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Sociodemographic influences on the early caregiving environment and likelihood of autism

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Sociodemographic influences on the early caregiving environment and likelihood of autism

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Content SOCIODEMOGRAPHIC INFLUENCES ON THE EARLY CAREGIVING ENVIRONMENT AND LIKELIHOOD OF AUTISM by Elizabeth Choi A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (OCCUPATIONAL SCIENCE) December 2023 Copyright 2023 Elizabeth Choi ii DEDICATION This manuscript is dedicated to Umma and Appa and to the PEACCh caregivers who shared their hearts and sorrows with me, revealing their hopes, fears, and concerns for their children. iii ACKNOWLEDGEMENTS I am grateful for being able to provide participant stipends through a Margaret Rood Dissertation Research award from the Mrs. T. H. Chan Division of Occupational Science and Occupational Therapy. I am also grateful for dissertation writing support through the Ostrow School of Dentistry and USC Graduate School Joint Fellowship. To Dr. Grace Baranek, thank you for believing in my potential from the beginning of my time as a PhD student. I have gained so much experience, knowledge, and confidence as your student, and my research, academic, and clinical skills grew in leaps and bounds under your mentorship. Most of all, thank you for your empathy, concern, and care as I navigated multiple health issues in the process of completing my degree and this dissertation. I was able to persevere because of your support, and I am proud of the researcher and scientist I have become through your guidance. To Dr. John Sideris, I have been so lucky to have learned from you and worked with you. Your enthusiasm for psychometrics has fully spread to me, and I was able to envision so much because of your thoughtful consideration and support. Your wry humor, comforting presence, and critical perspectives on social issues all shaped my time as a PhD student; thank you for your unfailing kindness and encouragement. To Dr. Mary Lawlor, I am forever grateful for having learned from you during my first semester in the PhD program. Our discussions in and out of the classroom as well as the research you’ve conducted and pointed me towards all lit a fire in me early on for recognizing the power of people’s narratives and lived experiences. Thank you for being supportive of me throughout the years and leading me to think more deeply and critically about my passions and ideas. To Dr. Linda Watson, I am so thankful for having had your mentorship during my PhD. Thank you for your constant encouragement, understanding, and instruction. You were a solid connection to the speech, language, and hearing sciences world for me, and I still felt grounded and tied to it because of your care to ensure that I had that support. To Drs. Susan Plann, Rosie Lowi, Netta Avineri, Bayo Ijagbemi, and Mary Alt, thank you for the conversations which motivated me to pursue my graduate and doctorate degrees and for your encouraging feedback that helped me trust in my abilities and potential. To Cass Faux and Dr. Janet Hawley, thank you so much for your guidance and mentorship from the beginning of my time as a graduate student and clinician. You both helped build my knowledge and confidence in my skills and supported my varied interests and passions during my time as a graduate student and beyond. To everyone at Milk and Honey in Tucson, AZ, thank you for sharing your passion and dedication for supporting parents as they navigate the postpartum period. Nina Isaac and Christina Arnold, your encouragement and interdisciplinary teamwork led me to this program in occupational science, and I’m so grateful for the path it has taken me. To everyone in the insp!re lab, especially Drs. Allison Phillips, Emily Campi, Julia Lisle, Cristin Holland, Elizabeth (Liz) Phelps, Yun-Ju (Claire) Chen, Emily Sopkin, and Savannah Gluck, thank you so, so much for creating such a supportive and caring lab environment. I learned so much from all of you, and I am a stronger researcher and clinician today because of our collective desire to do our best for the families we see and for whom we iv hope our research will support. To Kayla Brown, Dr. Emily Campi, Dr. Cindy Teow, Fatima Lizarde, Claire Needham, Savannah Gluck, Stephanie Magaña, Dr. Hanjun Xu, Rich Qian, and Alex Tucci, you have my endless gratitude for your insightful comments, dedicated work, and thoughtful questions about the data and analyses which then shaped the findings in this manuscript. To Aditya (AJ), Buwen, Ray, and Linah, thank you for the hangouts and camaraderie and support – from our first day together, I have appreciated your different perspectives and passions. I am so grateful for having had the opportunity to become an occupational scientist with you. To Rachel and Alexa, thank you for your friendship which has made me a better person than I was before I met either of you. I am constantly awed by the dedication and love you both have for the work you do and the children you serve, and I know that you have both already impacted so many lives because of your commitment. To Mielle, my soul friend and sister, thank you for sticking with me through the years and transforming our early serendipitous friendship into a life-long bond. I will gratefully carry the memories of our carefree times with me forever, but I am also so thankful for being able to share the difficult moments with you too. To Robin, thank you for your boundless love, care, and hope. I drew so much strength from your words of encouragement and support. To Gina, Kevin, and Gunnar, thank you for enfolding me so warmly and completely into the family. Your presence in our lives is so appreciated, and I enjoy every moment I spend with you. Thank you for always encouraging me and making me laugh. To Grandfather, thank you for always encouraging me to pursue higher education. Thank you for your pride in me and your unwavering belief in my ability to succeed. To Ben, thank you for your steady support and love in the face of the chaos that is our family. To little Zoey, thank you for your endless joy and curiosity. To Victoria, your own journey with neurodivergence and neurodiversity reinforced for me the importance of recognizing and addressing health disparities. Thank you for your frank belief in me, your hilarious honesty, and your passion for justice. To Margaret, thank you for understanding my oddities and supporting me throughout my life. There were times when you were my only friend, and I’m grateful for how much you protected me and looked out for me when we were younger and even now. Thank you for sharing your journey towards becoming a mother with me. To Umma and Appa, you have both sacrificed so much for us. Thank you for always listening to me and counseling me through my struggles, even when they seemed like nonsense. Thank you for being so open with me and being willing to grow and change with us. I am unbelievably fortunate to have your love, support, and instruction. To Alex, who sees me so clearly, who inspires me daily towards becoming a better person, who urges me to be kind to myself, thank you with all my heart for your love, encouragement, and discernment. Your insight and thoughtfulness enriched how I conducted my studies and wrote these manuscripts, and your unfailing support helped me push through some of the most difficult times in my life. This would not have been possible without you. v TABLE OF CONTENTS DEDICATION ................................................................................................................................ ii ACKNOWLEDGEMENTS...........................................................................................................iii LIST OF TABLES ....................................................................................................................... viii LIST OF FIGURES ....................................................................................................................... ix ABSTRACT................................................................................................................................... xi CHAPTER 1: A Mixed-Methods Intersectional Investigation of Autism Likelihood in Infancy... 1 1. INTRODUCTION .......................................................................................................... 1 1.1 Background and significance .................................................................................... 1 1.2 Theoretical foundations............................................................................................. 4 1.3 The present study....................................................................................................... 8 2. REFERENCES ............................................................................................................. 12 CHAPTER 2: Study 1 – A Critical Systematic Review of Sociodemographic Characteristics Associated with Autism Likelihood in Infancy ..................................................................... 17 1. INTRODUCTION ........................................................................................................ 17 1.1 Background and significance .................................................................................. 17 2. METHOD ..................................................................................................................... 21 2.1 Study design ............................................................................................................ 21 2.2 Search strategy and inclusion/exclusion criteria ..................................................... 22 2.3 Rating studies for quality and intersectional framing ............................................. 27 2.4 Data abstraction and analysis.................................................................................. 28 3. RESULTS...................................................................................................................... 30 3.1 Study characteristics................................................................................................ 31 3.2 Quality assessment .................................................................................................. 34 3.3 Measurement of autism likelihood.......................................................................... 36 3.4 Social status categories............................................................................................ 40 3.5 Intersectionality....................................................................................................... 55 4. DISCUSSION............................................................................................................... 57 4.1 Summary of findings related to race, ethnicity, and language ................................ 57 4.2 Summary of findings related to maternal education, income, and insurance status 60 4.5 Limitations of this systematic review...................................................................... 63 4.6 Conclusion............................................................................................................... 64 5. REFERENCES ............................................................................................................. 66 CHAPTER 3: Study 2 – An Intersectional Approach to Modeling Sociocultural Factors in Predicting Autism Likelihood in Infancy .............................................................................. 70 1. INTRODUCTION ........................................................................................................ 70 1.1 Social factors and health outcomes......................................................................... 70 1.2 Early identification of autism likelihood................................................................. 72 vi 1.3 Modeling intersectional factors using quantitative methods................................... 73 2. METHOD ..................................................................................................................... 75 2.1 Study design ............................................................................................................ 75 2.2 Recruitment ............................................................................................................. 76 2.3 Sampling and participants....................................................................................... 76 2.4 Measures and variables ........................................................................................... 79 2.5 Analysis................................................................................................................... 83 3. RESULTS...................................................................................................................... 86 3.1 Characteristics of the sample................................................................................... 86 3.2 Factor score mean comparisons by maternal race/ethnicity.................................... 87 3.3 Bayesian multilevel models .................................................................................... 89 3.4 Anticipated and perceived discrimination ............................................................. 126 4. DISCUSSION............................................................................................................. 129 4.1 Sociodemographic variables and covariates.......................................................... 129 4.2 Parent developmental concerns............................................................................. 134 4.3 Prenatal and perinatal risk factors......................................................................... 135 4.4 Anticipated and perceived discrimination ............................................................. 136 4.5 Additional limitations of this study ....................................................................... 137 4.6 Conclusion............................................................................................................. 138 5. REFERENCES ........................................................................................................... 139 CHAPTER 4: Study 3 – A Qualitative Exploration of Caregivers’ Hopes, Concerns, and Expectations for Their Children’s Development amongst Sociocultural Influences........... 145 1. INTRODUCTION ...................................................................................................... 145 1.1 Parenting amidst intergenerational discrimination................................................ 145 1.2 Systemic inequities in developmental surveillance of infants and toddlers.......... 147 1.3 Narratives and the lived experiences of caregivers............................................... 149 2. METHOD ................................................................................................................... 150 2.1 Study design .......................................................................................................... 150 2.2 Recruitment ........................................................................................................... 150 2.3 Interviews and transcription process..................................................................... 151 2.4 Data extraction and coding.................................................................................... 153 2.5 Authenticity of the data and findings .................................................................... 154 2.6 Trustworthiness of the data and findings............................................................... 160 2.7 Participants and measures ..................................................................................... 163 3. FINDINGS.................................................................................................................. 169 3.1 Parent experiences of developmental surveillance................................................ 169 3.2 Doing race, culture, and gender through parenting occupations........................... 178 3.3 Interrupting intergenerational cycles through parenting occupations................... 197 3.4 Considerations of this study .................................................................................. 209 4. CONCLUSION........................................................................................................... 209 5. REFERENCES ............................................................................................................211 CHAPTER 5: Integration of Findings from a Multilevel Mixed Methods Study of Sociocultural Influences on Infant Developmental Differences related to Elevated Autism Likelihood............................................................................................................... 216 vii 1. Overview ................................................................................................................. 216 2. The problem with checkboxes and aggregation of race/ethnicity identities ........... 217 3. Caregivers and developmental surveillance ............................................................ 218 4. Parenting occupations and critical consciousness................................................... 219 BIBLIOGRAPHY....................................................................................................................... 220 APPENDICES ............................................................................................................................ 238 APPENDIX 1: Study 1 – Literature Review Extraction Tool ........................................ 239 APPENDIX 2: Study 2 – Demographic Summary of InDI Subset ................................ 241 APPENDIX 3: Study 2 – Main R Packages used for Analyses and Workflow Management.................................................................................................................... 245 APPENDIX 4: Study 2 – Mediation SEM Parameter Estimates (path b only) .............. 247 APPENDIX 5: Study 2 – InDI Subscale Scores by Maternal Race/Ethnicity................ 248 APPENDIX 6: Study 3 – Guides for Semi-Structured Interviews................................. 249 viii LIST OF TABLES Table 1. Inter-rater reliability of the abstract and title screening phase between reviewers. ....... 24 Table 2. Inclusion and exclusion criteria for inclusion at the full-text screening phase. ............. 25 Table 3. Inter-rater reliability between raters during full-text screening...................................... 26 Table 4. Characteristics of the studies included in the final sample............................................. 32 Table 5. Quality assessment scores using the QuADS and ratings of intersectional framing of sociodemographic variables in studies making up the final sample...................................... 35 Table 6. Race/ethnicity breakdowns, collapsed analytic categories, and statistical results of studies in the final sample...................................................................................................... 41 Table 7. Breakdowns of social strata and other categories across studies in the final sample..... 47 Table 8. Breakdown of participant demographics of the full sample and the subsample included in Bayesian mixed model analyses. ........................................................................ 77 Table 9. Mean scores on FYIv3.1c factors by race/ethnicity group............................................. 88 Table 10. Receipt of WIC support by maternal race/ethnicity group..........................................115 Table 11. InDI subscale scores of participants with maternal educational level of a bachelor's degree or higher by maternal race/ethnicity group. ............................................................. 128 Table 12. Self-reported aspects of identity of participants......................................................... 163 Table 13. Self-reported educational history of participants' children's parents.......................... 164 Table 14. Self-reported household characteristics of participants.............................................. 165 Table 15. Parent report of developmental concerns on the DCQ version 1.6. ........................... 167 ix LIST OF FIGURES Figure 1. Conceptual diagram of infant neurodevelopment. ....................................................... 10 Figure 2. PRISMA flow diagram of screening process. .............................................................. 30 Figure 3. Mediation model path diagram..................................................................................... 85 Figure 4. Differences in median household income (centered) by maternal educational level and race/ethnicity groups....................................................................................................... 86 Figure 5. Parent report of developmental concerns by maternal race/ethnicity group. ............... 87 Figure 6. Parent report of developmental concerns by maternal educational level. .................... 87 Figure 7. Mean predicted CIP scores by parent report of developmental concerns..................... 91 Figure 8. Estimated relationship between adjusted age (centered) and predicted CIP scores. .... 92 Figure 9. Mean predicted CIP scores by maternal race/ethnicity group. ..................................... 92 Figure 10. Mean predicted SAE scores by parent report of developmental concerns. ................ 95 Figure 11. Estimated relationship between adjusted age (centered) and predicted SAE scores. . 96 Figure 12. Mean predicted SAE scores by maternal race/ethnicity group................................... 96 Figure 13. Mean predicted HYPER scores by parent report of developmental concerns............ 99 Figure 14. Estimated relationship between adjusted age (centered) and predicted HYPER scores. .................................................................................................................................... 99 Figure 15. Mean predicted HYPO scores by parent report of developmental concerns............ 103 Figure 16. Mean predicted HYPO scores by maternal educational level. ................................. 103 Figure 17. Mean predicted SREG scores by parent report of developmental concerns. ........... 106 Figure 18. Estimated relationship between adjusted age (centered) and predicted SREG scores. .................................................................................................................................. 106 Figure 19. Mean predicted SIRS scores by parent report of developmental concerns. ..............110 Figure 20. Mean predicted SIRS scores by maternal educational level and maternal race/ethnicity group. .............................................................................................................110 Figure 21. Estimated interaction effect between maternal race/ethnicity and adjusted age (centered) on predicted SIRS scores.....................................................................................111 Figure 22. Mean predicted MCM scores by parent report of developmental concerns..............114 Figure 23. Estimated interaction effect between maternal race/ethnicity group and adjusted age (centered) on predicted MCM scores.............................................................................114 Figure 24. Mean predicted SAE scores by receipt of WIC support............................................117 Figure 25. Mean predicted SAE scores by parent report of developmental concerns after adjusting for receipt of WIC support. ...................................................................................118 Figure 26. Estimated relationship between adjusted age (centered) and predicted SAE scores after accounting for receipt of WIC support.........................................................................119 Figure 27. Mean predicted SAE scores by maternal race/ethnicity group after accounting for receipt of WIC support. ........................................................................................................119 Figure 28. Estimated relationship between weeks gestation (centered) and predicted SIRS scores. .................................................................................................................................. 124 Figure 29. Predicted SIRS scores after accounting for the estimated moderating effect of maternal race/ethnicity group on the relationship between receipt of WIC support and predicted SIRS scores. ......................................................................................................... 124 Figure 30. Mean predicted SIRS scores by parent report of developmental concerns after accounting for receipt of WIC support and weeks gestation............................................... 125 x Figure 31. Estimated interaction effect between maternal race/ethnicity and adjusted age (centered) on predicted SIRS scores.................................................................................... 126 Figure 32. Average median household income (centered) by county of residence and maternal educational level. .................................................................................................. 133 xi ABSTRACT Despite rising national prevalence, autism diagnoses remain delayed and disproportionately low in historically marginalized groups. Early diagnosis, which is likely to lead to earlier intervention enrollment, can decrease lifelong costs to the family and to society through improved child outcomes, reducing the duration of – or the need for – more expensive and intensive services later in life. Persisting differences in diagnosis rates, particularly affecting Black/African American and Hispanic/Latine families, emphasize the need to investigate continuing disparities in autism screening and diagnosis. The primary goal of this dissertation was to explore how sociocultural processes impact infant autism likelihood and caregivers’ priorities for supporting their children’s development. An intersectional framework within a multilevel mixed methods research design was used to obtain a contextualized understanding of how infant neurodevelopment is situated amongst social systems and worlds. The first study was a critical systematic review of documented associations between sociodemographic variables and autism likelihood and evaluation of ways in which previous studies have conceptualized such variables. The second study was a proof-ofconcept quantitative investigation of intersectional factors on autism likelihood using Bayesian mixed modeling. The third study was a narrative analysis of the impact of sociocultural factors on caregivers’ hopes, concerns, and expectations for their children’s development. The findings of these three studies highlight key issues with current conceptualizations of race and ethnicity in research and healthcare; additionally, they provide a multilevel view of the contexts in which infants develop and ways in which social factors impact the health and wellbeing of individuals and communities. 1 CHAPTER 1: A Mixed-Methods Intersectional Investigation of Autism Likelihood in Infancy 1. INTRODUCTION 1.1 Background and significance Despite rising national prevalence since 2000 (Maenner et al., 2020; Yuan et al., 2021), autism diagnoses remain delayed and disproportionately low in historically marginalized groups (Baio et al., 2018; Durkin et al., 2017; Kelly et al., 2019; Mandell et al., 2007; Mandell et al., 2009; Palmer et al., 2010; Pedersen et al., 2012). Early diagnosis, which is likely to lead to earlier intervention enrollment, can decrease lifelong costs to the family and to society through improved child outcomes, reducing the duration of – or the need for – more expensive and intensive services later in life (Chasson et al., 2007). Persisting differences in diagnosis rates, particularly affecting Black/African American and Hispanic/Latine families, emphasize the need to investigate continuing disparities in autism screening and diagnosis. Much of the focus in studies examining early indicators of likelihood for eventual diagnosis of autism has centered on infant characteristics such as behavioral (e.g., attentional skills; Elsabbagh et al., 2013) or neural (e.g., connectivity; Dickinson et al., 2021) markers. These early indicators include behaviors across multiple domains such as social communication (e.g., atypical orientation to social stimuli; Baranek, 1999; Maestro et al., 2002; Miller et al., 2017) and sensory regulation (e.g., difficulty with visual attention disengagement; Baranek et al., 2018; Sacrey et al., 2013; Zwaigenbaum et al., 2005). Based on evidence that symptoms of autism can be identified in early childhood and given the potential for early identification to lead to earlier treatment, the American Academy of Pediatrics 2020 clinical report called for pediatric providers to use screening and routine surveillance to improve early identification, support 2 children’s participation in early intervention, and eliminate disparities to service access for children with autism (Hyman et al., 2020). One approach to improve identification efforts and uncover disparities impacting children with autism has been to explore prenatal and perinatal risk factors associated with autism likelihood (Hyman et al., 2020; Kolevzon et al., 2007). Risk factors such as gestational hypertension, preterm birth, and low birth weight are all inconsistently linked with increased likelihood of autism (Wang et al., 2017; Yirmiya & Charman, 2010), which may reflect complex dynamics between environmental and genetic factors associated with autism. These studies of prenatal and perinatal factors partially broaden our understanding of autism likelihood beyond individual neurodevelopmental differences, yet a more comprehensive understanding of autism likelihood should also account for the potential contributions of sociocultural processes. For example, research on social determinants of perinatal outcomes has found effects of advanced maternal age on increased risk of low birth weight specifically for Black/African American women in the presence of community-level factors such as generational residence in low-income communities (Lorch & Enlow, 2015). Existing studies of sociodemographic factors (reflecting categories of social difference) in relation to disparities in autism diagnoses or service access have largely examined these factors individually or modeled them in aggregate form (e.g., the additive effect of low educational level with racial group) without explicit consideration of how interactions amongst them might differentially impact outcomes. This is concerning given findings that greater available resources (e.g., advantageous SES, higher educational level) do not improve autism diagnosis rates across all racial and ethnic groups (Durkin et al., 2017; Palmer et al., 2010), suggesting greater complexity within these disparities than merely additive risks. Continuing to 3 treat sociodemographic factors as independent and thus disregarding the potential for compounded or differential effects at intersections of sociodemographic factors may lead to a failure to account for disparities in screening and diagnosis, with long-lasting impacts on children’s development. Moreover, sociodemographic factors may impact not only autism likelihood but also probability of timely screening, diagnosis, and referral for treatment. Caregiver concerns are a crucial aspect of pediatricians’ developmental surveillance practices (Hyman et al., 2020) and have been associated with positive autism screenings and eventual diagnosis (Ozonoff et al., 2009; Richards et al., 2016). Yet there is evidence of racial and ethnic differences in caregiver report of autism-related concerns (Donohue et al., 2019) as well as in pediatricians’ judgment of the validity and reliability of caregivers’ concerns (Angell & Solomon, 2014; Singh & Bunyak, 2019). These findings suggest that developmental surveillance may differentially benefit some families, highlighting the need to better understand the unique ways that intersections of social differences and sociocultural processes such as discrimination can influence developmental surveillance practices. Part of obtaining a better understanding of the impact of these factors is by using qualitative research methods to explore the phenomenological, lived experiences of families of infants undergoing developmental surveillance to uncover disparities and processes evident at the individual level that may not be detected when conducting quantitative analyses at the group level. Investigation of how infant neurodevelopmental differences and caregiver priorities for their children’s development are situated amongst sociocultural processes (e.g., perceived racism, sexism, and gendered racism) may help elucidate why developmental surveillance endeavors are unequal across subpopulations. 4 1.2 Theoretical foundations 1.2.1 Intersectionality The term “intersectionality” was first used in the 1980s in response to an institutionalized framing of discrimination along single axes (Cho et al., 2013; Crenshaw, 1989). Social justice endeavors and the legal and political fields they attempted to transform continued to approach the amendment of inequalities through a unidimensional lens, resulting in the subordination of voices and experiences of those located at intersections of discriminatory contexts. “Intersectionality” highlighted the paradox of “sameness” and “difference” of Black women (Cho et al., 2013) who were in the same breath too similar to Black men and White women to add to narratives of experiences of racism and sexism, yet too different for their experiences to stand on their own. This unidimensional conceptualization of race and gender by the courts resulted in legal ramifications that upheld discriminatory practices and perpetuated mutually exclusive framing of inequalities (Crenshaw, 1989). Intersectionality theory is a conceptual tool which foregrounds social and power dynamics (Choo & Ferree, 2010) and has been adopted into numerous fields of study, expanding its reach beyond its use as a term to describe a social phenomenon and becoming a methodological paradigm guiding research questions and methodologies. As a theoretical framework, intersectionality is crucial in disciplines such as public health as it enables researchers to better understand how intersectionality experienced at the micro, individual level is reflective of systematic and structural inequalities at the macro, societal level (Bowleg, 2012). Autism research is a field which has already demonstrated ties to intersectionality at multiple levels. Researchers have begun to move toward intersectional frameworks in examining health and service disparities experienced by individuals with autism and their families (Singh & 5 Bunyak, 2019). Organizations supporting autism research such as the International Society for Autism Research (INSAR) have also promoted the use of intersectional frameworks through an institute series exploring connections between autism research and autistic people’s experiences across identities and groups (International Society for Autism Research, n.d.). Intersectionality has the potential to serve as a powerful tool for elucidating hidden power dynamics and varied inequities across research, educational, and clinical contexts, making underlying processes explicit and supporting movements toward rectifying inequities. Consideration of an intersectional perspective – that positionality at the intersection of multiple social dimensions may be uniquely marginalizing – is especially important in understanding the caregiving experiences of parents of children with disabilities, as “ableness” is one socially determined dimension that may interact with other dimensions. These intersections may lead to unique experiences across social contexts that present challenges to families that cannot easily be addressed by rectifying inequities related to one dimension (e.g., low resources/social capital). Studies explicating lived experiences of families of children with autism using an intersectional lens have revealed unique barriers and sources of resilience at intersections of social categorizations (e.g., race, gender, class) when attempting to navigate the process of caregiving for their children (Angell & Solomon, 2014; Hannon, 2017; Lee & Zhu, 2020; Solomon et al., 2015). Even when caregivers explicitly express concerns about their infants’ development to their pediatricians, underlying discriminatory social processes related to race, ethnicity, and more can dictate which caregivers’ concerns will be deemed valid and worth pursuing (Angell & Solomon, 2014; Singh & Bunyak, 2019). The findings of these studies reveal that families’ journeys with autism and healthcare experiences are impacted by their own understanding of themselves as situated within a social world, their understanding of how the world perceives 6 them and their children, and their hopes, concerns, and expectations for their children – often in spite of the expectations of those they encounter. Given the effect that intersectional processes can have on the journeys of families with autism, it becomes imperative to investigate whether these processes are evident early in families’ journeys – that is, prior to the time diagnosis is typically considered – and if so, to explicate how they affect the early caregiving environment. 1.2.2 Occupational engagement and transactionalism The theory of the human need for occupation posits that purposeful activities, or occupations, are central to the maintenance of health by enabling humans to address immediate bodily needs; develop skills, structures, and tools needed to survive and thrive within their environment; and regularly exercise the mind and body to foster growth and advancement (Wilcock, 1993). Occupations are the means (or “transactions”) through which humans and their contexts co-define each other (Dickie et al., 2006). Moreover, occupations in this transformational sense influence and are influenced by health and well-being through four interdependent dimensions of doing, being, becoming, and belonging (Hitch et al., 2014; Wilcock, 2007). Over time, however, shifts in sociocultural and societal priorities to maintain institutional structures and powers have devalued engagement in occupations for the purpose of enhancing health and well-being (Wilcock, 1993). Acts of discrimination and oppression against individuals with marginalized and minoritized identities impact the ability to participate and engage equitably in occupations, adversely affecting health and well-being by limiting opportunities for health-improving activities (Wilcock, 1993; Ramugondo, 2020). Critical, contextualized evaluations of occupational injustices have the potential to unveil the influence of sociocultural processes, moving research endeavors towards contributing to social reform (Hocking and Whiteford, 7 2012). For instance, Africana womanism has been applied as a theoretical framework to reconceptualize and expand dominant framings of gender and gendered occupations within the field (Huff et al., 2018), and an occupation-centered analysis of social difference has supported critical investigation of ways in which social order is maintained, reorganized, and resisted through occupational engagement (Angell, 2014). Complementary to these critical frameworks is the construct of occupational consciousness (Ramugondo, 2012), or the awareness of and engagement with power dynamics at individual and collective levels through daily occupations. The early caregiving environment encompasses a time during which infants rapidly develop; however, it is also a time during which parents and guardians grow alongside their children as caregivers. Parenting a child who obtains a social label tied to a disability leads to significant shifts in parents’ understanding of caregiving, often increasing risk for lifestyle imbalance due to changes in employment, costs associated with services, and ability to engage in occupations (Lawlor, 2004; Stein et al., 2011). Additionally, by engaging in co-occupations with their children, parents support the development of children with disabilities in ways that require adaptation and growth in parents as well (Bonsall, 2014; Price & Stephenson, 2009). Part of this adaptation and growth involves a reframing and revisualizing of parents’ identities, roles, and goals while constantly negotiating with the cultural world in which they are situated (Lawlor, 2004). In addition, intersectional processes as experienced through everyday occupations may alter, strengthen, or hamper caregivers’ ever-changing hopes, concerns, and expectations for their children’s future. By drawing on past actions and future goals to make decisions and choices in the present, caregivers and their occupational engagement are co-constituted with their sociocultural and historical contexts (Sethi, 2021). This argument could be expanded further 8 through the application of an intersectional perspective: this co-constitution within mothers may be drastically different at specific intersections of race, sexual orientation, and class, so that the idea of a successful future differs across mothers. For mothers with more privilege across multiple axes, a successful future for children may involve their ability to explore possibilities and take risks, since there are likely multiple safety nets for those children to fall back upon if they meet temporary failures. For mothers that are less privileged, a successful future may need to be more secure, requiring a focus on following authority, focusing on immediate needs, and prioritizing safety. When including the possibility of a different developmental trajectory for children, then, mothers might differentially prioritize certain services, skills, and outcomes based on a complex collection of concerns in response to interlocking inequities. For infants who are at greater likelihood of eventual diagnosis of autism, caregivers may experience shifting hopes, concerns, and expectations not only as a result of their infants’ developmental trajectories but also due to sociocultural expectations, pressures, and inequities. For example, caregivers may notice delays in their infants’ development but lack access to professional or family support, thus making do with their existing resources; additionally, caregivers might bring up concerns to a professional but experience differential responses to their concerns based on factors such as race or ethnicity. These different situations and processes may then change caregivers’ hopes, concerns, and expectations for their infants, potentially shaping the occupational possibilities they envision for themselves as caregivers and for their children. 1.3 The present study I applied an intersectional framework to a multilevel mixed methods research design (Teddlie & Tashakkori, 2009) to explore the impact of sociocultural influences on infant 9 developmental differences related to elevated autism likelihood. Multilevel mixed methods studies produce meta-inferences about the structure, mechanisms, and relationships of a system within which a phenomenon of interest is contextualized (Headley & Plano Clark, 2020). This study design is particularly appropriate for investigating complex systems (e.g., with reciprocal interactions and interdependence within and across levels) and addressing questions of equity (McCrudden & Marchand, 2020). In this dissertation, infant neurodevelopment was conceptualized as nested within a multilevel system of biological, environmental, and sociocultural factors (see Figure 1). Individual infant neurodevelopment comprises the base system level and transacts with the next level of caregiver and family biological and environmental factors; this intermediate family level in turn transacts with the outermost level of community-associated environmental and sociocultural factors. Sociocultural processes such as discrimination and intergenerational transmission of knowledge as well as transactional processes such as infants’ and caregivers’ occupational engagement act within and across levels as mechanisms shaping infant neurodevelopment. Quantitative and qualitative data were collected concurrently and integrated with equal emphasis to draw conclusions about the multilevel system; findings resulting from both approach types influenced interpretation of data within the individual studies. 10 Figure 1. Conceptual diagram of infant neurodevelopment. The primary goal of this dissertation was to explore how sociocultural processes impact infant autism likelihood and caregivers’ priorities for supporting their children’s development. To that end, this dissertation targeted three main aims across three different studies. The first aim was to synthesize findings from studies documenting associations of sociodemographic characteristics and likelihood of autism in infants before three years of age and to determine the extent to which intersectionality is reflected in such studies. This first study was a critical review of the existing knowledge base of sociocultural influences on infant autism likelihood, and its findings guided analysis and interpretation of the second study and shaped interview questions and interpretation of the third study. The second aim was to examine how sociodemographic variables (e.g., race/ethnicity), pre-/perinatal risk factors (e.g., labor complications), and primary caregivers’ concerns for their infants’ development relate to infant neurodevelopmental differences related to autism likelihood. At its core, this second study was a novel application of an intersectional perspective 11 to a quantitative analysis of autism likelihood; it is a proof of concept of a paradigm shift in quantitatively modeling sociodemographic variables as interdependent. Findings of this study underscore the implications of statistical approaches of aggregation of race/ethnicity groups and highlight the need for further evaluation to determine whether differences across sociodemographic characteristics are true group differences as a result of sociocultural processes or are reflective of measurement bias. Finally, the third aim was to explore primary caregivers’ hopes, concerns, and expectations for their infants’ development and the extent to which cultural and sociodemographic variables impact those hopes, concerns, and expectations. This third study addresses a critical gap in representation of the lived experiences of caregivers within this early developmental context, particularly in relation to intersecting points of privilege and vulnerabilities. Findings from this study examine caregivers’ understanding of and priorities for their children’s development amidst their own meaning- and sense-making of how their lived experiences are embedded amongst sociocultural processes and power struggles. The findings of these three studies provide a contextualized understanding of factors impacting early likelihood for autism that is crucial for taking steps towards addressing existing service gaps and improving early care. Additionally, they shed new light on the impact of sociocultural factors on caregivers’ hopes, concerns, and expectations for their children’s development. These studies provide insight into how early factors influence the possibilities that caregivers foresee for their children and how developmental concerns are intertwined with other challenges and experiences caregivers face across different socioeconomic and sociocultural contexts. 12 2. REFERENCES Angell, A. M. (2014). 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International Journal of Developmental Neuroscience, 23(2-3), 143-152. doi:10.1016/j.ijdevneu.2004.05.001 17 CHAPTER 2: Study 1 – A Critical Systematic Review of Sociodemographic Characteristics Associated with Autism Likelihood in Infancy 1. INTRODUCTION 1.1 Background and significance Health is intricately tied to our socially constructed worlds: perceived racism and discrimination impact not only mental health (Pieterse et al., 2012) but also physical health (Pascoe & Richman, 2009). Large-scale studies have demonstrated that social factors and stressors shape long-term health and life outcomes (Braveman & Gottlieb, 2014), with marginalized or minoritized groups consistently experiencing adverse outcomes. Disentangling the effects of societal factors on health outcomes requires a conceptualization of social strata that can account for their complexities amongst social processes that privilege some characteristics over others in different contexts. Using an intersectional perspective, or conceptualizing sociocultural factors as interdependent and laden with power differentials, enables researchers to better detect relationships between sociocultural variables and health outcomes that systematically impact certain individuals and groups at intersections of disadvantaged social positions. Lewis et al. (2017), for instance, analyzed the intersectional impact of racism and sexism on Black/African American women’s health outcomes, finding evidence for significant associations between gendered racial microaggressions and both mental and physical health. Richardson and Brown (2016) tested whether differences in hypertension risk trajectories were best explained through an additive vs. intersectional approach and found evidence for multiplicative effects of race/ethnicity and gender such that the odds of hypertension for nonHispanic/Latine Black/African American and Mexican American women were increased beyond 18 risks separately imposed by race/ethnicity and gender. Testing race/ethnicity and gender as separate predictors in an additive approach resulted in significant findings only for race/ethnicity, suggesting that gender did not impact hypertension risk (Richardson & Brown, 2016). Use of an interaction term in a multiplicative approach, on the other hand, revealed that while being female was protective against hypertension for individuals who were White, being female was actually associated with greater risk for non-Hispanic/Latine Black/African American and Mexican American persons (Richardson & Brown, 2016). These examples demonstrate the importance of considering the impact of intersections of social strata on health outcomes to avoid drawing erroneous conclusions when these factors are assumed to be independent and equally representative of entire groups. Autism research as a field has begun to incorporate an intersectional perspective to highlight and uncover health and service disparities experienced by autistic individuals and their families (Singh & Bunyak, 2019). Studies in this vein have explored inequities and gaps in our current understanding of how individuals and communities from marginalized subgroups, particularly those at intersections of disadvantages, are impacted by societal processes that further intersect with neurodiversity (Cascio et al., 2020). Baker and Kim (2018) explored the impact of intersecting identities on the educational decision-making process of Somali American mothers of children with autism, revealing complexities in the barriers they had to face as a result of intersecting race/ethnicity-, disability-, and gender-based discrimination. Hannon (2017) used an intersectional framework to deconstruct the experience of being both a Black educator of school counselors and a father navigating educational services for his child with autism, exploring how his own empowerment and expertise as a result of being situated within the educational system were still challenged by inequitable institutional priorities and sociocultural 19 expectations. Angell and Solomon (2017), by analyzing ways that Latino parents engaged with or rejected an “autism parent” identity, examined the effects of discrimination by race, ethnicity, and class on Latino parents’ service-related occupational engagement. Much of the existing autism research focusing on disparities impacting young children and families aims to reveal inequities experienced at points of screening and/or diagnosis (e.g., delayed referrals or assessments after positive screening, misdiagnoses at first assessment). Multiple studies have examined differences in autism diagnoses by separate as well as intersecting social strata. Liptak et al. (2008) found that, despite similar overall autism rates in families both below and above the poverty line, children living below the poverty line had the lowest preschool prevalence of autism, suggesting underdiagnosis in this group with potential implications for long-term developmental outcomes due to delayed diagnoses. This study also identified higher proportions of parent-reported increased symptom severity in children from Latine families or children living below the poverty line, suggesting possible underascertainment of children with milder symptoms from traditionally underserved populations (Liptak et al., 2008). Becerra et al. (2014) examined records of 1,626,354 children (7,450 diagnosed with autism) born in Los Angeles County from 1995-2006 to examine the relative risks for varying comorbidities and severities by maternal race/ethnicity and nativity (Becerra et al., 2014). Compared to children born to White US-born mothers, children born to foreign-born mothers who were Black/African American, Filipina, or Vietnamese had higher relative risks of having a diagnosis of autism with comorbid intellectual disability (increase ranging from 127%-163%) and expressive language impairment (increase ranging from 86%-176%; Becerra et al., 2014). Lower unadjusted rates of autism in children of US-born Hispanic (relative risk reduction of 30%) and Black/African American mothers (relative risk reduction of 32%) were also reported in 20 comparison to US-born White mothers; however, adjusting for maternal race/ethnicity and nativity resulted in higher relative risks for these children (relative risk increase of 15% and 4%, respectively; Becerra et al., 2014). Further adjustment for regional centers resulted in a final relative risk increase of 14% for children of US-born Black/African American mothers and 76% for children of foreign-born Black mothers (Becerra et al., 2014). Findings of this study suggest that systematic disparities persist in ascertainment of US-born children with autism across racial/ethnic backgrounds. Additionally, considering only race/ethnicity and not also nativity would lead to underestimation of autism rates, potentially also precluding further investigation of important contributing factors that may be differentially impacting migrant families. These findings underscore the importance of closely examining potentially interacting factors with autism diagnoses that, if overlooked, would mask systematic differences. While it is important to use an intersectional framework to unveil systemic disparities impacting autism diagnoses and trajectories across life, it is also important to understand how sociocultural factors and processes impact autism likelihood prior to diagnosis. The underlying contributions of persistent, historical, and systematized inequalities to autism likelihood (i.e., screening for likelihood of a later diagnosis of autism) need to be better understood to identify families most likely to benefit from early referrals and intervention as well as crucial support and resources that are needed to rectify systemic disadvantages that may impact children’s developmental trajectories. The purpose of this study was to (1) synthesize previous findings in the literature documenting associations between sociodemographic characteristics and neurodevelopmental differences related to elevated likelihood of autism in infants before three years of age, (2) generate a theoretical basis and model to address gaps in the current literature, and (3) determine the extent to which intersectionality is reflected in such studies. In conducting 21 this synthesis, it was not my intention to establish sociodemographic characteristics as the primary sources of autism likelihood. Instead, this systematic review aimed to frame these characteristics as likely proxies of sociocultural processes which are themselves the forces inducing increased likelihood of prenatal and perinatal events that are then associated with elevated autism likelihood via a combination of biological and environmental influences. There is already evidence that disparities in birth outcomes between Black/African-American and White women remain even when accounting for class differences (Braveman et al., 2011), and the lifetime interactions with racism that Black/African-American women experience reflect not only enacted discrimination but also the potential for physiological and psychological impacts of persistent stress related to anticipated discrimination for themselves and their children (NuruJeter et al., 2011). This study had the following two research questions: 1) How do sociodemographic characteristics impact likelihood of autism in infants before three years of age? and 2) to what extent is an intersectional perspective incorporated or reflected in studies documenting these associations within this age group? 2. METHOD 2.1 Study design This study is a mixed-methods mixed research synthesis, a type of systematic review which applies principles of mixed-methods research designs to combine and integrate findings from qualitative, quantitative, and mixed-methods studies (Heyvaert et al., 2013). This type of study design is framed along three main dimensions (Heyvaert et al., 2013): (1) emphasis of approaches - equal vs. dominant status of qualitative and quantitative approaches; (2) temporal orientation - concurrent vs. sequential qualitative and quantitative approaches; and (3) integration - involving all vs. only some parts of qualitative or quantitative data within research 22 stages. Within this study, qualitative and quantitative review approaches were equally prioritized by aggregating findings via generation of comparable effect sizes and meta-analysis (or meta summary if there is inadequate data) and configuring findings by linking thematically diverse or disparate findings to generate a testable model or theory (Sandelowski et al., 2012). Findings of all data types were integrated across each research stage. This type of literature synthesis approach was chosen due to the nature of the research questions for this aim: (1) "How do sociodemographic characteristics impact likelihood of autism in infants before three years of age?" and (2) "To what extent is an intersectional perspective incorporated or reflected in studies documenting these associations within this age group?" While studies examining associations amongst sociodemographic variables and neurodevelopmental differences are likely to be quantitative, studies exploring those sociodemographic variables further and potentially explaining contextual factors impacting associations are more likely to be qualitative or mixed methods. A mixed-methods mixed research synthesis allows for integration of qualitative, quantitative, and mixed-methods data across multiple review stages, allowing researchers to use all relevant findings to better understand phenomena. 