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Essays examining nutrition behavior and policy in California

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Essays examining nutrition behavior and policy in California

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Content ESSAYS EXAMINING NUTRITION BEHAVIOR AND POLICY IN CALIFORNIA by Denise Diaz Payán 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 (PUBLIC POLICY AND MANAGEMENT) August 2015 Copyright 2015 Denise Diaz Payán i TABLE OF CONTENTS TABLE OF CONTENTS ........................................................................................................... i ACKNOWLEDGEMENTS ..................................................................................................... iii LIST OF TABLES AND FIGURES......................................................................................... v ABSTRACT .............................................................................................................................. 1 CHAPTER ONE. INTRODUCTION ....................................................................................... 3 Overview ........................................................................................................................... 6 CHAPTER TWO. MANDATORY MENU CALORIE LABELING IN CALIFORNIA: USING THE ADVOCACY COALITION FRAMEWORK TO EXAMINE THE HEALTH POLICYMAKING PROCESS ............................................................................................... 10 Introduction ..................................................................................................................... 10 Applicability of the Advocacy Coalition Framework to Health Policymaking .............. 12 Research Questions ......................................................................................................... 14 Methodology and Data Analyses .................................................................................... 16 Conceptualizing Belief Systems ..................................................................................... 20 The Case of Mandatory Menu Labeling in California .................................................... 26 Stakeholder Analysis ...................................................................................................... 31 Qualitative Analysis of Belief Systems .......................................................................... 37 Understanding the Policy Landscape .............................................................................. 40 Policy-Oriented Learning Events .................................................................................... 42 Coalition Coordination and Resources ........................................................................... 45 Use of Technical Knowledge .......................................................................................... 47 Discussion ....................................................................................................................... 51 Conclusion ...................................................................................................................... 54 CHAPTER THREE. ASSESSING BARRIERS AND FACILITATORS TO HEALTHY LIVING IN A LOW-INCOME COMMUNITY: HIGH SCHOOL STUDENT PERSPECTIVES ON NEIGHBORHOOD RESOURCE ENVIRONMENTS ...................... 57 Introduction ..................................................................................................................... 57 Methods........................................................................................................................... 59 Results ............................................................................................................................. 64 On- and Off-Campus Nutrition Resource Environment ................................................. 65 On- and Off-Campus Physical Activity Resource Environment .................................... 72 Health Care Resource Environment................................................................................ 76 ii Discussion ....................................................................................................................... 77 Strengths and Limitations ............................................................................................... 80 Conclusions ..................................................................................................................... 81 Implications for School Health ....................................................................................... 81 CHAPTER FOUR. FOOD INSECURITY AND BODY MASS INDEX (BMI) AMONG LOW-INCOME WOMEN IN CALIFORNIA IN 2009 ......................................................... 84 Introduction ..................................................................................................................... 84 Background ..................................................................................................................... 86 Developing a Revised Conceptual Model for Food Security and Weight Status for Adults .............................................................................................................................. 88 Methods........................................................................................................................... 96 Results ........................................................................................................................... 100 Discussion ..................................................................................................................... 108 Limitations .................................................................................................................... 109 Research and Policy Implications ................................................................................. 111 CHAPTER FIVE. CONCLUSION ....................................................................................... 114 REFERENCES ..................................................................................................................... 118 APPENDICES ...................................................................................................................... 136 Appendix A. Comparison of Bills Introduced in California (2003 - 2008) .................. 137 Appendix B. High School Student Focus Group Questionnaire................................... 