2.2 Search strategy and inclusion/exclusion criteria Following consultation with library scientists, I developed a systematic search protocol and tracker for databases likely to contain quantitative, qualitative, and mixed-methods studies relevant to the research questions. Search strings tailored to each database were entered into the following databases: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), APA PsycINFO, Embase, and Scopus. No restrictions were included regarding date or language of publication. Search strings were built to include a variety of keywords related to 23 the key terms of autism (e.g., “autism spectrum disorder”, “autism”) and sociodemographic factors (e.g., “socioeconomic”, “ethnic”) using OR operators within each term’s keywords and AND operators between key terms. The initial search on April 8, 2022 yielded 47,753 results. A trained graduate student replicated the search successfully with independently formed search strings using the same core keywords. Database search exports were imported into Covidence. Automatic removal of duplicates via Covidence in addition to hand marking of duplicates discovered during the screening process resulted in a remaining sample of 30,024 studies which were included in the initial round of title and abstract screening. Studies included in this round of screening included gray literature (e.g., dissertations, reports, posters) in efforts to reduce the potential effect of publication bias on conclusions drawn from this search. The first screening phase was completed by a team of nine trained reviewers including graduate- and undergraduate-level research assistants and volunteers. Each article’s title and abstract were reviewed in Covidence by myself and by another reviewer against inclusion criteria determining whether the study should move to the next screening stage. The following criteria were met to move to the next screening phase with a vote of “yes”: lack of indication that the article was a correction or erratum; inclusion of a sample of children from birth up to three years of age at elevated likelihood of autism (or their families) based on community-level screening results; and inclusion of least one sociodemographic variable in relation to at least one neurodevelopmental difference related to elevated autism likelihood either as a predictor or outcome. Sociodemographic variables of interest included race(s), ethnicity, educational level, employment status, income level, geographical location of residence, sex, gender, and sexual orientation. Neurodevelopmental differences related to autism likelihood included behavioral differences (e.g., differences across orienting, response to joint attention, language skills, 24 repetitive stereotyped behaviors) or positive autism screening result. Abstracts which only reported inclusion of child/parent sex or parental age and no other sociodemographic variables of interest were excluded from further screening with a “no” decision as the focus of this review was on sociodemographic variables likely to be associated with discrimination and social inequalities with subsequent differences across health outcomes, and ample reviews of associations amongst child sex and parental age on autism likelihood already exist. Abstracts that described samples of individuals at elevated likelihood of autism but did not specify an age range were moved to the next screening phase as a “maybe” decision if they met other inclusion criteria. Any studies receiving a vote of “no” from one reviewer and a “yes” or “maybe” from another reviewer were moved to a conflicts section and received a final consensus vote. Interrater reliability of title and abstract screening was assessed using percent agreement and Cohen’s Kappa and ranged from 91%-98% and 0.26-0.62 (fair to substantial), respectively (see Table 1). Table 1. Inter-rater reliability of the abstract and title screening phase between reviewers. Inter-rater Reliability: Abstracts & Titles Reviewer A Reviewer B Agreement Cohen's Kappa K Interpretation 2 1 92% 0.26 Fair 1 3 95% 0.37 Fair 4 1 98% 0.33 Fair 1 5 96% 0.44 Moderate 1 6 91% 0.44 Moderate 1 7 97% 0.31 Fair 8 1 92% 0.62 Substantial 1 9 96% 0.47 Moderate Studies receiving a “yes” or “maybe” decision during title and abstract screening were moved to full-text screening (see Table 2 for full inclusion and exclusion criteria). Each study with full text available in English was screened by a review team of four graduate- and undergraduate-level research assistants and volunteers for inclusion based on three main criteria: 1) the sample included children from birth up to three years of age at elevated likelihood of 25 autism (or their families); 2) a behavioral neurodevelopmental difference related to autism likelihood or a positive autism screening status were included as either a predictor or outcome; and 3) a sociodemographic variable was included as a predictor or covariate. Table 2. Inclusion and exclusion criteria for inclusion at the full-text screening phase. Systematic Review Inclusion and Exclusion Criteria Inclusion Exclusion Study sample includes children from birth to three years of age at elevated likelihood of autism OR families of such children Study sample is predominantly composed of children with other conditions (e.g., Fragile X syndrome, Down syndrome, very preterm birth status) or did not include a comparison group with lower likelihood (e.g., negative community-level screening of autism without increased familial likelihood) Study analyses include a behavioral neurodevelopmental difference related to likelihood of autism or positive screening for autism status as a predictor or outcome Study sample mean age is older than 36 months or not reported Study analyses include a sociodemographic variable as a predictor or covariate in relation to a behavioral neurodevelopmental difference related to likelihood of autism or positive screening for autism status Study sample was recruited from a country other than the United States Each study was also considered for exclusion based on three main criteria: 1) the study sample was predominantly composed of children with other conditions such as very preterm birth, Fragile X syndrome, Down syndrome, or language delay/disorder or did not include a comparison group with negative autism screening status; 2) the sample mean age was older than 36 months or was not reported, and 3) the study was conducted in a country other than the United States. Studies predominantly composed of children with pre-existing neurodevelopmental conditions (e.g., Fragile X syndrome) were excluded to narrow the focus of this review on sociodemographic characteristics that may be associated with increased autism likelihood in the absence of known comorbid or potentially contributing conditions. Studies with samples whose mean age was older than 36 months were excluded as our focus was on 26 associations between sociodemographic characteristics and autism likelihood in the first three years of life. Any studies which were retrospective in nature and included behavioral neurodevelopmental differences related to autism likelihood from birth to 36 months (via parent report or chart review) were not excluded. Studies which were conducted in a country other than the United States (n=29) were excluded under the assumptions that sociodemographic variables (and potential interactions amongst them) are likely to differ under varying sociocultural contexts across countries, and combining findings across countries would inadvertently merge findings which are not directly comparable due to those differences. I assumed that differences amongst states are likely smaller than differences amongst countries due to overarching federal policies. We also excluded systematic reviews and literature syntheses (n=99) in favor of including primary sources. We excluded articles that had been retracted (n=2). Any studies receiving a vote of “no” from one reviewer and a “yes” or “maybe” from another reviewer were moved to a conflicts section and received a final consensus vote. Additionally, any studies excluded for differing reasons by reviewers were also moved to a conflicts section to receive a final consensus reason for exclusion based on a predetermined hierarchy. Inter-rater reliability of full text screening was assessed using percent agreement and Cohen’s Kappa and ranged from 74-79% and 0.37-0.55 (fair to moderate), respectively (see Table 3). Table 3. Inter-rater reliability between raters during full-text screening. Inter-rater Reliability: Full Texts Reviewer A Reviewer B Agreement Cohen's Kappa K Interpretation 1 9 74% 0.41 Moderate 2 1 79% 0.55 Moderate 1 7 78% 0.37 Fair 27 2.3 Rating studies for quality and intersectional framing We used the Quality Assessment for Diverse Studies (QuADS; Harrison et al., 2021) tool to evaluate the quality of studies that passed full-text screening as this measure can be applied across study designs (e.g., mixed methods, quantitative). The QuADS is a revised version of the Quality Assessment Tool for Studies with Diverse Designs (QATSDD; Sirriyeh et al., 2012) and was primarily designed to broaden the use of this quality assessment tool for use beyond the field of psychology and across health service disciplines. An early iteration of the QuADS tool was reviewed by 10 researchers with experience in conducting systematic reviews of studies using different research designs to examine face and content validity. To use the QuADS, researchers rate articles along 13 criteria on a scale from 0-3 that allows studies using different methodologies to eventually be compared on those criteria. For this synthesis, I aimed to exclude any studies which appeared to have critical flaws (e.g., methods inappropriate to answer the research question, significant lack of information on participant characteristics); a cut-off quality score was not used as the authors of the QuADS determined that any cut-off would be arbitrary. Studies that passed both screenings and lacked critical flaws comprised the final core sample for analysis. Quality assessment of the included studies is reported narratively as recommended by QuADS authors. For the purposes of this review, intersectionality is defined as a theoretical paradigm that frames sociodemographic characteristics as interdependent and non-additive along with a recognition of underlying power dynamics and inequalities. We rated each study included in the final analyses for its level of intersectional framing of sociodemographic variables within the study’s method and findings. Ratings were made on a three-point scale: the study examined sociodemographic variables only as covariates or as single predictors (score=1); the study 28 examined interactions amongst sociodemographic variables in relation to outcomes (score=2); or the study contextualized intersecting sociodemographic variables amongst social and power dynamics (score=3). I chose to use these ratings as an initial step toward synthesizing the extent to which an intersectional perspective has been used to frame sociodemographic variables in relation to autism likelihood from birth to 36 months and to create a point of comparison across studies. 2.4 Data abstraction and analysis In order to preserve important contextual information from each study, we chose to use the “textin-context” method (Sandelowski et al., 2013) for data abstraction (see Appendix 1 for data abstraction protocol). This method formats data findings into “portable” statements that anchor findings to different aspects of study context including sample variation, sources of information, comparative reference points, and magnitude and/or significance. Texts-in-context were collated to preserve study-specific findings and aid interpretation of synthesis findings given contextual differences across studies such as parent vs clinician sources of information or variations in ways researchers operationalized certain variables. Originally, I had planned to analyze and synthesize abstracted data quantitatively using Bayesian meta-analysis, re-coding findings from each study to reflect whether a positive, negative, or no association between a sociodemographic variable and autism likelihood was found or whether the study did not address it. This re-coded data would then have been used as the basis for Bayesian data augmentation to create point estimates and credible intervals for each potential association (Crandell et al., 2012). Once our screening process was completed and we began data abstraction, I felt we needed to change our choice of analysis. The differences in operationalization of sociodemographic characteristics across studies, an issue which is 29 addressed later in this review, precluded valid aggregation of abstracted data. In this review, I discuss criticisms of the choice to aggregate certain variables such as race and ethnicity within the final studies. Applying this meta-analytic approach would likely compound this issue, and it was not possible to de-aggregate findings in studies in which certain variable categories were collapsed. Additionally, the number of studies meeting inclusion and exclusion criteria was small, and pooling results would likely lead to misleading conclusions. Thus, I chose to conduct a critical review of the existing literature to address what is currently known about associations between sociodemographic characteristics and autism likelihood and issues with the current approaches to studying sociodemographic factors in relation to autism likelihood. This begins with a narrative synthesis of findings across studies grouped by sociodemographic factors along with a critical evaluation of the conceptual assumptions made by researchers in their methodological approaches to operationalizing those factors. 30 3. RESULTS Figure 2. PRISMA flow diagram of screening process. 31 3.1 Study characteristics Despite beginning with a sample of over 30,000 studies prior to screening, only six studies met inclusion and exclusion criteria (see Figure 2 for PRISMA flow diagram). The primary reason articles were excluded was due to a mean sample age greater than 36 months or not clearly listing a mean sample age (n=388). Articles which followed cohorts of children (e.g., all children born in 2010) but did not include autism screening prior to 36 months of age were excluded. Studies which did not report a mean sample age but included outcomes from autism screenings at well-child visits (e.g., at 18 months or 24 months) were retained. A total of 29 articles that met other inclusion criteria but had samples recruited from non-US countries were excluded; none of those articles included a subsample from the US. Of the articles that were excluded because their samples were not representative of the target population of this systematic review (n=85), some articles screened children and analyzed subgroups based on positive vs. negative status, yet all of the children were still at elevated familial likelihood of autism. Of the final review sample (see Table 4 for descriptions of included studies), geographical locations of recruited samples were primarily from the eastern half of the US. One study used data from the National Children’s Study which had enrolled children from 31 states across the US before being discontinued (Moye, 2020), three studies recruited from a southern state (Georgia, Tennessee, Virginia), one study recruited from two eastern states (Pennsylvania and New Jersey), and one study recruited from an unidentified Midwest state. Three studies reported years of recruitment (2010-2015) while the other three did not specify cohort years. 32 Table 4. Characteristics of the studies included in the final sample. Systematic Review Final Sample Publication Mean age in months (SD) Child sex Stated purpose Guthrie et al. (2019) PA, USA NJ, USA N=25,999 Not provided; screening at approx. 18 and 24 months Male: 13,446 (51.7%) Female: 12,553 (48.3%) “Our goal with this study was to examine the real-world accuracy of universal screening for ASD by using an epidemiological design and long-term follow-up through 4 to 8 years of age. … Our secondary goals were to examine the accuracy of repeated screenings and the effect of child and/or family characteristics on screening rates and accuracy.” (Guthrie et al., 2019, p. 2) Heffler et al. (2020) Data from the National Children’s Study N=2,152 26.6 (2.1) at screening Male: 1,099 (51.1%) Female: 1,053 (48.9%) “…we examined the extent to which early screen exposure and social engagement through play and reading as well as previously identified perinatal and demographic factors were each associated with later risk of ASD and ASD-like symptoms as measured by the Modified Checklist for Autism in Toddlers (M-CHAT).” (Heffler et al., 2020, p. 691) Khowaja et al. (2015) GA, USA N=11,845 20.8 (3.2) at screening Male: 6,073 (51.3%) Female: 5,772 (48.7%) “Given the influence of early identification and treatment of ASD on prognosis, it is important to examine if and how sociodemographic factors (i.e., maternal education and race) might affect the screening process in a large, racially diverse sample.” (Khowaja et al., 2015, p. 1798) Peterman (2016) TN, USA N=773 24.48 (1.46) at screening Male: 391 (50.6%) Female: 382 (49.4%) “The first purpose of the current study was to extend this research by focusing exclusively on the African American subgroup of longitudinal participants from the CANDLE study, including more than 450 additional African American participants for whom data was collected since the study conducted by Gardner et al. (2013). …The second purpose of the present research was to determine whether the classification accuracy of the BITSEA could be 33 Systematic Review Final Sample Publication Mean age in months (SD) Child sex Stated purpose enhanced by including maternal factors associated with ASD. … Ultimately, the goal of this study is to inform screening practices in an effort to increase identification of African American toddlers at risk for ASD and facilitate early diagnosis and provision of intervention services.” (Peterman, 2016, pp. 12-14) Rea et al. (2019) Midwest state, USA N=999 23.87 (4.82) Male: 513 (51.4%) Female: 486 (48.6%) “This study sought to examine patterns of use of the Modified Checklist for Autism in Toddlers-Revised (MCHAT-R) as an ASD screening instrument for children presenting for a primary care appointment in an urban pediatric clinic setting, including a Hispanic clinic specifically designed to meet the needs of Spanishspeaking children and their families. … Finally, we aimed to better understand referral practices after a positive screening in this racially and ethnically diverse sample.” (Rea et al., 2019, p. 494) Scarpa et al. (2013) VA, USA N=447 Not provided; screening at approx. 18 and 24 months Male: 234 (52.3%) Female: 202 (45.2%) Not reported: 11 (2.5%) “This study aims to explore differences on M-CHAT performance in relation to educational and minority status in order to determine if the measure may need modifications for early detection in diverse groups and thus better focus intervention efforts in the future.” (Scarpa et al., 2013, p. 2272) 34 3.2 Quality assessment All studies were rated using the QuADS using a scale of 0-3 points on each of 13 criteria (total 39 possible points), resulting in a mean score of 27 (SD 3.5; range 21-31) across the six studies in the final sample (see Table 5 for QuADS ratings of each study). All studies received full points on two criteria: statement of research aims and appropriate design to address stated aims. All studies also received a score of at least 2 points on four criteria: theoretical or conceptual underpinning to the research, description of data collection procedure, appropriate method of analysis to answer research aims, and critical discussion of strengths and limitations. Scores ranged from 1-3 on four criteria: clear description of research setting and target population, appropriate sampling to address research aims, rationale for choice of data collection tools, and appropriate format and content of data collection tool to address aims. In general, lower scores on these criteria reflected a lack of rationale for operationalization of sociodemographic variables and minimal description of setting or sampling. Three studies received a score of 0 on justification for analytic method selected; those studies reported their choice of statistical approach without even a basic justification for choice (e.g., previously used by the research team). Notably, no studies received any points on one criterion: evidence that research stakeholders have been considered in research design or conduct. When providing background and rationale for study aims, most studies discussed important disparities in the screening process that impact children and families; however, no studies described involvement of families receiving screening or professionals providing screening through any methods (e.g., focus groups, interviews, advisory group) to guide the study design, collaboratively set research aims, or evaluate findings. 35 Table 5. Quality assessment scores using the QuADS and ratings of intersectional framing of sociodemographic variables in studies making up the final sample. QuADS Scores and Intersectionality Ratings of Final Sample QuADS Criteria Publication (first author’s last name) Guthrie Heffler Khowaja Peterman Rea Scarpa Average 1. Theoretical or conceptual underpinning to the research 2 2 3 3 3 3 2.67 2. Statement of research aim/s 3 3 3 3 3 3 3.00 3. Clear description of research setting and target population 3 2 2 1 3 3 2.33 4. Study design is appropriate to address stated research aim/s 3 3 3 3 3 3 3.00 5. Appropriate sampling to address research aim/s 3 1 3 3 3 3 2.67 6. Rationale for choice of data collection tool/s 1 1 2 3 2 2 1.83 7. Format and content of data collection tool is appropriate to address stated research aim/s 1 1 1 3 2 3 1.83 8. Description of data collection procedure 2 2 2 2 2 3 2.17 9. Recruitment data provided 2 1 1 2 1 2 1.50 10. Justification for analytic method selected 2 0 2 3 0 0 1.17 11. Method of analysis appropriate to answer research aim/s 2 2 2 2 2 2 2.00 12. Evidence that research stakeholders have been considered in research design or conduct 0 0 0 0 0 0 0.00 13. Strengths and limitations critically discussed 2 3 3 3 3 3 2.83 Total QuADS Score 26 21 27 31 27 30 27 SD 3.52 Intersectionality Rating 1 1 3 1 1 1 36 3.3 Measurement of autism likelihood 3.3.1 Autism screener All studies used a version of the Modified Checklist for Autism in Toddlers (M-CHAT; Robins et al., 1999) to screen infants for autism. The original M-CHAT consists of 23 yes-no items and is an extension of The Checklist for Autism in Toddlers (CHAT; Baron-Cohen et al., 1992). The CHAT was developed to identify early autism-related signs in toddlers at 18 months using a combination of parent report and clinical observation of behaviors such as pointing and engagement in pretend play (Baron-Cohen et al., 1992). The M-CHAT was designed to broaden the signs examined to identify a larger number of children with elevated likelihood for not only autism but also pervasive developmental disorders (Robins et al., 2001). Additional items added to the original CHAT items were intended to capture signs likely to be present in infants and toddlers with autism but also included items that almost all parents would endorse (Robins et al., 2001). Cut-off criteria indicating a flag are failing either two critical items or any three total items (Robins et al., 2001). In the initial validation study, physicians screened 16- to 30-monthold children at 18- and 24-month check-up visits with a community sample of 1,122 children and a sample of 171 children at elevated likelihood who had been referred for early intervention. Reliability using Cronbach’s α was determined to be adequate (α=.85 for all 23 items in a subset of 480 participants), and sensitivity, specificity, and positive predictive power values using discriminant function analysis of the full tool using a cutoff of 3 failed items were .97, .95, and .36, respectively, although these values were unable to account for children with false negatives and were calculated using the mixed sample at community-level and elevated likelihood (Robins et al., 2001). A follow-up study with 3,793 participants (3,309 from a community sample and 484 at elevated likelihood following a previous screening or referral) compared positive 37 predictive values (PPVs) between lower and elevated likelihood and found a PPV of .11 (95% confidence interval [.06-.15]) in the community sample and a PPV of .60 (95% CI [.53 to .67]) in the sample already determined to be at elevated likelihood (Kleinman et al., 2008). The M-CHAT, Revised with Follow-Up (M-CHAT-R/F; Robins et al., 2009), included both item and protocol revision and was validated using 15,612 toddlers from a community sample (recruited during well-child care visits) who were 16 to 30 months of age (Robins et al., 2014). Reliability, calculated using Cronbach’s α for the 20 yes/no items remaining on the revised version (M-CHAT-R), was determined to be as expected but lower than desired (α=.63) while the internal consistency of the questions with follow-up questions (M-CHAT-R/F) was higher and at an adequate level (α=.79; Robins et al., 2014). New scoring criteria for this version was a cutoff score of ≥ 2 on the total score after the follow-up to reduce the number of missed cases (i.e., children who screened negative on the M-CHAT-R but either screened positive on another autism screening tool or whose pediatricians had concerns; Robins et al., 2014). The new sensitivity, specificity, and PPV using only the M-CHAT-R items were .91, .96, and .14, respectively, while the same values for the new scoring procedure using the follow-up questions were .85, .99, and .48 (Robins et al., 2014). The authors noted that there was a disproportionately high number of African-American families who did not complete follow-up questions (Robins et al., 2014). Three studies (50%) explicitly stated that a Spanish translation of the M-CHAT was available to Spanish-speaking parents or caregivers, but only one (Scarpa et al., 2013) named the version used in that study (Spanish-Western Hemisphere version). Of the other two studies, one reported that the M-CHAT was available in both English and Spanish (Guthrie et al., 2019) while one reported that the authors could not determine whether a Spanish version was actually 38 used due to limitations in the data available for analysis from the electronic health records (Rea et al., 2019). In a comparison of the Spanish-Western Hemisphere M-CHAT-R version (627 children) to the English M-CHAT-R (1,964 children), DuBay et al. (2021) used a scoring cutoff of >2 failed items to compare performance between groups. Children screened using the Spanish-Western Hemisphere version demonstrated significantly higher scores above the cutoff for a positive initial screen (7.9% vs. 5.5%; χ 2 = 4.4, p<.05) and were 1.4 times more likely to have a higher risk score (p<.001) than children screened using the English version (DuBay et al., 2021). Missingness was also significantly different across groups: the Spanish-Western Hemisphere version was 3.6 times more likely to have blank responses than the English version (p<0.001; DuBay et al., 2021). For the purposes of this study, findings related to initial screening (i.e., use of the MCHAT or M-CHAT-R items prior to use of follow-up questions) are discussed in order to synthesize results across studies at the same point of first screening at community-level likelihood of autism. 3.3.2 Findings Three studies (50%) used the original M-CHAT consisting of 23 items. Using the MCHAT total score (i.e., raw score of 3 or higher) in a sample of 773 Black/African American toddlers, Peterman (2016) found 717 children (93%) to be at low risk and 56 (7%) to be at moderate-to-high risk. Of the children at moderate-to-high risk, 35 (62.5%) were boys (Peterman, 2016). Heffler et al. (2020) reported similar positive rates using the M-CHAT total score (with three questions removed that were dropped on the revised version of the tool) with a sample from the National Children’s Study which was predominantly composed of White children (71%) enrolled at birth from October 2010 to October 2012. Of 2,152 total children who 39 completed the M-CHAT, 150 (7%) were flagged with positive screens. Of those positive screens, 84 children (56%) were boys and 66 (44%) were girls (Heffler et al., 2020). In contrast to these findings, Scarpa et al. (2013) used the Spanish-Western Hemisphere and English versions of the M-CHAT in a sample of 447 toddlers of which 112 (25%) had Spanish-speaking caregivers. This study found a lower proportion of positive screens: 21 children (4.6%). The authors did not report a breakdown of positive screens by child sex. Notably, there were poorer-to-unacceptable levels of reliability of M-CHAT items in this sample across various maternal education (Cronbach’s α=0.428 for <12th grade/no GED completed and α=0.320 for 12th grade/GED completed groups), minority status (α=.399 for non-minority and α=0.533 for minority groups), and income strata (α=.585 for income <$20,000/year and α=.284 for income >$20,000/year), and both English (α=.653) and Spanish (α=.337) language versions demonstrated lower reliability levels than desired (Scarpa et al., 2013). One study (17%) used both the 23-item M-CHAT and the 20-item M-CHAT-R. In a sample of 11,845 children partially overlapping with the validation sample for the revised version of the M-CHAT with follow-up questions, Khowaja et al. (2015) reported 991 positive screens (8%) which was reduced to 225 (3%) after participants completed follow-up questions (excluding 204 participants who did not complete the follow-up questions). Breakdowns of positive screens by child sex were not reported. One study (17%) used only the M-CHAT-R. In a retrospective chart review study of 999 children (45.3% who completed the M-CHAT-R) who were seen at three different pediatric clinics including a Hispanic pediatric clinic, Rea et al. (2019) reported different positive screens by ethnicity: 5.4% of non-Hispanic/Latine children (Black/African-American and White children) and 12% of Hispanic/Latine children screened positive. Breakdowns of positive screens by child sex were not reported; additionally, language 40 of M-CHAT-R administration was not documented in medical charts. Finally, one study (17%) used the M-CHAT/F or the original M-CHAT items with follow-up questions and revised scoring protocol. In a sample of 25,999 children (90.9% of whom were screened via universal screening procedures), Guthrie et al. (2019) reported that 2,256 screened positive (9.5%) at their first screening using either English or Spanish versions of the M-CHAT/F. Following completion of the follow-up interview and exclusion of 1,177 participants who did not complete the followup, this was reduced to 297 (3%) for children whose caregivers completed the follow-up interviews or who scored in a range that did not require a follow-up interview to confirm positive status. However, demographic and clinical differences reported by the authors between those who completed the follow-up and those who did not suggested that children with positive screens who did not complete the follow-up should still be considered positive screens, resulting in a final rate of 6.2% (Guthrie et al., 2019). This differs from the approach taken in Khowaja et al. (2015); in that study, retaining the 204 participants who did not complete follow-up questions would result in a slightly higher final positive screen rate of 3.6% compared to 3%. Guthrie et al. (2019) also reported age-related differences in screening rates: toddlers between 21-26 months screened positive (8.9%) more often than toddlers between 16-20 months did (5.5%) on their first screenings. 3.4 Social status categories 3.4.1 Race and ethnicity All studies reported demographic breakdowns of race and/or ethnicity, the latter of which was operationalized as “Hispanic” or “Hispanic/Latino” status (see Table 6 for race/ethnicity breakdowns, collapsed categories, and statistical results). Five studies (83%) reported race and/or 41 Table 6. Race/ethnicity breakdowns, collapsed analytic categories, and statistical results of studies in the final sample. Race/Ethnicity Categories and Statistical Results Publication Descriptive categories n (%) Analytic categories Positive screen n (%) Odds ratio (95% CI) Guthrie et al. (2019) PA, USA NJ, USA Race of child White Black Other or multiple races Asian Not documented Ethnicity of child Not Hispanic or Latino Hispanic or Latino Not documented 11,118 (42.8%) 4,947 (36.5%) 4,232 (16.3%) 1,109 (4.3%) 43 (0.2%) 23,983 (92.2%) 1,929 (7.4%) 87 (0.3%) By race of child White Black Other or multiple races Asian By ethnicity of child Not Hispanic or Latino Hispanic or Latino 319 (3.0%) 752 (9.5%) 295 (7.6%) 108 (10.6%) 1305 (6.0%) 167 (9.6%) Version: M-CHAT Reference group 3.45 (3.01-3.94)* 2.70 (2.29-3.16)* 3.90 (3.11-4.90)* Reference group 1.67 (1.41-1.97)* Heffler et al. (2020) Data from the National Children’s Study Race/ethnicity of child Non-Hispanic White Hispanic Non-Hispanic Black Non-Hispanic Other Missing 1,527 (71.0%) 255 (11.9%) 157 (7.3%) 196 (9.1%) 17 (0.8%) By race/ethnicity of child Non-Hispanic White Hispanic Black Other N/A (4.1%) N/A (14.0%) N/A (14.6%) N/A (13.5%) Version: M-CHAT (adj. odds ratio) Reference group 3.38 (2.20-5.65)* 1.76 (0.91-3.44) 1.60 (0.81-3.17) Khowaja et al. (2015) GA, USA Race/ethnicity of child White, NOT Hispanic/Latino Black, NOT Hispanic/Latino Asian, NOT Hispanic/Latino White, Hispanic/Latino Unknown, Hispanic/Latino Black, Hispanic/Latino Asian, Hispanic/Latino 7,141 (60.3%) 3,088 (26.1%) 396 (3.3%) 199 (1.7%) 179 (1.5%) 62 (0.5%) 8 (0.1%) By race/ethnicity of child Minority White 584 (12.4%) 407 (5.7%) Version: M-CHAT(-R) 1.84 (1.60-2.13)* Reference group 42 Race/Ethnicity Categories and Statistical Results Publication Descriptive categories n (%) Analytic categories Positive screen n (%) Odds ratio (95% CI) Peterman (2016) TN, USA Race of child African American Race of mother African American White Other 773 (100%) 765 (99%) 7 (0.9%) 1 (0.1%) N/A N/A N/A Rea et al. (2019) Midwest state, USA Race/ethnicity of child African-American White Hispanic Asian Other/unknown 487 (48.8%) 285 (28.5%) 176 (17.6%) 14 (1.4%) 37 (3.7%) By race/ethnicity of child Hispanic Non-Hispanic 10 (12.0%) 20 (5.4%) Version: M-CHAT-R Chi-squared tests for independence African-American vs. White p=.86 Hispanic vs. NonHispanic p=.03* Scarpa et al. (2013) VA, USA Race/ethnicity of child Hispanic Black White Mixed Asian Native American Not reported 121 (27.1%) 113 (25.3%) 112 (25.1%) 40 (8.9%) 8 (1.8%) 3 (0.7%) 50 (11.2%) By race/ethnicity of child Minority Non-minority N/A (~4%) N/A (~4%) Version: M-CHAT Chi-squared test for independence Minority vs. Nonminority p=.79 43 ethnicity of child participants while one study (17%) reported race of both children and their mothers. Across studies, race and ethnicity information were obtained and coded differently. In a sample of children recruited from Tennessee who were all Black/African American, Peterman (2016) reported race of mothers using African American (99%), White (0.9%), and Other (0.1%) categories. Ethnicity of participants was not reported. Race/ethnicity were not statistically examined in relation to autism likelihood as the study was comprised of children with one reported race (African American); however, as discussed earlier, 7% of participants screened positive on the M-CHAT/M-CHAT-R items (Peterman, 2016). Guthrie et al. (2019) reported race and ethnicity separately of children recruited from Pennsylvania and New Jersey, using White (42.8%), Black (36.5%), Other or multiple races (16.3%), Asian (4.3%) as race categories and Not Hispanic or Latino (92.2%) and Hispanic or Latino (7.4%) as ethnicity categories. Children who were Asian (odds ratio (OR) 3.90; 95% CI [3.11 to 4.90]), Black (OR 3.45; 95% CI [3.01 to 3.94]), and Other or multiple races (OR 2.70; 95% CI [2.29 to 3.16]) were all 2-3 times more likely to screen positive on the M-CHAT-R than children who were White, and children who were Hispanic or Latino were also significantly more likely to screen positive than children who were not Hispanic or Latino (OR 1.67; 95% CI [1.41 to 1.97]; Guthrie et al., 2019). Notably, White children were screened more often than children from other race/ethnicity categories, and children who were not screened were more likely to be children of color (Guthrie et al., 2019), suggesting that even when universal screening procedures are in place, systematic racial disparities persist. The other four studies (67%) combined race and ethnicity categories. Khowaja et al. (2015) reported crossed Hispanic/Latino vs. non-Hispanic/Latino racial categories for children recruited from Georgia: White, NOT Hispanic/Latino (60.3%); Black, NOT Hispanic/Latino 44 (26.1%); Asian, NOT Hispanic/Latino (3.3%); White, Hispanic/Latino (1.7%); Black, Hispanic/Latino (0.5%); Asian, Hispanic/Latino (0.1%); and Unknown, Hispanic/Latino (1.5%). The “White, NOT Hispanic/Latino” category was retained and re-coded as “White” while the remaining categories were combined into one “Minority” category. Like Guthrie et al. (2019), Khowaja et al. (2015) also reported that children in the Minority group had higher odds of screening positive on M-CHAT/M-CHAT-R items than children in the White group had (OR 1.84, 95% CI [1.60 to 2.13]). Additionally, children in the Minority group were less likely to complete the follow-up in comparison to children in the White group (Wald’s χ 2 (1)=5.66, p=.017, b=-0.447, SE=0.188, OR 0.639, 95% CI [0.442 to 0.924]), and they were also more likely to not participate due to an invalid phone number (Wald’s χ 2 (1)=3.86, p=.049, b=0.826, SE=0.421, OR 2.29, 95% CI [1.02 to 5.21]; Khowaja et al., 2015). Heffler et al. (2020) similarly crossed race with ethnicity for children recruited from the National Children’s Study, but only for the group that did not report Hispanic/Latine background: Hispanic (11.9%), Non-Hispanic White (71.0%), Non-Hispanic Black (9.1%), and Non-Hispanic Other (9.1%). Children with Hispanic ethnicity had increased odds of positive screening on the M-CHAT (using M-CHAT-R scoring) than children with non-Hispanic White ethnicity (adjusted odds ratio (AOR) 3.38; 95% CI [2.20 to 5.65]) and of higher total scores on the M-CHAT (change 17.4%; 95% CI [8.7% to 26.7%]; Heffler et al., 2020). Children with Black (change 23.9%; 95% CI [12.9% to 35.8%]) and Other (change 16.5%; 95% CI [7.0% to 27.0%]) race/ethnicity were also more likely to have higher total scores on the M-CHAT compared to children with non-Hispanic white race/ethnicity but did not have significantly higher odds of positive screens (Heffler et al., 2020). Rea et al. (2019) combined race and ethnicity for children recruited from a Midwest state: African-American (48.8%), White (28.5%), Hispanic (17.6%), Asian (1.4%), and 45 Other/Unknown (3.7%). After finding no significant differences in positive screening rate between African-American and White children, the authors collapsed the African-American, White, Asian, and Other/unknown categories into a non-Hispanic group (Rea et al., 2019). Children with Hispanic ethnicity were significantly more likely to screen positive on the MCHAT-R than children with non-Hispanic ethnicity with a positive rate of 12.0% compared to 5.4%, respectively (χ 2 (1, N=999)=4.74, p=.03; Rea et al., 2019). Scarpa et al. (2013) also combined race and ethnicity for children recruited from Virginia to create race/ethnicity categories: Hispanic (27.1%), Black (25.3%), White (25.1%), Mixed (8.9%), Asian (1.8%), and Native American (0.7%). These categories were then aggregated into non-minority (White) and minority (all other categories) groups for analysis. It is worth noting that a sizable proportion of children did not have reported race/ethnicity (11.2%) and appear to have been excluded from both minority status groups in analyses. In one-way ANOVAs, there were no significant group differences between minority status groups on M-CHAT total or critical scores, and there were no group differences between proportions of children who screened positive (χ 2 (1, N=395)=0.072, p=.788; Scarpa et al., 2013). At the item level, there were significant differences across minority status groups on three items: the minority group failed items 1 (“Does your child like being swung, bounced on your knee, etc.?”; χ 2 (1, N=339)=5.447, p=.02), 11 (“Does your child ever seem oversensitive to noise? (e.g., plugging ears)”; χ 2 (1, N=344)=11.130, p=.001), and 18 (“Does your child make unusual finger movements near his/her face?”; χ 2 (1, N=339)=8.792, p<.003) more often than the non-minority group did (Scarpa et al., 2013). Interestingly, all three of these items are related to restricted, repetitive patterns of behaviors as outlined in the American Psychiatric Association’s Diagnostic and Statistical Manual, Fifth Edition (DSM-V; American Psychiatric Association, 2013), suggesting possible group 46 differences in understanding, measurement, or manifestation of sensory regulatory and repetitive behaviors related to autism likelihood. 3.4.2 Language Three studies (50%) reported languages of parents and caregivers of participants (see Table 7). Scarpa et al. (2013) reported that 112 participants (25.1%) had Spanish-speaking caregivers who completed the Spanish-Western Hemisphere version of the M-CHAT. Children of caregivers who spoke a language other than English or Spanish were excluded from the study. T tests comparing M-CHAT total and critical scores by language version were non-significant (t(445)=-0.953, p=.341 and t(445)=-0.103, p=.918, respectively). Analyses comparing items across language versions were not conducted. These results contrast with the aforementioned analyses conducted by DuBay et al. (2021) which found significant differences in risk scores across language versions. These conflicting findings may be due to differences in translation versions as Scarpa et al. (2013) used a translation of the original M-CHAT while DuBay et al. (2021) used a translation of the M-CHAT-R, suggesting that children screened using the latter version are more likely to receive higher risk scores. Guthrie et al. (2019) reported that 1,271 participants (4.9%) had Spanish documented as a language in their EHR, and a Spanish version of the M-CHAT/F was available at well-child visits in this study. Due to limitations of the data available from EHRs, language version of administration was not available to researchers; however, children whose EHR documented Spanish as a language were over 2 times more likely to screen positive on the M-CHAT than children whose EHR only listed English (OR 2.83; 95% CI [2.37 to 3.38]; Guthrie et al., 2019). Children whose EHR only listed English were also screened more often, and children who were not screened were more likely to have Spanish documented in their EHR (Guthrie et al., 2019). 47 Table 7. Breakdowns of social strata and other categories across studies in the final sample. Social Strata and Other Categories Publication Language, n, (%) Maternal education, n (%) Insurance, n (%) Income, n (%) Guthrie et al. (2019) PA, USA NJ, USA N=25,999 Male=13,446 (51.7%) Female=12,553 (48.3%) Mean age in months (SD): Not provided; screening at approx. 18 and 24 months Language documented (EHR) English only 24,371 (93.7%) Language other than English 1,271 (4.9%) No language documented 357 (1.4%) Not provided Payer at screening visit Private 14,087 (54.2%) Public/Medicaid 11,769 (45.3%) Self-pay or other 143 (0.6%) Sample income based on census tracts Median $59,596.56 SD $32,579.09 Note: missing data on 17 participants Heffler et al. (2020) Data from the National Children’s Study N=2,152 Male=1,099 (51.1%) Female=1,053 (48.9%) Mean age in months (SD): 26.6 (2.1) at screening Language of interview with caregiver English 2,013 (93.5%) Other 139 (6.5%) Not provided Not provided Annual household income <$30k 602 (28.0%) $30k-$49k 327 (15.2%) $50k-$99k 626 (29.1%) ≥$100k 346 (16.1%) Missing 251 (11.7%) 48 Khowaja et al. (2015) GA, USA N=11,845 Male=6,073 (51.3%) Female=5,772 (48.7%) Mean age in months (SD): 20.8 (3.2) at screening Not provided Note: inclusion criteria required English-speaking parents <High school 516 (4.4%) High school/GED 1,937 (16.4%) Associate/trade/some college 2,790 (23.6%) Bachelor’s degree 3,948 (33.3%) Graduate degree 2,654 (22.4%) Not provided Not provided Peterman (2016) TN, USA N=773 Male=391 (50.6%) Female=382 (49.4%) Mean age in months (SD): 24.48 (1.46) at screening Not provided Less than high school 55 (7.1%) High school diploma/GED 432 (55.9%) Technical school 95 (12.3%) College degree 132 (17.1%) Graduate/professional degree 54 (7.0%) Missing 5 (0.6%) Insurance status Medicaid 490 (63.4%) Other 208 (26.9%) None 68 (8.8%) Missing 7 (0.9%) Not provided Rea et al. (2019) Midwest state, USA N=999 Male=513 (51.4%) Female=486 (48.6%) Mean age in months (SD): 23.87 (4.82) Not provided Not provided Not provided Not provided 49 Scarpa et al. (2013) VA, USA N=447 Male=234 (52.3%) Female=202 (45.2%) Not reported=11 (2.5%) Mean age in months (SD): Not provided; screening at approx. 18 and 24 months Language of M-CHAT English 335 (74.9%) Spanish 112 (25.1%) <12 th grade/no GED 90 (20.1%) Completed 12 th grade/GED 143 (31.26%) >12 th grade/GED 84 (18.79%) Not reported 130 (29.08%) Not provided Household income Under $20k 232 (51.9%) $20k-$39k 73 (16.9%) $40k-$59k 18 (4.0%) $60k-$79k 3 (0.7%) Over $100k 1 (0.2%) Not reported 120 (26.84%) 50 Heffler et al. (2020) reported that 139 participants (6.5%) completed the interview in a language other than English. Having a parent whose interview was completed in English was associated with lower total score on the M-CHAT using M-CHAT-R scoring (change -19.3%, 95% CI [- 26.2% to -10.5%]) but not lower likelihood of screening positive (Heffler et al., 2020). 3.4.3 Maternal education Three studies (50%) analyzed maternal educational levels in relation to autism likelihood. Peterman (2016) reported maternal educational status across five levels based on highest reported level of education reached: less than high school (7.1%), high school diploma or GED (55.9%), technical school (12.3%), college degree (17.1%), and graduate/professional degree (7.0%). In univariate models, maternal education predicted M-CHAT outcome such that higher education was associated with increased likelihood of positive screen (using a p-value of <.25); however, this association no longer met the p-value cutoff for use in the larger multivariate model when health insurance type was entered into models and was thus dropped from further analysis (Peterman, 2016). Khowaja et al. (2015) reported maternal education across four levels: high school or less (20.8%), associate’s/trade/some college (23.6%), bachelor’s degree (33.3%), and graduate degree (22.4%). Khowaja et al. (2015) found that maternal education significantly predicted M-CHAT-R outcomes: participants with the lowest level of maternal education were 2.25 times more likely to screen positive than participants with the highest level of maternal education (95% CI [1.83 to 2.77]). A potential interaction between maternal education and race (minority vs. White) on screening outcome was tested and found to be non-significant (Khowaja et al., 2015). When testing whether there were associations between sociodemographic factors and non-participation in the next steps of screening, Khowaja et al. (2015) found that participants with the lowest level of maternal education (i.e., <high school education) had significantly less 51 odds of completing the follow-up than participants with the highest level (i.e., graduate degree; Wald’s χ 2 (1)=14.4, p<.001, b=-1.16, SE=0.305, OR 0.315, 95% CI [0.173 to 0.572]), and they had higher odds of not participating due to an invalid phone number than those with the highest level of education (Wald’s χ 2 (1)=4.67, p=.030, b=1.74, SE=0.801, OR 5.68, 95% CI [1.18 to 27.3]). For families who were able to be contacted, there was no significant difference in followup completion based on maternal education (p=.140), race (p=.231), or an interaction between maternal education and race (p=.985; Khowaja et al., 2015). Scarpa et al. (2013) reported maternal educational level across three levels: <12th grade/no GED (20.1%), completed 12th grade/GED (31.3%), and >12th grade/GED (18.8%). MCHAT total score significantly differed by maternal educational level (F(2, 314)=3.701, p=.026) such that participants with <12th grade/no GED had the most failed items while participants with >12th grade/GED had the least (Scarpa et al., 2013). The group with <12th grade education also significantly differed in M-CHAT total score from the group with >12th grade/GED (p=.017; Scarpa et al., 2013). Likelihood of positive screen did not significantly differ amongst the maternal educational level groups (χ 2 (2, N=309)=4.752, p=.093; Scarpa et al., 2013). At the item level, Scarpa et al. (2013) found significant group differences on three items: critical item 15 (“If you point at a toy across the room, does your child look at it?”) was failed more often by the group with >12th grade/GED while other groups did not fail the item at all (χ 2 (2, N=315)=11.141, p=.004); item 18 (“Does your child make unusual finger movements near his/her face?”) was failed most often by the group <12th grade/GED and least by the group >12th grade/GED (χ 2 (2, N=309)=15.778, p<.001); and item 22 (“Does your child sometimes stare at nothing or wander with no purpose?”) was failed most often by the group <12th grade/no GED and least by the group >12th grade/GED (χ 2 (2, N=313)=6.463, p=.040). Scarpa et al. 52 (2013) also examined distributions of maternal educational levels in the aggregated minority and non-minority groups and found them to be significantly difference (χ 2 (2, N=257)=14.956, p=.001). The largest proportion of minority group participants were in the 12th grade/GED group (41.9%) followed by participants in the <12th grade/no GED (36.9%) and >12th grade/GED (21.2%) groups (Scarpa et al., 2013). The largest proportion of non-minority group participants were in the 12th grade/GED group (56.7%) followed by participants in the >12th grade/GED (28.9%) and <12th grade/no GED (14.4%) groups (Scarpa et al., 2013). Additionally, a sizable proportion of participants did not have educational level reported (29.1%); missingness of maternal education data was significantly different across groups based on minority status, with more participants in the minority group missing data compared to the non-minority group (χ 2 (1, N=263)=10.196, p<.001; Scarpa et al., 2013). 3.4.4 Income Three studies (50%) analyzed income in relation to autism likelihood. Heffler et al. (2020) reported annual household income across four levels: <$30,000 (28.0%; “lowest”), $30,000-$49,999 (15.2%; “mid-low”), $50,000-$99,999 (29.1%; “mid-high”), and ≥$100,000 (16.1%; “highest”). In analyses comparing income level groups on likelihood of screening positive and being at elevated autism likelihood, children in the lowest annual household income group were more likely to screen positive than children in mid-low and mid-high income groups (mid-low AOR 0.34, 95% CI [0.16 to 0.74] and mid-high AOR 0.43, 95% CI [0.19 to 0.99]); however, they were not more likely to screen positive than children in the highest income group. In analyses comparing groups on total M-CHAT scores for which higher scores indicate more ASD-related symptoms, children in the lowest income group were more likely to have a higher total M-CHAT score than children in mid-high and highest income groups (mid-high change - 53 9.3%, 95% CI [-13.8% to -4.7%] and highest change -13.8%, 95% CI [-17.7% to -7.8%]) but were not more likely to have a higher score than children in the mid-low income group. In this study, findings suggest that being at the extremes of household income increases likelihood of screening positive while having lower income in general increases likelihood of exhibiting more autism-related signs/symptoms. Guthrie et al. (2019) reported median income based on participants’ census tracts, splitting participants via median split at $59,596.56 into binary lower and higher income groups. Children in the lower income group were over 2 times more likely to screen positive than children in the higher income group (OR 2.36; 95% CI [2.11 to 2.64]). Children in the higher income group were screened more often than children in the lower income group, and children who were not screened were more likely to be in the latter group (Guthrie et al., 2019). Scarpa et al. (2013) reported household income across five levels: under $20,000 (51.9%), $20,000-$39,000 (16.9%), $40,000-$59,999 (4.0%), $60,000-$79,000 (0.7%), and over $100,000 (0.2%). These levels were collapsed into binary groups, <$20,000 and >$20,000, and then tested for group differences in M-CHAT total and critical scores (Scarpa et al., 2013). MCHAT total score (t(325)=1.093, p=.275) and critical score (t(325)=1.165, p=.245) were not significantly associated with income level (Scarpa et al., 2013). There were no significant differences in missing maternal educational levels across income groups. Additionally, there were no significant differences in income levels across minority (Hispanic, Black, Asian, Native American, mixed) and non-minority (White) groups (χ 2 (1, N=271)=2.692, p>.10); however, a sizable proportion of participants did not have reported household income (26.84%), and potential systematic missingness of household income data across minority status groups was not explored (Scarpa et al., 2013). 54 3.4.5 Insurance status Two studies (33%) reported insurance status. Peterman (2016) reported insurance status of children across three categories: Medicaid (63.4%), other (26.9%), and none (8.8%). These categories were then collapsed into no insurance/Medicaid and private insurance binary categories for analysis (Peterman, 2016). Based on 95% confidence intervals, health insurance status did not have a statistically significant impact on autism likelihood when controlling for other variables in the model (year 1 model OR 2.22, 95% CI [0.98 to 5.05]; year 2 model OR 1.62, 95% CI [0.68 to 3.87]; Peterman, 2016). Guthrie et al. (2019) reported insurance payer of the well-child visit from which EHR data was collected across three categories: private insurance (54.2%), public insurance/Medicaid (45.3%), and self-pay or other (0.6%). Of these categories, only private insurance and public insurance/Medicaid appear to have been included in analysis of autism likelihood. Children whose visit was paid by public insurance/Medicaid were 3.5 times more likely to screen positive than children whose visit was paid by private insurance (OR 3.50, 95% CI [3.11-3.93]; Guthrie et al., 2019). Additionally, children whose visit was paid by private insurance were more likely to be screened more often than children whose visit was paid by public insurance/Medicaid, and when children were not screened, their visit was more likely to have been paid via public insurance/Medicaid (Guthrie et al., 2019). 3.4.6 Other social status categories One study (17%) reported maternal marital status but did not analyze differences in relation to autism likelihood or sociodemographic variables. Peterman (2016) reported marital status across six categories: never married (55%), married (22.5%), living with partner (18%), divorced (2.2%), separated (1.7%), and widowed (0.1%). Over half of the mothers in this sample had never married, and the implications of this in relation to other maternal factors (e.g., less 55 potential household income, less potential options for healthcare insurance and quality, increased likelihood of facing societal discrimination and barriers as an unmarried mother) were not discussed. One study (17%) analyzed population density in relation to autism likelihood. Guthrie et al. (2019) reported the population density of primary care sites (belonging to the Children’s Hospital of Philadelphia network within Pennsylvania and New Jersey) at which children were screened during well-child visits across two categories: suburban (57.9%) and urban (42.1%). Children whose visit was in an urban practice screened positive on the M-CHAT more frequently than children whose visit was in a suburban practice (OR 2.46, 95% CI [2.21-2.74]; Guthrie et al., 2019). Children whose visit was in a suburban practice were also screened more often, and when children were not screened, their visit was more likely to have been at an urban practice (Guthrie et al., 2019). 3.5 Intersectionality Each study was rated for its level of intersectional framing of sociodemographic variables within the study’s method and findings on a scale from 1-3 (see Table 5). All but one study received a score of 1 for examining sociodemographic variables only as single, independent predictors. Khowaja et al. (2015), which received a score of 3, tested a potential interaction effect between maternal education and minority status on autism likelihood. Additionally, those sociodemographic variables were briefly contextualized amongst social and power dynamics. For instance, discussion of reasons families did not participate in follow-up interviews included reference to potential differences in power (e.g., some families with higher maternal educational levels feeling more empowered to decline participation) and differences in mental load (e.g., some families with lower maternal educational levels or from minoritized backgrounds may experience more life stressors that can impact response rate; Khowaja et al., 2015). Despite these 56 strengths, this study did not explicitly frame sociodemographic characteristics as potentially intersectional, and the choice to collapse race/ethnicity categories into one “minority” group was inadequately justified. The non-significant test for an interaction effect between maternal education and minority status may have been affected by the choice to collapse all race/ethnicity groups except the “White, NOT Hispanic/Latino” group together, a decision that presumes adequate homogeneity in distributions of maternal education levels across race/ethnicity groups. It does not account for systemic historical discrimination in the U.S. resulting in persistent disparities in higher education attainment impacting Black/African American, Hispanic/Latine, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander individuals in particular (National Center for Education Statistics, 2023). Thus, even though analyses in Khowaja et al. (2015) demonstrate consideration of the possibility that the effect of maternal education on autism likelihood might be different across race/ethnicity groups, the approach taken was insufficient for yielding conclusive findings. Moreover, the true relationship between maternal education and autism likelihood for the most disadvantaged race/ethnicity groups may have been lost when those groups were combined and tested together. The remaining studies received a score of 1 and did not demonstrate use of an intersectional framing of sociodemographic variables. Scarpa et al. (2013) did consider variations in maternal educational levels and household income across race and ethnicity, but this was again tested in two groups representing minoritized status without adequate justification for binary grouping. Additionally, participants whose race was not reported (11.2%) were excluded from both groups without justification or discussion of this decision. Given the fact that individuals with non-Hispanic/Latine White race/ethnicity are the least likely to leave this demographic self-report question blank (Dembosky et al., 2019), choosing to simply exclude 57 these participants without justification and proceed with analysis, particularly if perpetuated across multiple studies with similar missing data, is likely to contribute to inaccurate estimates of potential systematic disparities impacting individuals with specific race/ethnicity backgrounds. Rea et al. (2019) tested for differences between Black/African American and White participants on M-CHAT-R positive screens before collapsing all participants except Hispanic/Latine participants into a non-Hispanic/Latine group; however, this also grouped participants who had reported Asian and Other race. No other justification was provided for the choice to group these categories together, and the resulting test across groups potentially conflates ethnicity with translated version as language of M-CHAT-R administration was not recorded. 4. DISCUSSION 4.1 Summary of findings related to race, ethnicity, and language Differences in the ways each study operationalized and/or aggregated race and ethnicity categories (and dealt with missing data) made it difficult to synthesize findings across studies. Overall, findings across the five studies that analyzed race and/or ethnicity in relation to autism likelihood suggest that children with minoritized race and/or ethnicity status are more likely to screen positive than children with White race and/or non-Hispanic/Latine ethnicity. Issues within and across studies that reduce confidence in this finding include potential conflation of Hispanic/Latine ethnicity with Spanish M-CHAT version measurement differences, collapse of all race categories except White participants into one “minority” group, aggregation of White participants with all other non-Hispanic/Latine participants into one group, and exclusion of participants without reported race without adequate justification. Of the two studies (40%) in which race and ethnicity categories were not collapsed into binary categories, Black, Asian, and “Other or multiple races” categories were associated with 58 increased likelihood of positive M-CHAT screen in one study (Guthrie et al., 2019). In another study, Black and “Other” race/ethnicity categories were not associated with increased likelihood of positive screen, but they were associated with higher total M-CHAT/M-CHAT-R scores (Heffler et al., 2020). Hispanic race/ethnicity was associated with both increased likelihood of positive screen and higher total M-CHAT/M-CHAT-R scores (Heffler et al., 2020). These two studies suggest between-group variation in increased autism likelihood when using the MCHAT/M-CHAT-R that may be overlooked when groups are combined or differences by race/ethnicity are not considered. Of the three studies (60%) in which race and ethnicity were collapsed into binary categories, one study reported that the minority group, comprised of all children who were Hispanic/Latino, Black, Asian, “Unknown race”, and “multiracial or other races”, was more likely to screen positive than children who were White (Khowaja et al., 2015). One study found no differences in positive screening rates between children who were African-American or White; once those two categories were aggregated with Asian and “Other/unknown” categories into a non-Hispanic group and then compared to the group of children who were Hispanic, children in the Hispanic group were more likely to screen positive than children who were not (Rea et al., 2019). Of note, this study was unable to control for the effect of language version as a Spanish translation was potentially available to some participants but language of administration was not documented (Rea et al., 2019). One study found no differences in total or critical scores on the M-CHAT between the non-minority group comprised of children who were White and the minority group comprised of children who were Hispanic, Black, Asian, Native American, and mixed; however, there were significant differences on three M-CHAT items with more children in the “minority” group failing those items (Scarpa et al., 2013). Aggregation of race/ethnicity 59 data in health research has been increasingly scrutinized for its potential to overlook heterogeneity between groups and obscure heterogeneity within groups (Kauh et al., 2021). Even the race/ethnicity categories in this review used in the studies that did not collapse groups were broad and do not capture significant within-group variation such as differences in immigrant subpopulations of some categories. More research examining autism likelihood in relation to disaggregated race/ethnicity groups is needed to build more confidence in findings of increased or decreased likelihood; additionally, more focus on determining the source of increased likelihood (e.g., measurement bias, construct bias) and addressing those sources in feasible ways is needed. The three studies that analyzed language in relation to autism likelihood found conflicting results in regards to whether language of caregivers or of screening administration impact autism likelihood. One study found no differences across English and Spanish M-CHAT total and critical scores (Scarpa et al., 2013) while another study found a significantly lower total MCHAT score using M-CHAT-R scoring for parents whose interviews were conducted in English (Heffler et al., 2020). One study found that children whose EHRs documented Spanish as a language were more likely to screen positive on the M-CHAT (Guthrie et al., 2019) while another study did not find differences in positive screen rate on the M-CHAT based on the language of the parent interview (Heffler et al., 2020). When considering these findings in light of DuBay et al. (2021) which showed increased likelihood of positive screen when using the Spanish-Western Hemisphere M-CHAT-R translation compared to the English M-CHAT-R, these studies suggest there may be measurement bias across language versions leading to increased chance of an initial positive screen rate when using a Spanish translation of the MCHAT/M-CHAT-R. Heffler et al. (2020) discussed using language of interview as a proxy of 60 caregiver immigrant status and interpreted results as supporting previous research showing increased rates of autism in children whose mothers are immigrants. As noted earlier, however, Scarpa et al. (2013) reported unacceptable reliability (internal consistency as measured by Cronbach’s α) of the Spanish-Western Hemisphere M-CHAT translation in their sample compared to the English M-CHAT version (α=.337 and α=.653, respectively). Given varying psychometric properties depending on the version of the M-CHAT/M-CHAT-R and the version of the translation, there is a high likelihood of measurement bias impacting scores of children screened for autism using different translation versions of the M-CHAT/M-CHAT-R that should be further explored and addressed to reduce the impact of measurement bias on families undergoing autism screening. 4.2 Summary of findings related to maternal education, income, and insurance status Evidence for an association between maternal education and autism likelihood is mixed. One study found no association between maternal education and autism likelihood (i.e., screening result above cut-off criteria) but did find an association between maternal education and M-CHAT total score (Scarpa et al., 2013); one study found that lower levels of maternal education were associated with increased autism (Khowaja et al., 2015); and one study found that maternal education was potentially associated with M-CHAT outcome but dropped the variable due to potential collinearity with health insurance (Peterman, 2016). One of the studies analyzing maternal educational levels in relation to autism likelihood also found evidence for item-level differences on the M-CHAT amongst the different educational level groups, with two items being failed most often by participants with the lowest level of maternal education and one item being failed most often by participants with the highest level of maternal education (Scarpa et al., 2013). One study found no interaction effect of maternal education and race (minority vs. 61 White) on autism likelihood (Khowaja et al., 2015) while another study found differences in educational level across minority and non-minority groups but did not test for an interaction effect on screening outcomes (Scarpa et al., 2013). Evidence for associations between household income and insurance status in relation to autism likelihood is also mixed but may reflect a pattern that also addresses the varied findings regarding maternal education. Heffler et al. (2020) found that autism likelihood decreases as income increases except in the highest level (≥$100,000), which was associated with lower total M-CHAT score (suggesting decreased severity) but not lower autism likelihood (i.e., likelihood of meeting cutoff criteria). Peterman (2016) did not find that insurance status impacted autism likelihood when controlling for other variables, while Guthrie et al. (2019) found that children whose visit was paid by public insurance/Medicaid were more likely to screen positive than children whose visit was paid by private insurance. Findings related to income and maternal education may reflect a non-linear relation amongst income and/or maternal education and autism likelihood. This may also explain why Scarpa et al. (2013), which grouped income by splitting at $20,000, did not find a significant association between household income and autism likelihood while Guthrie et al. (2019), which grouped income by splitting at $59,596.56, did find that children in the lower income group were more likely to screen positive than children in the higher income group. All children were screened using a parent-report measure, and it may be that caregivers at both extremes of household income and maternal education might spend more time with infants (e.g., as a stay-at-home parent) than caregivers who are not at either extreme, resulting in different response patterns on the M-CHAT/M-CHAT-R. The possibility of a maternal education gradient across parent report of children’s health conditions has previously been explored, with findings suggesting a non-linear maternal 62 education gradient may be explained by increased detection of subtle signs in mothers with the highest levels of maternal education (Berchick, 2016). Berchick (2016) also found that higher family income and paternal educational level were positively associated with decreased probability of reported health conditions. This mirrors recent findings showing that autism prevalence since 2000 is decreasing for White families living in California counties with more wealth compared to White families living in counties with less wealth (Nevison & Parker, 2020). That same study found that Hispanic families continued to demonstrate increasing prevalence across almost all counties, and the prevalence of autism in Black families exceeded prevalence in White families in a majority of counties in which data was available, with steeper increases in rate in all but one county (Nevison & Parker, 2020). Changes in autism prevalence for Asian families plateaued in some counties in which rates declined for White families while it increased in other counties in ways mirroring increases in Hispanic families living in those counties (Nevison & Parker, 2020). In general, findings from Nevison and Parker (2020) suggest that environmental factors associated with autism (e.g., prenatal and perinatal factors, exposures to toxins) are being addressed at a level impacting autism rates specifically for White families with greater wealth while those factors - or additional ones not impacting White families - remain unaddressed for families with other race/ethnicity backgrounds. Additionally, the differences Nevison and Parker (2020) found amongst county-level trends for Asian and Black families suggest that disparities differentially impacting subgroups of families within different race and ethnicity categories (especially with varying immigrant histories) may be uncovered if the overarching groups (e.g., “Asian,” Black,” “Hispanic”) are disaggregated. Altogether, findings of these studies and those included in this systematic review suggest complexities amongst the ways maternal education, income level, and race/ethnicity relate to 63 autism likelihood that may disproportionately impact children of families situated at disadvantaged and/or intersectional points of these social strata. More studies are needed to disentangle measurement-related differences (e.g., variations in response patterns) from autism likelihood at the first screening. 4.5 Limitations of this systematic review There are multiple limitations to this systematic review that should be taken into consideration. One major limitation is that the final dataset of studies all used the M-CHAT/MCHAT-R as the sole measure of autism likelihood. Thus, findings of this study should be considered in light of this limitation; associations found in these studies might not be replicated when using a different screening tool. Another limitation is that the information that was available on sampling across the studies in the final dataset varied. For instance, Guthrie et al. (2019) included the entire sample of eligible visits based on EHRs for analysis (excluding children with a language documented in the EHR that was not English or Spanish) and was able to report percentage of children screened vs. percentage of children that were missed. Peterman (2016) included only the sample of participants meeting inclusion criteria from a larger study of participants. There are likely to be differences between participants who were screened after agreeing to participate in a research study compared to participants who were screened as part of their well-child care visit and whose records are used in a retrospective chart review. Additionally, the focus of this study was on autism screening rather than diagnosis; therefore, we excluded studies which only reported information on children who had received a diagnosis rather than a positive screen. Those studies are likely to have different findings as they reflect final diagnostic status of children rather than initial likelihood as in this review. For the purposes of this study, I sought to identify sociocultural factors observable at the community level that 64 may impact infants and their families before they are ever referred for further evaluation. Khowaja et al. (2015) examined whether positive screens obtained at the community level remained so at the follow-up interview stage and led to positive diagnoses at the evaluation stage. Children of families with the lowest level of maternal education, while more likely to screen positive at first, were least likely to remain a positive screen after follow-up administration (Khowaja et al., 2015). Yet these children were also less likely to actually complete the follow-up and diagnostic evaluation (Khowaja et al., 2015), suggesting that the true change in positive screen rate may be overestimated in this group, particularly for the children who are most disadvantaged. The findings in this review reflect only the initial positive screen rates or initial total scores on M-CHAT/M-CHAT-R items. Given the fact that many families face barriers in completing the follow-up interview (Khowaja et al., 2015) and that physicians are variably likely to conduct the follow-up interview (Guthrie et al., 2019) or refer appropriately after a positive screen (Rea et al., 2019), the findings of this review are likely to represent the final screening status of a sizable proportion of children screened using the M-CHAT/M-CHATR. 4.6 Conclusion The goal of conducting this systematic review was to synthesize the existing evidence documenting associations between social strata categories and autism likelihood in infancy. We found only a handful of studies that met inclusion and exclusion criteria for this age group. While findings conflicted across studies, they indicated that children with historically disadvantaged identities across these social strata are at increased autism likelihood; further research is needed to differentiate true increased likelihood from measurement bias. 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Qualitative Health Research, 29(6), 796-808. doi:10.1177/1049732318808245 Sirriyeh, R., Lawton, R., Gardner, P., & Armitage, G. (2012). Reviewing studies with diverse designs: The development and evaluation of a new tool. Journal of Evaluation in Clinical Practice, 18(4), 746-752. doi:10.1111/j.1365-2753.2011.01662.x 70 CHAPTER 3: Study 2 – An Intersectional Approach to Modeling Sociocultural Factors in Predicting Autism Likelihood in Infancy 1. INTRODUCTION 1.1 Social factors and health outcomes Evidence of persistent disparities across health outcomes has culminated in an extensive but far from comprehensive illustration of the potential for sociocultural processes and power dynamics to manifest in physiological and psychological effects which disproportionately affect marginalized and minoritized communities. Systemic forms of oppression, such as residential segregation and environmental injustice, expose marginalized communities to conditions that damage health and reduce access to resources that can promote health and well-being (Braveman et al., 2022). During childhood, neighborhood poverty and segregation influence blood lead levels (Moody et al., 2016), educational attainment (South et al., 2003), and general self-rated health (Wang et al., 2022). Without extensive protective supports, the toxic stressors in adverse social and physical environments shaped by systemic discrimination disrupt the development of children’s brains, cognitive abilities, and self-regulation (McEwen & McEwen, 2017). Social factors impact individual health and well-being even prior to birth and are associated with adverse perinatal outcomes including higher risk of fetal and neonatal mortality (Lorch & Enlow, 2015). Pathways by which discrimination impacts health during pregnancy and birth include systemic modes such as reduced access to socioeconomic and healthcare resources as well as chronic stressors impacting long-term functioning such as through increased allostatic load (Dominguez, 2008). Black/African American women, who are positioned at an intersection of race and gender identities that are both often socially disadvantaged in the U.S., face 71 intersectional discrimination that is arguably its own unique stressor, particularly during pregnancy (Rosenthal & Lobel, 2011). Previous research has shown that Black/African American mothers of very low birthweight preterm infants were more likely to report lifetime exposure to interpersonal racial discrimination than Black/African American mothers of term infants with birthweight in the typical range (Collins et al., 2004). Additionally, White mothers (as compared to Black/African American mothers) of very low birthweight infants were more likely to receive prenatal treatments such as antenatal steroids and tocolytic medications (Paul et al., 2006). While the mortality rate of infants of Black/African American mothers who received a college education was found to be higher than infants of White mothers with the same educational status, once low birthweight was accounted for, mortality rates were equivalent (Schoendorf et al., 1992). Combined, findings such as these suggest that lifetime experiences of discrimination can have downstream effects impacting the health of individuals and their families. Moreover, the effects of social factors are not limited to the lifetime of one individual; rather, they accumulate across time and generations, even potentially to the level of altering gene expression (Braveman et al., 2011). In one longitudinal study, everyday discrimination that Black/African American and Hispanic/Latine women reported experiencing during pregnancy predicted difficulties with inhibition/separation and negative emotionality in their 6- and 12-month-old infants (Rosenthal et al., 2018). Similarly, everyday discrimination and acculturative stress predicted negative emotionality in 12-month-old infants in a study of Hispanic/Latine, Asian American, multiethnic, and Black mothers and their children (Liu et al., 2023). Documenting differences in health outcomes early in life and their associated social factors is a crucial first step towards addressing 72 and rectifying health disparities that continue to have deleterious effects on generations of marginalized communities. 1.2 Early identification of autism likelihood The “cybernetic model of autism” (Mundy & Crowson, 1997) proposes that an initial primary process involving decreased attention to, and processing of, social stimuli creates a feedback loop involving the development of “secondary” or “compensatory” behaviors (e.g., attenuation of social information processing) which are then re-incorporated into the child’s developmental systems, moving their overall development further from the typical trajectory. Early identification of risk and subsequent early intervention may interrupt these feedback loops and shift the child’s developmental trajectory closer to the norm (Barbaro & Dissanayake, 2009; Mundy & Crowson, 1997). Another conceptualization of early developmental processes related to autism proposes that early sensory differences (e.g., atypical visual-auditory integration) produce cascading effects on higher-level functions such as social communication (Baranek et al., 2018; Cascio et al., 2016). Findings from prospective longitudinal studies also suggest that though developmental skills may not appear to be significantly different in the earliest months of life, trajectories of infants later diagnosed with autism significantly diverge by 12 months of age. This divergence in trajectories may also be accompanied by a period of regression of skills, reportedly impacting up to 30% of children with autism (Baird et al., 2008). Establishing predictors that can be reliably detected prior to this age can thus enable allocation of resources for intervention at an earlier timepoint in children’s development, potentially even preventing the apparent regression of developmental skills that appears to take place between 6 to 12 months of age (Tager-Flusberg, 2010). 73 Health disparities stemming from systemic inequities limit the potential benefits of truncated identification (Hyman et al., 2020; Maenner et al., 2020; Mandell et al., 2009; Palmer et al., 2010) and intervention timelines (Angell et al., 2016; Singh & Bunyak, 2019; Smith et al., 2020; Wallis et al., 2020; Weitlauf et al., 2022): for children from marginalized and minoritized backgrounds, allocation of resources is often reduced and delayed. Crucially, systemic inequities and barriers have engendered the predominance of Non-Hispanic/Latine White participants in health research (George et al., 2014). While autism screening tools and procedures may be appropriate for identifying early signs in children from White backgrounds, they are likely less so for children from other backgrounds for whom presentation of autism-related signs may differ or who are exposed to different etiologic factors (Becerra et al., 2014; Tek & Landa, 2012). Bias in measurement tools and clinical judgment may also impact the validity and reliability of standardized procedures for identification of autism in ways that systematically disadvantage children from marginalized backgrounds (Donohue et al., 2019; Nowell et al., 2015). Pediatricians in the U.S. follow developmental surveillance procedures for autism which includes asking caregivers to report any developmental concerns, the presence of which may initiate formal autism screening or referrals (Becerra-Culqui et al., 2018); however, parent concerns and parent interpretation of early autism signs vary across race/ethnicity groups (Donohue et al., 2019; Tek & Landa, 2012). Further investigations into how identification of autism early in life may be impacted by sociocultural processes are needed to improve the rigor of autism screening and increase timely allocation of resources to those who are most vulnerable. 1.3 Modeling intersectional factors using quantitative methods One difficulty many studies using quantitative methodologies struggle to address is the potential impact of varied influences from social processes. Many studies include one variable 74 representing socioeconomic status (SES) such as maternal educational level. Inclusion of this one variable, however, fails to truly capture the complexities of socioeconomic status and the different external sources contributing to both the perceptions and realities of experienced SES. Studies attempting to determine whether SES variables influence health outcomes might erroneously conclude that SES does not impact health outcomes without any consideration for differential effects of low income, low education, and low social capital across different communities. Statistically, exclusion of sociodemographic variables might over- or underestimate the impact of socially rooted processes on specific subgroups in relation to health outcomes, drastically impacting the accuracy of research findings and potentially leading to policy and health decisions based on erroneous understandings of the underlying heterogeneity within lived experiences. Many studies also aggregate race/ethnicity categories, comparing outcomes for an aggregated group versus Non-Hispanic/Latine White. Such reliance on aggregated data obscures diversity across marginalized communities, perpetuating disparities in access to resources and benefits from policies and programs (Braveman et al., 2022; Kauh et al., 2021). Intersectionality is a key theoretical and methodological approach that has been applied across disciplines in an effort to better capture those social processes and account for them within studies (Choo & Ferree, 2010). Methods for doing so qualitatively, such as through grounded theory studies or participatory action research, have been detailed in the literature and readily incorporated in multiple social science fields (Bowleg, 2008; Cho et al., 2013; McCall, 2005). Methods for doing so quantitatively, however, have been more difficult to fully realize, as many quantitative approaches are not well-suited to capturing the complexities of intersectionality (Bowleg, 2008). This has led to a call for determining appropriate quantitative 75 and mixed methods for incorporating intersectionality in multiple fields (Agenor, 2020; Bauer et al., 2021; Bowleg & Bauer, 2016; Else-Quest & Hyde, 2016). Using an intersectional perspective – that is, exploring sociodemographic factors as interdependent, intertwined, and reflective of social and power dynamics – can reveal systemic inequities that may be overlooked if those factors are only treated as independent and static categories. More studies are needed that explicitly examine interactions amongst sociodemographic factors to better understand the intricate ways environmental and social factors influence autism likelihood. The purpose of this exploratory study was to examine the relationship amongst (1) disaggregated sociodemographic variables, (2) prenatal and perinatal risk factors, and (3) primary caregivers’ concerns for their infants’ development with infant neurodevelopmental differences related to likelihood for autism. The impact of perceived discrimination on the aforementioned relationships was also tested to determine whether part of those relationships could be explained by caregiver report of anticipated or enacted perceived discrimination. 2. METHOD 2.1 Study design The primary analyses of this study used extant data from the Southern California Child Development Survey (SoCal CDS), an ongoing study collecting normative data from a community sample on the First Years Inventory Version 3.1c (FYI 3.1c; Baranek et al., 2013) in the greater Los Angeles area encompassing five southern California counties. The parent SoCal CDS study was approved by both state and USC IRBs. The current study was also approved by the USC IRB. 76 2.2 Recruitment Participants in the parent SoCal CDS study were contacted through mailings based on vital records data (i.e., California birth and death certificates) following random selection based on their child’s age at the time of mailing (i.e., 6-16 months). Caregivers were eligible for contact if the birth mother was born in the United States, the child passed newborn hearing screening in both ears, and the child reached at least 34 weeks gestation. Recruitment was also supplemented through social media postings, resulting in 63 additional responses from participants who consented to linking survey results with vital records data and were successfully matched to California birth records. All questionnaires were completed either on paper copies which were mailed back in business reply envelopes or through Qualtrics XM. Participants who agreed to be contacted about future studies were invited to complete the Intersectional Discrimination Index (InDI; Scheim & Bauer, 2019) within approximately one month of completion of the surveys in the SoCal CDS study. Participants were invited to complete the InDI if they had consented to link vital records data with their survey responses and did not end the survey prematurely. Those participants were then included in separate drawings for $50 giftcards with 1 in 50 odds of winning. 2.3 Sampling and participants Deidentified extant data from the full existing SoCal CDS sample (818 total participants) was used in analyses for this study. Data for these analyses were obtained from caregivers of infants 5 to 17 months of age who completed the FYI 3.1c, an autism risk caregiver-report screener which contains 69 items tapping skills and behaviors across social communication, sensory regulatory function, and motor development related to early indicators of likelihood for autism. Only data from caregivers who had completed the FYIv3.1c and the Developmental 77 Concerns Questionnaire Version 1.6 (DCQ 1.6; Reznick et al., 2005) were included, resulting in a final subset of 635 participants which was used for Bayesian modeling (see Table 8). Due to restrictions limiting linkage of vital records information with SoCal CDS data to parents who provided additional consent, the data used for Bayesian modeling with pre-/perinatal risk factors included 523 of those participants. A subset of 100 caregivers also completed the InDI survey; of those, 94 agreed to link survey results with SoCal CDS data and vital records information, and their data were included in secondary analyses (see Appendix 2). Table 8. Breakdown of participant demographics of the full sample and the subsample included in Bayesian mixed model analyses. Participant Demographics All Responses Bayesian analyses Child age in months (range = 5 to 17) Mean SD Mean SD 10.61 3.21 10.57 3.20 Min Max Min Max 5 17 5 17 Child sex (male) n % n % 398 49.3% 322 50.7% Age in years of parent who gave birth Mean SD Mean SD 31.4 5.6 31.6 5.5 Min Max Min Max 16 47 17 47 Age in years of parent who did not give birth* Mean SD Mean SD 36.1 14.3 35.8 13.1 Min Max Min Max 17 62 17 62 Respondent's relationship to the child n % n % Biological Mother 708 91.4% 580 91.5% Biological Father 42 5.4% 35 5.5% Both Biological Mother and Father 22 2.8% 16 2.5% Other (e.g., Other Mother, Grandparent) 3 0.4% 3 0.5% Maternal race/ethnicity n % n % Hispanic/Latine single race 359 44.5% 278 43.8% Multiple races 44 5.5% 38 6.0% Non-Hispanic/Latine American Indian/Alaska Native 2 0.2% 0 0.0% Non-Hispanic/Latine Asian 71 8.8% 47 9.0% Non-Hispanic/Latine Black or African American 33 4.1% 24 3.8% Non-Hispanic/Latine Other 3 3.7% 0 0.0% Non-Hispanic/Latine White 282 34.9% 238 37.5% Not stated or unknown 13 1.6% -- -- 78 Maternal educational level n % n % Up to 12th, no diploma 25 3.1% 16 2.5% HSD or GED 135 16.7% 100 15.7% Some college/associate degree 177 21.9% 140 22.0% Bachelor’s degree 237 29.4% 200 31.5% Master’s/professional/doctorate degree 211 26.1% 179 28.2% Median household income of residence census tract/block Mean SD Mean SD 83,414.24 35,964.84 84,771.27 36,413.71 Min Max Min Max 11,062 248,036 20,554 248,036 County of residence n % n % Los Angeles 442 54.8% 355 56.0% Riverside 141 17.5% 104 16.4% Orange 116 14.4% 98 15.4% San Bernardino 70 8.7% 49 7.7% Ventura 37 4.6% 29 4.6% * Count and percentage exclude cases with no available information on the parent who did not give birth. Respondents of the final analyses were primarily children’s biological mothers (91.5%). The average respondent was in their early 30s when their child was born, of Hispanic/Latine or Non-Hispanic/Latine White race/ethnicity, and had at least some college education or an associate degree. While there was a large range of estimated median household income within this sample ($20,554 to $248,036), the average respondent’s estimated median household income was $84,771.27. Most participants in this study were recruited from Los Angeles County as half of recruitment mailings were deliberately intended to target individuals living in that county. The remaining half of recruitment mailings were equally split across Riverside, Orange, San Bernardino, and Ventura counties, although response numbers differed across those counties (see Table 8). As noted previously, the vast majority of residential addresses for participants in this study were classified as “urban” by Census Bureau guidelines. 79 2.4 Measures and variables 2.4.1 First Years Inventory Version 3.1c (FYI 3.1c) The FYI 3.1c is a 69-item parent-report autism screening tool that has been expanded for use with infants ages 6 to 16 months of age. It is currently undergoing additional collection of norms within the SoCal CDS study. Psychometric analyses of the original 61-item FYI Version 2.0 demonstrated that the measure identified 44% of 12-month-old infants who received a diagnosis of autism spectrum disorder by 3 years and that 85% of all positive-screened infants experienced developmental challenges requiring evaluation and/or services by 3 years (Reznick et al., 2007). Factor analysis of the revised 69-item FYI 3.1c demonstrated that items loaded onto seven factors across social communication, sensory-regulatory functions, motor coordination, and milestones. In a large community sample of 6,454 children from North Carolina of which a subset of 2,236 children who had outcome data at three years of age, comparisons of mean factor scores revealed that the group of children with parent-reported clinical autism diagnoses by three years of age had significantly higher factor scores than children with no known diagnoses or concerns (Baranek et al., 2022). For the analyses in the current study, scores for each of the following seven factors were included as continuous outcome variables: Communication, Imitation and Play (CIP; 14 items); Social Attention and Affective Engagement (SAE; 14 items); Sensory Hyper-responsiveness (HYPER; 7 items); Sensory Hypo-responsiveness (HYPO; 8 items); Self-regulation in Daily Routines (SREG; 8 items); Sensory Interests, Repetitions, and Seeking Behaviors (SIRS; 7 items); and Motor Coordination and Milestones (MCM; 7 items). 2.4.2 Developmental Concerns Questionnaire Version 1.6 (DCQ 1.6) The DCQ 1.6 is a parent-report questionnaire which asks parents to report whether they or anyone they know have any concerns about their child's development; additionally, parents are 80 asked whether the child's biological relatives have a diagnosis of autism spectrum disorder or any type of developmental disability/disorder. If parents report that they or others have concerns about their child's development, they are asked what those concerns are, who they have talked with about those concerns, and whether their child has been evaluated (and if so, what the results of the evaluation were). Parent report of concerns was used in this study’s analyses as a categorical predictor with the following categories: (1) No one has expressed any concerns about my child's development; (2) I have no concerns about my child's development, but someone else has expressed concerns; (3) I have some concerns about my child's development; (4) I have many concerns about my child's development. Type of developmental concern (e.g., speechlanguage, sensory, general) and any professionals parents may have spoken to about those concerns (e.g., pediatrician, social worker) were also coded and included in posthoc analyses. 2.4.3 Intersectional Discrimination Index (InDI) The InDI is a set of three measures with 39 items addressing anticipated (InDI-A) and enacted (day-to-day [InDI-D] and major [InDI-M]) discrimination that was designed for use in quantitative intersectionality studies. The InDI questions do not ascribe discrimination to any particular axis of social status or position, thus potentially allowing the index to be used with groups of individuals that are stratified across different axes. The InDI-A subscale is scored using a mean score of nine items which are scored from 0 (strongly disagree) to 4 (strongly agree). The InDI-D lifetime sum score is generated from nine items which are coded as 1 if any option other than “no” is endorsed and has a total range of 0 to 9. The InDI-D past year sum score is generated from the same nine items which are re-coded on a scale from 0 (no) to 2 (many times) and has a total range of 0 to 18. The InDI-M lifetime sum score is generated from 13 items which are coded from 0 (never) to 2 (more than once) and has a total range of 0 to 26. The InDI-M past 81 year sum score is generated from follow-up questions to those 13 items which are coded as 1 (yes) and 0 (no) and has a total range of 0 to 13. An exploratory factor analysis supported unidimensionality of the InDI-A, and tests of the index in known-group comparisons supported construct validity as individuals within minority groups exhibited significantly higher discrimination results; additionally, each InDI component was significantly positively associated with psychological distress as measured on the Kessler 6 (Kessler et al., 2002 as cited in Scheim & Bauer, 2019). The intraclass correlation (ICC) demonstrating test-retest reliability for the InDI-A was .72 while ICCs were .70 for lifetime InDI-D, and .72 for lifetime InDI-M (Scheim & Bauer, 2019). Subscale mean (InDI-A) and sum scores were used in analyses since differences in anticipated vs. enacted discrimination may have differential impacts on predictor variables. 2.4.4 Sociodemographic variables Information including caregivers' race(s), ethnicities, highest educational levels, employment status, residential geographic area (urban vs. rural), and age were obtained from vital records data which were reported by children’s biological parents on their birth certificates as well as input by medical professionals at children’s birth hospitals. Median household income for each participant was approximated by matching 2020 Census tract and block median household income values with birth mothers’ residential addresses at the time of their children’s birth and was included as a continuous predictor which was grand-mean centered. Maternal race/ethnicity group membership was included in analyses as a categorical predictor based on vital records labeling for race/ethnicity (Hispanic/Latine single race, Non-Hispanic/Latine White race only, Non-Hispanic/Latine Black, American Indian/Alaska Native, Native Hawaiian/Other Pacific Islander, Non-Hispanic/Latine Asian, Other-specified, More than one race, and Unknown). For the main analyses of this study, only the following race/ethnicity categories were 82 included due to small sample sizes (or no participants) in the groups that were not included: Hispanic/Latine, Non-Hispanic/Latine White, Non-Hispanic/Latine Black/African American; Non-Hispanic/Latine Asian. Maternal highest educational level was included as an ordinal predictor (12th grade or less with no diploma; high school graduate degree; some college or associate degree; bachelor’s degree; master's, professional, or doctorate degree). Maternal educational level was collapsed first into three groups for summary analyses (Up to HSD/GED, Some college/associate degree, Bachelor or more) and then into two groups for Bayesian modeling (Up to some college/associate degree, Bachelor or more). Residential geographic area was initially included as a binary variable (urban or rural) but due to the negligible sample size of participants whose residential addresses were classified as rural by the Census Bureau, this variable was dropped from analyses. 2.4.5 Prenatal and perinatal risk factors Information about the infant's "parent who gave birth" including number of prenatal care visits, receipt of Women, Infants, and Children (WIC) program support, number of cigarettes per day, pregnancy complications, and labor complications were obtained from vital records data. Number of prenatal care visits and Apgar score at 5 minutes (range 0-10) were included as discrete predictors using grand-mean centering; pregnancy complications and labor complications were included as discrete predictors using grand-mean centered sum totals; weeks of gestation was included as continuous predictor using grand-mean centering; and receipt of WIC program support was included as a dichotomous predictor. Number of cigarettes per day was also initially planned for inclusion; however, due to lack of variability, this variable was dropped from analyses. 83 2.5 Analysis All analyses were conducted using R Statistical Software (v4.1.1; R Core Team, 2021) and RStudio (RStudio Team, 2021; see Appendix 3 for a list of R packages used in this study). One-way analysis of variance (ANOVA) tests assuming equal variances were used to determine whether there were differences in FYIv3.1c factor score means across maternal race/ethnicity groups. For HYPER factor scores, the assumption of homogeneity of variances (assessed using Bartlett’s test) was violated, and so Welch’s ANOVA was conducted instead. Post-hoc Tukey Honest Significant Difference (HSD) and Games-Howell tests (which automatically adjust for multiple comparisons) were conducted to determine which pairwise comparisons were significantly different. In order to determine whether differences across sociodemographic variables were observed when controlling for other factors, Bayesian multiple linear regression models were tested for each FYIv3.1c factor. For each FYIv3.1c factor, an initial Bayesian multiple linear regression model with a set of predetermined predictors and covariates was tested (Base Model). Numeric predictors (e.g., adjusted age) were grand-mean centered. Additionally, predetermined potential interactions were tested for each factor: maternal race/ethnicity x maternal educational level, maternal race/ethnicity x median household income, and maternal race/ethnicity x adjusted age. These interactions were tested regardless of whether each predictor had a credible effect on its own. Base Model: FYIv3.1c Factor Score ~ Child sex + Developmental concern + Maternal educational level + Median household income (centered) + Adjusted age (centered) + Maternal race/ethnicity group membership 84 Additionally, to account for within-group variance being the primary driving factor for race/ethnicity group differences, Bayesian random intercepts mixed effects models were compared to fixed effects models. Evaluation of model convergence was completed for each model by examining MCMC chain traceplots, posterior predictive check visualizations, Rhat values (R̂=1.00), effective sample sizes (ESS>1,000), and Bayesian R 2 values. Model comparisons/rankings for each factor were completed using leave-one-out cross-validation information criterion (LOOIC) to determine differences in expected predictive accuracy of the models based on expected log predictive density differences. Once a final parsimonious model was evaluated, each FYIv3.1 factor was further tested for associations with prenatal and perinatal risk factors using the final model. Each factor was initially tested with a set of possible prenatal and perinatal predictors in addition to any predictors that had previously been found to have a credible effect on each factor score (Pre- /Perinatal Factors Base Model). Numeric predictors (e.g., number of pregnancy complications) were grand-mean centered. Fixed effects and random intercept models for pre-/perinatal factors were compared as described above. Pre-/Perinatal Factors Base Model: FYIv3.1c Factor Score ~ Predictors from previous analyses + Number of pregnancy complications (centered) + Number of labor complications (centered) + Receipt of WIC + Apgar at 5 minutes + Weeks gestation (centered) + Number of prenatal visits (centered) Caregiver-reported anticipated and perceived/experienced discrimination were tested as mediators of the relationship between race/ethnicity and FYIv3.1c factor scores using the Baron and Kenny procedure (1986) with 10,000 bootstraps to produce estimates and confidence intervals. For these exploratory analyses, only one predictor was included: maternal race/ethnicity group. Each FYIv3.1c factor was included in separate models as dependent 85 outcome variables, and each InDI subscale score was tested as a mediator. Due to small sample sizes of participants with Non-Hispanic Black/African American or Non-Hispanic Asian maternal race/ethnicity who completed the InDI (see Appendix 3 for demographic details), analyses were conducted with two race/ethnicity groups based on whether or not participants had reported Non-Hispanic/Latine White maternal race/ethnicity. Figure 3. Mediation model path diagram. Mediation models (see Figure 3) were tested for each FYIv3.1c factor and for each InDI subscale using structural equation modeling (SEM). Formulae: InDI subscale score ~ a * Maternal race/ethnicity grouping FYIv3.1c factor score ~ b * InDI subscale score FYIv3.1c factor score ~ cp * Maternal race/ethnicity grouping Regression estimates of path b, representing the direct effects of caregiver-reported perceived discrimination on child FYIv3.1c factor scores, were retained and reported in this study. Estimates of path a and c prime were not retained or reported in this study due to the use of aggregated race/ethnicity groups. Descriptive comparisons of mean scores and ranges are instead reported as preliminary findings to characterize the sample and consider future directions. 86 3. RESULTS 3.1 Characteristics of the sample Across this sample, participants who reported a bachelor’s degree or higher educational attainment also lived in residences with the highest median household income based on census data while participants who reported attaining up to a high school (HSD) or general education diploma (GED) lived in residences with the lowest median household income (see Figure 4). There were two notable exceptions across maternal race/ethnicity groups: for participants reporting Non-Hispanic/Latine (NHL) Black/African American maternal race/ethnicity, having up to some college credits or an associate degree did not correspond with a higher average median household income than that of participants reporting up to a HSD or GED; and for participants reporting NHL Asian maternal race/ethnicity, the same level of maternal education corresponded with a higher average median household income than that reported by participants with the highest level of maternal education. Figure 4. Differences in median household income (centered) by maternal educational level and race/ethnicity groups. The majority of participants reported that no one had ever expressed a developmental concern about their child (88%; see Figure 5 for developmental concerns by race/ethnicity group 87 and Figure 6 for developmental concerns by maternal educational level). No participants with NHL Black/African American or NHL Asian maternal race/ethnicity selected the response stating, “I have no concerns about my child’s development, but someone else has expressed concerns.” The Hispanic/Latine maternal race/ethnicity group had the highest proportion reporting that someone else had expressed concerns about their child (6% compared to 2% and 3% of participants with NHL White or multiple maternal races/ethnicities, respectively). Only four participants across the entire sample reported that they had many developmental concerns about their child (0.6%). Figure 5. Parent report of developmental concerns by maternal race/ethnicity group. Figure 6. Parent report of developmental concerns by maternal educational level. 3.2 Factor score mean comparisons by maternal race/ethnicity One-way analysis of variance (ANOVA) was conducted separately for each FYIv3.1c factor to test whether there were statistically significant differences in factor scores across race/ethnicity groups (see Table 9). For HYPER, the assumption of homogeneity of variances 88 was violated, and so Welch’s ANOVA was used instead of traditional ANOVA. Five of the seven factors had statistically significant mean differences across groups: CIP, SAE, HYPER, SREG, and SIRS. Table 9. Mean scores on FYIv3.1c factors by race/ethnicity group. FYIv3.1C Factor Scores CIP SAE HYPER HYPO SREG SIRS MCM Full sample (N=635) mean 0.03 0.00 -0.02 -0.01 -0.01 0.00 0.03 min -1.75 -1.90 -1.85 -2.25 -2.02 -1.79 -1.66 max 2.43 4.33 4.01 4.61 3.87 2.76 2.30 Hispanic/Latine and any single race (n=278) mean -0.09 -0.09 0.12 0.06 0.09 0.10 -0.04 min -1.75 -1.84 -1.62 -2.09 -1.78 -1.68 -1.66 max 2.27 4.33 4.01 4.61 3.87 2.76 2.30 NHL Asian (n=57) mean 0.32 0.16 0.14 0.15 0.17 0.12 0.34 min -1.37 -1.291 -1.13 -1.26 -1.00 -1.29 -1.41 max 2.31 2.28 2.36 2.04 2.30 1.56 2.16 NHL Black/African American (n=24) mean -0.19 -0.35 -0.09 -0.20 -0.17 0.18 -0.13 min -1.71 -1.83 -1.85 -2.25 -2.02 -1.55 -1.62 max 1.43 0.98 1.65 1.81 0.96 1.93 1.23 Multiple races (n=38) mean 0.01 0.02 -0.26 -0.20 -0.13 -0.14 0.04 min -1.61 -1.90 -1.42 -1.90 -1.68 -1.79 -1.37 max 1.73 2.31 1.79 1.38 1.39 1.75 1.91 NHL White race only (n=238) mean 0.12 0.10 -0.18 -0.09 -0.13 -0.13 0.06 min -1.69 -1.84 -1.73 -1.85 -1.97 -1.66 -1.65 max 2.43 2.44 2.93 2.55 2.50 1.96 2.23 One-way ANOVA F 3.221 2.991 5.781 2.22 3.251 3.665 2.148 p .012* .018* <.001* .065 .012* .006* .073 *Significant difference based on p < .05; NHL = Non-Hispanic/Latine; standardized factor scores Post-hoc Tukey HSD tests across each factor resulted in multiple significant pairwise comparisons. For CIP, participants with maternal race/ethnicity in the NHL Asian group had significantly higher mean scores (suggesting higher risk) than participants with maternal race/ethnicity in the Hispanic/Latine group (0.411, p=.027). For SREG, participants with 89 maternal race/ethnicity in the NHL White group had significantly lower mean scores (suggesting lower risk) than participants with maternal race/ethnicity in the Hispanic/Latine group (-0.220, p=.026). For SIRS, participants with maternal race/ethnicity in the NHL White group had significantly lower mean scores (suggesting lower risk) than participants in the Hispanic/Latine group (-0.238, p=.008). For SAE, HYPO, and MCM, no pairwise comparisons were statistically significant. For HYPER, Games-Howell test was used due to unequal variance across groups and resulted in multiple significant pairwise comparisons. There were significant differences between participants with maternal race/ethnicity in the Hispanic/Latine group compared to the NHL White group (-0.302, p<.0004) and between participants with maternal race/ethnicity in the NHL Asian group compared to the NHL White group (-0.316, p=.041). 3.3 Bayesian multilevel models 3.3.1 Communication, imitation, and play (CIP) The first factor tested, Communication, imitation, and play (CIP), consists of 18 items targeting social communication behaviors such as pointing to communicate and imitation of sounds or noises (Baranek et al., 2022). Model 0 (Fixed effects): CIP ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership Model 1 (Nesting in race/ethnicity groups with random intercepts): CIP ~ 1 + Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + (1 | multiple membership across Maternal race/ethnicity groups) 90 CIP MODEL 0 CIP MODEL 1 Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept 0.08 0.00 0.16 0.30 -0.24 1.20 DCQ No someone 0.21 -0.06 0.48 0.21 -0.06 0.48 DCQ Yes some 0.37 0.18 0.56 0.38 0.19 0.57 DCQ Yes many 1.96 1.34 2.58 1.97 1.35 2.59 Adjusted age -0.72 -0.78 -0.67 -0.73 -0.78 -0.68 Group: Hispanic/Latine -0.19 -0.30 -0.08 -0.36 -0.79 -0.04 Group: Black/African American -0.30 -0.57 -0.04 -0.69 -1.70 -0.03 Group: Asian -0.03 -0.21 0.16 -0.31 -1.20 0.27 Group: Multiple races -0.13 -0.37 0.10 -0.35 -0.95 0.08 Random Part sd(Intercept) --- --- --- 0.42 0.02 1.63 Sigma 0.63 0.59 0.67 0.63 0.59 0.66 Alpha -0.25 -1.31 0.86 -0.30 -1.37 0.84 Bayesian R2 0.58 0.55 0.61 0.59 0.55 0.61 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, Model 0 was estimated to have a slightly higher expected predictive accuracy than Model 1 (ELPD difference smaller than 4.0). The variance ratio of Model 1 taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Model 1, which was determined to be the final model for interpretation, was estimated to explain 58.5% of the variance in CIP scores (R2 = 0.59, Est. Error = 0.02, 95% CI 0.55 to 0.61). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 14,259 to 86,797; Tail ESS range 15,694 to 44,613) indicated adequate model convergence. 91 CIP MODEL 1: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept 0.30 0.37 -0.24 1.20 DCQ No someone 0.21 0.14 -0.06 0.48 DCQ Yes some 0.38 0.10 0.19 0.57 DCQ Yes many 1.97 0.31 1.35 2.59 Adjusted age -0.73 0.03 -0.78 -0.68 Group: Hispanic/Latine -0.36 0.20 -0.79 -0.04 Group: Black/African American -0.69 0.44 -1.70 -0.03 Group: Asian -0.31 0.38 -1.20 0.27 Group: Multiple races -0.35 0.27 -0.95 0.08 Random Part sd(Intercept) 0.42 0.45 0.02 1.63 Sigma 0.63 0.02 0.59 0.66 Alpha -0.30 0.62 -1.37 0.84 Bayesian R2 0.59 0.02 0.55 0.61 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution For CIP scores, a positive increase in score is an increase in risk. Parent report that they had some and many developmental concerns about their child were associated with an estimated increase in CIP score of 0.38 (SD 0.10; 95% CI 0.19 to 0.57) and 1.97 (SD 0.31; 95% CI 1.35 to 2.59), respectively, compared to parent report that no one had ever expressed developmental concerns about their child (see Figure 7). Parent report that they did not have any developmental concerns but someone else had expressed concerns did not have a credibly different effect on CIP scores than report that no one had ever expressed concerns. Figure 7. Mean predicted CIP scores by parent report of developmental concerns. 92 Adjusted age, which was included as a covariate, had a credible effect on CIP scores of -0.73 (SD 0.03; 95% CI -0.78 to -0.67), suggesting that younger children had higher CIP scores while older children had lower CIP scores (see Figure 8). Figure 8. Estimated relationship between adjusted age (centered) and predicted CIP scores. Maternal race/ethnicity group membership also had a credible effect (see Figure 9). Children of mothers reporting Hispanic/Latine ethnicity (-0.36, SD 0.20; 95% CI -0.79 to -0.04) or NHL Black/African American race (-0.69, SD 0.44; 95% CI -1.70 to -0.03) had lower CIP scores than children of mothers reporting only NHL White race/ethnicity. Children of mothers reporting NHL Asian race or multiple races did not have credibly different estimated group mean CIP factor scores than children of mothers reporting only NHL White race/ethnicity. Figure 9. Mean predicted CIP scores by maternal race/ethnicity group. Tests for interactions between race/ethnicity groups and maternal education, median household income, and adjusted age did not have credible effects that were different from 0. 