139 Appendix C. High School Student Focus Group Script ............................................... 142 Appendix D. Informed Consent for Non-Medical Research (Parental Permission - English) ......................................................................................................................... 145 Appendix E. Informed Consent for Non-Medical Research (Parental Permission - Spanish)......................................................................................................................... 148 Appendix F. Child Assent to Participate in Research ................................................... 152 Appendix G. CHIS 2009 Six Items to Measure Food Security Status and Hunger (from the U.S. Household Food Security Survey Module)..................................................... 154 iii ACKNOWLEDGEMENTS This body of work would not have been possible without the help and support of several individuals. First of all, my deepest gratitude to my dissertation committee who provided invaluable advice. Thank you, Dr. Michael Nichol, Dr. LaVonna Lewis, and Dr. Michael Cousineau, for being exemplary professors, researchers, and mentors. I am grateful to Dr. David Sloane and the CUIDARSE team for affording me the opportunity to work as a project assistant at USC and as a research assistant at the RAND Corporation, respectively. Chapter three was developed as part of the evaluation of the REACH Demonstration Project (Co-Principal Investigators Dr. Sloane and Dr. Lewis). Moreover, I would like to extend my thanks to the student focus group participants, school personnel, and USC evaluation team for their assistance. I would also like to acknowledge external reviewers who provided useful feedback on specific chapters, including the discussant and presentation attendees from the Midwest Political Science Association (MPSA) Conference and session attendees from AcademyHealth’s Annual Research Meeting. Additionally, Dr. Daniel Mazmanian from USC’s Price School of Public Policy and Dr. Tim Biblarz from USC’s Dornsife Department of Sociology contributed to the shape and analytical framework of chapters two and four, respectively. In terms of financial resources and support, I extend my gratitude to USC’s Provost Ph.D. Fellowship Program, the EDGE Institute, and the Sol Price School of Public Policy. Furthermore, I am forever indebted to the institutional resources afforded to me by Harvard College, Harvard Kennedy School, and the University of Southern California. iv My sincere thanks to numerous educators who encouraged and supported me along the way. My earliest mentors in the health policy field consisted of faculty and staff from Harvard’s Health Policy Program, including Dr. Robert (“Bob”) Blendon, Dr. David Cutler, Dr. Joseph Newhouse, Dr. Mary Ruggie, Dr. Judith Palfrey, Joan Curhan, and Deborah Whitney. Your mentorship and advice have forever shaped the trajectory of my career. A huge thank you to my family and friends for their support throughout this arduous process. To my parents, Gregorio and Marisela Diaz, who sacrificed their careers in Mexico to provide me with access to a better education. Thank you to my family (Claudia, Maribel, Josue, Joshua, Caleb, and Carolina), the Payán family (Ramona, Norberto, Nelly, Ursula, Andy, Joey, Sandra, Trent, Vanessa, David, David Jr., Michael, Andrew, and Nicholas), my extended family, and my friends for their continuous friendship and support. A special note of gratitude to my talented and gifted twin sister and husband who served as my dissertation editors. Last, mil gracias to my husband, Daniel Payán, for providing endless encouragement and support. He—along with Ribski and T-Bone—have been there every step of the way and have helped me through the hills and valleys of life. This accomplishment would not have been possible without Daniel by my side all of these years. v LIST OF TABLES AND FIGURES Figure 2.1. Mandatory Menu Labeling Primary Source Search Process (1999-2009) ……....19 Table 2.1. Belief Categories by Coalition ……………………………………………………25 Table 2.2 Timeline of Menu Labeling Policy in California (2003-2009)……………………31 Table 2.3. Organization Level Coalition Measures by Group ……………………………….35 Table 2.4. Sources of Technical Knowledge in the Menu Labeling Debate…………………49 Table 3.1. List of Focus Group Topic Areas and Selected Questions ……………………….62 Table 3.2. Socio-Demographic Characteristics of Focus Group Participants (N=28) ……….64 Table 3.3. Recommendations to Improve the On-Campus Nutrition Resource Environment.68 Table 3.4. Top Barriers to Physical Activity…………………………………………………73 Figure 4.1. Conceptual Framework of Food Security and Weight Status among Adults ……96 Table 4.1. Weighted Variable Distributions for Selected Characteristics among Low-Income Adults (<200% FPL) in California, CHIS 2009 ……………………………………………101 Table 4.2. Hierarchical Linear Regression Model Results Examining the Associations between Food Insecurity and BMI for Low-Income, Female Adults in California (2009) ...103 1 ABSTRACT This dissertation consists of three studies examining facets of the obesity epidemic and related policies in California. Chapter two uses the advocacy coalition framework (ACF) to analyze the health policymaking process from 1999 to 2009 and the role of policy-oriented learning in California. During that period, six menu labeling bills were introduced in the state legislature, which resulted in the enactment of SB 1420 in 2008. The study examines how advocacy coalitions engaged in a policy debate within a policy subsystem and used technical knowledge to promote their agendas. Primary data collection consists of a systematic search for legislative bills (n=87 documents). Qualitative methods are used to identify coalition members and to examine their underlying deep core beliefs, policy core beliefs, and secondary beliefs. Two coalitions are identified—a public health coalition and an industry coalition. Compared to their counterpart, the public health coalition more effectively used policy-oriented learning events and information to influence the legislative process. Key leaders from the two coalitions consisted of state level stakeholders (rather than national level stakeholders), which may have been why the debate continued after the policy initially failed to pass. The study highlights the use of technical knowledge in the state health policymaking process, namely the emphasis on