93 3.2.2 Social attention and affective engagement (SAE) The Social attention and affective engagement (SAE) factor consists of 14 items targeting social communication behaviors such as response to name and use of direct eye contact (Baranek et al., 2022). Model 0 (Fixed effects): SAE ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Adjusted age (centered) x Maternal race/ethnicity group membership Model 1 (Nesting in race/ethnicity groups with random intercepts): SAE ~ 1 + Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Adjusted age (centered) x Maternal race/ethnicity group membership + (1 | multiple membership across Maternal race/ethnicity groups) SAE MODEL 0 SAE MODEL 1 Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept 0.04 -0.07 0.14 0.23 -0.38 1.21 DCQ No someone 0.27 -0.08 0.60 0.26 -0.09 0.60 DCQ Yes some 0.46 0.21 0.70 0.46 0.22 0.70 DCQ Yes many 2.67 1.86 3.44 2.68 1.88 3.45 Adjusted age -0.24 -0.35 -0.14 -0.24 -0.35 -0.14 Group: Hispanic/Latine -0.20 -0.33 -0.06 -0.29 -0.77 0.10 Group: Black/African American -0.50 -0.84 -0.16 -0.88 -2.17 -0.09 Group: Asian -0.03 -0.28 0.21 -0.29 -1.34 0.40 Group: Multiple races -0.10 -0.40 0.20 -0.29 -1.01 0.21 Group: Hispanic/Latine x Adj age 0.15 0.01 0.29 0.14 0.00 0.29 Group: Black/African American x Adj age 0.23 -0.13 0.58 0.23 -0.12 0.58 Group: Asian x Adj age -0.16 -0.42 0.10 -0.16 -0.42 0.11 Group: Multiple races x Adj age -0.19 -0.53 0.16 -0.21 -0.57 0.15 Random Part sd(Intercept) --- --- --- 0.45 0.01 1.75 Sigma 0.84 0.79 0.89 0.84 0.79 0.89 Alpha 2.03 1.14 2.98 2.00 1.11 2.94 Bayesian R2 0.16 0.11 0.20 0.16 0.11 0.21 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution 94 Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, Model 0 was estimated to have a slightly higher expected predictive accuracy than Model 1 (ELPD difference smaller than 4.0). The variance ratio of Model 1 taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Notably, once random intercepts were allowed in Model 1, the moderating effect of maternal membership in Hispanic/Latine race/ethnicity group on adjusted age was no longer credibly different from 0. This change was interpreted to be due to reallocation of variance; that is, the effect of membership in Hispanic/Latine group on SAE factor scores appeared to be explained more by within-group than between-group differences. A final random intercepts model (Model 2) was specified based on Model 1 with the interaction term dropped to provide more accurate predictors. Model 2 was estimated to explain 13.8% of the variance in SAE scores (R2 = 0.14, Est. Error = 0.02, 95% CI 0.09 to 0.19). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 17,723 to 79,663; Tail ESS range 17,649 to 44,930) indicated adequate model convergence. Model 2 (Nesting in race/ethnicity groups with random intercepts): SAE ~ 1 + Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + (1 | multiple membership across Maternal race/ethnicity groups) 95 SAE MODEL 2: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept 0.19 0.35 -0.39 1.05 DCQ No someone 0.25 0.18 -0.10 0.59 DCQ Yes some 0.46 0.12 0.21 0.70 DCQ Yes many 2.64 0.41 1.84 3.43 Adjusted age -0.19 0.03 -0.25 -0.12 Group: Hispanic/Latine -0.29 0.21 -0.78 0.08 Group: Black/African American -0.75 0.47 -1.89 -0.02 Group: Asian -0.18 0.40 -1.16 0.47 Group: Multiple races -0.25 0.28 -0.91 0.22 Random Part sd(Intercept) 0.39 0.43 0.01 1.53 Sigma 0.84 0.03 0.79 0.89 Alpha 1.96 0.47 1.07 2.90 Bayesian R2 0.14 0.02 0.09 0.19 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution For SAE scores, a positive increase in score is an increase in risk. Parent report of either some or many developmental concerns about their child was associated with an estimated increase in SAE score of 0.46 (SD = 0.12; 95% CI 0.21 to 0.70) and 2.64 (SD = 0.41; 95% CI 1.84 to 3.43), respectively, compared to parent report that no one had ever expressed a concern (see Figure 10). Parent report that they did not have any developmental concerns but someone else had expressed concerns did not have a credibly different effect on SAE scores than report that no one had ever expressed concerns. Figure 10. Mean predicted SAE scores by parent report of developmental concerns. 96 Adjusted age, which was included as a covariate, had a credible effect on SAE scores of -0.19 (SD 0.03; 95% CI -0.25 to -0.12), suggesting that younger children had higher SAE scores while older children had lower SAE scores (see Figure 11). Figure 11. Estimated relationship between adjusted age (centered) and predicted SAE scores. Maternal race/ethnicity group membership also had a credible effect (see Figure 12). Children of mothers reporting NHL Black/African American race were estimated to have lower mean SAE scores (-0.75, SD 0.47; 95% CI -1.89 to -0.02) than children of mothers reporting only NHL White race/ethnicity. Children of mothers reporting Hispanic/Latine ethnicity, NHL Asian race, or multiple races did not have credibly different group mean SAE factor scores than children of mothers reporting only NHL White race/ethnicity. Figure 12. Mean predicted SAE scores by maternal race/ethnicity group. Tests for interactions between race/ethnicity groups and maternal education, median household income, and adjusted age did not have credible effects that were different from 0. 97 3.2.3 Sensory hyperresponsiveness (HYPER) The Sensory hyperresponsiveness (HYPER) factor consists of seven items targeting sensory regulatory behaviors such as sensitivities to tactile or auditory stimuli (Baranek et al., 2022). Model 0 (Fixed effects): HYPER ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership Model 1 (Nesting in race/ethnicity groups with random intercepts): HYPER ~ 1 + Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + (1 | multiple membership across Race/ethnicity groups) HYPER MODEL 0 HYPER MODEL 1 Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept -0.19 -0.29 -0.09 -0.21 -0.81 0.37 DCQ No someone 0.12 -0.20 0.41 0.12 -0.20 0.42 DCQ Yes some 0.42 0.19 0.64 0.42 0.19 0.64 DCQ Yes many 1.32 0.61 1.96 1.32 0.61 1.96 Adjusted age 0.19 0.13 0.25 0.19 0.13 0.25 Group: Hispanic/Latine 0.22 0.10 0.35 0.27 -0.06 0.65 Group: Black/African American 0.01 -0.32 0.32 0.02 -0.73 0.76 Group: Asian 0.39 0.18 0.60 0.38 -0.33 1.05 Group: Multiple races -0.11 -0.38 0.16 -0.11 -0.58 0.35 Random Part sd(Intercept) --- --- --- 0.30 0.01 1.26 Sigma 0.79 0.74 0.84 0.79 0.74 0.84 Alpha 2.61 1.77 3.67 2.60 1.77 3.69 Bayesian R2 0.12 0.08 0.16 0.13 0.09 0.17 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, Model 0 was estimated to have a slightly higher expected predictive accuracy than Model 1 (ELPD difference smaller than 4.0). The variance ratio of Model 1 taken from 98 calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Notably, once random intercepts were allowed in Model 1, the effect of maternal membership in Hispanic/Latine or NHL Asian race/ethnicity groups was no longer credibly different from 0. This change was interpreted to be due to reallocation of variance; that is, the effect of membership in Hispanic/Latine or NHL Asian race/ethnicity groups on HYPER factor scores appeared to be explained more by within-group than between-group differences. A final random intercepts model (Model 2) was specified based on Model 1 with maternal membership in race/ethnicity groups dropped to provide more accurate predictors. Model 2 was estimated to explain 11.4% of the variance in HYPER scores (R2 = 0.11, Est. Error = 0.02, 95% CI 0.07 to 0.16). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 18,735 to 67,570; Tail ESS range 17,797 to 42,978) indicated adequate model convergence. Model 2 (Nesting in race/ethnicity groups with random intercepts): HYPER ~ 1 + Developmental concern + Adjusted age (centered) + (1 | multiple membership across Maternal race/ethnicity groups) HYPER MODEL 2: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept -0.04 0.20 -0.43 0.35 DCQ No someone 0.13 0.16 -0.19 0.43 DCQ Yes some 0.43 0.12 0.19 0.65 DCQ Yes many 1.36 0.35 0.63 2.00 Adjusted age 0.19 0.03 0.13 0.24 Random Part sd(Intercept) 0.30 0.27 0.07 1.04 Sigma 0.79 0.02 0.74 0.84 Alpha 2.49 0.45 1.71 3.46 Bayesian R2 0.11 0.02 0.07 0.16 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution 99 For HYPER scores, a positive score increase is an increase in risk. Parent report of either some or many developmental concerns about their child was associated with an estimated mean increase in HYPER score of 0.43 (SD = 0.12; 95% CI 0.19 to 0.65) and 1.36 (SD = 0.35; 95% CI 0.63 to 2.00), respectively, compared to parent report that no one had ever expressed a concern (see Figure 13). Parent report that they did not have any developmental concerns but someone else had expressed concerns did not have a credibly different effect on HYPER scores than report that no one had ever expressed concerns. Figure 13. Mean predicted HYPER scores by parent report of developmental concerns. Adjusted age, which was included as a covariate, had a credible effect on HYPER scores of 0.19 (SD = 0.03; 95% CI 0.13 to 0.24), suggesting that younger children had lower HYPER scores while older children had higher HYPER scores (see Figure 14). Figure 14. Estimated relationship between adjusted age (centered) and predicted HYPER scores. Once within-group variation was accounted for in the random intercepts model, maternal membership in race/ethnicity group did not have a credible effect on HYPER scores. 100 3.2.4 Sensory hyporesponsiveness (HYPO) The Sensory hyporesponsiveness (HYPO) factor consists of eight items targeting sensory regulatory behaviors such as orientation and attention to stimuli (Baranek et al., 2022). Model 0 (Fixed effects): HYPO ~ Developmental concern + Maternal educational level (binary split) + Maternal race/ethnicity group membership + Maternal educational level (binary split) x Maternal race/ethnicity group membership Model 1 (Nesting in race/ethnicity groups with random intercepts): HYPO ~ Developmental concern + Maternal educational level (binary split) + Maternal race/ethnicity group membership + Maternal educational level (binary split) x Maternal race/ethnicity group membership + (1 | multiple membership across Maternal race/ethnicity groups) HYPO MODEL 0 HYPO MODEL 1 Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept -0.37 -0.59 -0.15 -0.23 -0.84 0.60 DCQ No someone 0.36 0.02 0.69 0.36 0.02 0.69 DCQ Yes some 0.65 0.42 0.88 0.65 0.42 0.88 DCQ Yes many 2.47 1.70 3.20 2.48 1.70 3.21 Bachelor or more 0.27 0.03 0.52 0.27 0.03 0.51 Group: Hispanic/Latine 0.34 0.10 0.58 0.22 -0.29 0.64 Group: Black/African American 0.19 -0.28 0.66 0.04 -0.97 0.84 Group: Asian 0.53 -0.22 1.21 0.28 -0.90 1.24 Group: Multiple races 0.44 -0.13 0.98 0.32 -0.40 0.95 Group: Hispanic/Latine x Bachelor or more -0.29 -0.60 0.02 -0.29 -0.59 0.02 Group: Black/Afr. Amer. x Bachelor or more -0.55 -1.24 0.13 -0.55 -1.25 0.13 Group: Asian x Bachelor or more -0.36 -1.09 0.42 -0.36 -1.09 0.42 Group: Multiple races x Bachelor or more -0.79 -1.43 -0.12 -0.83 -1.49 -0.15 Random Part sd(Intercept) --- --- --- 0.39 0.01 1.49 Sigma 0.80 0.75 0.85 0.80 0.75 0.85 Alpha 1.69 1.00 2.38 1.71 1.01 2.41 Bayesian R2 0.14 0.09 0.18 0.14 0.10 0.18 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution 101 Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, Model 0 was estimated to have a slightly higher expected predictive accuracy than Model 1 (ELPD difference smaller than 4.0). The variance ratio of Model 1 taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Notably, once random intercepts were allowed in Model 1, the effect of maternal membership in Hispanic/Latine race/ethnicity group was no longer credibly different from 0. This change was interpreted to be due to reallocation of variance; that is, the effect of membership in Hispanic/Latine race/ethnicity group on HYPO factor scores appeared to be explained more by within-group than between-group differences. Model 1 was estimated to explain 13.7% of the variance in HYPO scores (R2 = 0.14, Est. Error = 0.02, 95% CI 0.10 to 0.18). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 20,533 to 85,126; Tail ESS range 20,897 to 50,614) indicated adequate model convergence. 102 HYPO MODEL 1: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept -0.23 0.36 -0.84 0.60 DCQ No someone 0.36 0.17 0.02 0.69 DCQ Yes some 0.65 0.12 0.42 0.88 DCQ Yes many 2.48 0.39 1.70 3.21 Bachelor or more 0.27 0.12 0.03 0.51 Group: Hispanic/Latine 0.22 0.24 -0.29 0.64 Group: Black/African American 0.04 0.45 -0.97 0.84 Group: Asian 0.28 0.54 -0.90 1.24 Group: Multiple races 0.32 0.35 -0.40 0.95 Group: Hispanic/Latine x Bachelor or more -0.29 0.16 -0.59 0.02 Group: Black/African American x Bachelor or more -0.55 0.35 -1.25 0.13 Group: Asian x Bachelor or more -0.36 0.38 -1.09 0.42 Group: Multiple races x Bachelor or more -0.83 0.34 -1.49 -0.15 Random Part sd(Intercept) 0.39 0.41 0.01 1.49 Sigma 0.80 0.02 0.75 0.85 Alpha 1.71 0.36 1.01 2.41 Bayesian R2 0.14 0.02 0.10 0.18 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution For HYPO scores, a positive score increase is an increase in risk. Parent report that they did not have any developmental concerns but someone else had expressed concerns was associated with an estimated mean increase in HYPO score of 0.36 (SD = 0.17; 95% CI 0.02 to 0.69) compared to parent report that no one had ever expressed a concern (see Figure 15). Parent report that they had either some or many developmental concerns about their child was associated with an estimated mean increase in HYPO score of 0.65 (SD = 0.12; 95% CI 0.42 to 0.88) and 2.48 (SD = 0.39; 95% CI 1.70 to 3.21), respectively, compared to parent report that no one had ever expressed a concern. 103 Figure 15. Mean predicted HYPO scores by parent report of developmental concerns. Maternal membership in race/ethnicity groups had a credible moderating effect on the relationship between maternal educational level and HYPO factor scores: a child of a mother reporting multiple races and an educational level of a bachelor’s degree or higher had a decrease in slope of -0.83 (SD = 0.34; 95% CI -1.49 to -0.15) compared to a child of a mother reporting NHL White race/ethnicity and an educational level of up to an associate degree (see Figure 16). Figure 16. Mean predicted HYPO scores by maternal educational level. 3.3.5 Self-regulation in daily routines (SREG) The Self-regulation in daily routines (SREG) factor consists of eight items targeting sensory regulatory behaviors such as ability to be soothed and functioning during daily routines (Baranek et al., 2022). Model 0 (Fixed effects): SREG ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership 104 Model 1 (Nesting in race/ethnicity groups with random intercepts): SREG ~ 1 + Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + (1 | multiple membership across Maternal race/ethnicity groups) SREG MODEL 0 SREG MODEL 1 Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept -0.16 -0.27 -0.06 -0.13 -0.73 0.56 DCQ No someone 0.15 -0.20 0.50 0.15 -0.19 0.50 DCQ Yes some 0.53 0.28 0.77 0.53 0.29 0.77 DCQ Yes many 1.28 0.50 2.03 1.28 0.50 2.04 Adjusted age -0.13 -0.20 -0.07 -0.13 -0.20 -0.07 Group: Hispanic/Latine 0.18 0.04 0.32 0.17 -0.24 0.55 Group: Black/African American -0.08 -0.42 0.26 -0.19 -1.13 0.52 Group: Asian 0.26 0.03 0.49 0.26 -0.51 1.00 Group: Multiple races -0.04 -0.34 0.25 -0.07 -0.61 0.42 Random Part sd(Intercept) --- --- --- 0.95 0.05 2.89 Sigma 0.82 0.77 0.87 0.82 0.77 0.87 Alpha 1.60 0.74 2.31 1.57 0.74 2.27 Bayesian R2 0.09 0.05 0.13 0.09 0.05 0.13 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, Model 0 was estimated to have a slightly higher expected predictive accuracy than Model 1 (ELPD difference smaller than 4.0). The variance ratio of Model 1 taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Notably, once random intercepts were allowed in Model 1, the effect of maternal membership in Hispanic/Latine or NHL Asian race/ethnicity groups was no longer credibly different from 0. This change was interpreted to be due to reallocation of variance; that is, the effect of membership in Hispanic/Latine or NHL Asian groups on SREG factor scores appeared to be 105 explained more by within-group than between-group differences. A final random intercepts model (Model 2) was specified based on Model 1 with maternal race/ethnicity group membership dropped to provide more accurate predictors. Model 2 was estimated to explain 7.7% of the variance in SREG scores (R2 = 0.08, Est. Error = 0.02, 95% CI 0.04 to 0.12). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 15,847 to 64,101; Tail ESS range 14,511 to 45,457) indicated adequate model convergence. Model 2 (Nesting in race/ethnicity groups with random intercepts): SREG ~ 1 + Developmental concern + Adjusted age (centered) + (1 | multiple membership across Maternal race/ethnicity groups) SREG MODEL 2: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept -0.06 0.16 -0.38 0.26 DCQ No someone 0.17 0.18 -0.18 0.51 DCQ Yes some 0.54 0.12 0.29 0.78 DCQ Yes many 1.31 0.39 0.52 2.07 Adjusted age -0.14 0.03 -0.20 -0.07 Random Part sd(Intercept) 0.23 0.23 0.02 0.85 Sigma 0.82 0.02 0.77 0.87 Alpha 1.55 0.41 0.64 2.26 Bayesian R2 0.08 0.02 0.04 0.12 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution For SREG scores, a positive score increase is an increase in risk. Parent report that they had either some or many developmental concerns about their child was associated with an estimated mean increase in SREG score of 0.54 (SD = 0.12; 95% CI 0.29 to 0.78) and 1.31 (SD = 0.39; 95% CI 0.52 to 2.07), respectively, compared to parent report that no one had ever expressed a concern (see Figure 17). Parent report that they did not have any developmental 106 concerns but someone else had expressed concerns did not have a credibly different effect on SREG scores than report that no one had ever expressed concerns. Figure 17. Mean predicted SREG scores by parent report of developmental concerns. Adjusted age, which was included as a covariate, had a credible effect on SREG scores of -0.14 (SD = 0.03; 95% CI -0.20 to -0.07), suggesting that younger children had higher SREG scores while older children had lower SREG scores (see Figure 18). Figure 18. Estimated relationship between adjusted age (centered) and predicted SREG scores. Once within-group variation was accounted for in the random intercepts model, maternal membership in race/ethnicity group did not have a credible effect on SREG scores. 3.3.6 Sensory interests, repetitions, and seeking behaviors (SIRS) The Sensory interests, repetitions, and seeking behaviors (SIRS) factor consists of seven items targeting sensory regulatory behaviors such as perseveration and hand or arm flapping (Baranek et al., 2022). 107 Model 0 (Fixed effects): SIRS ~ Developmental concern + Adjusted age (centered) + Maternal educational level (binary split) + Maternal race/ethnicity group membership + Maternal educational level (binary split) x Maternal race/ethnicity group membership + Adjusted age (centered) x Maternal race/ethnicity group membership Model 1 (Nesting in race/ethnicity groups with random intercepts): SIRS ~ 1 + Developmental concern + Adjusted age (centered) + Maternal educational level (binary split) + Maternal race/ethnicity group membership + Maternal educational level (binary split) x Maternal race/ethnicity group membership + Adjusted age (centered) x Maternal race/ethnicity group membership + (1 | multiple membership across Maternal race/ethnicity groups) SIRS MODEL 0 SIRS MODEL 1 Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept -0.14 -0.35 0.06 -0.11 -0.76 0.60 DCQ No someone 0.44 0.11 0.75 0.44 0.11 0.76 DCQ Yes some 0.20 -0.02 0.42 0.20 -0.03 0.42 DCQ Yes many 0.52 -0.25 1.19 0.52 -0.24 1.19 Adjusted age -0.28 -0.37 -0.18 -0.28 -0.37 -0.18 Bachelor or more 0.01 -0.21 0.24 0.01 -0.22 0.24 Group: Hispanic/Latine 0.24 0.01 0.47 0.21 -0.23 0.62 Group: Black/African American 0.55 0.07 1.01 0.61 -0.18 1.51 Group: Asian 0.25 -0.47 0.89 0.10 -0.99 0.99 Group: Multiple races 0.32 -0.21 0.84 0.31 -0.32 0.93 Group: Hispanic/Latine x Bachelor or more -0.14 -0.43 0.14 -0.14 -0.44 0.15 Group: Black/Afr. Amer. x Bachelor or more -0.80 -1.52 -0.09 -0.80 -1.52 -0.09 Group: Asian x Bachelor or more -0.06 -0.75 0.69 -0.06 -0.75 0.70 Group: Multiple races x Bachelor or more -0.66 -1.27 -0.04 -0.72 -1.37 -0.07 Group: Hispanic/Latine x Adjusted age 0.01 -0.11 0.14 0.01 -0.11 0.14 Group: Black/Afr. Amer. x Adjusted age 0.56 0.21 0.92 0.56 0.21 0.92 Group: Asian x Adjusted age 0.19 -0.03 0.41 0.19 -0.03 0.41 Group: Multiple races x Adjusted age -0.13 -0.44 0.18 -0.12 -0.43 0.20 Random Part sd(Intercept) --- --- --- 0.36 0.01 1.45 Sigma 0.78 0.73 0.83 0.78 0.73 0.83 Alpha 2.30 1.50 3.26 2.29 1.48 3.25 Bayesian R2 0.16 0.12 0.20 0.16 0.12 0.20 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution 108 Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, Model 0 was estimated to have a slightly higher expected predictive accuracy than Model 1 (ELPD difference smaller than 4.0). The variance ratio of Model 1 taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Model 1 was estimated to explain 16.0% of the variance in SIRS scores (R2 = 0.16, Est. Error = 0.02, 95% CI 0.12 to 0.20). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 22,972 to 88,264; Tail ESS range 19,380 to 49,953) indicated adequate model convergence. 109 SIRS MODEL 1: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept -0.11 0.33 -0.76 0.60 DCQ No someone 0.44 0.17 0.11 0.76 DCQ Yes some 0.20 0.11 -0.03 0.42 DCQ Yes many 0.52 0.36 -0.24 1.19 Adjusted age -0.28 0.05 -0.37 -0.18 Bachelor or more 0.01 0.12 -0.22 0.24 Group: Hispanic/Latine 0.21 0.21 -0.23 0.62 Group: Black/African American 0.61 0.41 -0.18 1.51 Group: Asian 0.10 0.50 -0.99 0.99 Group: Multiple races 0.31 0.32 -0.32 0.93 Group: Hispanic/Latine x Bachelor or more -0.14 0.15 -0.44 0.15 Group: Black/African American x Bachelor or more -0.80 0.36 -1.52 -0.09 Group: Asian x Bachelor or more -0.06 0.37 -0.75 0.70 Group: Multiple races x Bachelor or more -0.72 0.33 -1.37 -0.07 Group: Hispanic/Latine x Adjusted age 0.01 0.06 -0.11 0.14 Group: Black/Afr. Amer. x Adjusted age 0.56 0.18 0.21 0.92 Group: Asian x Adjusted age 0.19 0.11 -0.03 0.41 Group: Multiple races x Adjusted age -0.12 0.16 -0.43 0.20 Random Part sd(Intercept) 0.36 0.01 1.45 Sigma 0.78 0.73 0.83 Alpha 2.29 1.48 3.25 Bayesian R2 0.16 0.12 0.20 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution For SIRS scores, a positive score increase is an increase in risk. Parent report that they did not have any developmental concerns but someone else had expressed concerns was associated with an estimated mean increase in SIRS score of 0.44 (SD = 0.17; 95% CI 0.11 to 0.76) compared to parent report that no one had ever expressed a concern (see Figure 19). Parent report that they had either some or many developmental concerns about their child did not have a credibly different effect on SIRS scores than parent report that no one had ever expressed a concern did. 110 Figure 19. Mean predicted SIRS scores by parent report of developmental concerns. Maternal membership in race/ethnicity groups had a credible moderating effect on the relationship between maternal educational level and SIRS factor scores and between adjusted age and SIRS factor scores (see Figure 20). Being a child of a mother reporting NHL Black/African American race/ethnicity or multiple races and an educational level of a bachelor’s degree or higher was likely to have a decrease in slope of -0.80 (SD = 0.36; 95% CI -1.52 to -0.09) and -0.72 (SD = 0.33; 95% CI -1.37 to -0.07), respectively, compared to being a child of a mother reporting NHL White race/ethnicity and an educational level of up to an associate degree. Figure 20. Mean predicted SIRS scores by maternal educational level and maternal race/ethnicity group. Additionally, for children of mothers reporting NHL Black/African American race/ethnicity, adjusted age was positively associated with SIRS factor scores with a mean increase of 0.56 (SD = 0.18; 95% CI 0.21 to 0.92) in slope compared to children of mothers reporting NHL White race/ethnicity (see Figure 21). 111 Figure 21. Estimated interaction effect between maternal race/ethnicity and adjusted age (centered) on predicted SIRS scores. 3.3.7 Motor coordination and milestones (MCM) The Motor coordination and milestones (MCM) factor consists of seven items targeting gross and fine motor skills such as use of pincer grasp as well as developmental milestones such as use of early consonants (Baranek et al., 2022). Model 0 (Fixed effects): MCM ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Maternal race/ethnicity group membership x Adjusted age (centered) Model 1 (Nesting in race/ethnicity groups with random intercepts): MCM ~ 1 + Developmental concern + Maternal race/ethnicity group membership + Adjusted age (centered) + Maternal race/ethnicity group membership x Adjusted age (centered) + (1 | multiple membership across Maternal race/ethnicity groups) 112 MCM MODEL 0 MCM MODEL 1 Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept 0.03 -0.02 0.08 0.12 -0.21 0.60 DCQ No someone 0.17 0.01 0.33 0.17 0.00 0.33 DCQ Yes some 0.26 0.15 0.38 0.26 0.15 0.38 DCQ Yes many 1.48 1.11 1.86 1.49 1.11 1.86 Adjusted age -0.90 -0.95 -0.85 -0.90 -0.95 -0.85 Group: Hispanic/Latine -0.08 -0.15 -0.01 -0.16 -0.40 0.03 Group: Black/African American -0.19 -0.35 -0.03 -0.36 -0.93 0.01 Group: Asian -0.02 -0.14 0.09 -0.10 -0.57 0.25 Group: Multiple races -0.09 -0.23 0.06 -0.16 -0.48 0.08 Group: Hispanic x Adjusted age 0.07 0.01 0.14 0.07 0.01 0.14 Group: Black x Adjusted age 0.15 -0.01 0.31 0.15 -0.01 0.31 Group: Asian x Adjusted age -0.07 -0.19 0.05 -0.07 -0.19 0.05 Group: Multiple races x Adjusted age -0.07 -0.22 0.09 -0.07 -0.23 0.08 Random Part sd(Intercept) --- --- --- 0.23 0.01 0.96 Sigma 0.38 0.36 0.40 0.38 0.36 0.40 Alpha 0.13 -0.97 1.21 0.11 -0.99 1.20 Bayesian R2 0.84 0.83 0.85 0.84 0.83 0.85 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, Model 0 was estimated to have a slightly higher expected predictive accuracy than Model 1 (ELPD difference smaller than 4.0). The variance ratio of Model 1 taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Model 1 was estimated to explain 84.0% of the variance in MCM scores (R2 = 0.84, Est. Error = 0.005, 95% CI 0.83 to 0.85). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 18,826 to 86,454; Tail ESS range 17,978 to 45,075) indicated adequate model convergence. 113 MCM MODEL 1: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept 0.12 0.20 -0.21 0.60 DCQ No someone 0.17 0.08 0.00 0.33 DCQ Yes some 0.26 0.06 0.15 0.38 DCQ Yes many 1.49 0.19 1.11 1.86 Adjusted age -0.90 0.02 -0.95 -0.85 Group: Hispanic/Latine -0.16 0.11 -0.40 0.03 Group: Black/African American -0.36 0.24 -0.93 0.01 Group: Asian -0.10 0.20 -0.57 0.25 Group: Multiple races -0.16 0.14 -0.48 0.08 Group: Hispanic/Latine x Adjusted age 0.07 0.03 0.01 0.14 Group: Black/Afr. Amer. x Adjusted age 0.15 0.08 -0.01 0.31 Group: Asian x Adjusted age -0.07 0.06 -0.19 0.05 Group: Multiple races x Adjusted age -0.07 0.08 -0.23 0.08 Random Part sd(Intercept) 0.23 0.27 0.01 0.96 Sigma 0.38 0.01 0.36 0.40 Alpha 0.11 0.61 -0.99 1.20 Bayesian R2 0.84 0.00 0.83 0.85 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution For MCM scores, a positive score increase is an increase in risk. Parent report of either some or many developmental concerns about their child was associated with an estimated mean increase in MCM score of 0.26 (SD = 0.06; 95% CI 0.15 to 0.38) and 1.49 (SD = 0.19; 95% CI 1.11 to 1.86), respectively, compared to parent report that no one had ever expressed a concern (see Figure 22). Parent report that they did not have any developmental concerns but someone else had expressed concerns did not have a credibly different effect on MCM scores than report that no one had ever expressed concerns. 114 Figure 22. Mean predicted MCM scores by parent report of developmental concerns. Maternal membership in race/ethnicity groups had a credible but very small moderating effect on the relationship between adjusted age and MCM factor scores (see Figure 23). For children of mothers reporting Hispanic/Latine race/ethnicity, adjusted age had a small, credibly different effect on MCM factor scores such that their slope was 0.07 higher than that of children of mothers reporting NHL White race/ethnicity (SD = 0.03; 95% CI 0.01 to 0.14). Figure 23. Estimated interaction effect between maternal race/ethnicity group and adjusted age (centered) on predicted MCM scores. 3.3.8 Prenatal and perinatal risk factors Each FYIv3.1 factor was tested for associations with prenatal and perinatal risk factors using the most explanatory model from the previous analyses. Each factor was initially tested with a set of possible prenatal and perinatal predictors in addition to any predictors that had 115 previously been found to have a credible effect on each factor score. Pre-/perinatal risk factors which were tested included the following: number of pregnancy complications (centered); number of labor complications (centered); receipt of Women, Infants, and Children (WIC) support; infant Apgar score at 5 minutes after birth; infant weeks gestation (centered); and number of prenatal visits (centered). If any pre-/perinatal risk factors were found to have a credible effect, non-nested (0P) and nested (1P) models with non-predictive effects dropped were compared. For CIP, HYPER, HYPO, SREG, and MCM factors, none of the prenatal or perinatal risk factors had a credible effect on factor scores. Table 10. Receipt of WIC support by maternal race/ethnicity group. Participants Who Received WIC support Maternal race/ethnicity group n (of group) % (of group) NHL White 13 6.5% Hispanic/Latine single race 108 47.6% NHL Black/African American 9 39.1% NHL Asian 4 9.8% Multiple races 8 24.2% WIC = Women, Infants, and Children; NHL = Non-Hispanic/Latine For SAE, the only pre-/perinatal risk factor that had a credible effect on SAE factor scores was receipt of WIC support (see Table 10 for group breakdowns). Non-nested and nested models were fitted based on SAE Model 2. SAE Model 2 (Nesting in race/ethnicity groups with random intercepts): SAE ~ 1 + Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + (1 | multiple membership across Maternal race/ethnicity groups) SAE Pre-/Perinatal Factors Model 0P (Fixed effects only): SAE ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Receipt of WIC 116 SAE Pre-/Perinatal Factors Model 1P (Nesting in race/ethnicity groups): SAE ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Receipt of WIC + (1 | multiple membership across Maternal race/ethnicity groups) SAE MODEL 2 SAE MODEL 1P Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept 0.19 -0.39 1.05 0.20 -0.46 1.08 DCQ No someone 0.25 -0.10 0.59 0.50 0.07 0.92 DCQ Yes some 0.46 0.21 0.70 0.43 0.16 0.69 DCQ Yes many 2.64 1.84 3.43 2.33 1.40 3.20 Adjusted age -0.19 -0.25 -0.12 -0.21 -0.28 -0.14 Group: Hispanic/Latine -0.29 -0.78 0.08 -0.25 -0.77 0.16 Group: Black/African American -0.75 -1.89 -0.02 -0.81 -1.98 -0.04 Group: Asian -0.18 -1.16 0.47 -0.14 -0.95 0.57 Group: Multiple races -0.25 -0.91 0.22 -0.14 -0.70 0.32 Receipt of WIC --- --- --- -0.23 -0.40 -0.06 Random Part sd(Intercept) 0.39 0.01 1.53 0.45 0.01 1.71 Sigma 0.84 0.79 0.89 0.83 0.77 0.88 Alpha 1.96 1.07 2.90 1.83 0.80 2.81 Bayesian R2 0.14 0.09 0.19 0.15 0.10 0.21 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, the non-nested Model 0P was estimated to have a slightly higher expected predictive accuracy than the nested Model 1P (ELPD difference smaller than 4.0). The variance ratio of Model 1P taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was negligible. Model 1P was estimated to explain 15.3% of the variance in SAE scores (R2 = 0.15, Est. Error = 0.03, 95% CI 0.10 to 0.21). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 13,928 to 70,286; Tail ESS range 13,894 to 32,643) indicated adequate model convergence. 117 SAE MODEL 1P: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept 0.20 0.39 -0.46 1.08 DCQ No someone 0.50 0.20 0.07 0.92 DCQ Yes some 0.43 0.13 0.16 0.69 DCQ Yes many 2.33 0.46 1.40 3.20 Adjusted age -0.21 0.04 -0.28 -0.14 Group: Hispanic/Latine -0.25 0.24 -0.77 0.16 Group: Black/African American -0.81 0.50 -1.98 -0.04 Group: Asian -0.14 0.37 -0.95 0.57 Group: Multiple races -0.14 0.26 -0.70 0.32 Receipt of WIC -0.23 0.09 -0.40 -0.06 Random Part sd(Intercept) 0.45 0.47 0.01 1.71 Sigma 0.83 0.03 0.77 0.88 Alpha 1.83 0.51 0.80 2.81 Bayesian R2 0.15 0.03 0.10 0.21 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Receipt of WIC support was associated with a decrease in SAE score of -0.23 (SD 0.08; 95% CI -0.40 to -0.06), suggesting that children of mothers who had received WIC support had lower SAE factor scores than children of mothers who had not received WIC support (see Figure 24). Figure 24. Mean predicted SAE scores by receipt of WIC support. A possible interaction between receipt of WIC support and maternal race/ethnicity groups was tested but was not found to have a credible effect on SAE scores. Once accounting for receipt of 118 WIC support, parent report that they did not have any developmental concerns but someone else had expressed concerns had an effect on SAE scores such that children of those parents had credibly higher SAE scores than children of parents who reported that no one had ever expressed a concern about their children’s development (0.50, SD = 0.22; 95% CI 0.07 to 0.92). Parent report of either some or many developmental concerns about their child was associated with an estimated increase in SAE score of 0.43 (SD = 0.13; 95% CI 0.16 to 0.69) and 2.33 (SD = 0.46; 95% CI 1.40 to 3.20), respectively, compared to parent report that no one had ever expressed a concern (see Figure 25). Figure 25. Mean predicted SAE scores by parent report of developmental concerns after adjusting for receipt of WIC support. Adjusted age still had a credible effect on SAE scores of -0.21 (SD 0.04; 95% CI -0.28 to -0.14) such that younger children had higher SAE scores while older children had lower SAE scores (see Figure 26). 119 Figure 26. Estimated relationship between adjusted age (centered) and predicted SAE scores after accounting for receipt of WIC support. Maternal race/ethnicity group membership also still had a credible effect (see Figure 27). Children of mothers reporting NHL Black/African American race were estimated to have lower mean SAE scores (-0.81, SD 0.50; 95% CI -1.98 to -0.04) than children of mothers reporting only NHL White race/ethnicity. Children of mothers reporting Hispanic/Latine ethnicity, NHL Asian race, or multiple races did not have credibly different group mean SAE factor scores than children of mothers reporting only NHL White race/ethnicity. Figure 27. Mean predicted SAE scores by maternal race/ethnicity group after accounting for receipt of WIC support. For SIRS, both receipt of WIC support and weeks gestation had credible effects on SIRS factor scores. Non-nested and nested models were fit based on SIRS Model 1. 120 SIRS Pre-/Perinatal Factors Model 0P (Fixed effects and interaction term): SIRS ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Maternal educational level (binary split) + Receipt of WIC + Weeks gestation (centered) + Maternal race/ethnicity group membership x Maternal educational level (binary split) + Maternal race/ethnicity group membership x Adjusted age (centered) + Maternal race/ethnicity group membership x Receipt of WIC SIRS Pre-/Perinatal Factors Model 1P (Nesting in race/ethnicity groups): SIRS ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Maternal educational level (binary split) + Receipt of WIC + Weeks gestation (centered) + Maternal race/ethnicity group membership x Maternal educational level (binary split) + Maternal race/ethnicity group membership x Adjusted age (centered) + Maternal race/ethnicity group membership x Receipt of WIC + (1 | multiple membership across Race/ethnicity groups) 121 SIRS MODEL 1 SIRS MODEL 1P Fixed Part Est. 95% CI-L 95% CI-U Est. 95% CI-L 95% CI-U Intercept -0.11 -0.76 0.60 -0.36 -1.06 0.26 DCQ No someone 0.44 0.11 0.76 0.50 0.12 0.86 DCQ Yes some 0.20 -0.03 0.42 0.25 0.01 0.48 DCQ Yes many 0.52 -0.24 1.19 0.28 -0.59 1.03 Adjusted age -0.28 -0.37 -0.18 -0.27 -0.37 -0.17 Group: Hispanic/Latine 0.21 -0.23 0.62 0.24 -0.24 0.72 Group: Black/African American 0.61 -0.18 1.51 -0.01 -0.98 1.04 Group: Asian 0.10 -0.99 0.99 0.22 -0.98 1.36 Group: Multiple races 0.31 -0.32 0.93 0.48 -0.58 1.48 Bachelor or more 0.01 -0.22 0.24 0.16 -0.14 0.49 Receipt of WIC --- --- --- 0.09 -0.40 0.56 Weeks gestation --- --- --- 0.09 0.03 0.15 Group: Hispanic/Latine x Bachelor or more -0.14 -0.44 0.15 -0.12 -0.50 0.25 Group: Black/Afr. Amer. x Bachelor or more -0.80 -1.52 -0.09 -0.22 -1.12 0.71 Group: Asian x Bachelor or more -0.06 -0.75 0.70 0.03 -0.91 1.08 Group: Multiple races x Bachelor or more -0.72 -1.37 -0.07 -0.72 -1.68 0.32 Group: Hispanic/Latine x Adjusted age 0.01 -0.11 0.14 0.02 -0.11 0.16 Group: Black/Afr. Amer. x Adjusted age 0.56 0.21 0.92 0.71 0.35 1.08 Group: Asian x Adjusted age 0.19 -0.03 0.41 0.18 -0.07 0.43 Group: Multiple races x Adjusted age -0.12 -0.43 0.20 0.01 -0.29 0.31 Group: Hispanic/Latine x Receipt of WIC --- --- --- 0.14 -0.37 0.66 Group: Black/Afr. Amer. x Receipt of WIC --- --- --- 1.38 0.40 2.38 Group: Asian x Receipt of WIC --- --- --- -0.16 -1.16 0.76 Group: Multiple races x Receipt of WIC --- --- --- -0.21 -1.32 0.94 Random Part sd(Intercept) --- --- --- 0.32 0.01 1.33 Sigma 0.75 0.70 0.80 0.75 0.70 0.80 Alpha 2.39 1.40 3.66 2.38 1.40 3.62 Bayesian R2 0.20 0.15 0.25 0.20 0.15 0.25 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Based on comparison of expected log pointwise predictive density (ELPD) using crossvalidation, the non-nested Model 0P was estimated to have a slightly higher expected predictive accuracy than the nested Model 1P (ELPD difference smaller than 4.0). The variance ratio of Model 1P taken from calculating the difference between variances of draws from the posterior predictive distribution conditional on random effects and not conditioned on random effects was 122 negligible. Once receipt of WIC support and weeks gestation were accounted for in the model, the moderating effect of maternal membership in NHL Black/African American race/ethnicity group on the relationship between maternal educational level and SIRS factor scores no longer had a credible effect. Additionally, the main effect of maternal educational level no longer had a credible effect. A final multiple regression model (Model 2P) was specified based on Model 1P with the interaction between maternal membership in race/ethnicity groups and maternal educational level dropped to provide more accurate predictors. Model 2P was estimated to explain 19.4% of the variance in SIRS scores (R2 = 0.19, Est. Error = 0.03, 95% CI 0.14 to 0.24). All Rhat (R̂=1.00) and effective sample size values (Bulk ESS range 17,857 to 77,747; Tail ESS range 18,069 to 46,574) indicated adequate model convergence. SIRS Pre-/Perinatal Factors Model 2P (Nesting in race/ethnicity groups): SIRS ~ Developmental concern + Adjusted age (centered) + Maternal race/ethnicity group membership + Receipt of WIC + Weeks gestation (centered) + Maternal race/ethnicity group membership x Adjusted age (centered) + Maternal race/ethnicity group membership x Receipt of WIC + (1 | multiple membership across Race/ethnicity groups) 123 SIRS MODEL 2P: RANDOM INTERCEPTS Fixed Part Est. Est. Error 95% CI-L 95% CI-U Intercept -0.21 0.28 -0.87 0.33 DCQ No someone 0.50 0.19 0.12 0.85 DCQ Yes some 0.23 0.12 0.00 0.46 DCQ Yes many 0.29 0.41 0.59 1.03 Adjusted age -0.27 0.05 -0.37 -0.17 Group: Hispanic/Latine 0.11 0.18 -0.27 0.48 Group: Black/African American -0.15 0.39 -0.83 0.75 Group: Asian 0.28 0.34 -0.39 1.03 Group: Multiple races -0.18 0.24 -0.64 0.33 Receipt of WIC -0.04 0.21 -0.46 0.36 Weeks gestation 0.08 0.03 0.02 0.15 Group: Hispanic/Latine x Adjusted age 0.03 0.07 -0.11 0.16 Group: Black/Afr. Amer. x Adjusted age 0.70 0.18 0.35 1.05 Group: Asian x Adjusted age 0.17 0.13 -0.07 0.42 Group: Multiple races x Adjusted age 0.02 0.15 -0.28 0.31 Group: Hispanic/Latine x Receipt of WIC 0.25 0.23 -0.19 0.70 Group: Black/Afr. Amer. x Receipt of WIC 1.52 0.41 0.73 2.34 Group: Asian x Receipt of WIC -0.08 0.44 -0.98 0.72 Group: Multiple races x Receipt of WIC 0.39 0.37 -0.35 1.12 Random Part sd(Intercept) 0.33 0.37 0.01 1.32 Sigma 0.75 0.03 0.70 0.80 Alpha 2.48 0.60 1.46 3.80 Bayesian R2 0.19 0.03 0.14 0.24 CI = Credible Intervals (L = Lower bound; U = Upper bound); DCQ = Developmental Concerns Questionnaire version 1.6; Sigma = Residual standard deviation; Alpha = Shape parameter of skew normal distribution Higher weeks gestation was associated with a very small increase (demonstrating higher risk) in SIRS scores (0.08, SD = 0.03; 95% CI 0.02 to 0.15; see Figure 28). 124 Figure 28. Estimated relationship between weeks gestation (centered) and predicted SIRS scores. Receipt of WIC support had a credible effect on SIRS scores specifically for children of mothers who reported NHL Black/African American race/ethnicity such that receiving WIC support was associated with an increase in SIRS score of 1.52 (SD = 0.41; 95% CI 0.73 to 2.34) compared to children of mothers who reported NHL White race/ethnicity who did not receive WIC support (see Figure 29). Figure 29. Predicted SIRS scores after accounting for the estimated moderating effect of maternal race/ethnicity group on the relationship between receipt of WIC support and predicted SIRS scores. Parent report that they did not have any developmental concerns but someone else had expressed concerns was still associated with higher SIRS factor scores with an estimated mean increase of 125 0.50 (SD = 0.19; 95% CI 0.12 to 0.85) compared to parent report that no one had ever expressed a concern (see Figure 30). Once receipt of WIC support and weeks gestation were accounted for in the model, parent report that they had many developmental concerns about their child had a credible effect on SIRS scores, resulting in a 0.29 mean increase (SD = 0.41; 95% 0.59 to 1.03) compared to parent report that no one had ever expressed a concern. Figure 30. Mean predicted SIRS scores by parent report of developmental concerns after accounting for receipt of WIC support and weeks gestation. For children of mothers reporting NHL Black/African American race/ethnicity, adjusted age was still positively associated with SIRS factor scores with a mean increase of 0.70 (SD = 0.18; 95% CI 0.35 to 1.05) compared to children of mothers reporting NHL White race/ethnicity. 126 Figure 31. Estimated interaction effect between maternal race/ethnicity and adjusted age (centered) on predicted SIRS scores. 3.4 Anticipated and perceived discrimination Structural equation modeling (SEM) was used to test anticipated and enacted discrimination as a mediator of the relationship between maternal race/ethnicity and FYIv3.1c factor scores. The following direct and indirect effects were tested: maternal race/ethnicity on discrimination (path a); discrimination on FYIv3.1c factor scores (path b); and race/ethnicity on FYIv3.1c factor scores (path c prime). Parameter estimates for path b are available in Appendix 4. InDI Anticipated discrimination mean scores significantly predicted HYPER factor scores across the subsample (unstandardized estimate 0.20; SD = 0.10; p = .04; 95% CI 0.02 to 0.41) such that a one-unit increase in caregivers’ mean reported anticipated discrimination was associated with a small increase in child’s HYPER factor score of 0.20. InDI Lifetime Day-toDay enacted discrimination sum scores significantly predicted SREG factor scores across the subsample (unstandardized estimate 0.08; SD = 0.03; p = .01; 95% CI 0.02 to 0.14). The magnitude of this effect was also small, with a one unit increase in caregivers’ reported lifetime day-to-day enacted discrimination leading to a minor increase in child’s SREG factor score of 0.08. 127 One major limitation of these exploratory analyses was the use of aggregated race/ethnicity groupings to address the issue of small sample sizes within subgroupings (e.g., five participants reporting NHL Black/African American race/ethnicity). The full results of these mediation analyses are therefore not presented or interpreted in this study to avoid drawing conclusions without accounting for the possibility that aggregation may have masked disproportionate impacts of discrimination on underrepresented subgroups. Another major limitation of these analyses stems from respondent characteristics. All five participants reporting maternal education of up to a high school or general education diploma were in the Hispanic/Latine maternal race/ethnicity group. Additionally, all five participants in the NHL Asian group and four of five participants in the NHL Black group reported maternal education of a bachelor’s degree or higher. Overall, the majority of participants in this subsample reported maternal education of a bachelor’s degree or higher (74.5%). A summary comparison of InDI scores of all respondents reporting similar maternal education of a bachelor’s degree or higher across maternal race/ethnicity groups is provided here to characterize caregiver experiences of discrimination within this sample (Table 11); a summary comparison of the entire subsample is available in Appendix 5. 128 Table 11. InDI subscale scores of participants with maternal educational level of a bachelor's degree or higher by maternal race/ethnicity group. Intersectional Discrimination Index (InDI) Subscale Scores by Maternal Race/Ethnicity of Participants with Maternal Education of a Bachelor’s Degree or Higher Group InDI-A InDI-D lifetime InDI-D past year InDI-M lifetime InDI-M past year Subset reporting maternal education of a bachelor’s degree or higher (n=70) mean 0.92 4.41 2.37 2.63 0.21 SD 0.79 2.83 3.62 3.61 0.956 min 0.00 0.00 0.00 0.00 0.00 max 3.00 9.00 17.00 13.00 3.00 Non-Hispanic/Latine and White race only (n=34) mean 0.64 3.47 1.97 1.82 0.06 SD 0.68 2.50 3.41 2.88 0.24 min 0.00 0.00 0.00 0.00 0.00 max 2.67 9.00 11.00 12.00 1.00 Hispanic/Latine and any race (n=17) mean 1.12 4.29 2.53 2.29 0.35 SD 0.65 3.08 4.71 3.55 0.79 min 0.33 0.00 0.00 0.00 0.00 max 2.78 9.00 17.00 13.00 3.00 Multiple races (n=10) mean 0.69 5.60 2.60 3.50 0.30 SD 0.50 2.46 2.50 4.03 0.68 min 0.00 0.00 0.00 0.00 0.00 max 1.44 8.00 7.00 14.00 2.00 Non-Hispanic/Latine Asian race only (n=5) mean 1.29 6.20 1.60 2.40 0.40 SD 0.72 2.49 3.05 3.36 0.55 min 0.00 2.00 0.00 0.00 0.00 max 1.67 8.00 7.00 7.00 1.00 Non-Hispanic/Latine Black/African American only (n=4) mean 2.61 7.75 5.50 9.00 0.50 SD 0.53 1.89 3.00 3.65 1.00 min 1.89 5.00 2.00 5.00 0.00 max 3.00 9.00 8.00 13.00 2.00 InDI-A = Anticipated discrimination (range 0-4); InDI-D = Day-to-Day enacted discrimination (lifetime range 0-9; past year range 0-18); InDI-M = Major enacted discrimination (lifetime range 0-26; past year range 0-13). Of this subset of participants reporting a bachelor’s degree or higher, 66 (94%) respondents reported cis female gender. The average median household income for this subset 129 was $99,282.44 (SD = $41,069.21; range $21,519 to $248,036). Median household income varied widely by maternal race/ethnicity group, which may be partially attributable to large differences in sample sizes: the lowest average was in the Hispanic/Latine group ($66,862), and the highest average was in the NHL Asian group ($117,693). Notably, participants with NHL Black/African American maternal race/ethnicity had the highest mean and minimum InDI scores across all subscales, and their InDI major lifetime enacted discrimination sum score was markedly high amongst the groups (9.00 compared to the next highest mean of 3.50). Participants with NHL White maternal race/ethnicity had the lowest mean InDI scores across all subscales except day-to-day enacted discrimination in the past year sum score. 4. DISCUSSION The goal of this exploratory study was to use disaggregated sociodemographic variables to explore predictors of infant neurodevelopmental differences related to autism likelihood and to test whether experiences of discrimination explain any part of those relationships. 4.1 Sociodemographic variables and covariates Bayesian random intercepts models provided estimates of between-group comparisons by race/ethnicity after accounting for within-group variance and potential interdependence amongst race/ethnicity groupings. Being a child of a mother reporting Hispanic/Latine or NHL Black/African American race/ethnicity was associated with a lower CIP score (suggesting lower risk) compared to being a child of a mother reporting NHL White race/ethnicity. Being a child of a mother reporting NHL Black/African American race/ethnicity was also associated with a lower SAE score (suggesting lower risk) compared to being a child of a mother reporting NHL White race/ethnicity. These effects remained credible, suggesting that membership in these groups has a 130 fixed effect on scores. Lower factor scores on CIP and SAE reflect less endorsement of risk in related behaviors. If children from these groups consistently score as having lower risk in these behaviors than children from other groups, whether due to measurement bias or true group differences, the level indicating a concerning amount of endorsed risk for these groups should be set to be lower than for other groups. Otherwise, children from these groups showing milder signs will be systematically under-identified. Previous studies of children already diagnosed with autism have repeatedly found evidence suggesting under-ascertainment of children with milder signs and symptoms of autism in these two race/ethnicity groups (Becerra et al., 2014; Durkin et al., 2017; Tek and Landa, 2012). For HYPER and SREG, the effect of having a mother with Hispanic/Latine or NHL Asian race/ethnicity was no longer credible once random intercepts were allowed. For SAE, HYPO, and SIRS, the effect of having a mother with Hispanic/Latine race/ethnicity was no longer credible once random intercepts were allowed. For MCM, the effect of having a mother with Black/African American race/ethnicity was no longer credible once random intercepts were allowed. For these factors, the loss of credibility of effects associated with maternal race/ethnicity group membership suggests that the effect of being in each group on factor scores varies within each group and does not always have a credible fixed effect. When conceptualizing maternal race/ethnicity as used in this study as a proxy for being affected by certain sociocultural processes associated with race/ethnicity, these results suggest that those processes variably affect each child’s factor scores. Additionally, potential interdependence amongst race/ethnicity groups may reduce the impact of group membership such that there is no fixed effect of race/ethnicity group on these scores. Further investigations into contextual factors that might impact measured differences across race/ethnicity groups are warranted. 131 Adjusted age, which was included as a covariate, had a credible effect on all FYIv3.1c factor scores except for HYPO. Estimated mean CIP, SAE, and SREG scores were lower for older children (suggesting lower risk) and higher for younger children (suggesting higher risk). Conversely, mean scores for HYPER were estimated to be higher for older children (suggesting higher risk) than for younger children. For MCM, race/ethnicity moderated the effect of age such that, compared to younger children in the group with NHL White maternal race/ethnicity, younger children in the Hispanic/Latine maternal race/ethnicity group had slightly but credibly lower estimated MCM scores (suggesting lower risk) and older children had higher estimated MCM scores (suggesting higher risk). This finding suggests that the effect of age on MCM scores for children in the Hispanic/Latine maternal race/ethnicity group is slightly magnified. For SIRS, the relationship between age and factor score was credibly different for children in the group with NHL Black/African American maternal race/ethnicity compared to children in the NHL White group. While the general effect of age across the sample resulted in lower SIRS estimates for older children and higher estimates for younger children, there was an opposite relationship between age and SIRS estimates for the NHL Black/African American group. In a previous study using earlier versions of the FYIv3.1c with a different cohort of children from North Carolina, Chen et al. (2022) found an association between race and SIRS scores such that the group of children who did not have NHL White race had higher scores. Similarly, Scarpa et al. (2013) found a difference across minority status on three items of the Modified Checklist for Autism in Toddlers (M-CHAT) which were related to restricted, repetitive patterns of behaviors. The current study adds to the previous literature through its use of disaggregated race/ethnicity categories and highlights an important area for future research: to determine whether increased mean SIRS scores and/or restricted, repetitive patterns reported in 132 children with NHL Black/African American maternal race/ethnicity in this age range are indicative of a true increase in this group compared to other groups or are a product of measurement bias. In either case, if this relationship continues to be replicated across studies, the potential for higher endorsement of SIRS behaviors of children with NHL Black/African American maternal race/ethnicity should be accounted for when conducting autism screenings using parent report to prevent misidentification of autism likelihood. One limitation of this study was the lack of variability in respondents with a maternal education level of up to a high school or general education diploma (HSD/GED). Due to the small sample sizes of participants in this group, it was combined with participants who had some college experience or up to an associate degree, resulting in a binary split between those who had attained at least a bachelor’s degree and those who had not. This dichotomous variable was then used to estimate potential interactions between maternal education and maternal race/ethnicity. Having a bachelor’s degree or higher was associated with a large, credible effect on HYPO scores, resulting in lower scores for children whose mothers reported multiple races compared to children of mothers with NHL White race/ethnicity and up to an associate degree. Maternal educational level also had an effect on SIRS scores for children in the NHL Black/African American and multiple races groups; however, once weeks gestation and receipt of WIC support were included in that model, the moderating effect of maternal education disappeared. In general, the results of this study suggest that maternal educational level is not as explanatory as other predictors; with the exception of HYPO, maternal educational level does not appear to credibly influence FYIv3.1c scores. Previous research findings have conflicted with regard to whether higher maternal education is associated with increased or decreased autism likelihood (Berchick, 2016; Croen et al., 2002; Khowaja et al., 2015; Peterman, 2016; Scarpa et al., 2013). Nevison 133 and Parker (2020) found differences in autism prevalence across California counties and amongst families of different race/ethnicity and suggested that within-state differences might be explained by differential exposure to environmental factors. In the current study, which recruited from five counties in the greater Los Angeles area, there was a marked difference in estimated median household income by maternal education across counties (see Figure 32). Future studies with larger samples from different counties with varying wealth, healthcare resources, and subcommunity profiles might be able to examine whether conflicting findings in the literature regarding maternal education could be explained by interactions with community-level factors. Figure 32. Average median household income (centered) by county of residence and maternal educational level. Estimates of median household income did not have a credible effect on estimated means for any FYIv3.1c factors, contrary to expectations given the documented effects of poverty on developmental outcomes. One possible explanation is that this variable, to an extent, reflects community-level socioeconomic resources derived from participants’ residential census tracts and blocks and therefore might not capture individual-level socioeconomic resources that could be related to FYIv3.1c factor scores. Another possible explanation is that median household income has different effects in the presence of other factors (e.g., for specific subgroups living in certain counties). Finally, the findings in this study may be impacted by response bias since 134 participants were families with enough time and other resources to complete the survey; families who were most affected by low income and reduced access to resources are unlikely to have participated. 4.2 Parent developmental concerns Parent report that they had some developmental concerns about their child was predictive of somewhat elevated scores on all factors (including SIRS once accounting for pre-/perinatal factors) compared to parent report of no concerns. Parent report of many developmental concerns had a strong positive effect on estimated means of all factors except SIRS compared to parent report of no concerns. Notably, all reports of many parent developmental concerns were from the NHL White or Hispanic/Latine maternal race/ethnicity groups. Thus, while many parent developmental concerns might be predictive of higher scores across most factors for children in these groups, there is not enough evidence to determine whether it is as predictive for children in other maternal race/ethnicity groups. In general, the results of this study suggest that parent report of any amount of developmental concerns is associated with increased scores (suggesting higher risk) on FYIv3.1c factors, a relationship that was expected since the FYIv3.1c is a parent report measure. Parent report that they did not personally have any developmental concerns but someone else had expressed concerns about their child was associated with increased scores for HYPO, SIRS, and SAE (the latter two factors after including pre-/perinatal factors in the models). This finding might suggest that increased behaviors on these factors are not highly concerning to parents even though they are frequent enough to be noted and reported by them. Those behaviors, however, may be more likely to be noted by professionals who express their developmental concerns to those parents. 135 4.3 Prenatal and perinatal risk factors WIC support provides access to classes and health services for caregivers who are pregnant or caring for a child under five years of age and whose income meets federal poverty level-based guidelines (Public Health Foundation Enterprise WIC, 2023). For SAE, receipt of WIC support was associated with less risk in social communication behaviors related to social attention and affective engagement in this age range (e.g., response to name). The support WIC provides might bolster early parenting that scaffolds development of social skills, or it might mitigate some of the effects of poverty on early infant development that might otherwise impact affective engagement. Families who participate in WIC may also be more likely to seek and/or receive other community supports and resources. Previous research has shown a relationship between WIC participation and autism rates, with significantly lower autism rates in states with higher WIC participation (Shamberger, 2011). In the current study, receipt of WIC support also had an inverse relationship with SIRS factor scores specifically for children of mothers with NHL Black/African American race/ethnicity. Receipt of WIC support had a large estimated positive effect on SIRS scores for these children, resulting in higher endorsed risk in SIRS behaviors than that endorsed for children of NHL White mothers who had not received WIC support. As noted previously, there was a relationship between age and SIRS scores specifically for children of mothers with NHL Black/African American race/ethnicity (i.e., as they age their scores increase, reflecting higher risk or endorsement of SIRS behaviors). Of the children in this race/ethnicity group, only one child above the centered mean of adjusted age had a mother who received WIC support. One interpretation of this finding is that WIC support was not protective against increased incidence of SIRS behaviors in this group. It might also indicate that parents of these children, who 136 qualified for WIC support under income guidelines, may be more likely to endorse SIRS behaviors on this parent report measure than parents in other race/ethnicity groups who had met the same guidelines are. Inclusion of receipt of WIC support along with weeks gestation changed model estimates so that maternal educational level no longer had an effect for children of mothers with NHL Black/African American race/ethnicity, suggesting that this moderating effect may be attributable to the relationship between income/poverty and maternal educational level in this group. A comparison of results from a parent report measure with an observational measure of SIRS behaviors would provide more insight into whether this finding reflects parent response patterns to questions about these behaviors or is a true difference in this group. Unexpectedly, most prenatal and perinatal risk factors as modeled in this exploratory study were not predictive of differences in FYIv3.1c factor scores. It is possible that specific individual complications (e.g., diagnosis of gestational diabetes during this pregnancy) rather than the number of complications overall may be associated with increased risk across FYIv3.1c factors. The sampling procedures used for the parent study (e.g., passing newborn hearing screening in both ears) may also have excluded infants from recruitment who would have been more likely to be affected by pre-/perinatal risk factors. Future studies with more variation in pre-/perinatal risk exposure across the samples should test individual risk factors in relation to infant behaviors related to autism likelihood while taking into account the potential for differences by maternal race/ethnicity as a result of systemic discrimination. 4.4 Anticipated and perceived discrimination In this sample, anticipated discrimination as reported by caregivers was significantly associated with HYPER factor scores: caregivers who reported higher anticipated discrimination on the InDI also endorsed more HYPER-related behaviors in their children on the FYIv3.1c. 137 Caregiver-reported day-to-day discrimination experienced across the lifetime was also associated with a minor increase in child SREG factor scores, suggesting that those children had slightly more difficulty with self-regulation during daily routines. These findings add to the current literature demonstrating that caregiver exposure to discrimination is associated with measurable differences in infants’ behaviors, but they would be strengthened by corroboration from similar changes noted by an observational measure. Previous studies have found that exposure to racial discrimination – particularly the experience of interpersonal racism, which is captured in day-today discrimination on the InDI – impacts perinatal outcomes (Collins et al., 2004) and infants’ social-emotional development (Rosenthal et al., 2018). Interpretation of findings from the current study is limited by the low sample sizes of participants reporting maternal race/ethnicity aside from Hispanic/Latine and NHL White. Future studies with larger sample sizes could better determine whether the associations found in this study are found across all groups or if there are differences as a function of race/ethnicity group membership (or other characteristics such as type of employment) and whether discrimination mediates the effects of other predictors. 4.5 Additional limitations of this study The race/ethnicity groups used in this study, while more specific than the dichotomous “White vs. Non-White” variable used in many other studies, are still aggregates of varied subgroups with different historical and ongoing experiences with discrimination, access to resources, and intersectional disadvantages. For example, the NHL Asian group is an aggregate of all Asian subgroups even though immigration histories are different across subgroups and time. Additionally, the Hispanic/Latine category used in this study as pulled from vital records data is a combination of any single race with additional report of Hispanic/Latine ethnicity. Thus, this group combined participants with Hispanic/Latine ethnicity and Black race with participants 138 with Hispanic/Latine ethnicity and White race, which may have obscured differences between those participants. Similarly, the multiple races group combined all participants with any combination of races together even though the lived experiences at different intersections of racial and ethnic categories are likely to be very different. Despite these issues with the use of these race/ethnicity categories, the hope is that this level of grouping captures at least a meaningful amount of shared variance within groups, including experiences of systemic discrimination and access to community resources. Potential ways to address this limitation in future studies might include collection of socioeconomic indicators (e.g., history of changing location of residence, generational refugee/asylum status) and access to resources (e.g., childcare at religious institutions, additional caregivers in the home) to examine whether factors such as stability of such indicators or extent of community supports might explain part of the withingroup variance. 4.6 Conclusion Results from these analyses revealed that there were differences in infant factor scores on the FYIv3.1c across race/ethnicity groups in ways that may affect the accuracy of factor scores and determination of autism likelihood. Additionally, findings provided preliminary support for a relationship between experiences or anticipation of discrimination as reported by caregivers and adverse infant outcomes. 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S., Vehorn, A., Miceli, A., Pinnock, T., Dada, Y., Hine, J., & Warren, Z. (2022). Black families’ experiences of developmental screening: Review of well-child visits to inform enhanced autism spectrum disorder risk assessment. Journal of Developmental and Behavioral Pediatrics, 43(9), 503-510. doi:10.1097/DBP.0000000000001129 145 CHAPTER 4: Study 3 – A Qualitative Exploration of Caregivers’ Hopes, Concerns, and Expectations for Their Children’s Development amongst Sociocultural Influences 1. INTRODUCTION “But I do catch myself, if we’re out in a different area, where I’m like, ‘Oh my God! I don’t want him to act up in front of the White people.’” 1.1 Parenting amidst intergenerational discrimination Oppression against marginalized and minoritized communities in the United States has been perpetuated by historical and persistent discrimination centered on maintenance of a power imbalance favoring the already rich, influential, and powerful. This has included systemic discrimination such as political disenfranchisement and disempowerment, racial residential segregation with associated environmental injustice, inequitable lending policies, and disproportionate mass incarceration and police brutality (Bailey et al., 2017; Braveman et al., 2022; Lynch et al., 2021). Reinforcing systemic forms of oppression are the incremental, taxing experiences of interpersonal bias, prejudice, and discrimination that individuals with marginalized and minoritized identities or backgrounds encounter across varied contexts. These experiences of systemic and interpersonal discrimination are linked with increased likelihood of adverse health outcomes through reduced engagement in health-sustaining behaviors and increased engagement in health-harming behaviors (Pascoe et al., 2022), increased and detrimental levels of toxic stress (McEwen and McEwen, 2017), and effects on parenting practices and context (Berry et al., 2021). Over time, chronic exposure to harmful conditions associated with discrimination have intergenerational ramifications (Berry et al., 2021; Rosenthal et al., 2018). 146 Individuals at intersectional points of discrimination, facing oppression along multiple socially constructed axes such as gender and race/ethnicity, are especially vulnerable to experiencing adverse outcomes (Turan et al., 2019). Actions taken to address inequities while considering only the effects along one axis (e.g., sexism) are inadequate for addressing intersectional injustices experienced by individuals with membership in multiple groups targeted by discrimination (Crenshaw, 1989). The intersectional discrimination Black/African American women experience across their lives presents as a unique stressor, exacerbated during pregnancy and tied to sexuality and motherhood, that contributes to adverse prenatal and perinatal outcomes (Rosenthal and Lobel, 2011). The consequences of intersectional discrimination are especially apparent in reports of prenatal and perinatal disparities experienced by Black/African American and Hispanic/Latine mothers compared to Non-Hispanic/Latine White mothers, including higher infant mortality rates and lower-quality hospital environments and care (Berry et al., 2021). Moreover, discrimination experienced by caregivers during pregnancy can affect infants’ health and well-being even a year after birth (Rosenthal et al., 2018). Critical consciousness, which involves an awareness of systemic oppression as well as a recognition of one’s own ability to potentially enact change within their socially constructed world (Freire, 1973/2005), is a potential protective mechanism supporting resilience against the long-term effects of discrimination. Children’s development of critical consciousness has the potential to ameliorate the effects of systemic barriers associated with adverse educational and health outcomes and is heavily impacted by the socialization they receive from parents and peers and (Diemer et al., 2016; Heberle et al., 2020). Following the murder of George Floyd in 2020, Black/African American parents discussed race and inequality more often with their children than they had before; however, White parents remained unchanged in the frequency of their 147 discussions of race or inequality according to one study of family racial socialization practices (Sullivan et al., 2021). Additionally, compared to White parents, Black/African American parents reported more worry that their children could be targets of racial bias or be biased themselves both before and after the murder of George Floyd; their worry also increased significantly after the event, while White parents’ level of worry did not (Sullivan et al., 2021). As systemic and interpersonal discrimination continue to perpetuate oppression within the environments in which children are raised, parents’ socialization practices and priorities for their children’s development are likely to be impacted. Mothers of Black/African American children with autism, particularly sons, experience high levels of stress and fear about their children’s future potential interactions with law enforcement. Their children, who are located at the intersection of discrimination against race, sex, and disability, are affected simultaneously by negative societal perceptions of Black/African American males as being “dangerous” and the systemic lack of law enforcement training and capacity to appropriately recognize and respond to neurodiverse individuals (Dababnah et al., 2022). 1.2 Systemic inequities in developmental surveillance of infants and toddlers The American Academy of Pediatrics recommends that primary care providers conduct developmental surveillance at each visit to identify children at elevated likelihood of developmental delays or conditions such as autism at earlier ages to maximize the potential benefits of early evaluation and intervention on children’s long-term developmental trajectories (Lipkin et al., 2020). Developmental surveillance involves six overarching practices that combine informal and formal screening with actions promoting collaboration amongst providers, families, and other professionals (Lipkin et al., 2020). Evidence of adherence to these guidelines has been mixed, and rates of developmental surveillance especially differ between states across 148 the U.S. (Hirai et al., 2018) and for children from marginalized or minoritized backgrounds (Wallis et al., 2020). Adherence to autism-specific screening recommendations as part of developmental surveillance is additionally impacted by a number of provider-specific factors: confidence and self-efficacy (Fenikilé et al., 2015), differing perceptions of the increased incidence of autism and potential benefits of universal screening for autism (Fenikilé et al., 2015), perceived costs of performing screening (Zwaigenbaum et al., 2015), and variability in developmental screening practices such as use of informal judgment instead of validated screening tools (Swanson et al., 2014; Thomas et al., 2016). Though providers should elicit and address caregivers’ concerns about their children’s development at each well-child visit as part of developmental surveillance (Lipkin et al., 2020), elicitation of caregivers’ concerns appears to differ by providers in relation to family social characteristics. In analyses adjusting for socioeconomic factors, providers were still less likely to elicit developmental concerns from parents of Black/African American children or from parents of Hispanic/Latine parents in households with a primary language besides English than from parents of non-Hispanic/Latine White children (Guerrero et al., 2011). Providers have been shown to be biased in terms of their perceptions of parents’ knowledge about autism, in general reporting that parents who were non-Hispanic/Latine White had more knowledge than parents of other race/ethnicity backgrounds (Zuckerman et al., 2013). Such biases may impact whether providers will initiate discussion of developmental concerns with parents or how they respond when parents do raise concerns; additionally, parents with minoritized or marginalized identities may be able to sense provider bias or discriminatory behaviors, which would further reduce the likelihood that parents would discuss developmental concerns. Given evidence that some providers base decisions to screen for autism in part on whether concerns were raised by parents 149 (Fenikilé et al., 2015, Johnson et al., 2007), disparities in elicitation and interpretation of parent concerns across race/ethnicity groups by providers are concerning. 1.3 Narratives and the lived experiences of caregivers Narratives as a method of exploring lived experiences are likely to elicit rich, contextualized descriptions that, particularly in relation to illness or disability, provide participants with an opportunity to recover their "voice" and bring to light "invisible" perspectives (Mattingly & Lawlor, 2000). Narratives of redemption, for instance, have revealed ways in which fathers of children with disabilities resisted or accepted sociocultural expectations through their occupations (Bonsall, 2020); moreover, through their narratives, these fathers engaged in analysis of their own fathering occupations in relation to their constructed worlds. Similarly, by taking individual vs. collective stances within narratives, Black/African American mothers of children with disabilities or special needs negotiated the possibilities of alternative fates for their children (Jacobs et al., 2011). During primary care visits, caregivers mediate their children’s access to services and resources while providers mediate caregivers’ access to the broader healthcare system. As caregivers and providers interact to monitor children’s development, their constructed worlds overlap, resulting in shared states of consciousness when actions and goals align (Lawlor, 2004) or dissonance when collaboration is unsuccessful or impossible, priorities differ, or decisionmaking power is not shared. Through their narratives of parenting experiences in relation to and embedded within their sociocultural worlds, caregivers have opportunities to meld critical consciousness with analysis of their daily occupations. Engaging in this occupational consciousness, or awareness and analysis of the impact of hegemonic practices on everyday occupations (Ramugondo, 2012), can reveal how caregivers’ parenting occupations interplay 150 with their environments and sociocultural processes such as discrimination in ways that support, maintain, or harm the health and well-being of themselves and their families. The purpose of this study was to use primary caregivers’ narratives to explore their hopes, concerns, and expectations for their infants’ development and to investigate the extent to which sociocultural factors impact caregivers’ priorities for supporting their child’s development. The findings of this study provide a contextualized understanding of one component of developmental surveillance – caregiver concerns – that is crucial for taking steps toward addressing existing service gaps, changing public policies for promoting infant and child health, and improving early care. 2. METHOD 2.1 Study design For this study, I chose video-recorded narrative elicitation as the primary method of data collection to obtain contextualized phenomenological descriptions of caregivers’ lived experiences. Narrative analysis was used on participants’ narratives and verbalized meaning- and sense-making with particular attention to social processes and power dynamics as they were revealed through and interwoven across participants’ stories. 2.2 Recruitment Approximately 54 participants from the parent SoCal study were invited via email to participate in this study using purposive sampling on the basis of the following criteria: (1) the participant had a child 6-18 months of age, completed all questionnaires in the parent SoCal CDS study and reported a neurodevelopmental concern (e.g., communication or sensory regulatory issues); (2) the participant’s infant passed newborn hearing screening in both ears; and (3) the participant gave permission to link survey responses with vital records variables. In efforts to 151 ensure an adequate number of participants across racial and ethnic demographics representative of the greater Los Angeles area, recruitment of participants self-reporting Black/AfricanAmerican race was prioritized. Due to the low number of participants self-reporting Black/African-American race who also reported a developmental concern for their child, recruitment criteria was broadened to include participants of non-White race whether or not they reported a developmental concern for their child. This was done in an attempt to prevent conflation of race with developmental concern. All participants who were invited reported that they had lived at least three years in the United States and were able to speak English fluently enough to engage in interviews and complete Aim 2 questionnaires (i.e., reported at least "very fluent" English language ability in the parent study). All eventual participants signed informed consents as approved by the USC IRB. Additionally, participants gave informed consent on maintaining video and audio recordings for the purposes of this study as well as for presentations and training/teaching. 2.3 Interviews and transcription process Interviews took place between July 28, 2022 and February 5, 2023. Semi-structured interviews were conducted using interview guides (see Appendix 6) with open-ended and personalized questions (e.g., "Tell me about [child]" and "It seemed like __ is important to how you see your child’s development, would you agree?"). I moved off-script in order to pursue follow-up questions based on participants’ responses, but they attempted to ask all questions that had been included in the interview guides. I also attempted to encourage participants to tell stories to elaborate on their responses (e.g., "Can you give me an example of a time when you had to rely on your support system?"). Interviews were scheduled for 1.5-2 hours each during times preferred by participants, and all were completed over Zoom web conferencing software. 152 Each caregiver was interviewed twice: (1) an initial interview with open-ended questions and a focus on prompting narratives about the infant's development and (2) a follow-up interview within a month of the first interview with targeted open-ended questions to gain greater depth on emerging themes within and across participant interviews (with a focus on sociocultural factors). Recordings (and transcripts, if available) of initial interviews were reviewed before each second interview in order to tailor certain questions based on each participant’s data. The majority of interviews were completed only with those who self-reported being mothers; two (9%) interviews were completed with both the mother and father (per self-report) participating together in the same interviews. The majority of participants completed interviews within the home setting while one (9%) participant completed interviews within the work setting. Some participants who completed interviews within the home setting occasionally paused the interview to attend to children. The one participant completing interviews within the work setting paused the interview and appeared to relocate to a more private location within the work setting before continuing with the interview. Initial text transcriptions of Zoom audio recordings were automatically performed via Zoom software. I then reviewed transcriptions side-by-side with video recordings to correct text, include text that was not captured initially (often due to overlapping speech), and add extralinguistic information. All participant interviews were transcribed using a standardized set of conventions. Non-narrative dialogue was transcribed verbatim, including filler words and phrases (e.g., "uh") and contractions (e.g., “’cause” for “because”). Transcripts were chunked as much as possible into my questions vs. the participant’s responses, prioritizing chunking of participants’ responses as much as possible to retain the context within which participant’s narratives emerged. 153 2.4 Data extraction and coding For each transcript, I conducted a “chaptering process.” Each transcript was read completely through at least once and then given a title reflecting my interpretation of overarching themes emerging from that transcript. I then re-read the transcript while marking chapters at points of significance. These included points at which the data exhibited a type of shift which encompassed changes in topic, emotional tone, temporality, and agreement. These chapters were named “in vivo” (i.e., using participants’ own words and phrasing). Finally, mini biographies were drafted for each participant once transcripts of both interviews were fully chaptered; these biographies attempted to capture each participant’s narrative in relation to the study’s research questions when taking both interviews into account. In vivo chapter titles served as the first round of coding. A second round of coding retained chapter codes and added additional in vivo codes for transcript segments directly related to the study’s research questions. Clean narratives without coding information were excerpted from transcripts and additionally coded for participants' values, attitudes, beliefs, and expressions of conflict using versus coding. When coding values, researchers annotated portions of narratives expressing what the participant thinks/feels is important (values), how the participant thinks/feels about something or someone (attitudes), and what the participant personally thinks/fees to be true (beliefs). Researchers were encouraged to apply the label they thought best fit with the participant’s expression, but they were not held to strict guidelines differentiating amongst values, attitudes, and beliefs. When coding conflicts, or using versus coding, researchers annotated portions of narrative in which there appeared to be individuals, concepts, processes, systems, and beliefs that were in conflict with each other. 154 2.5 Authenticity of the data and findings This research is conducted within a naturalistic paradigm. Such a paradigm assumes that data are partial representations and reflections of socially constructed realities, thus requiring a grounding of data to context, time, and perspective (Lincoln and Guba, 2007). Moreover, the researcher is understood to be integrally tied to the phenomenon of interest rather than maintaining objectivity, infusing their values across the research process (Lincoln and Guba, 2007). To address concerns of “rigor” when evaluating research conducted within a naturalistic paradigm, Lincoln and Guba (2007) proposed five criteria of “authenticity.” These criteria, discussed in the following section, were intended to address the aforementioned ontological and epistemological considerations and to provide a framework for researchers to assess the “rigor” of naturalistic research studies. 2.5.1 Fairness This research is expected to elicit differing situational constructions from individuals or groups with varied or conflicting value systems (Lincoln and Guba, 2007). The criterion of fairness asks whether researchers recognize and represent different value and belief systems and respect those differences in empowering community members when producing recommendations or actions based on the research (Lincoln and Guba, 2007). To address the first step of this criterion, ascertainment and representation of different value and belief systems, narrative coding methods were chosen for this study (see section 2.4 Data extraction and coding) that helped reveal these conflicting values and supported in-depth analysis of the value systems reflected in participants’ narratives. Narratives were simultaneously coded using values coding to capture values, attitudes, and beliefs expressed through participants’ sense- and meaning-making as well as versus coding to capture internal and external conflicts that were either explicitly 155 highlighted by participants or proposed by researchers during analysis. During interviews, “member checks” were also incorporated through interspersed paraphrased summaries of interviewees’ own statements to ensure that my immediate interpretation and rephrasing were valid to participants. These interpretive checks often started with me stating, “So I’m hearing that you’re saying…” and ending with, “is that right?” or “would you agree with that?” Participants often responded in the affirmative or agreed and then provided further contextualized examples. I occasionally asked whether perspectives or conflicts that other participants had mentioned resonated with participants from similar and different intersecting social strata. Responses to these check-ins helped contextualize data provided by participants and evaluate whether these themes were representative across the data or were felt more strongly by participants with similar intersecting social strata. Additionally, at the beginning of the second interview with each participant, I provided a summary statement of the participant’s main priorities for their child’s development based on review of the video recording of the first interview. I asked whether this summary statement was accurate and whether participants wished to highlight anything else that had not been included in that summary statement. On one occasion, a participant noticeably hesitated while agreeing to a summary statement of letting the child develop at his own pace. I followed up with a probing question and attempted to reassure the interviewee that it was alright to disagree and that this was a check-in to make sure that the summary was accurately capturing her priorities for child development. The participant then stated that she was doing “the best that she can do” to support her child’s development, prompting me to realize and subsequently verbalize that the original summary statement did not seem to take into account the supportive actions and activities the participant had previously done and continued to do to support her child’s development. In this 156 instance, the wording of the summary did not align with the participant’s values and beliefs about her priorities for her child’s development, and this interpretive check provided an opportunity for the participant to reject this framing of her priorities that I made based on the first interview. Data analysis and interpretation of findings then took into account the importance of this participant’s clarification on the potential implications of describing parents’ priorities in ways that inadvertently frame parents as inactive observers rather than active supporters of their children’s development. The second aspect of this criterion, negotiation of recommendations and subsequent actions, was not incorporated into the conceptualization of this study from the beginning. The primary purpose of this study was to explore parent experiences related to their children’s development rather than to propose recommendations or actions, although it naturally follows that recommendations may be made once these phenomena are better understood. Two questions that may have supported caregivers’ negotiation within the research process itself (i.e., to shape the research focus, goals, and findings) were added after approximately half of the total sample had completed the study. The first question asked participants for the best advice they had ever received or would give to other parents. The second, which became the final question of each interview, asked if there was anything I should have asked them (sometimes with added clarification: “to better understand you as a parent to your child”). Responses to these questions were used to expand the base questions posed to later participants (e.g., adding “What is something from your background that you do NOT want to pass on to your children?”) and provided additional insight into difficulties or issues participants had faced and overcome or continued to struggle with in relation to caregiving that were not always explicitly discussed when responding to other questions. The fact that these questions were not asked to the first half 157 of participants may have impacted the findings since potential insights from those participants were not present in the data. Whenever possible, participants’ own words were used as in-vivo codes to preserve as much of participants’ original intent as possible and to reduce the influence of researcher bias on final interpretations and findings of this study. 2.5.2 Ontological authentication Based on the assumption that reality is constructed and reconstructed via transactional processes, Lincoln and Guba (2007) argue that there should be evidence of increased consciousness of the complexities experienced across constructed worlds. This increased awareness could be demonstrated within participants, researchers, or both. In the present study, participants actively engaged in both sense-making and meaning-making while sharing their stories and responding to questions. Through verbalized sense-making of their external worlds, participants shared their evolving understanding of their contexts in the present as well as in the past. Through verbalized meaning-making of their contextualized experiences in relation to their selves, participants revealed their interpretations of events in their lives and how they shaped their understanding of themselves and their children in their past, present, and future. At the outset of this study, intersectionality theory was chosen as one of the primary frameworks for analyzing data as it allows for a complex, dynamic perspective of social realities and experiences. The application of intersectionality amongst other theories such as transactionalism and occupational consciousness was intended to expand my consciousness of how the effects of different intersecting social strata and the caregivers/children they affect interact to create their constructed worlds. 158 2.5.3 Educative authentication Achieving educative validity requires opportunities for interested parties, particularly individuals serving as gatekeepers of community partners and constituencies, to increase their awareness and understanding of others’ constructed worlds as well as their underlying value systems (Lincoln and Guba, 2007). In the present study, as they produced narratives or responded to questions about their sociocultural backgrounds and childhood expectations, participants engaged in sense-making and meaning-making while reminiscing about the individuals in their lives and the communities in which they grew up. They often made comments about what others valued (e.g., expectations their own caregivers had for them and why) and how those values influenced the ways the participants were raised as well as the choices participants made with their own children. Additionally, a few participants were employed within the healthcare systems themselves or educated in child development, and they referenced those experiences in relation to the new understandings and conflicts that emerged as they became parents. Some participants also remarked that they had never considered the topics covered during the interviews, particularly those related to their own identities as caregivers or expectations for their children in the far future. On my part, coding and analysis of these interviews as well as the experience of engaging in conversation with participants provided a deeper understanding of how participants’ value systems shaped their actions with and aspirations for their children. Moreover, engagement in the research also highlighted how the shift in identity and reality of becoming caregivers altered multiple participants’ value systems and world-constructions. 159 2.5.4 Catalytic authentication Promising methods to facilitate action in response to the research inquiry and/or evaluation include involving community partners at the outset of the research, honoring their input, sharing decision-making power for the focus of the research and ongoing procedures, and supporting re-balancing of power and foreground voices (Lincoln and Guba, 2007). Community partners were not directly involved in the conceptualization of this study. Instead, findings from previous research studies highlighting health disparities impacting families from marginalized and minoritized communities motivated the initial questions and goals of the research. Future studies stemming from this research will directly involve community partners from the beginning; additionally, the findings of this study based on the recommendations of participants will guide initial procedures and questions of future studies. Participants in this research were provided an opportunity to guide future interviews by discussing any questions they felt were important to understand their lived experience; additionally, they were asked multiple times during interviews whether there was anything else they wished to add or anything I had missed or misunderstood. Amplification of the voices of individuals and communities often overlooked in research and in healthcare encounters (and redistribution of power within the research) was supported through the elicitation of narratives in this study which offered opportunities for participants to apply their own framing to their recollections, confront the relevance of interview questions, address past experiences in which they felt powerless, and infuse their own perspectives and analysis of their social worlds on the subsequent findings of the research. 2.5.5 Tactical authenticity Finally, Lincoln and Guba (2007) argue that the research should involve “joint emic-etic elaboration” (p. 24) allowing for distribution of power and negotiating the construction of the 160 evaluation or inquiry in the hope that the evaluation is empowering to those who have been disempowered. As noted previously, the act of producing narratives was in its own way a redistribution of power, enabling participants to provide their interpretation and analysis of the events in their own lives and the ways that societal pressures and their own contexts shaped each other. The semi-structured nature of the interviews also made it possible to follow the lead of participants so that topics and issues they deemed important or relevant were elaborated upon and discussed in more detail. I provided reworded check-ins with participants about their expressed or implicit values underlying their recollected experiences, sense-making, and meaning-making so that participants had opportunities to agree, disagree, or amend my stated interpretations. 2.6 Trustworthiness of the data and findings Lincoln and Guba (1985) also proposed four criteria of trustworthiness as a parallel of the criteria demonstrating rigor within a conventional positivist paradigm (i.e., internal validity, external validity, reliability, and objectivity). These criteria were then augmented by the aforementioned criteria of authenticity which were rooted in naturalism rather than developed as analogies to issues relevant to a positivist paradigm (Lincoln and Guba, 1985). 2.6.1 Credibility (Lincoln and Guba, 2007) As an analog to internal validity, credibility in this study may be evaluated firstly through the use of repeated member checks through which I confirmed my understanding or interpretation of participants’ statements and their underlying intended messages. Member checks were also used to confirm reconstructions of participants’ verbalized sense- and meaningmaking. I also conducted member checks across participants, occasionally asking whether an issue or topic raised by one participant was relevant/similar to or different from other 161 participants’ experiences. A future step related to member checks that I hope to take is informally reviewing the final analyses with participants who are interested prior to manuscript submission for publication. I also triangulated data by collaborating with coders across different identities and race/ethnicity backgrounds during the coding and analysis phases. Advisors also provided guidance as interviews progressed, helping to shape future questions and provide guidance on addressing ethical quandaries which emerged during interviews. Finally, comparative case analysis supported credibility in this study: I compared participants’ narratives for patterns and standout exemplars as well as conflicting experiences. I also noted when a particular experience or topic did not seem to be relevant or applicant to certain participants compared to other participants. 2.6.2 Transferability (Lincoln and Guba, 2007) As an analog to external validity, transferability in this study may be evaluated by the “thickness” of descriptive data, or contextualized information, details, and analysis necessary to support the ability of readers to judge whether the findings might be applicable beyond this study and these participants. Issues that impacted the transferability of data and findings from this study included the inability to include some aspects of participants’ contexts that would significantly affect anonymity. Notably, within the relatively short span between 2020 to 2023, participants in this study experienced multiple nationwide health crises which impacted their caregiving to various degrees: COVID-19 mass shutdowns beginning in 2020, infant formula shortages following product recall and supply chain issues in 2022, and an unprecedented rise in respiratory syncytial virus (RSV) in 2022. 162 2.6.3 Dependability (Lincoln and Guba, 2007) As an analog to reliability, dependability in this study may be evaluated across the process of data collection, coding, and analysis. Multiple people edited auto-transcribed interview transcripts to try to capture as much of what participants said as accurately as possible. Protocols with examples of codes and relevant excerpts were created for this study along with data collection methods that can aid reproducibility. Trained student researchers coded portions of the data, and differences in those codes were examined with consensus decisions and in-depth discussions as needed. Issues that may have affected dependability included limitations of the recording software or stability of internet connections that led to minor data loss in some interviews. Additionally, I was the only editor or coder for some transcripts (with multiple passes). 2.6.4 Confirmability (Lincoln and Guba, 2007) As an analog to objectivity, confirmability in this study may be evaluated through steps taken to reduce bias. I attempted to engage in constant reflexivity through the interview, coding, and analysis process. The use of analytic memos aided reflexivity and documented interpretive leaps prior to final construction of findings. In an effort to triangulate interpretations while trying to reduce biased expectations from driving those interpretations, some participant narratives were reviewed and coded by student researchers who did not have access to audio/video or additional background information on participants besides what emerged naturally in interviews. I acknowledge that no research is truly objective; the guiding questions of this study were already biased by what I had initially anticipated would be important to ask participants. Additionally, my own understanding and lived experience of intersectionality are, in some ways, inseparable from my application of intersectionality on this data and findings. 163 2.7 Participants and measures Table 12. Self-reported aspects of identity of participants. A total of 12 participants from 11 families from the parent SoCal CDS study were eventually recruited into this study. Participants were provided openended blanks to capture demographic information related to identity (see Table 12). The majority of participants reported cisgender female identity (92%) and straight/heterosexual sexual orientation (75%). Open-ended blanks enabled participants to respond with labels they preferred for race/ethnicity, and two participants opted to leave race blank. These aspects of identity are reported separately to preserve anonymity of participants. Educational histories of parents from the 11 families in this study were obtained from the parent SoCal study after participants agreed to link data from the two studies together. Participants in this study had predominantly completed a college degree or higher (82%), resulting in a study sample with greater representation from mothers who had completed higher education (see Table 13). The general Interviewee’s Self-Reported Aspects of Identity (fill in the blank) Sex assigned at birth n (of 12) % Female/Woman 11 92% Male 1 8% Gender n (of 12) % Female/Woman 11 92% Male 1 8% Sexual orientation/sexual identity n (of 12) % Straight/heterosexual 9 75% Bisexual 1 8% Cis 1 8% Female 1 8% Race(s) n (of 12) % Hispanic 3 25% White 2 17% No response 2 17% Latino 1 8% Caucasian + Native American 1 8% Mexican 1 8% Black 1 8% African American 1 8% Ethnicity n (of 12) % White 4 33% Mexican 2 17% African Latina 1 8% Latina 1 8% Black 1 8% African American 1 8% Hispanic 1 8% Caucasian + Native American 1 8% 164 reported educational level of children’s fathers showed more spread across educational levels, with a smaller proportion completing a college degree or higher (36%) and a larger proportion completing up to an associate degree (63%). Table 13. Self-reported educational history of participants' children's parents. Parent Self-Reported Educational History Mother’s or 1st Parent’s highest educational level completed (select one) n (of 11) % Less than 8th grade 0 0% 9 th -11th grade 0 0% High school degree or GED 2 18% Vocational or trade degree 0 0% Associates/2 year degree 0 0% Courses toward college degree 0 0% College degree 6 55% Master’s degree 3 27% Professional degree (MD, PhD, JD) 0 0% Father’s or 2nd Parent’s highest educational level completed (select one) n (of 11) % Less than 8th grade 0 0% 9 th -11th grade 0 0% High school degree or GED 3 27% Vocational or trade degree 2 18% Associates/2 year degree 2 18% Courses toward college degree 0 0% College degree 2 18% Master’s degree 1 9% Professional degree (MD, PhD, JD) 1 9% Participants were asked to report on their household characteristics as part of this study. A combination of household characteristics reported in this study as well as in the parent SoCal study are presented here (see Table 14). In general, this study sample was representative of families across a wide range of income levels when broken into $20,000 yearly household income increments. A report from KTLA in March of 2023 suggested that individuals in Los Angeles city and Anaheim in 2022 needed to make an after-tax salary of $76,710 to “live comfortably” using a budgeting method splitting 50% of salary on basic living expenses, 30% on personal spending, and 20% on savings or debt reduction (Palm, 2023). The after-tax salary for 165 Riverside was considerably lower ($67,060) while the after-tax salary for San Diego and Chula Vista was higher ($76,710). Table 14. Self-reported household characteristics of participants. According to the California Department of Public Health (2022), based on yearly income, the poverty guideline in California for 2022 was defined as $13,590 for a oneperson household, $18,310 for a twoperson household, and $27,750 for a four-person household. In this sample, six participants fell in the “living comfortably” range Self-Reported Household Characteristics Household yearly income range (select one) n (of 12) % Under $20,000 2 17% $20,000 - $39,999 1 8% $40,000 - $59,999 2 17% $60,000 - $79,999 1 8% $80,000 - $99,999 0 0% $100,000 - $119,999 0 0% $120,000 - $139,999 1 8% $140,000 - $159,999 3 25% $160,000 - $179,999 0 0% $180,000 - $199,999 1 8% Over $200,000 1 8% Type of residential community (select one) n (of 12) % Suburban 7 58% Urban 5 42% Rural 0 0% Current parenting situation in the home (all that apply) n (of 12) % Two-parent home 10 83% Single-parent home 2 17% Multigenerational home (i.e., grandparents) 0 0% Other 0 0% Languages spoken in the home (fill in the blank) n (of 12) % English 5 42% English and Spanish 6 50% English and German 1 8% Child’s age at first interview n (of 12) % 8-10 months 3 25% 11-13 months 3 25% 14-16 months 5 42% 17-18 months 1 8% Child’s sex n (of 12) % Male 9 75% Female 3 25% Child’s birth order n (of 12) % First child born to this mother 5 42% Second child born to this mother 5 42% Third or other child born to this mother 2 17% 166 while the other six participants did not report a household income within that range. Three of the participants whose reported household income did not fall into a range to “live comfortably” were potentially under or close to the poverty guideline. These are broad estimates given that the “living comfortably” after-tax salary estimates are based on individuals and not family units. Most participants (83%) reported their current household situation as a two-parent home. One participant reported living with their parents during the interviews but did not select “multigenerational home” to describe their current parenting situation. All families spoke English in the home, and a majority (58%) reported also speaking another language (predominantly Spanish). A majority of the children that interviews focused on were male (75%), and the mean child age at the first interview was 12.8 months (SD 3.0) with a majority of children (42%) falling within the 14-16 month age range. The mean reported age of mothers at birth was 29.8 years (SD 2.8) while the mean reported age of fathers at birth was 32.6 years (SD 3.4). In order to preserve anonymity, participant extracts in the findings of this study were labeled by the number of the section pertaining to the excerpt and a letter chosen in order of reference (e.g., Participant 1A, 3B). Additionally, some participants may have excerpts with different letters within sections if combining their excerpts with the same identifier might impact the preservation of anonymity. Pronouns of children may have been replaced with “[child’s]” or a different pronoun (e.g., “his,” “their”). Names of family members and locations were changed, replaced with a generic label (e.g., “[spouse]”), or removed. If a participant’s narrative referred to a specific profession or unique situation that may impact anonymity, a substitute profession or situation may have replaced it that was chosen to preserve the intent of the participant’s narrative as much as possible. 167 2.7.1 Developmental Concerns Questionnaire version 1.6 Parents completed the Developmental Concerns Questionnaire version 1.6 (DCQ version 1.6; Reznick et al., 2005) as part of their participation in the larger SoCal CDS study and reported their current level of concerns about their child’s development (see Table 15). The majority of participants (58%) expressed no concerns about their child’s development, and of the subgroup reporting no concerns, five of seven participants (71%) reported non-White/Caucasian and/or Hispanic/Latine race/ethnicity. Table 15. Parent report of developmental concerns on the DCQ version 1.6. Developmental Concerns Questionnaire version 1.6 Concerns about child’s development (check one) n (of 12) % No one has expressed any concerns about my child's development 7 58% I have no concerns about my child's development, but someone else has expressed concerns 2 17% I have some concerns about my child's development 3 25% I have many concerns about my child's development 0 0% Of the subgroup reporting that either they or someone else had a concern about their child’s development, three of five participants (60%) reported non-White/Caucasian and/or Hispanic/Latine race/ethnicity. Despite attempts to overrecruit Black/African-American participants to prevent conflation of lack of developmental concerns with racial/ethnic categories, there were no participants reporting Black/African-American race/ethnicity who reported that they or someone else had a concern about their child’s development. Over the course of interviews, however, multiple parents who did not have concerns about their youngest child expressed that they did have concerns about an older child’s development, including participants reporting Black/African American race/ethnicity. Of seven participants who did not have concerns about their youngest child’s development, three participants expressed concerns about an older child or reported that an older child had received a diagnosis such as attentiondeficit/hyperactivity disorder or was receiving a service such as speech-language therapy. 168 2.7.2 Intersectional Discrimination Index The Intersectional Discrimination Index (InDI) was developed to support analyses of discrimination along intersectional social identities and positions (Scheim and Bauer, 2019). The InDI contains three subscales measuring three dimensions of discrimination (Scheim and Bauer, 2019): (1) Anticipated (InDI-A; 9 items), (2) Day-to-day (InDI-D; 9 items), and (3) Major (InDIM; 13 items). A mean score for the InDI-A is generated after scoring all items from 0 (strongly disagree) to 4 (strongly agree). Sum scores are generated on the InDI-D following binary coding (1=yes, 0=no) of items asking about lifetime day-to-day discrimination and re-coding of items asking about experiences in the past year as 0 (no, never, not in the past year), 1 (once or twice), or 2 (many times). Sum scores are also generated on the InDI-M following coding of items asking about lifetime major discrimination as 0 (never), 1 (once), or 2 (more than once or in more than one place) and binary re-coding of items asking about experiences in the past year (1=yes, 0=no). The InDI-A demonstrates acceptable unidimensionality and internal consistency (Cronbach’s alpha =0.93; item-total correlations 0.69 to 0.81), and test-retest reliability for the InDI was 0.72 (InDI-A), 0.70 (InDI-DI), and 0.72 (InDI-M; Scheim and Bauer, 2019). Results of known-group comparisons and tests of convergent validity supported the construct validity of the InDI (Scheim and Bauer, 2019). In total, 10 of 12 participants in the current study completed the InDI. In this sample, nine out of ten (90%) participants reported anticipating at least one type of discriminatory experience based on who they are (e.g., being treated poorly by a health care provider, being harassed or stopped by police or security). The mean InDI-A score, 1.01, reflected general disagreement that they anticipated discrimination; however, two participants’ mean scores (2.56 and 2.78) reflected general agreement that they anticipated discrimination. The average InDI-A mean score within 169 this sample was comparable to the mean score for US respondents from the initial validation study of the InDI (1.01; Scheim and Bauer, 2019). Seven of ten (70%) participants reported having at least one lifetime experience of discrimination based on who they are (e.g., being asked inappropriate questions), and five of ten (50%) participants reported having at least one experience of discrimination within the past year. Four of ten (40%) participants reported having a major discrimination experience because of who they are (e.g., being threatened with a physical or sexual attack), and one (10%) participant reported experiencing one within the past year. 3. FINDINGS Participants’ narratives reveal a complex and dynamic picture of their hopes, concerns, and expectations for their children amidst competing sociocultural pressures, institutional barriers, and intergenerational cycles. 3.1 Parent experiences of developmental surveillance […] when I talk to the pediatrician about things, my expectation is typically that like I'm going to need to convince them that this is something that it makes sense to be concerned about and I'm not just like walking in as an overly anxious firsttime mom. 3.1.1 A Catch-22 For some parents in this study, attaining professional support to address their developmental concerns about their children involved a process that was far from straightforward. The stories of Participants 1A and 1B underscore the paradoxical nature of attempting to address developmental concerns within a system fraught with barriers. […] I'm concerned, and I was called at one of my appointments a "Tiger Mom." [Interviewer: You were called that by someone?] Yes. By my pediatrician. […] I do think there is some type of stigma with like pediatricians being like, “Oh,” like… “They're a Tiger Mom,” or “They’re…” you know, whatever. 