government agency reports in the legislative process. Parallels are drawn between the tobacco and obesity policy debates. Schools are well-positioned to serve as points of health promotion interventions to address the childhood obesity epidemic in California. Chapter three aims to understand adolescents’ perceptions of barriers and facilitators to healthy eating, active living, and well- being. This qualitative study identifies prevailing beliefs, attitudes, perceptions, and experiences among high school students in an underserved community. Four focus groups 2 were conducted (N=28) at three high schools in South Los Angeles between July and October 2014. Most participants were Hispanic/Latino (57.1%) or African-American (32.1%) and almost a third were overweight or obese (32%). Barriers to healthy eating are identified on- and off-campus. Environmental concerns about safety were a key barrier to mobility— differences in the social environments existed across school sites. Participants exhibited a high level of awareness of school-based health centers for access to health care services. The findings suggest the school can be leveraged as a place for health promotion activities for adolescents in underserved communities. Success depends on recognizing barriers in the institutional, built, and social environments. Recommendations to increase access to school lunches and utilization of health care services, after-school programs, and community gardens, are provided. Although most obesity research has focused on the contributing individual level factors, the role of structural factors (including access to food) is equally important to addressing the obesity epidemic. Chapter four examines the role of food security and its contribution to Body Mass Index (BMI) using data from the 2009 California Health Interview Survey (CHIS), and provides a conceptual framework of food security and weight status among adults based on existing literature. Controlling for several variables (including SES), food insecurity and hunger was found to be, on average, associated with a 1.23 kg/m 2 increase in BMI score compared to food security among low-income women in California. This relationship was statistically significant. Future obesity treatment and intervention programs should consider targeting populations with greater rates of food insecurity and hunger, in addition to low-income, minority populations. 3 CHAPTER ONE. INTRODUCTION The prevention and treatment of obesity in the United States (U.S.) is a national public health priority. In the past twenty years, there has been a substantial increase in the obesity prevalence rate among adults and children in the U.S. Every state has experienced a significant increase since 1990 (Flegal, Carroll, Ogden, & Curtin, 2010; Pan et al., 2009). In 1990, all states in America had an obesity prevalence rate that was less than 14%. By 2010, every state had an obesity prevalence rate that surpassed 20%, with several states in the South reporting obesity rates over 30%. In 2011-2012, almost a third of youth (31.8%) were obese or overweight (Ogden, Carroll, Kit, & Flegal, 2014). The burden of the obesity epidemic is a visible and pervasive problem across the country with severe population health and economic implications. The health risks associated with being overweight or obese are well known in the public health and medical literature. In general, higher body mass scores are associated with premature death (Calle, Thun, Petrelli, Rodriguez, & Heath, 1999). Obese persons 1 face a higher risk of coronary heart disease, type 2 diabetes, hypertension, high cholesterol, certain cancers, stroke, gallbladder disease, sleep apnea, and respiratory problems (U.S. Department of Health and Human Services, 2001), and obese/overweight individuals have a greater likelihood of self-reporting fair or poor health compared to normal weight individuals (Mokdad et al., 2003). In terms of a life course perspective, obese youth are at greater risk for negative physical and mental health outcomes compared to normal weight youth. Childhood obesity often persists into adulthood, particularly if at least one of the child’s parent is obese 1 Adult obesity is defined as having a body mass index (BMI) ≥30 kg/m 2 . Overweight is defined as a BMI between 25 kg/m 2 and 30 kg/m 2 . A disadvantage from using BMI as an obesity measure is that fat mass is not differentiated from lean mass. 4 (Whitaker, Wright, Pepe, Seidel, & Dietz, 1997). The long-term impact on children includes physical and mental health issues—the latter resulting from bullying, social stigmatization, and discrimination (Dietz, 1998). From a population health perspective, the vast increase in obesity rates poses a considerable threat to the country’s public health as people age with worse health outcomes. The primary cause of obesity stems from a discrepancy between caloric consumption and expenditure that leads to excess fat. In most cases, dietary and physical activity behaviors largely determine a person’s weight. A few other causes exist, including an underactive thyroid (hypothyroidism), certain antidepressant and antipsychotic medications, and genetic factors, although these factors have had limited impact in their contribution to the epidemic. The majority of obese and overweight people in the U.S. are the result of increased caloric consumption and a sedentary lifestyle. In terms of childhood obesity, experts cite a number of risk factors, including: family history of diabetes; exposure to maternal diabetes in utero; race, ethnicity, and gender; and contributing environmental factors. Experts agree most cases of obesity are preventable (U.S. Department of Health and Human Services, 2001). The obesity problem is unequally distributed among the U.S. population. Higher obesity prevalence rates are seen among Black and Hispanic groups, and the lowest rates are present in Whites and Asian populations. Obesity is also significantly more prevalent among low-income groups. Research suggests the impact of socioeconomic status on obesity varies by racial/ethnic group. Obesity is also more common among low-income Blacks and Hispanics compared to other groups (Wang & Beydoun, 2007). Addressing the widespread epidemic of obesity requires individual level behavioral changes as well as modifications to the built environment for at-risk populations. Given the 5 range of contributing factors, obesity-related behavioral