170 “Munchhaus–” I'm not saying he's got a rare- or like a rare DISEASE! I'm saying he's got- I- I just want to make sure he's okay. Because I don't want ithim to- to be seven years old and, “By the way, why didn't you get this treated?” “Oh, well, I TRIED but I was–” You know what I mean? - Participant 1A A tug of war over power is evident in both participants’ recollections of visits with their children’s pediatricians. The pediatrician’s use of the term “Tiger Mom” in response to Participant 1A’s expression of concern over her child’s communication development effectively censures her for being worried that her child may not be meeting expected milestones. “Tiger Mother,” which was popularized by Battle Hymn of the Tiger Mother by Amy Chua, is associated with the image of a mother who holds high expectations for her children and uses a strict parenting style to help her children achieve those expectations. “Tiger Mother” also evokes the stereotype of “traditional” Chinese (or more broadly, Asian) parents as being restrictive and authoritarian according to “Western” parenting style typology (Xie & Li, 2019). The use of this term within this context serves to trivialize Participant 1A’s concern, characterize her as having extreme expectations for her child, and gatekeep whose concerns or expectations are reasonable and worthy of full consideration. This excerpt also reveals the vulnerabilities mothers, in particular, face within these interactions. Terms such as “Tiger Mom” – and “Munchausen Syndrome,” which Participant 1A herself referenced – carry sexist overtones and are likely to be used against women who express concerns about their children that health professionals feel are unreasonable. Though mothers are expected to be diligently aware of their children’s current development in order to accurately report any concerns to pediatricians, they must also be wary of appearing overly concerned or overbearing. Moreover, their own expectations for child development must match their pediatrician’s often unspoken expectations to warrant further investigation. Asian mothers may feel additional pressure to be just worried enough or they may 171 be even less likely to have their concerns addressed as they face the intersectional stereotype of being a “Tiger Mom.” Participant 1A alternately resisted and embraced the term “Tiger Mom” when discussing her feelings about the interaction. After she first mentioned being called a “Tiger Mom,” she paused and stared at the camera, effectively inviting me as her interviewer and audience to react and become an active partner in her recollection of the interaction. Her use of sarcasm relocates censure back onto the pediatrician, highlighting the absurdity of his reaction: “I’m not saying he’s got a rare- or like a rare DISEASE!” In her second interview, she argued, “I’m like, ‘Okay, so what? You want the best for your kid. Oh, that’s– You’re a terrible human being for wanting your kid to succeed…’” She described herself as “an advocate of [her] kids” and reframed “Tiger Mom” when discussing how she felt about the label: I think that basically I don’t want them to have to struggle, so if I can step in and do whatever I need to do, whether that be, you know, like a “Tiger Mom,” or advo[cate]– I think it’s a little bit of both! I think it’s a little bit of both. I think there’s a nice balance there, and I think… You know, I think “Tiger” has a… a negative connotation, but I- I don’t think that’s what it is. - Participant 1A During her interviews, Participant 1A cited the importance of early intervention as her motivation for pursuing her concerns regardless of her pediatrician’s response. Through the hypothetical situation she raised of later being confronted with negative consequences of ignoring her developmental concerns (“I don’t want it- him to- to be seven years old and, ‘By the way, why didn’t you get this treated?’”), Participant 1A demonstrated her own awareness of the paradoxical situation in which she is mired: in the present, she is censured by her child’s pediatrician for being overly concerned, and in the future, she is criticized and held responsible for her child’s development if those concerns prove to be valid but were never addressed. Imbalanced power dynamics are again apparent in Participant 1B’s narrative of her 172 discussion of developmental concerns with her child’s pediatrician. In this interaction, the source of concern is shifted to the pediatrician while Participant 1B is less convinced that there is currently cause for concern: […] except the pediatrician um said, “Oh, okay, my minimum for- for speech at a year old is saying Mama and Dada ‘cause those are-” You know, we asked if she’s- she’s bilingual, could that be why? And she said that’s pretty um standard across languages. And so she uh… referred us to a- um a clini- clinic from the city, I think, to get an evaluation, and I called them and they’re like, “HOW old is she? A year? No, we don’t do- we– That’s too young!” Kinda like, why– You shouldn’t be worried yet um was kind of… the impression I got from them. […] I mean, she was probably 6 months old, and I was telling people, “I think she’s gonna be a little bit later with the- with the communicating piece,” just ‘cause I could tell- um I felt like I could already tell what- what her focus was. - Participant 1B This pediatrician referenced a personal criterion for children’s speech-language development that Participant 1B’s child did not meet. By asking whether her child’s bilingualism might impact her development, Participant 1B countered the pediatrician’s criterion and concern by drawing on her knowledge of her child’s developmental context. Her child’s pediatrician, by asserting that this criterion is the same across languages (despite evidence of cultural and linguistic differences in forms and order of language acquisition in infancy), rejected this possibility. This interaction is the inverse of Participant 1A’s experience; despite a lack of concern expressed by Participant 1B, the pediatrician moved forward with a clinical referral. When Participant 1B followed through with the referral and contacted the clinic, she then received contradictory information regarding whether her daughter’s abilities relative to her age evinced the need for concern about her development. Individual variability in milestone expectations across clinics and providers adds to bottlenecks in the early intervention system, resulting in delayed services, confused families, and cases lost to attrition at each juncture. Participant 1B’s tale is just one example of the frustrating, sinuous journey countless caregivers face when navigating the healthcare system 173 to follow up on referrals and attempt to access early intervention services for their children. In her second interview, which took place a few months after her visit with her child’s pediatrician, Participant 1B reported that her daughter’s language was “really starting to take off” and that she was growing in both language expression and understanding. She cited her recent observable language development as one reason she would no longer worry about her daughter’s ability to meet her pediatrician’s criterion of saying the specific words Mama or Dada. Additionally, she tapped expert information that only she had access to as her child’s primary caregiver with daily observations of her child’s growth and development. She noted: I thought from early on like she’s gonna be a little bit later with talking, just ‘cause I didn’t see- or- or with communication, just ‘cause like- I just felt like she didn’t- that wasn’t her interest. She wanted to learn to- She wanted to learn to crawl and walk, and now that she’s mastered that physical piece where, you know, she’s farther along than some other kids are with her– like what she can do with her hands and her balance and everything, and what she can climb. So I feel like now that she’s achieved what she wanted to, now she’s moving on to the communication a bit more. And she says, “All done,” like– She’s a pro at “all done.” - Participant 1B Participant 1B’s attribution of intentionality and drive to her daughter’s developmental sequence thus far (i.e., exhibiting age-appropriate and possibly advanced development of motor skills prior to use of expressive vocabulary) demonstrates intersubjectivity between mother and daughter. Participant 1B connected her daughter’s present level of development with her developmental history, personality, actions, and environment. Through this perspective, her daughter’s development and mastery of skills was not occurring by chance; instead, it was intrinsically tied to her daughter’s understanding of her world, her desire to engage physically with it, and her motivation to move freely in it. For Participant 1B, her daughter’s developmental trajectory matched her own parental knowledge of her daughter’s interests and motivations, and so she did not feel that concern about her development was warranted. 174 3.1.2 “Okay, how many people do we need to see before someone takes us seriously?” In both Participant 1A’s and 1B’s cases, despite evidence of positive associations between caregiver concerns and true presence of certain developmental conditions (Ozonoff et al., 2009; Richards et al., 2016), the distinct institutionalized hierarchy in which the pediatrician’s clinical judgment ranks higher than parents’ expertise results in a referral system heavily dependent on each pediatrician’s personal criteria for concern. Within such a skewed system, the repercussions of providers’ conscious and unconscious biases when determining which children receive referrals loom large. In one retrospective study of 18- to 36-month primary care visits of Black/African American children who were eventually diagnosed with autism, providers did not initiate referrals if parents alone expressed concerns that their providers did not share (Weitlauf et al., 2022). In another study of children who had screened at elevated likelihood of eventual diagnosis of autism, both Black/African American and Asian children were less likely than White children to be referred to early intervention, and Asian children were additionally less likely to be referred for an autism evaluation (Wallis et al., 2020). One study of primary care providers in California found that providers generally experienced more self-reported difficulties recognizing early signs and symptoms of autism in Hispanic/Latine and Black/African American children compared to non-Hispanic/Latine White children; moreover, providers with less self-reported knowledge about autism were more likely to report that parents of Latine children were less knowledgeable about autism than parents of non-Hispanic/Latine White children (Zuckerman et al., 2013). Compounding the difficulties that caregivers and their children often encounter as a result of systematic biases against race and ethnicity is the fact that most primary caregivers are women who have often already faced sexism in their own medical interactions prior to having children. 175 In the following excerpts, Participant 1C couples her past interactions with medical professionals with her present expectations for visits with her child’s pediatrician. In the process, she touches on how sexism against her also impacts her son’s medical care. I think part of like my FEAR around that is when we found out [child] had the tongue tie in the hospital, […] like seven to eight different people assessed [child] for tongue tie, and all of them said that it wasn't a problem and that it's not why we're having issues with feeding and it's not why I was in so much pain. And it wasn't until like I saw yet another lactation consultant out of the hospital and she was like, “No, no–” Like, “This- This is a problem!” And she referred us to the dentist. We thought- And then the dentist was the first person to catch the lip tie, so we saw like eight or nine people who all agreed [child] had a TONGUE TIE, only one of them said that was a problem, and then none of- NONE of those people caught the lip tie! And so I think like I get nervous about that kind of happening again if I'm not like- kind of like beating a dead horse with some of these things, because I kept being like, “This doesn't feel right,” like, “I don't think this is right,” like- like, you know? And it took SO many– And it's like once we had the ties released, he did SO much better, you know? So I think like it feels like every time a new medical thing comes up again, I feel like it's like- kind of like that again. It's like, “Okay, how many people do we need to see before someone takes us seriously?” Right– OR before you find the right PERSON who like really knows. Yeah, so it does- it sort of feels like we're through that process all over again. […] it feels like I always have to sort of like convince them that I'm not OVERREACTING to the situation. […] I think some of it probably is just yeah previous experience like with my own medical doctors. Where it's felt like I'm not necessarily like listened to, or they're sort of like, “Well, alright, if you REALLY think this is an issue, we can order some blood work.” Or, you know, you're kind of like, “I feel like crap all the time, this isn't NORMAL, like, why is this?” And you're like– And they're like, “Well, if you really want us to check your iron level…” […] but I think like the other piece, being like a mom, like I think people are generally dismissive of concerns that moms have a lot of the time. Like- Like when I was like voicing over and over again, like when I was trying to breastfeed, like, “This doesn't feel right,” like, “This feels wrong,” right? They're like, “This is just what it's like, you just– you've never been- you've never done this before, so you don't KNOW.” Right? So I think like some of it's being a mom and like, you know, they're right. Like I had never done it befonever done it before, right? But like– So I think like, there, that- that is DEFINITELY I think part of it, especially when you're interacting as a mom 176 with like any kind of medical professional, probably, except for like a select group of lactation consultants that don't work in hospitals, probably. And then I think also like being a woman- a woman, like– I think people are– not EVerone, but I think a lot of doctors, even FEMALE DOCTORS are like REALLY dismissive of women's concerns for some reason. Which feels crazy. Um like, you're also female, like this shouldn't be happening. And so– But I think like in terms of like with my son's pediatrician, I think it's more like first-time mom and doctor and less to do with like my gender. - Participant 1C Participant 1C’s concern about chronic fatigue and her report of pain while breastfeeding were minimized by different medical providers through skepticism (“Well, alright, if you REALLY think this is an issue…”) and condescension (“…you've never done this before, so you don't KNOW.”). Gendered bias in medical providers’ assessment and treatment of women’s reports of pain are well-documented (Prego-Jimenez et al., 2022; Samulowitz et al., 2018). Participant 1C notably separated the pushback she faced as a woman seeking medical care from the pushback she faced as a first-time mother (“I think it’s more like first-time mom and doctor and less to do with like my gender.”). Her phrasing suggested that, based on her experience, the pushback firsttime mothers face in response to their concerns is more likely due to providers’ assumptions that their concerns are related to inexperience rather than gendered stereotypes about women. When I followed up with a question asking if she felt that pediatricians were likely to dismiss first-time fathers’ concerns as easily as they did those of first-time mothers, she responded, “Probably less dismissive of dads. I think like moms just are generally like– I think WOMEN generally are just dismissed more especially with- with medical concerns.” She also noted, “[…] that's hard for me to answer because… my husband's really only come to like two or three pediatrician appointments over [child]'s life.” Ironically, Participant 1C’s past experiences in which her concerns were dismissed by medical providers unless she persisted helped her prepare to advocate for her child’s 177 developmental needs. If she had stopped expressing concerns and accepted her providers’ initial responses, the timing of service delivery for her son would be delayed until her providers finally became concerned (which might not even be until many well-child visits had passed). She shared: […] like even with our pediatrician, I've had to really, really, push. Like, “No, actually, like I REALLY don’t think it’s normal that [child] WILL NOT USE his left hand for things.” And my pediatrician was like, “Well, he just prefers to use his right hand!” And I was like, “No, like you DON’T UNDERSTAND, like he CANNOT pick up a BALL with his left hand.” - Participant 1C Participant 1C’s pediatrician downplayed her concern, ascribing her child’s present level of motor skills to his preferences rather than his functioning and developmental abilities. Yet Participant 1C pushes back with a clear concerning sign about her son’s development. The systemic failure to efficiently and appropriately address Participant 1C’s concerns about her own health as a woman as well as her early experiences as a mother whose pain and concerns were dismissed by multiple professionals generated distrust with the system and clinical judgment. Moreover, those experiences engendered a fear about needing to be hypervigilant and unfaltering to address her son’s support needs (“And so I think like I get nervous about that kind of happening again if I'm not like- kind of like beating a dead horse with some of these things”). Similarly, Participant 1A’s understanding of critical failures in the healthcare system appeared to contribute to her desire to push against her pediatrician’s lack of concern about her child’s development. In the following excerpt, she described observing the process that a close relative had endured to obtain therapy services for her children. I remember it was just such an intensive process, like it's- because it's so bureaucratically red taped, all of it. It's just so hard. You're going from one doctor to another, because one doctor isn’t familiar with it, they’re: “Well, I don't know how to help you.” Or you're going at the government or at the city level, like for [region name removed for anonymity] Regional-type things, it's, 178 you know, a lot of assessments, it's a lot of, uh, “Well, they don't fit here,” or they– It's just- It's- It's- It seems like a very frustrating process unless it's like a clear-cut, easily definable… disorder, or whatever. - Participant 1A Participant 1A’s knowledge about the “intensive process” of the early intervention system in her area supported her position on raising and hopefully addressing her developmental concerns in spite of the implicit censure she faced from her pediatrician (e.g., by using the term “Tiger Mom”). She expressed a desire to ensure that her child does not fall behind because she did not bring up her observations and concerns: “I think he might just be slower [compared to his older sibling]. I mean I'll bring it up at his 18-month appointment, but I'm just so concerned like i- if we need to get, you know, speech therapy and– well, start early, let's get going. So that way he's not behind; he doesn't suffer for it.” With regard to her developmental concerns, knowledge about the system through other parents who had already undergone the process contributed to Participant 1C’s resilience against the institutionalized power imbalance pressuring her to back down. 3.2 Doing race, culture, and gender through parenting occupations [Interviewer: Could you describe some hopes or aspirations that you have for (child)?] Yeah. Um. I hope the world isn't on fire! I hope we don't have to live through a real-life version of Parable of the Sower, so… I am hopeful for- thatum that she can enjoy like the at LEAST same quality of life that I enjoy. Conscientização, as conceptualized by Freire (1973/2005), is the process of developing a critical awareness and consciousness of the forces of systemic oppression within one’s reality alongside engagement with one’s own context towards enacting systemic change. It calls for and represents critical awareness and intentionality in the transactions amongst people and their social and physical environments, leading to social transformations. Within family units, and especially between primary caregivers and their children, conscientização may be experienced 179 both individually and collectively, spanning generations. As was revealed through their narratives, caregivers engaged in conscientização in their day-to-day parenting occupations through which they resisted, perpetuated, and confronted societal pressures. 3.2.1 The perpetual act of mind reading and intersubjectivity Intersubjectivity – defined for the purposes of this study as encompassing a range of interactions and relations between perspectives (Gillespie and Cornish, 2010) – dynamically impacts parenting across contexts to an extent particularly unbalanced towards caregivers facing intersectional pressures. Many of these caregivers manage constant awareness of the perspectives of observers and the cultural expectations or stereotypes tied with those perspectives; additionally, their consciousness of the dynamics of race/ethnicity, culture, and power shapes their everyday parenting occupations. In the following excerpt, Participant 2D’s description of “mind reading” is a clear example of the intersubjectivity she engages in while parenting her son in different contexts. Sometimes– Like I do a lot of like the “mind reading.” Like I just feel like, “Damn! Like you're probably thinking like I'm not being strict enough or stern enough, or…” Or um like maybe they're thinking that I'm letting… especiallyThis is especially with my son, where it’s like, “UGH! Like should I be meaner?” Or like, “Oh, she can't control her kid.” […] But I do catch myself, if we're out in a different area, where I'm like, “Oh my God! I don't want him to act up in front of the White people.” […] See, I'm doing the mind reading, where I'm like, “Oh, like maybe they'll be more judgmental, because, you know, he's like a Brown child.” And um… maybe they won't see like– or they'll associate the behavior to- like to his culture. Um but it's like, “Trust me, it's not– In my culture, too, they do the same!” But… But I guess I just feel like– My whole thing, sometimes, it's like I don't want to stand out, I don't want to stand out. So I think if we're like in a different um setting, and it's like we already stand out, and then you're standing out more with behavior? It's like, “No, no, no!” But thing is, he loves attention, and he loves talking to everyone. So like it's definitely helped me in a way to 180 not- to not think that way? Where I'm like, “Okay, it's not– Like, it's not like that. Just relax.” - Participant 2D Participant 2D was cognizant of multiple perspectives at once: her child’s behaviors may be viewed as a reflection of her parenting style and abilities (“…she can’t control her kid.”), people around her may hold her responsible for her child’s behaviors (“…you’re probably thinking like I’m not being strict enough…”), and racism was likely to impact people’s reactions and perceptions of her child’s behaviors (“…maybe they’ll be more judgmental, because, you know, he’s like a Brown child.”). Her worry that people in a different area may attribute her child’s behaviors to her culture also highlights a crucial, unfair burden that she and others with marginalized or minoritized identities carry due to imbalanced power dynamics in the U.S. The actions and behaviors of her and her family may be viewed as an embodied extension of her cultural background, particularly if judged negatively, to the extent that some parents may feel pressured to “perform” parenting well in order to protect a collective cultural image. “I don’t want him to act up in front of the White people” captures this pressure and expectation to do race or to do culture while parenting, reflecting an awareness that, just by existing in a space, she and her son may be seen not just as a parent and child but as racial and cultural representatives. Participant 2D’s statement that her son’s behaviors would be criticized by people “in [her] culture, too” counters external stereotypes that her son’s behaviors are a result of his cultural upbringing. Moreover, it hints at the lived experiences of her and her son at the intersection of racism and ableism in which her parenting and her son’s behaviors “stand out” in physical, embodied ways. Participant 2D’s son, who is neurodivergent, interacts with the world in ways that often do not match how many adults would prefer children to behave. When describing the expectations of her child within her “little bubble” of society, Participant 2D 181 stated, “[…] your child has to be… almost like– just very well-behaved, and like um proper, and… My first one has ADHD, so–” Behaviors in children that are associated with ADHD (e.g., impulsivity, restlessness) and behaviors that participants described as being frowned upon by society overlap. Similar societal expectations about children’s behavior were described by Participant 2E; she shared that she faced expectations from within her extended family for her children’s behaviors and her role in managing them: Yeah, I think they expect me to control [my children’s] behavior. Or they would expect me to be aware of what they're doing and to limit their disruption within their lives. Um I– At a very personal level with like family, I feel like they expect me to CONstantly be a helicopter parent, where I’m watching over them to make sure that EVERY single action is- is um corrected, and every single task is safe. - Participant 2E Attention deficit hyperactive disorder (ADHD) is often misdiagnosed or diagnosed later in Black/African American or Hispanic/Latine children (Rainer et al., 2023). When Participant 2D initially raised her concerns about her son, her son’s difficulty disengaging his attention when his name was called was viewed by the pediatrician as a voluntary choice to ignore his mother: And I would ask the doctor, I'm like, “Look. He SELECTIVELY doesn't pay attention to me, or he'll just like– yeah.” […] And I would call him by a different name, like a cartoon character name, and he would turn. So at that time, that pediatrician told me that… um that sometimes kids just– that he's just choosing to ignore you. So at first, I was like, “Do you think he doesn't hear me?” “Or he doesn’t like–” um “Does he not know hi– I know he knows his name, I don't know why he's doing that.” And he's like, you know, “He's just choosing to ignore you. He's just more interested in watching TV, that's it.” Like he kind of made it seem like it wasn't a big deal. - Participant 2D And when Participant 2D’s son entered school, his behaviors were viewed by teachers as potentially stemming from the transition into starting school: I would ask the teacher, I'm like, “Do you think like [ADD/ADHD] is what it 182 is?” And a lot of them would be like, you know, “He's still very young, so we don't know.” Yeah, and they're like, “I feel like his behavior is, um, you know, maybe because he's just starting school.” Because there would be behavior where like, “Oh, he–” I kind of was like, “Oh, no!” Every day I would get a note. Yeah, like, “[Child] stood on top of the table today.” Or… “He was […] stepping on people's shoes.” He liked doing that for a while. He would just randomly go and step on people's feet. - Participant 2D In the following excerpt, Participant 2D discussed her parenting approach with her son in relation to external societal pressures. The thing is, I think I’ve- I've learned over the years with him. It's just like… the way that I approach him or talk to him about certain expectations and behavior. It's different than how I grew up, for sure. Like- Like if I was to be loud in a restaurant, I would just get smacked or something. Yeah. Um whereas with him, if- if I raise my voice at him or like come at him aggressively, or like match his energy, he just gets more like amped up. So I'm trying to model the behavior that I want him to- to have, like "Hey," or like have him look at me. And then he'll start to like kind of reflect on what he's doing, um and I know that, at least in my culture, like if a kid is like wildin’ out and stuff, it's like, “Oh, that mom can't control him,” or like “She's getting walked all over.” But um I know I feel like that when I'm around my husband's family sometimes, but I feel like I'm not gonna… hit him, and I'm not gonna yell at him because he's making you uncomfortable, because his energy is just so- It's just like a lot of energy. Um… And… And yeah, I'm trying to learn to not care about that. […] not to jump back to the ADHD but there's certain things where it's like, “Oh, you know, that's not real, or that's not a thing,” or– [Interviewer: Oh, like in the culture?] Maybe for the older generation? The culture, or like– Or medication is like frowned upon. Um… Even in some of like those groups that I have as friends. Yeah. So I'm like, “Okay. But.” I try not to let it influence me because I'm like, “Okay, at the end of the day, I need to make a decision- a wellinformed decision based on what the doctor tells me, and my child, and how I feel.” […] Because when he was [his younger sibling’s] age, I would avoid places. I wouldn't like… kids’ playgrounds, the park… I would like– I STILL don't like the park, but it's gotten better where when he was like [younger], I was just like, you know, I'm scared. Because he just had a lot of energy, and he would sometimes just bite someone for no reason. Yeah or like or run, and then I'd be like, “Oh, my God,” I’d feel like I have to be constantly watching him, and I'm 183 on edge, or is he being too loud and people are looking at me? Um so I was just like, you know what? I'd rather not do it. Which is probably not the healthiest thing. But- It WORKS eventually! - Participant 2D Participant 2D described deliberate choices she made to avoid responding to her child’s behaviors and energy through the use of parenting strategies she had been raised with such as corporal punishment. Her choice to alter her responses to model the desired behaviors or to bring his attention to her to encourage reflection demonstrated her understanding of the nature of his behaviors and how they relate to her actions. Additionally, she recognized ways in which her parenting style rejected demands of external pressures for the mother-child dyad to conform to expectations (“I’m not gonna…hit him, and I’m not gonna yell at him because he’s making you uncomfortable…”). These external pressures are magnified when considering cultural context; cultural/societal beliefs about ADHD (“Oh, you know, that's not real, or that's not a thing…”) and stigma related to medication (“…medication is like frowned upon…”) erase her child’s neurodiversity and confine mother and child to neurotypical norms of parenting and child behaviors. Participant 2D’s use of the phrases “wildin’ out” here in this excerpt and “act up” in an earlier excerpt may reflect her understanding of how his behaviors are viewed negatively by others as reflective of misbehavior or rebellion; they may also reflect her own view of his behaviors as disruptive despite her evolving understanding of how some of his behaviors reflect his neurodiversity. Participant 2D expressed a fear that her child would “stand out” in an unfamiliar setting due to his behaviors when they already stood out due to their physical appearances (“…it's like we already stand out, and then you're standing out more with behavior?”), suggesting that she was worried her child’s behaviors might be seen as connected with his cultural worlds in rebellion against dominant societal expectations for children’s 184 behaviors. Her fears reflect the potential for repercussions from people around her towards her as a mother, her child, or her cultures/communities – avoiding standing out in a different area could be protective when anticipating discrimination. These fears may have also contributed to her decision to “avoid places” and limit her family’s activities outside of the home, referring to both physical and mental strain (“I’d feel like I have to be constantly watching him, and I’m on edge…”). Participant 2D also expressed worries that observers might attribute her son’s behaviors to a misconception that she was a single mother. In the following narrative, she described comments from people in her community that highlight the added complexity of facing gendered racism in her parenting occupations. […] and there's times where I'm like, “Oh, I hope people don't–”… I'm like, I hope people don't think like I'm a single mom and that's why he's like- like acting up and stuff. Not that that would mean ANYthing, but… [Interviewer: There's just a lot of stereotypes or like ways people feel–] There's a lot– YES. Especially like in like our… I guess community or culture. Yeah, like I know this lady once um… We were like at a family party– I had never seen her. And [my husband] was helping me with my son, and then she's like, “Oh,” like, “Good thing he didn't leave you!” And I was like, “What the f– Who are you?!” Like, no. WHAT? And then he was like, “Why would I leave her? She's my wife.” And then she was just like, “Oh, no, no, it's because a lot of men like they just leave.” And I'm like, “Wow.” Or I've had some family friends ask like, “How many babies do you have now, two? Is it with the same dad?” And it's like, of course. - Participant 2D Hispanic/Latine and Black/African American women have historically faced stereotypes related to their sexuality and motherhood which results in pregnancy-specific stress (Rosenthal and Lobel, 2020). The comments Participant 2D recalled suggest that interlocutors already held stereotyped assumptions about the likelihood of her children being fathered by the same man and whether he would raise them with her. Having heard these comments from people within close 185 circles of her community, Participant 2D was afraid that her son’s behaviors would lead to additional judgment that she was a single mother with a “misbehaving” child because of stereotypes associated with the intersection of her race and gender. Gendered racist stereotypes such as these have the potential to underlie biased clinical judgment in providers’ assessments of children’s behaviors, contributing to disparities in identification and diagnosis of neurodevelopmental conditions. 3.2.2 “I’m not the right indigenous. And- And I’m not the right Black or the right White…” Participants were asked whether the options for demographics questions about race and ethnicity reflect them accurately, and if not, what they might be missing. Their responses reflect critical gaps in the ways race and ethnicity are conceptualized and applied across multiple systems and environments including research, healthcare, and education. This gap was especially evident amongst participants who reported non-White/non-Caucasian race and/or ethnicity for this study. Oh, I hate [filling out race and ethnicity checkboxes]. […] I don't think it's a- it's uh representative of necessarily– I mean, for some people, it is, but now, I think it's not really representative of who you are at this point, like. […] I mean, yes, I HAVE to checkmark the box ‘cause I’m Hispanic, but I- I have no Hispanic input. I didn't know my dad, so there was no Hispanic INPUT in my… in my life. But- But BECAUSE of that, I have to- I HAVE to mark it. But then what RACE do you identify as? Caucasian, but I’m NOT Caucasian. It’s just- It's- It– That doesn't FIT anybody anymore, like just get a- do away with it. Like it doesn’t- It shouldn't matter, anyway. - Participant who responded “Heinz 57” when asked to describe their cultural background Like growing up and have- havi- being of mixed race, um I always feel like I always check off more boxes. Or the box that represents me is not in that box. And so I'm forced to make concessions just to fill out surveys, go to the doctor’s, when you fill out the back to school stuff. - Participant who described growing up Puerto Rican 186 It's always confusing now, I think, with like the- the question of race, ‘cause it's, you know, it's White, or Black, Asian, things like that, but. Um. I don't fit into any of those. So I always just put “other.” But then when it comes to the ethnicity, it’s like Hispanic, or Latino, like Mexican, and I– like that's where I feel like, okay, yeah. But when it comes to like the race and stuff like that, I always put like “other,” ‘cause I don't really know what to put for it? - Participant who left race blank when given the option while reporting race and ethnicity for this study Because… when you have boxes there, you’re […] assuming that you’re one of these five. Right? And then you have a box that says “Other.” That- That’s– Yeah. Nobody wants to feel othered! - Participant who noted they wouldn’t be able to find a certain race to check off for their child When I applied to my job, I clicked White, because I was like, if I'm– “Google! What am I?” […] Yeah, so I'm like, “Okay, whatever! I'll put White.” But then I feel really guilty just putting that! [Interviewer: Oh, interesting.] Yeah, because– So in at least in my experience with my… culture. My people. There's a very um, I guess, Euro-centric obsession, almost? Like if your- Like if a baby's born, the first thing most people will ask, it's like, “Oh, what color are the baby's eyes?” There's like a fascination with like… Um I guess White features? And like, “Oh, how's the baby's nose?” And, you know, like little… weird things like that. Um and to insult someone you'll call them like– To me, I feel like the biggest insult to tell uh a Hispanic person is to call them an indio, which is like an indigenous person? But literally, we're all mixed here! Um but so I think when I- when I do click just White, I'm like, “No! I'm not White!” […] The best way to encapsulate it? It's kind of like– I don't know if you ever saw Selena? When– [Interviewer: The singer- movie about the singer? Yeah.] Yeah, when her dad's like, “You have to be more Mexican than the Mexicans, and more American than the Americans.” ‘Cause you're never Mexican enough for this or American enough for that? And I feel like, “Yeah!” I- I completely agree with that! Where it's like, “Well, I'm not the right indigenous. And- And I'm not the right Black or the right White or the right–” But I think that's why um… you just kind of embrace the country? Maybe that's why… I'm like, “Yes, I'm Mexican American.” Neither here nor there, no! - Participant who initially checked “Indigenous,” “White,” and “Black” on demographic forms after receiving ancestry test results In the process of filling out sociodemographic information on routine forms, caregivers were required to “make concessions” as one participant described, marking options to define themselves that they did not agree with or found insufficient. For some participants whose 187 cultural communities experienced historical abuse, mistreatment, and erasure, being forced to choose a race category such as “White” or “Other” due to technical (and arbitrarily assigned) definitions perpetuated oppression against their cultural identities. Providing fill-in-the-blank options for reporting race and ethnicity may better represent important race- and ethnicity-based disparities that could be obscured through the use of large race/ethnicity categories. Additionally, a fill-in-the-blank option for reporting cultural backgrounds may lead to collection of demographic information that is perceived as more authentic to lived experience than “race” or “ethnicity.” 3.2.3 “My husband doesn’t feel the same sort of split between his identities that I do with mine.” Changes in the U.S. labor force related to the COVID-19 pandemic were associated with more equitable division of domestic labor between different-gendered parenting partners at the beginning of the pandemic; however, division of domestic labor division returned towards prepandemic levels after the first few months (Carlson and Petts, 2022). Increased involvement of fathers in childcare within the home appeared to be associated with increased time at home during and after the pandemic; however, traditional gender attitudes of parenting – as opposed to attitudes associated with gender equality – increased during this time (Carlson and Petts, 2022). Despite an increase in remote work during and after the pandemic, the gender gap in domestic labor remained imbalanced, with mothers increasing their share of responsibilities for childcare (Dunatchik et al., 2021). Both internal and external expectations in the U.S. pressure mothers rather than fathers to devote a large portion – if not the entirety – of their own identity and wellbeing towards supporting their children. In the following excerpt, Participant 2F responded to my question asking if she felt that she and her husband shared caregiving equally or if she felt that it was weighed more heavily 188 towards one parent. [Child] and I are like a duo, you know, so um. I'm REALLY the one that's going to the appointment- scheduling the appointments, calling people, um like trying to figure things out. I also– I used to take classes in child development. Um so I would have been the default parent anyways just because of like how we have our schedule set up, but, like, I think that that extra sort of like- My husband's just kind of like, “Oh,” like, “You've got this!” Like, “You know the answer to this question, right?” or like– And I'm like hhhhh okay, I probably… I- I- I think like that has made– Like the way that I think I've seen that affect the dynamics of my relationship, is my husband just ASSUMES like I- I know, or I WILL know? And he doesn't like, for example, READ the like “What to expect” or Healthline or whatever. Um like he just like– It's my- It's MY job just to know, or to be on top of that, and it's like he doesn't really consider that his responsibility. So. I think it like MOSTly makes sense to be my responsibility, but I also think like, okay, like, you shou- you should know too. Shouldn't JUST be me. - Participant 2F Participant 2F described multiple time-consuming parenting occupations associated with supporting child development for which she was primarily responsible. She noted that their schedule would have resulted in her being “the default parent,” suggesting that the gendered division of parenting labor in her household was supported by logistical decisions and limitations due to their different work situations and time availability. For their situation, this made sense; Participant 2F had already addressed multiple developmental concerns for her child since the time of his birth, which included numerous physical therapy sessions, pediatrician visits, phone calls, and tracking of developmental milestone achievements. Yet she expressed frustration with her husband for his role in perpetuating this gendered division in which she assumed most of the physical and mental and emotional burdens of childcare. Her husband, by deferring questions about their son’s development to his wife and expecting that she would be monitoring their son’s development primarily on her own, implicitly assigned the bulk of developmental care to his wife, care that was likely to multiply as their son became old enough to undergo testing and 189 evaluation for receiving additional developmental support services. Gendered division of parenting labor even extends to the digital realm; one study in three urban cities in China found that mothers were more likely than fathers to perform the digital labor associated with parenting involving finding parenting information, communicating with children’s teachers, and managing online shopping and online education services (Peng, 2022). Participant 2F echoed this in her reflections on the parenting dynamic in their household: “…my husband just ASSUMES like I- I know, or I WILL know? And he doesn't like, for example, READ the like “What to expect” or Healthline or whatever. […] It's MY job just to know, or to be on top of that, and it's like he doesn't really consider that his responsibility.” The “extra sort of” that Participant 2F briefly mentioned included her knowledge in child development from classes she had taken in the past. While the gendered division of labor may appear to be “natural” given the initial difference in knowledge about child development across the two parents, it is also reflective of a larger gendered division of childcare in society that then influences which gender is more likely to have childcare experiences and knowledge of child development. This gendered division of parenting, which included additional labor to understand and address multiple developmental concerns, resulted in adverse effects on Participant 2F’s identities or sense of self and her relationship with her husband: I- it feels like there is competition between… Like I feel like I haven't quite figured out how to like combine all of my identities into one that's like full identity, and I don't- I don't know if like I just haven't figured that out yet, or maybe it's not fully possible. Um, but I feel like- like and to add in like the- like the third major identity like mom, wife, [work identity removed for anonymity]… um like- like those identities to me feel really separate, and it's hard for me to sort of like put them like together. Um. Like- And I- I think also what feels challenging is like I operate most of my time as like [child]'s mom. Um and when I’m interacting with my husband like I- like the interactions are as different if I’m in like MOM mode or like wife mode. Like BUSINESS mode or like relaxed, and um like I just– And like my husband has said like, “Oh, I feel like sometimes you just treat me like [child]'s father.” I mean, yeah, that's 190 kind of fair! Like sometimes I definitely do that because I’m in like that mode, especially, and I think maybe it will be easier when we've moved through a lot of the developmental concerns, because I think like, especially if I’m talking with my husband about things with [child]- about [child], like I’m much more like, “Okay! Like these– We need to make these phone calls and these are who– This is where we're at on this thing, and this is, you know, the concern right now, and like these are the developmental s-, you know, milestones he should be reaching, and he's not.” Um. Like it's just like a very different stance than like, “Oh, like I made you- I made you dinner, like let's sit down and eat together,” and like, “How was your day?” Like it's just- It just is different. Um. And I think it feels… it also feels very weird, because my husband and I have been together for [removed] years. Um like. Suddenly we have this new- like we have this new person and these new identities, and my husband doesn't feel the same sort of split between his identities that I do with mine. Like we’ve TALKED about it, and he's like, “Well, I don't really understand why you- why there's a difference between like treating me this way and that way.” I’m just like, “I- I don't know either!” Like there just- there just happens to be right now- like there's a difference for me. Um. So yeah, I think like the identity pieces is tough for me, and I don't know if it's like that for everyone? Or if it's just sort of like that for me right now. Um. And then I think also because my job has a lot of caregiving elements, I think that sort of further complicates it. […] I- I spend a lot of time sort of… I mean, I wouldn't like– It's not EXACT– It's not like Mom Mode, but it is like being organized around another person and their needs and their feelings. - Participant 2F Participant 2F struggled to combine her identities (“…those identities to me feel really separate…”), briefly mentioning a “competition” amongst them and the feeling that there was not one “full identity.” The intensive mental labor and organization associated with each of her stated identities and required in order to be “organized around another person and their needs and their feelings” may contribute to feeling a split sense of self. Once their son was born, Participant 2F embraced a complete, new identity to meet all of their child’s needs, while her husband was not required to take on so many new tasks and perspectives that he felt a similar need to embrace a new identity (“Suddenly we have this new- like we have this new person and these new identities, and my husband doesn't feel the same sort of split between his identities that 191 I do with mine.”). Of note, all three identities Participant 2F listed relate to another person, suggesting that her sense of self at this point in her life was primarily centered around others. Developmental concerns for her son, in tandem with an uneven gendered distribution of parenting labor between the parenting couple, appeared to complicate Participant 2F’s issues related to her sense of self and identities. Her belief that “… it will be easier when [they’ve] moved through a lot of the developmental concerns” suggested that it was presently overwhelming to (almost single-handedly) balance constant monitoring of development to promptly address concerns that arose with managing information gathering and health-seeking from medical providers. Her description of “Mom Mode” reveals a significantly one-sided workload of information gathering and health-seeking, and she provides the information found to her husband (“…these are the developmental s-, you know, milestones he should be reaching, and he’s not.”) instead of pursuing this information together. Women disproportionately bear the mental load (cognitive and emotional labor) for families and for society, a load that has increased with the shift to remote work and is particularly draining and health-harming because it is invisible, boundaryless, and enduring (Dean et al., 2022). The expectation that mothers should take responsibility for managing the majority of childcare and caregiving needs persists even when mothers face acute or chronic health conditions. While recovering from a major injury resulting in impacted ambulation and reduced mobility, Participant 2G continued to act as the primary caregiver and consciously prioritized her family before her own health/wellbeing: I mean like I really, mentally, don’t give myself a chance to like- There’s no time to sit immobile. I have kids that need me, like I have– I told my husband, and– it’s, you know, if as a mother, if you get injured, you know, m- as a mom, normally, you’re on the back burner. But like, right now, I’m not even on the STOVE. It’s like, my kids and my family come first, and that’s okay. That’sthat is what happens when you have small kids. I- I’m sure I would be better if I 192 BALANCED a little bit more, but I- I- for some reason I’m not good at doing that. - Participant 2G Participant 2G noted that her husband was also involved in parenting, but due to her life experiences and her dependability, major parenting responsibilities automatically fell primarily to her: I think because I feel like I always have to be the one… the touchstone. Like I always have to be– If- If shit is going sideways, Mom HAS to be the one to keep it together. It doesn't matter how difficult, I HAVE to keep it together, because I'M the one who HAS to take care of the situation. Not that my husband is incapable of doing it. He's not! But because I have the training of– I HAVE to be the one. - Participant 2G Participant 2G acknowledged that there was less than ideal balance in her caregiving situation, but she expressed a willingness to sacrifice her health and well-being in the present in order to support her children during a crucial period of development (“I have kids that need me…” and “…that is what happens when you have small kids.”). Her comment that there is “no time to sit immobile,” in light of the injury she was recovering from, emphasized her struggle with how her physical injury limited her ability to parent her child the way she desired (“I couldn’t sit and rock him for like maybe two, three months after, I couldn't rock him to put him down for the night.”). Towards the beginning of her interview, she noted that she felt like she “personally lost out a lot of time with him because of this injury.” Critically, developmental concerns as explored in this study were inextricably tied to guilt for many parents and appeared to contribute to a sense of urgency for Participant 2G to sacrifice her own well-being. She felt a strong sense of guilt in relation to her developmental concerns for her child, believing that her significant injury inadvertently contributed to her child’s potential developmental delay. She shared, “I feel like I have a lot of guilt involved with this, because I 193 feel– Like, you know, because I was so concerned with my own injury and just trying to get through the day-to-day minutia… like that maybe I didn’t talk to him enough in just trying to get through?” Notably, during the time of her injury and in the present, her husband was still parenting in the house; her conflicted feelings and attribution of her child’s developmental delay to her “trying to get through the day-to-day minutia” suggest that her guilt is tied to her feelings and expectations of what she, as a mother, can provide for her children that is not as easily provided by her husband. External gendered expectations about caregivers and who bears responsibility for children’s behaviors are reinforced through interactions within the community. In the following excerpt, Participant 2H recalled times when she and her husband were differentially greeted by community members when picking up their child. […] when we were going to church, and I would take him, um, they would have I guess like this– where you can drop off your kids and they can watch them during the service. Um. Whenever I picked him up, it would be like, "Oh, you know. Um he's very ANXIOUS and he's very… like THIS and he was climbing here, and he hit this little girl,” and it was just like a list of stuff that he had done. And I just felt like, “Well, damn!” And then whenever my husband would pick him up, ‘cause I would tell him, “You know what? I don't want to hear it, and I feel like those ladies are judging me every time I pick him up, like oh, eeeh.” And at one point they're like, “You know, he mainly hits the girls.” And it's like… He's TWO. I really doubt he's targeting all the girls. But whatever. And um so I would tell him like, “I need you to go, because I don't want to hear it.” But then HE would go, and it was like, “What did they tell you?” And he's like, “They didn't tell me anything.” I'm like what the HELL? Like why did they tell JUST ME? And I’m– I feel like if YOU go pick him up, it's like "Oh, awwww!" I'm like, “That's not fair!” And he's like… And he's just like, “I don't know, like maybe he had a good day!” I’m like, I DOUBT that. They just don't tell you anything. - Participant 2H In Participant 2H’s narrative, the church members providing childcare told her about her son’s behaviors throughout that day not only to provide information on his actions but also to censure 194 her for how he behaved. By doing so, they implied that there was something wrong with her parenting which then resulted in his behaviors. The difference in information provided to each parent can be seen as reflecting who they felt was responsible for the child’s behavior and who they could censure. Their tacit assumption that Participant 2H, rather than her husband, bore responsibility for their child’s behavior and should be the recipient of censure reinforced gendered expectations about childcare. 3.2.4 “It's very frowned upon when your children don't speak Spanish.” Historically, introduction of an external dominant language and sanctioning of linguistic repression has served as a strategy of deculturalization (Martínez-Roldán and Quiñones, 2016). In the U.S., both voluntary and involuntary assimilation have been associated with loss of heritage languages and deculturalization. Multiple participants in this study expressed conflicts regarding their desires to pass on language and culture ties to their children. I'm not gonna lie, when I go to Mexico, they don't see me as Mexican. It's just like, you know, you're American. And it's like, “Um, I guess so.” BUT the fact that I can have a conversation, it's like it helps? To be, you know, part of the group as opposed to like– And there's like a very um… It's very frowned upon when your children don't speak Spanish. It's like, “Oh, well, you're not real Mexican.” Or, “You're not a real Latino.” - Participant 2I For Participant 2I, speaking Spanish was important because of what it represented to her family and community members outside of the U.S.: though the ability to speak Spanish was insufficient for Participant 2I to be ascribed Mexican or Latine identities, it was still seen as an authentic link to the culture and communities. Inability to speak heritage languages may be seen as a rejection of the heritage culture, resulting in being seen as “not real Mexican” or “not a real Latino.” The importance of language to the maintenance of cultural ties is echoed in the following 195 excerpt by Participant 2J, who reflects on her own experience growing up without speaking Spanish within the home: And then um… like growing up, too, like we didn't speak Spanish in my household. I learned it because m- my grandma spoke Spanish, and I’m– Like, me being the oldest, I like spent more time with her when I was very small. So I learned it, and like my dad speaks it fluently, but he never spoke it in the house, and I always felt like, “Okay?” Like I don't know! I always felt WEIRD, like I was like more American than Mexican, but I wanted to be so much more Mexican than I want– Yeah, like I always felt like– Or people would be like, “Wow, you're Mexican? Like you're real light-skinned! or “You look real White.” And I would just be like, “No, like I really am,” and I would just feel like very self- self-conscious about… like my culture, ‘cause I didn't KNOW like a lot about it, or we didn't do things in our house that would be like “traditional” or like um… that they would be like, ”Oh, okay,” like my dad spoke Spanish, but he never spoke it to us. So I’d always feel like, “Well, why did you do that?” or… um like it- like it- yeah, it was just weird, like- or “Were you not like proud of your culture? Like you didn't want us to learn- like you didn't want us to be that like Mexican, you wanted us to be more American?” So yeah, like I always just felt like that way, or just very like… self-conscious when talking about, “Oh, I AM ha- like Mexican,” and people would be like- or, you know, like sometimes I wouldn't understand. “What is that?” Or like– Why do you– You know? Like whatever it is that we're talking about, or like traditions and stuff, and I would just be like, “We don't do that.” Or like, I don't know, and I had always felt like I want to be somewhere where like I could feel in the community that there is that um… like how- like culture, kind of. So I didn't want to live somewhere where it was not like h- the- the Mexican culture wasn't like influencing community? I felt like I wanted to be somewhere where um… it felt like, “Okay, I could feel that presence,” or you know what I mean? Or I could see it like within the community, somehow. Or yeah, I just- I don't know, I just kind of felt that way after a while in becoming an adult, because I didn't speak Spanish like at home, so growing up like I lost whatever I had learned. And I remember like feeling like, “Oh, my gosh! Like I’m never going to speak it again! Like this is the last–” I felt like it was the last part I HAD of my culture to hold onto. [Interviewer: Ah the language piece of it?] Yeah, like the language. And after my grandma died, ‘cause I did like to make an effort to visit her, and like took my kids to um see her um only because like I felt like that small connection like to her. And after she had passed away, I just felt like, “Oh, my goodness! Like who am I going to speak Spanish to?” Or like FORCI- or like force, because she didn't speak English. So I had to like FORCE myself TO 196 PRACTICE or like TO talk to her or TO like remember, you know what I mean? Like how to- how to SPEAK. So I would just– I had felt like, “Oh, my goodness! Like who am I going to practice with?” Or like, you know, I just felt like a very– after she– like that piece like died, kind of. And it- And I didn't want to feel that way, so like I tried um… Like now, like I DO try to like speak it to my kids, even if it is whatever. But I do, and I try to, you know, expose them to the culture, or expose them to um Spanish-speaking like whatever, people or vendors, or whatever. Just so they can understand like, you know, there are different types of languages, there are different types of cultures, but this is OURS. Yeah. - Participant 2J Participant 2J’s experience lacking a strong connection to her culture as a child and finding a link through her ability to speak the language with her grandmother motivated her present desire for her children to learn Spanish. The community in which she grew up, to her, did not feel as though there was a strong Mexican cultural influence, contributing to her feeling of disconnect. When combining her feeling that she lacked a Mexican cultural influence in her home with people’s comments on her light skin and subsequent implications that she looked White, not Mexican, her ability to speak Spanish was a physical, audible way for her to establish and show her connection to the culture. For Participant 2J, speaking Spanish and having a conversational partner in her grandmother were ties that no longer existed once her grandmother passed away. Spanish was clearly critical for Participant 2J’s connection to Mexican culture and Mexican identity. Advice from some pediatricians or other health providers and even other people in the community suggesting that parents use only English in the home so that children aren’t “confused” or so that they can adjust more easily when transitioning to monolingual English school environments – both rationales which are not supported by evidence – effectively supports continued erasure of cultural ties via promotion of language attrition and devaluing of heritage languages. Participants who expressed a desire to pass on Black/African American history and 197 culture to their children described the necessity of overcoming the long-standing silence impacting Black/African American history and destruction of culture: Participant 2K: Of course our children are gonna be more cultural than they areor we are. [Participant 2L: Mhm.] But at the same time, we’re learning things too [Participant 2L: Right.] as we’re teaching them too [Participant 2L: Everyday.], so it’s not just THEM being cultured, we’re gonna be cultured as well. Participant 2L: Right. Because I’m in that situation where I’m going, “Well, how come there isn’t more about Black history? XX our job and our culture and I go, “Well, what do I know about Black history?” And it’s like– Well, the stuff that’s in school is just all about slavery and there’s a lot more about Black history since the Black Lives Matter movement coming out and lots of documentaries and stuff, so, you know, when I turn on a documentary, ‘cause I LOVE documentaries, “Oh, well, would the kids watch this with me? Would this be something they were interested in? What parts of this could I share with them, even if they didn't watch the documentary with me that we could explore later on?” - Participant 2K (Father) and 2L (Mother) These excerpts underscore the inadequacy of education on Black/African American history in schools and, consequently, the need for many families to incorporate learning about Black/African American culture and history as a family co-occupation instead of a top-down transfer from parents to children. Participant 2L also demonstrated critical consciousness, recognizing that Black/African American history and culture have been minimized or ignored in formal education and searching for alternative ways to support her family’s cultural edification. 