and environmental factors pose an opportunity for policy interventions. In recent years, numerous policies and public health programs aimed at decreasing the obesity prevalence and incidence rates have been developed and implemented. Several policy responses were developed to address the individual and environmental components of obesity. Examples of policies range from “sin taxes” for sugary beverages to restrictions on vending machine items. A greater focus on the contribution of the built environment to the obesity epidemic has arisen in recent years given evidence that low-income communities have fewer healthy options available (and more unhealthy food options) compared to more affluent communities (Lewis et al., 2005). Research is needed at the state level on the types of policies introduced and the effectiveness of obesity prevention and reduction policies (McKinnon et al., 2009). The increase in the prevalence of obesity and overweight rates across the country has challenged professionals across the medical and public health fields. The issue poses a considerable problem for the government, private, and nonprofit sectors due to the negative health and economic impacts of obesity. The direct costs include the medical cost of treating the effects of obesity and being overweight (e.g. treating type 2 diabetes). The indirect costs include loss of productivity or premature death. Obese adults have an average of 36% higher annual medical expenditures compared to normal weight adults (Sturm, 2002). The estimated direct lifetime medical cost for an obese child who becomes an obese adult is $19,000 compared to a normal weight child (Finkelstein, Graham, & Malhotra, 2014). Furthermore, the estimated national cost of obesity is $147 billion—a sizable sum that disproportionately 6 burdens publically financed insurance programs such as Medicaid (Finkelstein, Trogdon, Cohen, & Dietz, 2009). Overview This dissertation contains several research questions related to the nutrition policy landscape and the consequences of a specific policy in California. The state of California was selected as the target focus for the three studies for the following reasons: the high obesity- related costs, the state’s extensive experience with obesity policy, and its diverse population. First, the economic impact of California’s estimated obesity-related costs are the highest in the country given its large population. At $15.2 billion per year, California has the highest estimated state medical expenditures attributable to obesity—an estimated 41.5% are financed by public programs, such as Medicare and Medicaid (Trogdon, Finkelstein, Feagan, & Cohen, 2012). Second, in the last decade the state has seen a high influx of obesity-related policies. Various stakeholders in the state, such as legislators and advocacy groups, have attempted to tackle this problem in the policy and political streams. The state has been one of the first- movers in the area of obesity policy—influencing national policies and politics. Thus, examining the state’s policymaking process could reveal patterns in other legislative environments. Lastly, California was selected due to its racial and ethnic diversity. According to the U.S. Census Bureau (2013), an estimated 38.4% of the 38 million residents identify as Hispanic or Latino compared to 17.1% of the general U.S. population. The remaining state residents identify as White (39%), Black/African-American (6.6%), Asian (14.1%), multi- 7 racial (3.7%) American Indian or Alaska Native (1.7%), or Native Hawaiian or other Pacific Islander (0.5%). Understanding the effect of obesity-related policies in the state can help to address existing disparities across the country. These essays are organized by conceptual framework, discipline, and related literature in the subsequent sections. A mixed methods approach was selected for this dissertation since mixed methods are appropriate for complex social problems requiring comprehensive analyses of the magnitude and the meaning of an issue (Creswell, 2009). Mixed methods are ideal for research questions on obesity-related policies. A person’s dietary behavior is affected by numerous factors, including their education, genetic makeup, availability of resources in the built environment, and setting (e.g. school, home, restaurant, etc.). On a population-wide basis, the country’s obesity problem is a complex and multi-faceted problem. Given the varied dimensions of the problem, a mixed methods research approach is the preferable route to answering the research questions posed by this study. This dissertation aims to analyze the nutrition policy environment in California and to examine barriers and facilitators to healthy food consumption. The study will draw from different sets of literature around nutrition policy and health behavior. Each has a distinct conceptual framework and methodology. Theories about the policymaking process will be used as the basis for chapter two and the socio-ecological model will serve as the basis for the third and fourth chapters. Specific information about each chapter is provided below. Chapter two examines the political process surrounding the enactment of a specific nutrition policy in California. In recent decades, the obesity epidemic has been at the forefront of public health and health policy discussions in the U.S. A plethora of policies to stem and decrease the rates of obesity have emerged in response. In the state health 8 policymaking process, advocacy coalitions play a formidable role. This study uses the advocacy coalition framework (ACF) to analyze the health policymaking process from 1999 to 2009 and the role of policy-oriented learning in California. During the selected time period, six mandatory menu labeling bills were introduced in the state legislature, which resulted in the enactment of SB 1420 in 2008. The study examines how advocacy coalitions engaged in a policy debate within a policy subsystem and used technical knowledge to promote their arguments. Primary data collection consisted of a systematic search for legislative bills and newspaper articles. Qualitative methods are used to identify coalition members and to examine their underlying deep core beliefs, policy core beliefs, and secondary beliefs. The case study provides valuable insight into the state health policymaking process. Recommendations are provided for researchers interested in disseminating their work among policymakers. Chapter three aims to improve the understanding of barriers and facilitators to high school students’ access to healthy food, active living environments, and health care services in a low-income community. The study assesses participants’ level of awareness of school- based and neighborhood health promoting resources, thus allowing one to evaluate how their environments might influence their behaviors. High school adolescents’ perspectives can improve our understanding of the relationship between the intrapersonal, social, and environmental factors that impact utilization of resources in and around school campuses. Understanding existing barriers and facilitators is important for designing (and implementing) successful school-based interventions and may help to decrease health disparities among adolescents. 