3.3 Interrupting intergenerational cycles through parenting occupations And then… other challenges like your kids reflecting that anger back to you. That's the scariest thing, and they DO do it now, too, and my husband is a very angry person. Uh and that's probably– It doesn't help at all. And so I feel like– So our kids do reflect anger upon us. And I– Again, it might be more normal than I think, but– They will yell at us, and they will, like, you know, say, “No, no, no, no!” And then- So that gets the WHOLE family very tense. And I just feel like as they get older, if that's not checked, or– How does someone deal with this- such a rebellious toddler? I uh- I think that could get problematic. 198 Um so even though my husband was– No. So my husband turned out okay. He's a great guy, he does a great job, but you know, during his childhood and his teenage years, he was in a lot of fights, and he was in a lot of iss- problems, and like he- he did go to jail a couple times for- for fighting, and he– that's part of just his anger, and his family's anger, like his brother INstigated most of those fights. So the challenge of growing up is understanding that he might have that in his genes and might have that in like- just as a reflection of who his parents are. And then– So trying to understand that and then trying to work with him but then not wanting my kids to end up in jail and not wanting my kids to um- to focus on that anger so much, because it is not healthy. Freire argued that it is in the combination of occupation and preoccupation, or reflection, that actions are human and thus able to transform social worlds (1970/2005). Caregivers in this study mapped their parenting occupations in the present amongst their own histories and their envisioned futures for their children. Their narratives, sense-making, and meaning-making explore deliberate choices caregivers made to prevent intergenerational transmission of parenting or their recognition of how their present actions related to their childhoods. In their reflections, the complex interplay amongst societal pressures, caregivers’ expectations for themselves, and their concerns for their children is clearly visible. 3.3.1 “…to make up for what my mom didn't do, I have to go above and beyond.” In a study of mothers of young children using transactional and life course perspectives, intersectional factors influenced mothers’ lives across time, shaping their parenting perceptions and decisions in the present and future (Sethi, 2021). Participants in this study described occupations they pursued as a family as well as individually to create a different family environment for their children than the ones in which they grew up. Participant 3M felt that her relationship with her mother during her childhood influenced her parenting occupations in the present: I feel like, as a mother, to make up for what my mom didn't do, I have to go above and beyond. So if it makes my life harder, who cares? So be it. Like my mom didn't do pictures, my mom didn't do homemade, like I'm learning how to 199 sew so I can make homemade advent calendars. WHY? Why make it hard on myself? So they know that mom cared, mom was there, mom cared, I loved you guys. Like I feel like I OVERdo it now because of my mom- experiences with my mom. You know, I'm OVERLY affectionate, I'm OVERLY loving, because my mom wasn't that way, and she's still not. I mean, I know she loves us, but she's just not- that's just not who she is. You know? So I feel like I have to go [above and beyond]. […] I feel like it is somewhat of a reflection on me. Like they– you know, ‘cause I know I look back at my mom, my mom didn't give– I mean. I'm– pardon my– but my mom didn't give a shit, and she didn't– I mean, we were there, we were fed, we were clothed, but there was nothing more, she didn't need anything more. - Participant 3M For Participant 3M, the effort involved in doing pictures and homemade projects was a way of showing her care and love for her children; the activities they engaged in together or the fun items she created were tangible proof of her time with them and of who she was as their mother. The dearth of co-occupations with her mother during her childhood, to her, showed that her mother “didn’t give a shit” and “didn’t need anything more” than making sure that Participant 3M’s basic needs were met. For Participant 3M, parenting needed to go beyond meeting basic needs, requiring in-depth engagement with her children, which motivated her to learn new occupations such as sewing to provide the experience she was unable to have for her children. Participant 3N described family activities and occupations as times deliberately set aside to encourage her children’s sense of belonging and feeling wanted: And so on movie night, we will- I'll cook a meal, um I'll put out snacks so they can eat while I'm cooking the meal. And then we'll all sit down and have dessert to FINISH the movie, right, 'cause these movies are like 2 hours long now. You don't have time just to sit through this! […] It was so like these are the times that we like really get to sit and enjoy each other. And that's really why like I schedule these on the calendar, these movie nights, and- and try to make sure that dinners are all held together, because I remember, as a kid, I didn't get a lot of time with my family just to like sit around each other. You know? And just like um fill my bucket with, you know, at- the love and attention that I needed. And so I try to make sure that my kids feel wanted and feel a sense of belonging 200 in their home, and so I- I- I try to do these activities and- and schedule these dinners so that we can do that. - Participant 3N Incorporating family time into daily routines like dinner, particularly when members of this family often did not eat other meals during the day together, served as times deliberately set aside for their children to “fill [their] bucket[s]” with love and attention. For Participant 3N, these family occupations were especially important when she considered her recent realizations about how the way she and her siblings were raised affected their development and continued to affect their lives now: I have come to the realization of the things that my mother had passed on to us. And like seeing how it affected me and my development and even still watching like my [siblings] um leaving the house last year and my [sibling] still there this year and seeing how my mom is STILL influencing [them]. And you know the things that THEY get into, and, you know, how she's not taking her mental health seriously at all times. You know? They– Uh sometimes she's like, “I’m fine!” and other times she's like, “Well, I don't want to take this medication because it makes me feel like this or XX” and it’s like, “Well we need to like really get a grasp on why you feel like this” and, you know, break it down. We can't just blame mom for everything, we have to, you know, figure out a way to move past this. I can already see that [older sibling] is probably gonna have all- trouble with anxiety. And I hope that we're able to assist him in that and managing it because I know it's something that runs in the family. And mm you can definitely see it trickling down and I always have to like check my own anxiety and make sure I'm not like making him feel anxious because I feel anxious. […] I– Like [his] first day of school was like that, like my mom showed up and I did NOT need my mom to show up to the first day of school. Because it made ME anxious and we were just there for three hours for like orientation, but SHE was uncomfortable around so many people, and so it was making ME worried about whether she really needs to be there. And I wasn’t able to focus on [the children] like uh XX [older sibling]’s first day. Yeah. Like. She’s like, “Come on! Grandparents do this!” And I’m just like, “I don’t neeeed you to be here!” So I had hoped that my anxiety wasn’t uh influencing HIS anxiety about being there, especially since this was a NEW school, and this is a new space for him. He had had no problems going to preschool, but I don’t know what the difficulty was 201 about the first five days of kindergarten. They were- They were difficult for sure. - Participant 3N Participant 3N was wary of intergenerational transmission of anxiety (“something that runs in the family” and “see it trickling down”) and was able to see it in action with one of her sons across three generations from her mother to her and through her down to her son. Her narrative also revealed her conscious efforts to address and manage her own anxiety as she recognized the impact her own mental health had on her son’s ability to manage anxiety. For Participant 3O, maintaining a balanced relationship between parenting and working were especially important because of what she experienced in her family being raised by parents who immigrated: I mean that's one of the things, I think– at least as a Hispanic person, I don't- I don't know if everyone feels like this. I know my sister always talked about like the generational um impact or… the barriers you want to eliminate. Um. So yeah, one of them is the– and I- I kind of went through this. It's just having them– enough time to talk to my kids is one thing that my parents, who decided to come to this country just work, work, work when there was never enough um sit-down time to- to understand your kids, and you just ASSUME that you know them? […] So before, I was like, “Oh, well, [my dad]'s working, he's working. I can't do anything. All right, I guess I have to understand that.” But then like as you grow up, you just realize like, “No!” Like that's BS. If you get– Obviously, yes, you'll lose wages, but you HAVE the flexibility at a certain time in your life to say, “I’m going to miss it because it's an important day.” He missed my wedding. He was working. - Participant 3O Her realizations over time that her parents’ choice to prioritize their work negatively impacted their relationship with their children and served as physical barriers against developing that relationship motivated her to avoid repeating the same behavior with her own children. Additionally, in recognizing their negative impact on her childhood and adulthood, she made the 202 decision to close familial relationships, going low- to no-contact with them to prevent their influence from affecting another generation of children. So yeah, extended families is- is very important with Mexicans. But I feel like I’ve broken that? […] And even on [husband]'s side, like we used to visit theirtheir family more, but then he got into like arguments with them, and so now HE no longer wants to have involvement with his grandparents, or I mean with HIS parents. Which puts our kids in a moment of like, oh, well will they get to know their grandparents? Will they learn from them? Do we WANT them to learn from them? You know? Because obviously… that just doubles the weird stuff that you learn, like– And they were also not the best parents, you know? And [husband] talks about- My husband talks about that a lot, like, “Oh, my mom messed up in this way, this way, and this way,” and when she was taking care of our kids, like that– she was probably TEACHing them these bad habits, you know? “And then I don't want my kids to turn out the way I did,” he would say. Um and his dad recently called him and told him like, “Hey, are you coming over for Christmas?” And since he is in a quarrel with his BROTHER, he's like, “No, I don't want to go. I don't want to see him.” And then his dad said, “Oh, you're gonna be like that with your kids, where they’re not gonna give him- give them the experiences to, you know, be involved with us and to get to know us?” It's just like- it's shocking! It's like, “Well, fuck, like we- we have a lot of stuff going on in our life!” You know? And then it's- it's an expectation of the family to go visit often and to be there at every event, whether it's the major holidays or every single holiday that comes up like MONTHly. And that's VERY Hispanic, and I don't know what other cultures do? I feel like there, it's more common to like, “Oh, I’m only going once a year or twice a year.” Yeah. And I don't know what standards we want to set for our kids, but yeah, that’s- This year, we decided that we will do just holidays at our house, so Thanksgiving, we spent it doing nothing. And then Christmas, we're planning to just have a bunch of presents for our kids. Um and that's it. It's just going to be our nuclear family. - Participant 3O Participant 3O expressed feeling conflicted about having “broken” ties to extended families, which was highly valued culturally. She described wanting her children to know their grandparents but also being afraid of their influence not only on her children but also on herself and her husband. She and her husband broke with cultural and familial expectations to gather with extended family multiple times a year, choosing not to continue family traditions or submit 203 to family pressures to overcome their misgivings. 3.3.2 “‘Stop crying like a girl!’” Caregivers described internal conflicts surrounding their interactions with their children in light of their reflections on gendered expectations that they themselves faced while growing up. Participant 3P linked two different responses to his sons’ emotions with his childhood experiences: I think for me, uh not being close with my dad at all kind of like fuels… the way I- I approach things about things of that nature. Like- Like [Participant 3Q] calls me like “Superdad” because the smallest things. Like, you know, one of our other children– Like we were walking through Target one time, and he had like these little toys, and one of them fell off the cart. And [Participant 3Q] was like, “WE DON’T HAVE TIME, WE GOTTA GO!” And I walked back through the store and- and FOUND IT, yeah! And I don't wanna DO that, I don’t it to be one of those things where it's like… He holds onto THAT ONE THING forever. “REMEMBER THAT TIME WE WERE AT TARGET, YOU WOULDN’T GO BACK TO GET–” You know what I mean? Like I don’t want it to be something like that. So I think that’s why I try to, in MY eyes, go above and beyond. Um wi- So. I don’t wanna have– I don’t want that. same. type. of– I wouldn’t- I wouldn’t say resentment, but almost kinda like LONGING? I wouldn’t want my kids to- to have that same type of feeling. […] I don’t think it’s bad– Well, I think it’s bad now because we’ve been talking ‘bout it, but- but YES. I- I do [feel like there are more cultural expectations for how a boy should grow up]. [Participant 3Q: So what are your cultural expectations for boys?] What do you mean? What ar– What- What- What do you mean? [Participant 3Q: I mean the way that, you know, you say things like, “Boys don’t cry, stop [crying]!”] Okay, okay, but- bokay, but- I– ‘cause I don’tI don’t– First off, I never said that boys DON’T cry. [Both participants say simultaneously: “Stop crying like a girl!”] Yes, that. […] Right, right, and that comes through my child sports and things of that nature, so– And then being the only like– uh like wi– Like I said, I grew up with my [removed], and then [removed] lived there too, and SHE’s also female, so I’m the ONLY MALE of the house. So I gotta have that like AAHHH bravado type thing, and it’s likeit’s HARD to just turn it off after being the “man of the house,” quote unquote, FOREVER. - Participant 3P (Father) and Participant 3Q (Mother) Participant 3P’s relationship with his own father influenced his actions and decisions in the 204 present. His narrative about being “Superdad” demonstrated his awareness of how his son may look back on his parenting actions in the future. His present reflections on what his actions meant to his son and the fact that they would show his care and love for him prompted him to make his son’s feelings a priority despite time constraints and pressures to respond in the way he might been treated as a child. His use of “Stop crying like a girl!” with his sons demonstrates the difficulty he faces of trying to parent his sons in a different way than the way he was raised. He refers to his childhood growing up with only women and facing gendered expectations as the “man of the house” and as a member of child sports to have bravado and not cry. His comments also show that his wife’s commitment to her own values and priorities supported his current actions towards breaking intergenerational transmission of gendered expectations and socialization (“I don’t think it’s bad– Well, I think it’s bad now because we’ve been talking ‘bout it…”). Participant 3R described similar cultural expectations for men that she was aware of and did not fully embrace for her own son: […] they're always like don't show your emotions, you know, they- they're more um. Uh. How do you- How do you call it? Um– They're more manly, you know? Like th– Machos, you know? That's how- That's how people were raised back then, you know! Like- Like the men, you weren't able to cry. If you show emotion, you know– But no, I mean, yeah, I want him to be tough, you know, but- but at the same time, I mean he is human. He is a person, you know? So everybody has emotions. - Participant 3R In this excerpt, Participant 3R’s use of “That’s how people were raised back then…” emphasized her framing of these expectations as more reflective of the past and not necessarily what she wanted for her son in the present. She juxtaposed showing emotion with being “tough,” “manly,” and “Macho.” Her final comments demonstrated that she recognized her son’s personhood 205 beyond his masculinity and desired to support his emotional development in a way that would allow him to express his feelings while still being able “to be tough.” Other cultural expectations related to gender that participants discussed include gendered division of household labor: Yeah, I don't want there to be a- like a- a difference in- in expectations and gender? You know, in my Hispanic hou- household, it was less my parents but more my grandparents were always pushed. So my brother was more– like he needed more attention than the female, sort of thing. I try to push for my kids to um overall have ALL the tools that they need to thrive in the future. So I want um my kids to be able to cook, I want my kids to be able to um yeah, just keep their space tidy, because I know it's so important for the mind. - Participant 3S Participant 3S made parenting choices against the cultural expectations for gendered household roles that she experienced as a child. For her, promoting less gendered division of roles was a way to support her sons to “have ALL the tools that they need to thrive in the future”; additionally, she viewed teaching her sons to cook and clean as important for their mental health and not as gendered activities that should only be taught to daughters. In the following excerpt, Participant 3T similarly described trying to avoid gendering roles for her sons but highlighted the societal differences she suddenly became very aware of while parenting her daughter as opposed to parenting her sons. But yeah, just making those decisions in regards to her ‘cause she's a girl makes me like very nervous! […] Like I feel like, yeah, I could see the difference between my boys and her. Like the decisions that kinda need to be made or thought about as in regards to as she's grow up, you know, um… like EVERYTHING! Like what she'll wear, or what she's gonna wear NOW, or like if she'll play SPORTS or like– I don't know, it's just like so strange! Like with my boys like it was just so easy to think like, “Yes, Yes, I’m gonna do this, like, okay, yeah.” But like with her, it's just like, “Oh, my gosh.” It's like I feel like I have to think a little bit extra. […] Like I look at the clothes, and I’m like, “Oh, my gosh! These are so MUCH.” Not much like COST wise, but just like, “Oh, my gosh! There's so much like SPARKles or like, you know, PINK or like. There's so much more like RUFFLES or like–” I don't know! And I’m just like, 206 “Oh, my gosh! I just want like a- something plain! Or like neutral, or like– you know what I mean?” It's weird. […] like when I was in school, I took a gender… like gender class? Um, this was like before I had her, and I just had my two boys […] and um like we would like talk about like um the difference, and like in society, and like, you know, when you see it at the stores, when you're shopping, or in the clothes, or like, you know, just like beliefs and stuff. And I don't know, I didn't have a daughter, so I was like, “Well, yeah, I guess you could see that,” or I would just kind of think like ME growing up. But now that I have a daughter, I completely understand! And it's just like I DID see it kind of with my boys, because there was like certain things, like, you know, like you would see in the boys’ “kids” section, and not the girls, or like baby dolls, and like… stuff like that, but– I don't know, I mean I was always trying to be inclusive with them, too! Like I got my son a kitchen because I was like, well, I mean, you know, what's wrong getting a kitchen? Everybody likes to cook, or, you know, small stuff like that, or like I would always encourage, “Look at all of the babies, they're so cute!” Because I’m like, “Wow, they're gonna, you know, be around babies, too.” So yeah, but like as– I have a daughter now, it's just like… yeah, different. - Participant 3T Her description underscores the pervasive presence of gendered expectations in choices for clothing and toys even for children as young as hers. Her lived experience of having a daughter seemed to change her understanding of societal expectations and pressures for girls in a way that exploring those pressures in a formal class didn’t. Moreover, having a daughter helped her recognize how she treated herself and spoke of herself (in dialogue with gendered expectations) as someone who is the same gender as her daughter to model for her how she wanted her to see herself: “Okay, like, what direction am I going in? Or like who do I want her to be? Or like what do I have to do now to instill in her for later?” […] like I have to be already like conscious of how I talk to her. How I talk to myself or like what I say about myself. Um. I mean, like I was already pretty conscious about that like with my boys, but now it's a girl like I HAVE to be careful, you know? Like I can't be like, “Wow, I look so gross.” Or like, “Gosh, like my hair is so ugly, or like, you know, like I hate it,” or something like that, like I have to like be conscious of like the words I use um or like how I talk about myself. - Participant 3T 207 3.3.3 Social media engagement Finally, social media engagement emerged as a mode for parents to engage in collective parenting occupations in concert with larger dynamics of conflicting societal pressures. Participant 3U, who shared that she received most of her parenting information through Tiktok, highlighted its importance given the limitations of her physical context for parenting support. It's such a great way to connect, because it SHOULD be through family members, right, where you get all this information passed down. But I don't HAVE that type of relationship with my mom, and I don't think she ever sat down to think about it herself? She just parented, because that's how it came naturally to her. So you don't have someone that you can connect with. Um and then through Tiktok, you just- you either find those videos that resonate with you, or you actively look for the information that- that you need. - Participant 3U Being able to engage on social media with parenting videos and parenting-related accounts enabled Participant 3U to connect with experienced community members who could provide parenting information and support that she was unable to receive from her own parents. Despite these benefits, social media engagement also came with its own drawbacks. Participant 3U found that she judged herself for not being able to engage on social media the way many mothers did by posting pictures and taking professional family shots: But at some point I wanted to show off my kids and my family, but then it just became too annoying to me. And I feel like that might be- like I judge myself for it, too. I feel like people… who post about their kids on social media look so much happier as a family. And so I feel like the perspective of someone who DOESN’T do that is that they're not happy. […] like even just like the- the professional child pictures. Like I have no energy to schedule that. And part of it is like, “Oh, well, are you not doing it ‘cause…” I don't know. I feel like my mom's perspective is like, “Oh, you- you're WRONG for not doing that.” Yeah. - Participant 3U Her judgment of herself and her mother’s reported perspective illustrate the influence of both external perceptions and internalized expectations for parents’ (and particularly mothers’) social 208 media engagement. Those pressures caused Participant 3U to judge herself for not having enough energy beyond her ongoing paid and unpaid labor to put into presenting a particular static framing of her family for outsiders to see. Participant 3V also changed her social media engagement patterns in response to external pressures related to child development and parenting expectations. I do tend to follow a lot of other um influencer moms um […] who kind of have a more upbeat, kind of funny way of showing how parenthood is going for them? Um… versus- like I did follow some like doctors at some point, and um I kind of stopped following them ‘cause they really were just like by the books, everything by the books. Um. You know, or, “Here's how these studies go.” And things like that. And for me, it's like, I understand all this stuff is very important. […] But I think AS a parent, there's already so much pressure on yourself to make sure that you're… not going to mess this human up? Um that like the extra kind of pressures of, “Oh, if you do this exactly how you do this at this age, then they're going to grow up to be, you know, Rhode Scholars or whatever.” […] there's already so much pressure of is he– like is he crawling when he's supposed to crawl, like is he– You know, am I feeding him the right things? It– Like is he getting enough nutrients? Is he drinking enough water? There's already those kinds of things that the other pressures… trying to stay away from. So I try to find more moms who do uplifting things um or show uplifting things, or are just funny. Um and really just show you like, “Oh yeah, you remember that time your living room was a mess? Well, here's video of mine currently a mess right now, and I want you to know everybody's is a mess right now, even if they make you think they're not. They're a mess.” So, you know, I try to– I r– I– Like I think it's like a kind of like social media camaraderie? - Participant 3V Though she initially followed educational accounts on social media for information seeking purposes to support child development, those accounts began to feel prescriptive in their posts about what developmental skills to expect at what age. For Participant 3V, following mothers “who do uplifting things” was a way to find “social media camaraderie” in the face of pressing guilt associated with trying to make sure children develop well. 209 3.4 Considerations of this study In the course of conducting these interviews, it became clear that there may be some aspects of parenting and caregiving that were essentially “lost in translation”; multiple times, participants who also spoke a language other than English would say, “How do you say it…” There may be different culturally/linguistically embedded expressions or embodied ways of understanding parental hopes, concerns, and expectations for children that could not be adequately captured in English. Different findings would likely have emerged if these interviews had been multilingual with interviewers who more closely shared participants’ cultural backgrounds. Additionally, my own positionality amongst different social axes such as race/ethnicity and gender is likely to have impacted the types or extent of information participants felt comfortable sharing as well as my perspectives while analyzing their narratives and interview data. Thus, the findings of this study were shaped dynamically not only by the unique lived experiences and social worlds of the participants but also by participants’ shifting engagement and alignment with me and with my social worlds as we negotiated understanding, meaning, and empathy through these interviews. 4. CONCLUSION Intersectional analysis of caregivers’ occupational engagement, particularly in relation to their children’s development, revealed how conscientização manifested in daily life through parenting occupations. 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L., Yirmiya, N., Estes, A., Hansen, R. L., McPartland, J. C., Natowicz, M. R., Choueiri, R., Fein, D., Kasari, C., Pierce, K., Buie, T., Carter, A., Davis, P. A., Granpeesheh, D., Mailloux, Z., Newschaffer, C., Robins, D., Roley, S. S., … Wetherby, A. (2015). Early identification of autism spectrum disorder: Recommendations for practice and research. Pediatrics, 136 Suppl 1(Suppl 1), S10–S40. doi:10.1542/peds.2014- 3667C 216 CHAPTER 5: Integration of Findings from a Multilevel Mixed Methods Study of Sociocultural Influences on Infant Developmental Differences related to Elevated Autism Likelihood 1. Overview My primary goal in conducting this research was to critically examine the impact of sociocultural processes on infants’ autism likelihood and early caregiving environment. I used a multilevel mixed methods research design to obtain a contextualized understanding of how infant neurodevelopment is situated amongst social systems and worlds. In Study 1, I systematically reviewed the literature for documented associations between sociodemographic variables and autism likelihood. The results of Study 1 indicated important gaps in the conceptualization of variables related to social strata and the need for more research both examining the potential influence of sociocultural processes on autism likelihood and applying an intersectional perspective. In Study 2, I used Bayesian mixed modeling to test whether interactions amongst sociodemographic variables predicted differences scores reflective of autism likelihood across neurodevelopmental domains in a sample of children from the greater Los Angeles area. I also examined whether there were race/ethnicity or other sociodemographic group differences in associations amongst pre-/perinatal risk factors, experiences of discrimination, and domain factor scores. The results of Study 2 demonstrated the importance of disaggregation of race/ethnicity groups and consideration of potential interactions in predicting autism likelihood in infancy. Additionally, findings provided preliminary support for an association between maternal experiences and anticipation of discrimination and elevated infant risk scores in sensory regulation and hyperresponsivity, expanding the growing number of studies examining the generational impact of discrimination on health outcomes. In Study 3, I used narrative analysis to 217 explore the impact of sociocultural factors on caregivers’ hopes, concerns, and expectations for their children’s development. The findings of Study 3 contextualize the process of developmental surveillance in the broader landscape of families’ lives, highlighting ways that caregivers’ identities, developmental concerns, and sociocultural expectations are co-constituted through parenting occupations and merging of social worlds. The findings of these three studies provided a multilevel view of the contexts in which infants develop and ways in which social factors impact the health and well-being of individuals and communities. 2. The problem with checkboxes and aggregation of race/ethnicity identities The ways that race and ethnicity are often conceptualized in research and healthcare emerged as an issue cutting across these three studies. Aggregation of race and ethnicity groups reduced clarity in interpretation of findings from previous research studies and potentially obscured important differences between and within groups. Quantitatively, there were credible differences across race/ethnicity categories in predictors of infant outcomes related to autism likelihood. Qualitatively, caregivers described important examples of variation in lived experience within their racial, ethnic, and cultural groups that influenced their priorities for their children’s development. Caregivers’ reflections on whether demographic checkboxes adequately capture their race and ethnicity stressed the inadequacy of current practices for collecting and reporting race and ethnicity information. Each time individuals are required to self-report their racial or ethnic identities by choosing them from predetermined options, they must reconcile how they themselves identify with society’s expectations for their identities. This becomes an issue particularly for people whose racial/ethnic categorizations do not match their sociocultural experience growing up and people whose racial/ethnic identities are not represented in 218 predetermined options which implicitly rank the options (by naming certain options and othering the unavailable options) as most pertinent to the purposes of data collection. In this dissertation, participants were given checkboxes in Study 2 and fill-in-the-blank lines for Study 3; while selfreport of race and ethnicity overlapped across options for the majority of participants, there were notable differences in what some participants considered as their race vs. their ethnicity. Future studies could examine whether discordance between self-reported vs. institutionally assigned race/ethnicity categories differentially predicts outcomes from participants exhibiting concordance across categories. Additionally, future studies could test whether a “cultural background” grouping as reported by participants is more explanatory or predictive of differences in health outcomes than race/ethnicity groupings. 3. Caregivers and developmental surveillance Though developmental surveillance is recommended for improving long-term health outcomes for children through timely access to evaluations and services, disparities produced by sociocultural processes impact the extent to which those benefits reach children with marginalized or minoritized backgrounds. These studies identified systemic and interpersonal barriers within developmental surveillance practices that hampered early identification and intervention efforts. While parent report of developmental concern was predictive of higher risk in certain developmental domains, there was not enough evidence to indicate that it was predictive across all race/ethnicity groups, suggesting that differences in caregiver report of concern might impact the effectiveness of current developmental surveillance practices. Future research should explore developmental surveillance practices holistically to examine how caregiver report of developmental concern is elicited across providers, how structural differences in access to and availability of developmental resources relate to caregivers’ developmental 219 concerns and priorities, and whether melding of different cultural backgrounds within families impacts caregivers’ expectations for their children’s development, particularly with regard to timing of developmental milestones and child behaviors or signs prompting caregiver concerns. 4. Parenting occupations and critical consciousness Combined, the findings of these studies revealed the importance of parenting occupations in caregivers’ expansion of and engagement in critical consciousness. Envisioning expectations and hopes for their children’s development often forced caregivers to confront societal expectations for their children, requiring them to reflect on how they themselves were raised while considering how they wanted to parent their own children. Through their parenting occupations, caregivers resisted, altered, perpetuated, and even renewed ties with sociocultural expectations. Additionally, both anticipation and experience of discrimination impacted caregivers’ developmental priorities for their children and their approaches to parenting. Critical consciousness was transformative, freeing, and daunting as caregivers with marginalized or minoritized identities engaged in complex layers of intersubjectivity while parenting. Particularly for caregivers facing intersectional points of discrimination, parenting occupations served to make manifest competing sociocultural processes affecting families intergenerationally. 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International Journal of Developmental Neuroscience, 23(2-3), 143-152. doi:10.1016/j.ijdevneu.2004.05.001 238 APPENDICES APPENDIX 1: Study 1 Literature Review Extraction Tool ....................................................... 239 APPENDIX 2: Study 2 Demographic Summary of InDI Subset ............................................... 241 APPENDIX 3: Study 2 Main R Packages Used for Analyses and Workflow Management ...... 245 APPENDIX 4: Study 2 Mediation SEM Parameter Estimates (path b only) ............................. 247 APPENDIX 5: Study 2 InDI Subscale Scores by Maternal Race/Ethnicity............................... 248 APPENDIX 6: Study 3 Guides for Semi-Structured Interviews................................................ 249 239 APPENDIX 1: Study 1 – Literature Review Extraction Tool (Based on Sandelowski et al., 2013 Rules of thumb for text-in-context extraction) Data Extraction 1. Note MEAN age of participants; if mean age falls past 36 months, mark as “older child age range” and move to the next record 2. Note country (findings should be for the US only) 3. Anchor findings to sample information a. Specify sample according to extent of variation and/or whether these variations are addressed in the findings i. “Parents of children with cystic fibrosis…” or “Low-income mothers of adolescents with cystic fibrosis” or “In families with children with cystic fibrosis mostly adherent to their prescribed regimen…” 4. Anchor findings to source of information a. Insert source of information whenever there are multiple sources of information about an index person or event i. “…parent-reported child behaviour problems were significantly associated with more sibling-reported conflict and rivalry…” 5. Anchor findings to information about time a. Insert time phrase in longitudinal and intervention studies and if time is featured as a variation in the target phenomena in the findings i. “…an average of 4 years after injury… or “prior to receiving these interventions predicted better weight gain immediately after receiving these interventions” 6. Anchor findings to comparative reference points a. Insert between-group and between-theme comparisons i. “were almost four times more likely to display…than children whose caregivers…” or “Trapped mothers with end-stage renal disease…” vs “Adaptive mothers…” vs “Embedded mothers…” 7. Anchor findings to information about magnitude and significance a. Insert frequency, per cent participants associated with a finding in surveys and statistical significance i. “Most (70%) parents of children with cystic fibrosis most frequently used as coping strategies...” b. If findings address statistical significance, also include all relationships relevant to research synthesis aims found to NOT be statistically significant i. or “Siblings reported significantly more conflict and rivalry…but there were no statistically significant differences in conflict and rivalry with…” 8. Anchor findings to study-specific conceptions of phenomena a. Specify target phenomena as operationalized in study 240 b. “…demonstrating more balance between cohesion and flexibility (neither overly rigid nor flexible) had higher rates of reported adherence than families demonstrating less balance between cohesion and flexibility (overly rigid or overly flexible).” 9. Rate level of intersectional framing of sociodemographic variables in methods and findings: a. 1 – covariates or as single predictors b. 2 – interactions with other sociodemographic variables c. 3 – contextualization of intersecting sociodemographic variables in sociocultural context and amongst power dynamics 241 APPENDIX 2: Study 2 – Demographic Summary of InDI Subset Demographic Characteristics Multilevel analyses InDI analyses Child age in months (range = 5 to 17) Mean SD Mean SD 10.57 3.20 10.21 3.24 Min Max Min Max 5 17 5 16 Child sex (male) n % n % 322 50.7% 49 52.1% Age in years of parent who gave birth Mean SD Mean SD 31.6 5.5 32.6 5.8 Min Max Min Max 17 47 20 47 Age in years of parent who did not give birth* Mean SD Mean SD 35.8 13.1 35.0 9.7 Min Max Min Max 17 62 21 50 Respondent's relationship to the child n % n % Biological Mother 580 91.5% 90 95.7% Biological Father 35 5.5% 0 0.0% Both Biological Mother and Father 16 2.5% 4 4.3% Other (e.g., Other Mother, Grandparent) 3 0.5% 0 0.0% Maternal race/ethnicity n % n % Hispanic-single race 278 43.8% 32 34.0% Multi-race 38 6.0% 13 13.8% Non-Hispanic/Latine American Indian/Alaska Native 0 0.0% 0 0.0% Non-Hispanic/Latine Asian 47 9.0% 5 5.3% Non-Hispanic/Latine Black or African American 24 3.8% 5 5.3% Non-Hispanic/Latine Other 0 0.0% 0 0.0% Non-Hispanic/Latine White 238 37.5% 39 41.5% Not stated or unknown Maternal educational level n % n % Up to 12th, no diploma 16 2.5% 1 1.1% HSD or GED 100 15.7% 4 4.3% Some college/associate degree 140 22.0% 19 20.2% Bachelor degree 200 31.5% 38 40.4% Master/professional/doctorate degree 179 28.2% 32 34.0% Median household income of residence census tract/block Mean SD Mean SD 84,771.27 36,413.71 88,908.63 41,044.69 Min Max Min Max 20,554 248,036 21,519 248,036 242 County of residence n % n % Los Angeles 355 56.0% 51 54.3% Orange 98 15.4% 14 14.9% Riverside 104 16.4% 15 16.0% San Bernardino 49 7.7% 10 10.6% Ventura 29 4.6% 4 4.3% Aspects of identity (fill in the blank) n (of 94) % Respondent’s Relationship to Child Mother 89 94.7% Parent 3 3.2% Father 2 2.1% Sex assigned at birth Female/Woman 89 94.7% Blank 3 3.2% Male 2 2.1% Gender Female/Woman 89 94.7% Blank 3 3.2% Male 2 2.1% Sexual orientation/sexual identity (overlap allowed) Straight/Heterosexual 74 78.7% Female 9 9.6% Bisexual 5 5.3% Blank 5 5.3% Pansexual 1 1.1% Lesbian 1 1.1% Race(s) Caucasian/White 45 47.9% Hispanic/Hispanos 10 4.8% Blank/Decline to state 10 4.8% Mixed/Multiracial 4 4.3% Asian 3 3.2% Black 3 3.2% Mexican 3 3.2% White and Hispanic 2 2.1% American 1 1.1% Asian and Hispanic 1 1.1% Caucasian + Native American 1 1.1% Hispanic/Latino 1 1.1% Human Race 1 1.1% Latina 1 1.1% Latino 1 1.1% 243 Latino, Asian 1 1.1% Mexican American 1 1.1% Mexican, Filipino, Indigenous 1 1.1% Mixed Black, White, Indigenous Colombian 1 1.1% Mixed White and Asian 1 1.1% White, Filipino 1 1.1% White, Alaskan Native American 1 1.1% Ethnicity Caucasian/White 34 36.2% Blank 8 8.5% Hispanic 6 6.4% Mexican 6 6.4% Mexican American 5 5.3% Latina 4 4.3% Not Hispanic/Latine 4 4.3% Black 3 3.2% American 2 2.1% Chinese 2 2.1% Latino 2 2.1% African Latina 1 1.1% Asian American 1 1.1% Caucasian + Native American 1 1.1% Chinese and Taiwanese 1 1.1% Filipino and Mexican 1 1.1% Hispanic/Latino 1 1.1% Italian 1 1.1% Japanese 1 1.1% Japanese and Portuguese 1 1.1% Jewish 1 1.1% Latin American 1 1.1% Latino and Asian 1 1.1% Mexican, Filipino, Indigenous 1 1.1% Mixed 1 1.1% White and Filipino 1 1.1% Navajo 1 1.1% White Hispanic 1 1.1% White, Alaskan Native American 1 1.1% Parent educational history at child’s birth n (of 94) % Highest educational level completed of parent who gave birth Up to 12th grade, no diploma 1 1.1% High school degree or GED 4 4.3% Some college or associate degree 19 20.2% 244 Bachelor degree 38 40.4% Master, doctorate, or professional degree 32 34.0% Blank/Withheld 0 0.0% Highest educational level completed of parent who did not give birth Up to 12th grade, no diploma 3 3.2% High school degree or GED 18 19.1% Some college or associate degree 26 27.7% Bachelor degree 23 24.5% Master, doctorate, or professional degree 22 23.4% Blank/Withheld 2 2.1% Household characteristics n (of 94) % Household yearly income range (select one) Under $20,000 10 10.6% $20,000 - $39,999 5 5.3% $40,000 - $59,999 11 11.7% $60,000 - $79,999 12 12.8% $80,000 - $99,999 2 2.1% $100,000 - $119,999 0 0.0% $120,000 - $139,999 7 7.4% $140,000 - $159,999 11 11.7% $160,000 - $179,999 4 4.3% $180,000 - $199,999 7 7.4% Over $200,000 20 21.3% Blank/Prefer not to disclose 5 5.3% Type of residential community (select one) Suburban 56 59.6% Urban 31 33.0% Rural 4 4.3% Blank 3 3.2% Current parenting situation in the home (all that apply) Two-parent home 74 78.7% Multigenerational home (i.e., grandparents) 9 9.6% Single-parent home 5 5.3% Blank 3 3.2% Two-parent home and Multigenerational home (i.e., grandparents) 2 2.1% Single-parent home and Multigenerational home (i.e., grandparents) 1 1.1% Languages spoken in the home (fill in the blank) English (only language listed) 62 66.0% English and other languages 29 30.9% Multiple other languages (English not listed) 0 0.0% One language (English not listed) 3 3.2% 245 APPENDIX 3: Study 2 – Main R Packages Used for Analyses and Workflow Management Arnold, J. B. (2021). ggthemes: Extra Themes, Scales and Geoms for 'ggplot2'. R package version 4.2.4. https://CRAN.R-project.org/package=ggthemes Bolker, B., & Robinson, D. (2022). broom.mixed: Tidying Methods for Mixed Models. R package version 0.2.9.4. https://CRAN.R-project.org/package=broom.mixed Bürkner, P.-C. (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1-28. doi:10.18637/jss.v080.i01 Bürkner, P.-C. (2018). Advanced Bayesian Multilevel Modeling with the R Package brms. The R Journal, 10(1), 395-411. doi:10.32614/RJ-2018-017 Bürkner, P.-C. (2021). Bayesian Item Response Modeling in R with brms and Stan. Journal of Statistical Software, 100(5), 1-54. doi:10.18637/jss.v100.i05 Fernández-i-Marín, X. (2016). ggmcmc: Analysis of MCMC Samples and Bayesian Inference. Journal of Statistical Software, 70(9), 1-20. doi:10.18637/jss.v070.i09 Gabry, J., & Mahr, T. (2022). bayesplot: Plotting for Bayesian Models. R package version 1.10.0. https://mc-stan.org/bayesplot Gabry, J., Cesnovar, R., & Johnson, A. (2023). cmdstanr: R Interface to 'CmdStan'. https://mcstan.org/cmdstanr, https://discourse.mc-stan.org Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society Series A: Statistics in Society, 182, 389-402. doi:10.1111/rssa.12378 Kay, M. (2023). ggdist: Visualizations of Distributions and Uncertainty. R package version 3.3.0. https://mjskay.github.io/ggdist. doi:10.5281/zenodo.3879620 Kay, M. (2023). tidybayes: Tidy Data and Geoms for Bayesian Models. R package version 3.0.4. https://doi.org/10.5281/zenodo.1308151. doi:10.5281/zenodo.1308151 Long, J. A. (2019). interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions. R package version 1.1.0. https://cran.r-project.org/package=interactions Long, J. A. (2022). jtools: Analysis and Presentation of Social Scientific Data. R package version 2.2.0. https://cran.r-project.org/package=jtools Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., & Makowski, D. (2021). performance: An R Package for Assessment, Comparison and Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139. doi:10.21105/joss.03139 Neuwirth, E. (2022). RColorBrewer: ColorBrewer Palettes. R package version 1.1-3. https://CRAN.R-project.org/package=RColorBrewer 246 Ooms, J. (2021). writexl: Export Data Frames to Excel ‘xlsx’ Format. R package version 1.4.0. https://CRAN.R-project.org/package=writexl Stan Development Team (2022). RStan: the R interface to Stan. R package version 2.21.7. https://mc-stan.org Wickham, H. (2022). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.5.0. https://CRAN.R-project.org/package=stringr Wickham, H. (2023). modelr: Modelling Functions that Work with the Pipe. R package version 0.1.11. https://CRAN.R-project.org/package=modelr Wickham, H., & Bryan, J. (2019). readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.2. https://CRAN.R-project.org/package=dplyr Wickham, H., Hester, J., & Bryan, J. (2023). readr: Read Rectangular Text Data. R package version 2.1.4. https://CRAN.R-project.org/package=readr Wickham, H., Vaughan, D., & Girlich, M. (2023). tidyr: Tidy Messy Data. R package version 1.3.0. https://CRAN.R-project.org/package=tidyr Wilke, C. O. (2021). ggridges: Ridgeline Plots in 'ggplot2'. R package version 0.5.3. https://CRAN.R-project.org/package=ggridges 247 APPENDIX 4: Study 2 – Mediation SEM Parameter Estimates (path b only) FYIv3.1c Factor InDI Subscale Score Estimate Std. Error p 95% CILower 95% CIUpper CIP Anticipated mean -0.08 0.13 .56 -0.31 0.19 Day-to-day Lifetime sum -0.01 0.04 .75 -0.09 0.07 Day-to-day Past year sum -0.04 0.03 .21 -0.04 -0.13 Major Lifetime sum -0.03 0.03 .27 -0.09 0.03 Major Past year sum -0.04 0.12 .76 -0.30 0.24 SAE Anticipated mean -0.03 0.12 .82 -0.25 0.22 Day-to-day Lifetime sum -0.03 0.04 .41 -0.11 0.04 Day-to-day Past year sum -0.04 0.02 .16 -0.08 0.02 Major Lifetime sum -0.04 0.03 .10 -0.09 0.01 Major Past year sum -0.14 0.10 .17 -0.37 0.08 HYPER Anticipated mean 0.20 0.10 .04* 0.02 0.41 Day-to-day Lifetime sum 0.06 0.03 .03 0.00 0.11 Day-to-day Past year sum 0.01 0.02 .53 -0.02 0.05 Major Lifetime sum -0.01 0.02 .60 -0.05 0.03 Major Past year sum -0.05 0.09 .62 -0.17 0.20 HYPO Anticipated mean 0.11 0.10 .25 -0.07 0.33 Day-to-day Lifetime sum 0.03 0.03 .39 -0.03 0.08 Day-to-day Past year sum -0.01 0.02 .76 -0.04 0.04 Major Lifetime sum -0.02 0.02 .23 -0.06 0.02 Major Past year sum -0.09 0.08 .25 -0.19 0.10 SREG Anticipated mean 0.20 0.11 .07 -0.01 0.42 Day-to-day Lifetime sum 0.08 0.03 .01* 0.02 0.14 Day-to-day Past year sum -0.01 0.02 .59 -0.05 0.03 Major Lifetime sum -0.01 0.02 .73 -0.05 0.04 Major Past year sum 0.00 0.09 .99 -0.17 0.22 SIRS Anticipated mean 0.07 0.09 .45 -0.11 0.24 Day-to-day Lifetime sum 0.04 0.03 .19 -0.02 0.09 Day-to-day Past year sum 0.02 0.02 .43 -0.03 0.06 Major Lifetime sum 0.01 0.02 .72 -0.03 0.05 Major Past year sum 0.11 0.10 .26 -0.01 0.37 MCM Anticipated mean -0.03 0.13 .82 -0.27 0.23 Day-to-day Lifetime sum 0.01 0.04 .89 -0.07 0.08 Day-to-day Past year sum -0.02 0.03 .51 -0.08 0.04 Major Lifetime sum -0.01 0.03 .72 -0.07 0.05 Major Past year sum 0.10 0.11 .38 -0.08 0.38 CI = Confidence Interval; *Significant (p<.05) and 95% CI does not cross 0 248 APPENDIX 5: Study 2 – InDI Subscale Scores by Maternal Race/Ethnicity Intersectional Discrimination Index (InDI) Subscale Scores by Maternal Race/Ethnicity Group InDI: A InDI: D lifetime InDI: D past year InDI: M lifetime InDI: M past year Full subset (n=94) mean 0.99 4.29 2.46 2.83 0.29 SD 0.84 2.83 3.63 3.63 0.91 min 0.00 0.00 0.00 0.00 0.00 max 3.00 9.00 17.00 14.00 7.00 Non-Hispanic/Latine and White race only (n=39) mean 0.69 3.64 2.36 2.31 0.15 SD 0.69 2.49 3.77 3.14 0.54 min 0.00 0.00 0.00 0.00 0.00 max 2.67 9.00 12.00 12.00 3.00 Hispanic/Latine and any race (n=32) mean 1.07 3.78 2.09 2.19 0.22 SD 0.82 3.06 3.77 3.16 0.61 min 0.00 0.00 0.00 0.00 0.00 max 2.89 9.00 17.00 13.00 3.00 Multiple races (n=13) mean 1.00 5.46 3.08 4.23 0.77 SD 0.83 2.40 3.30 4.59 1.96 min 0.00 0.00 0.00 0.00 0.00 max 2.89 8.00 11.00 14.00 7.00 Non-Hispanic Asian race only (n=5) mean 1.29 6.20 1.60 2.40 0.40 SD 0.72 2.49 3.05 3.36 0.55 min 0.00 2.00 0.00 0.00 0.00 max 1.67 8.00 7.00 7.00 1.00 Non-Hispanic Black/African American only (n=5) mean 2.51 7.60 4.80 7.80 0.40 SD 0.51 1.67 3.03 4.15 0.89 min 1.89 5.00 2.00 3.00 0.00 max 3.00 9.00 8.00 13.00 2.00 InDi: A = Anticipated subscale; InDI: D = Day-to-Day subscale; InDI: M = Major subscale 249 APPENDIX 6: Study 3 – Guides for Semi-Structured Interviews Interview #1 Guide: 1. Tell me about (your child). a. Supporting questions if needed: Is there a memory you could share about a time that really captures who (your child) is? 2. Could you tell me about some moments that are meaningful to you and your family? a. Supporting questions if needed: Do you have any special traditions you do with your family? How do you see (your child) being involved with those as they grow up? b. Are there things that are important for you to pass on to your children? Like cooking or specific experiences? Carry on traditions around specific holidays? 3. Was there a time when you had a concern for (your child)? a. Supporting questions if needed: Did anything ever come up that was a concern about (your child) before they were born? Looking back, how have your concerns changed? When did you first become concerned? Did your concerns lead to any changes in your caregiving? 4. Where do you get your information on child development and parenting/caregiving from? a. Supporting questions: What was your last visit with your pediatrician like? What do you expect when you talk to your pediatrician? Who have you talked with in the past about any concerns you have about your child and why? b. Do you get any advice from websites or social media? Could you describe the last post or link or anything else like a movie or tv show you saw that made you think about your child or yourself as a caregiver? Checking child’s behaviors with different resources online? Disciplining/getting child to sleep? c. Anonymous source vs. relatives vs. professionals 5. Is there a time when you had to lean on your support system? a. Supporting questions if needed: What happened the last time you had to depend on someone else for caregiving for your child? b. What is some of the best advice you’ve been given or found? 6. What were some expectations you faced growing up as a child yourself? Would you say you have the same expectations for your own child? Are there things you don’t want to pass on for your child? a. Supporting questions if needed: In my own family, my grandfather expected all the grandchildren to prioritize their education above everything else. Are there any expectations like that in your family, either for yourself as a caregiver or for (your child)? How do you feel about those expectations? 7. When you think way out into the future, what are some hopes or aspirations or dreams you have for (your child)? a. Supporting questions if needed: What do you imagine when you picture (your child) growing up? How would you say your hopes or aspirations relate to your concerns for your child? b. What do you see them doing? Where do you see them working/living? What kind of friends will they have? What do you think it’ll take for them to be happy? 250 c. Think to the first day of… or think about an important holiday party or a certain birthday… where were you at... Interview #2 Guide: 1. Has anything changed in your child’s life since our last interview? 2. From our last interview, it seemed like making sure [child] … (summary using responses from Interview #1) is important to how you think about your child’s development. Would you agree with this? a. Would you say there’s anything else you really prioritize when thinking about your child’s development? (e.g., what will you be monitoring as he moves through his second year of life?) b. Could you walk me through your day yesterday or the day before? i. Can you walk me through __ routine? What do you think about when you’re doing it? 3. When was a moment that you remember being really aware of being [child’s] caregiver? a. Supporting questions if needed: How would you describe your identity as a caregiver? b. Could you describe a time when you felt a conflict in terms of being your child’s caregiver? 4. Could you share a story or two that captures how you grew up within your social or cultural background? a. Supporting questions if needed: Would you say that the boxes you check for questions asking about your demographic information (like what’s your ethnicity and race) are good reflections of your social and cultural backgrounds? What do those boxes capture, and what is missing? b. What do you feel like people expect from you as a parent to your child when they see you? Are there expectations for parents on social media that resonate with you or that don’t? c. Can you tell me about some of the places you take your child and some of the places you won’t take them? 5. Can you tell me about how you chose your pediatrician or how you chose different professionals who work with your child? 6. What are some difficulties you anticipate facing as a parent or caregiver? a. Could you provide some examples of what you envision already in terms of what your child will face? Is some of that based on your own personal experiences? 7. What is one of your happiest moments with your child? a. What was the last memorable event with your child that you recorded? Why did you record it?

Abstract (if available)

Abstract Despite rising national prevalence, autism diagnoses remain delayed and disproportionately low in historically marginalized groups. Early diagnosis, which is likely to lead to earlier intervention enrollment, can decrease lifelong costs to the family and to society through improved child outcomes, reducing the duration of – or the need for – more expensive and intensive services later in life. Persisting differences in diagnosis rates, particularly affecting Black/African American and Hispanic/Latine families, emphasize the need to investigate continuing disparities in autism screening and diagnosis. The primary goal of this dissertation was to explore how sociocultural processes impact infant autism likelihood and caregivers’ priorities for supporting their children’s development. An intersectional framework within a multilevel mixed methods research design was used to obtain a contextualized understanding of how infant neurodevelopment is situated amongst social systems and worlds. The first study was a critical systematic review of documented associations between sociodemographic variables and autism likelihood and evaluation of ways in which previous studies have conceptualized such variables. The second study was a proof-of-concept quantitative investigation of intersectional factors on autism likelihood using Bayesian mixed modeling. The third study was a narrative analysis of the impact of sociocultural factors on caregivers’ hopes, concerns, and expectations for their children’s development. The findings of these three studies highlight key issues with current conceptualizations of race and ethnicity in research and healthcare; additionally, they provide a multilevel view of the contexts in which infants develop and ways in which social factors impact the health and well-being of individuals and communities.

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Asset Metadata

Creator Choi, Elizabeth (author)

Core Title Sociodemographic influences on the early caregiving environment and likelihood of autism

School School of Dentistry

Degree Doctor of Philosophy

Degree Program Occupational Science

Degree Conferral Date 2023-12

Publication Date 12/06/2023

Defense Date 12/05/2023

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag autism,Bayesian modeling,Child development,intersectionality,mixed methods,narratives,systematic review

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Baranek, Grace (committee chair), Lawlor, Mary (committee member), Sideris, John (committee member), Watson, Linda (committee member)

Creator Email choieliz@usc.edu,elizahch@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113782236

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Legacy Identifier etd-ChoiElizab-12527

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Format theses (aat)

Rights Choi, Elizabeth

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Source 20231207-usctheses-batch-1112 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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