9 Although most obesity research has focused on identifying individual level factors, the role of structural factors, including access to food, is equally important to addressing the obesity epidemic. The fourth chapter examines the role of food security and its contribution to Body Mass Index (BMI) using data from the 2009 California Health Interview Survey (CHIS). The primary objectives of the study are to provide a robust theoretical framework and to explore the relationship between food security and adult BMI among minority, low- income populations. A revised conceptual framework modelling the potential pathways from food insecurity to weight status was developed based on a thorough review of the food security literature. Moreover, the study utilizes a quantitative research design to empirically test specific mechanisms. The essay approach was selected as the preferred dissertation format since it allows for the exploration of a set of related questions that cannot be comprehensively answered by a sole conceptual framework or method. Ultimately, this dissertation is a compilation of diverse methods and approaches aimed at better understanding research questions related to nutrition policy and behavior in California and will hopefully move the needle towards addressing the obesity epidemic in the state. 10 CHAPTER TWO. MANDATORY MENU CALORIE LABELING IN CALIFORNIA: USING THE ADVOCACY COALITION FRAMEWORK TO EXAMINE THE HEALTH POLICYMAKING PROCESS Introduction In 2000, addressing the obese and overweight problem became a top public health priority in the United States. Today, almost two-thirds of American adults are either overweight or obese. In 2011-2012, almost a third of youth (31.8%) were obese or overweight (Ogden et al., 2014). The vast increase in the prevalence of obese and overweight individuals in the country has challenged professionals in the medical and public health sectors. The obesity epidemic is a pervasive problem in the U.S. with population health and economic implications (Kersh & Morone, 2005). Treating and preventing obesity is difficult from a strictly medical perspective since the causes of obesity are multi-factorial. A person’s weight may be determined by genetic, metabolic, behavioral, socioeconomic, cultural, and environmental factors. Increased caloric consumption and increased sedentary behaviors have played a role in the obesity epidemic (U.S. Department of Health and Human Services, 2001). Considering the wide range of factors, the behavioral and environmental factors related to obesity present an opportunity for policy responses to promote improved dietary behaviors and physical activity. Several policy responses have emerged to address obesity at the state level. In California, various stakeholders have attempted to tackle this problem in the policy stream. The state has been one of the early adopters in the area of obesity-related policy, which has subsequently influenced the national policy and political streams. Kingdon’s multiple 11 streams theory provides valuable insight into the policymaking process. He identified several key factors involved in the political stream, such as public opinion, election results, administrative changes, partisan factors, and interest group campaigns (1995). Davis and Davis describe advocacy coalitions as including a wide variety of participants—“officials from local, state, federal, or special district governments, members of the business community, interest group representatives, journalists and researchers” (1988, p. 3-4). This study primarily focuses on the role of government officials, agencies, and interest groups in the state health policymaking process while taking into account the use of technical knowledge. This work uses a theoretical lens to analyze the development of a specific nutrition policy. Specifically, I use the advocacy coalition framework (henceforth, ACF) to examine the role of advocacy groups and coalitions involved in the state health policymaking process. Several organizations and individuals are involved in the state health policymaking process. Since the mid-1950’s, the number of organizations interested in health care policy has significantly increased and the health policy community has transformed from an iron triangle into a series of policy networks (Peterson, 1994). Interest groups coalesce and seek to define political debates in order to influence legislative outcomes and to advocate for their policy positions (Sabatier, 1988). Advocacy groups and coalitions have formed to engage in the health policy process in support or opposition of specific obesity-related policies, such as trans-fat bans and menu labeling policies. However, “policy research in health promotion is still largely an atheoretical enterprise” (Breton & Leeuw, 2010, p. 82). Policies that have emerged at the state level to address the obesity epidemic have not previously been studied using a conceptual approach. 12 Previously, the ACF has been used to examine the policy process surrounding the passage of a menu labeling policy in King County, Washington (Johnson, Payne, McNeese, & Allen, 2012). A public health coalition and industry coalition were identified based on key participant interviews and document reviews (i.e. agendas, summaries, media reports, etc.). Shared values and beliefs were identified for each coalition. In this article, I present a theoretical approach using the ACF to analyze the public policy process that led to the passage of a mandatory calorie labeling policy (SB 1420) in California in 2008. The menu labeling bill specified that restaurants with at least 20 locations in the state would have to disclose calories, carbohydrates, saturated fat, and sodium content for each of their menu items through either a brochure or menu board (McGreevy, 2008). Using the ACF’s theoretical lens, individual stakeholders and organizations leading up to the bill’s passage will be identified. The types of beliefs espoused during the process and key policy-oriented learning events will be closely examined within the health policy subsystem. Applicability of the Advocacy Coalition Framework to Health Policymaking Medical and public health studies play an important role in the health policymaking process and can shape the health priorities of the state or inform the decision making process. There are clear examples where public health research led to the adoption of federal health policy changes. For instance, anti-smoking regulations resulted from epidemiological studies indicating an increased lung cancer risk for recipients of second hand smoke. However, technical knowledge plays a limited role in the policymaking process. The ACF is valuable because it explicitly considers the role of technical analyses in the process of policy change, yet does not qualify technical analyses as being sufficient for policy change (Sabatier & 13 Weible, 2007). Unlike the traditional policy iron triangle, the ACF lays out a more expansive and inclusive set of individual stakeholders and organizations who are involved in the policy process (Sabatier, 1988). Social history methods and primary document collection were used in this study to identify coalition stakeholders and their corresponding belief systems in the policy and political streams. The ebb and flow of this health policy debate in California will be evaluated by conceptualizing participatory groups and their policy arguments as belief systems. Intuitively, values and beliefs on whether the individual or the environment is predominantly responsible for a person’s unhealthy weight gain lead to differing policy opinions and approaches. The task to comprehensively assess individual beliefs or values regarding obesity would be a difficult task to undertake. Obesity debates have engaged topics of morality, political ideology, and cultural attitude, among others, as contributing factors to attitudes about obesity (Barry, Brescoll, Brownell, & Schlesinger, 2009). The ACF approach does not aim to evaluate individual beliefs or values. Instead, it considers the beliefs of the participants engaged in the policy process and the context of the policy debate as relatively stable parameters. The ACF is based on the assumption that advocacy coalitions are comprised of individuals with similar belief systems who engage in coordinated activities over a period of time (Jenkins-Smith & Sabatier, 1994). One of the premises of the ACF is that the policy change and learning processes take more than a decade (Sabatier & Weible, 2007). The process of defining the problem and the causal relationship between individual choice, genetics, and the environment has been raging for more than a decade. The link between obesity and diabetes was determined in the 1970’s. The 1990’s were a decade marked by mounting evidence that obesity was a major risk factor 14 for coronary heart disease and other diseases (Manson et al., 1990). In the 1990’s, public health research on the increase in the prevalence of obesity and related costs drew attention to the problem. Based on research on the legislative health policymaking process—which suggests scientific research is not sufficient for political attention and public policy—, one must consider other actors who are involved in the policy process. The ACF meets these guidelines and considers actors in the media and at different levels of government involved in the formulation of policy. Research Questions The set of research questions identified for this study draw on questions previously posed by policy scholars as being pertinent questions to further refining the ACF and developing testable hypotheses (Jones & Jenkins-Smith, 2009) and ACF stakeholder analysis questions (Weible, 2007). Specifically, this study considers three primary research questions and a secondary question related to the health policymaking process at the state level.  Who were the stakeholders and participants involved in the mandatory menu calorie labeling debate in California?  What were the primary arguments in support of or against the policy? o What types of beliefs were reflected in the policy arguments?  How is health policy informed by technical knowledge and research? One of the four premises of the ACF is that public policies can be conceptualized as belief systems (Jenkins-Smith & Sabatier, 1993). These belief systems “involve value priorities, perceptions of important causal relationships, perceptions of world states (including the magnitude of the problem), perceptions of the efficacy of policy instruments, etc.” (Sabatier, 15 1988, p. 132). These sets of beliefs are theorized to undergird policy prioritization by individual and organizational stakeholders in a policy debate. Previously, the ACF has been used to identify stakeholders and coalitions and to examine conflicting arguments and values between groups (Brecher, Brazill, Weitzman, & Silver, 2010). After identifying documents about mandatory menu calorie labeling, a qualitative analysis of the documents will be conducted. Akin to the menu labeling study from King County, two coalitions are expected to emerge—a public health coalition and industry coalition (Johnson et al., 2012). Similarly, advocacy coalition studies on smoking policy have found the anti-tobacco coalition consisted of health care organizations, public health NGOs, and government agencies whereas the pro-tobacco coalition consisted of the tobacco industry (Breton, Richard, Gagnon, Jacques, & Bergeron, 2008; Princen, 2007). Another study that used the punctuated equilibrium theory to assess the tobacco lobby’s state level policy monopoly found “a sharp rise in the ongoing and acrimonious conflict between the tobacco industry and public health groups” stemming back to the 1980’s (Givel, 2006, p. 415). Thus, the selected policy subsystem may be more susceptible to adversarial relationships where advocacy coalitions compete with one another (Weible, 2008). Literature on these topics will serve as the basis for a codebook to guide the qualitative analysis. Additional in-vivo codes will be identified if a different type of argument (or belief system) arises. Ultimately, the goal will be to attribute belief system codes to the identified stakeholders and to create a table listing the participants in each respective coalition. The ACF was selected because it explicitly considers the role of learning processes in a policy debate. The translational research process, from public health and health services research to policy, includes learning processes that occur at the individual and organizational 16 levels. Scientific evidence has played an important role in highlighting the obesity problem and elevating the issue to the forefront of health policy debates. Concern over the issue is in response to the mounting scientific and economic evidence that obesity is associated with major population health problems and economic costs. However, the role of evidence in informing health policy is a lingering policy process question (Breton & Leeuw, 2010). Several studies point to numerous barriers that preclude the role of learning in the health policymaking process. The challenges of translating technical knowledge into policy implications are formidable. Unlike policymakers, scientists conduct research over long periods of time, prioritize objectivity, rely on previous scientific data, and look to peer- reviewed sources for information (Brownson, Royer, Ewing, & McBride, 2006). In the legislative process, institutional features, lack of evaluation skills, competing interests, or budget constraints minimize the role of scientific studies in the health policymaking process (Jewell & Bero, 2008). Political costs, competing policy subsystems, and time constraints are other factors to consider (Kingdon, 1995). There is limited discussion regarding the use of scientific studies and research by advocacy groups and stakeholders in support of their positions with the broader public. This study aims to address the gap in knowledge regarding the use of technical knowledge in the legislative debate (Gagnon, Turgeon, & Dallaire, 2007). Methodology and Data Analyses The study identified multiple levels of stakeholders involved in the policy subsystem, including individuals and organizations. A stakeholder analysis was conducted to identify the primary organizations and individuals involved (Weible, 2007). Similar stakeholder analyses 17 have been conducted in ACF articles on expanded after-school programs (Brecher et al., 2010) and federal environmental policy (Ellison & Newmark 2010). Albright utilized a similar qualitative analysis approach to examine key participant interview data and documents about the flood policy subsystem in Hungary (2011). Another ACF study on Quebec’s tobacco act utilized interviews, newspaper articles, and documents (Breton et al., 2008). For the purpose of this analysis, organizations will be defined as groups of individuals who work toward a common goal and share interests (North, 1990). The unit of analysis will focus on the meso level, where policy networks and coalitions are key factors (Kim & Roh, 2008). Various types of stakeholders are included in the analysis. At the organizational level, not-for-profit and for-profit organizations or companies will be considered. At the individual level, politicians and other individual stakeholders will be identified. Public health departments, the governor, and legislators are involved in the policymaking process at the state level (Sabatier & Weible, 2007; Schneider, 1989). Other stakeholders interested in the outcome of restaurant menu labeling policies may include business owners, restaurant associations, patrons, local health department leaders, and lobbyists (Dodson et al., 2009). Generally, public health groups have been identified as supporters while the restaurant industry has been labeled as the opposition (Pomeranz & Brownell, 2008). This study relies on legislative bill documents since they are the product of the policy venue and they are places where policy elites communicate their policy positions, values, and beliefs (Jenkins-Smith, St. Clair, & Woods, 1991). While several ACF studies use key participant interviews as their primary source (Brecher et al., 2010; Albright, 2011; Ellison & Newmark 2010), interviews are often difficult and time-consuming. Moreover, a 18 disadvantage of using interviews is that they can exclude individuals or organizations who have exited the policy subsystem or who are not available for an interview. Rather, the systematic data collection process and qualitative analysis of documents described in this study can serve as an example of how to integrate historical analysis methods with content analysis methods to identify stakeholders and to examine belief systems. Data Collection Primary sources include legislative bill documents and newspaper articles. Newspaper articles have previously been used as data sources for other ACF articles to identify policy oriented learning events and conflicts (Davis & Davis, 1988). Secondary sources include case studies, policy briefs, and reports. The time period selected for the collection of primary documents is 1999 through 2009. A decade or more is recommended to understand the process of policy change (Sabatier, 1991). The lower bound of the range, 1999, was selected in order to exclude the nutrition labeling debate for pre-packaged food products in supermarkets; moreover, 1999 has been identified as the year when the fast food industry became “demonized” and “the most visible target” of the political obesity debate (Kersh & Morone, 2002). The upper bound of the range, 2009, was selected because that was one year after the policy was passed, thus allowing for the identification of additional documents related to the passage of the law. Newspaper articles on menu labeling policies in other states or cities were excluded from the analysis. Refer to Figure 2.1 which depicts the primary source search and elimination process for the legislative documents. 19 Figure 2.1. Mandatory Menu Labeling Primary Source Search Process (1999-2009) Qualitative Data Analysis The analysis primarily focused on identifying: 1) advocacy coalition members - supporters or opposition; 2) arguments - in favor or against menu labeling policy; 3) general beliefs espoused about obesity or nutrition policies; and 4) learning-related events (i.e. meetings or events) or use of technical products. The pilot coding process comprised of coding 15% of the documents identified per data category type. The pilot coding largely consisted of an open coding process to identify and develop a standard set of codes to employ (Swigger & Heinmiller, 2014). The first round 20 of coding focused on identifying the coalition stakeholders. The newspaper articles were primarily used to identify stakeholders’ public arguments and policy-oriented learning events. The use of technical knowledge was also examined. First, the initial codes were piloted with at least one version of each of the following type of legislative document: bill version, amendment, analyses, and veto message. Other legislative documents were not coded and instead used to develop the timeline and bill comparison diagrams. The second step consisted of coding arguments and specific belief constructs identified in the literature, as well as enumerating the normative core, policy core, and instrumental policy beliefs. This process is an actualization of the proposed mapping of beliefs and policies (Jenkins-Smith & Sabatier, 1994). The belief constructs are discussed in more detail in the following section. Qualitative data was analyzed using the qualitative analysis software NVivo (2012). Conceptualizing Belief Systems One of the premises of the ACF is that public policies can be conceptualized as belief systems. The structure of belief systems includes three categories—(1) deep core beliefs, which are representative of normative and ontological axioms, (2) policy core beliefs, which delineate the set of policy strategies, and (3) secondary beliefs, which are more specific policy strategies or proposals (Sabatier, 1988; Sabatier & Weible, 2007; Weible, Sabatier, & McQueen, 2009). On the surface, a policy such as menu labeling focuses on providing information in a specific setting to inform decisions about consumption. Menu labeling policy focuses on improving consumer nutrition information where variables such as price, promotion, and 21 placement influence dietary decision making (Glanz, Sallis, Saelens, & Frank, 2005). Menu labeling seeks to reduce information asymmetry between the buyer and the seller by providing calorie and nutrition information for the consumer. Although there is a potential problem with selection (e.g. healthy people will seek the information), the basic premise of menu labeling legislation is to provide nutrition information to individuals who may not be aware of the amount of calories in restaurant food items. The public health rationale consisted of the “principle that consumers have the right to know the nutrition content of these items to enable them to make choices better suited to their nutritional needs” (Pomeranz & Brownell, 2008, p. 1578). The notion of providing nutrient information in an easily visible location was based on the notion that consumers would make informed decisions and potentially decrease their caloric intake. Moreover, if the food industry is manipulating or misleading consumers about nutritional information, the public may be more likely to support government involvement and regulation (Kersh & Morone, 2005). The industry’s values centered on limited government regulation and involvement in private sector settings. In 1994, a study asked foodservice company research and development directors about potential obstacles related to nutrition labeling in restaurants. The most commonly cited obstacles consisted of operational barriers (such as excessive menu variations, limited menu space, and lack of flexibility) and cost. Moreover, only 35% of the respondents “believed they had a responsibility to provide consumers with nutrition information” of any kind (Almanza, Nelson, & Chai, 1997). The debate between the public health and industry coalitions is premised on contrasting beliefs regarding the “importance of environmental change to make it easier to choose healthy foods versus a reliance on 22 individual responsibility to select healthier foods among an array of less-healthy foods” (Johnson et al., 2012, p. S133). Leaders in the medical and public health fields have explicitly discussed public health advocacy lessons to control tobacco smoking and the applicability of these lessons to obesity. The first lesson listed is to “address the issue of individual responsibility versus collective or environmental action early and often” (Yach, McKee, Lopez, & Novotny, 2005, p. 898). One of the most pervasive issues in the smoking debate was the government’s role in limiting individual choice, which is a frame that has moved into the obesity policy debate (Kersh, 2009). Organizations such as the American Heart Association (henceforth, AHA) and the Center for Science in the Public Interest (henceforth, the CSPI) have drawn on this smoking policy background and framework as a foundation—using the lessons learned and adapting them to the obesity problem (Kersh & Morone, 2002; Kersh 2009). Broadly speaking, the obesity debate has been similar to the tobacco debate. Obesity became a national public health priority when The Surgeon General’s Call to Action to Prevent and Decrease Overweight and Obesity was published in 2001. The release of this report was comparable to the Surgeon General’s 1964 report entitled Smoking and Health. In health policy, the Surgeon General’s report serves to align national health priorities and foci in the political stream; the report is essentially a linchpin for a shift in the political prioritization of health topics among policymakers and elites (Sabatier, 1991; Sabatier, 1988). In 2001, the report identified the increase in the obesity rate as an epidemic and emphasized prevention and treatment options to address the problem. The report outlined a campaign to promote awareness of the problem, encourage individual behavioral change, and motivate community level improvements. After the CDC published data on the rise in the 23 prevalence of obesity and the Surgeon General’s Call to Action was published, research, advocacy, and funding on different dimensions of the obesity problem increased and the obesity problem transitioned into a political issue (Barry, Brescoll, Brownell, & Schlesinger, 2009; Kersh & Morone, 2005). The media’s attention to obesity also began to increase during this time (Oliver & Lee, 2005). The 2001 report portrays the debate revolving around obesity as being analogous to the tobacco debate. Surgeon General David Satcher commented on the public’s underlying beliefs about obesity: “Many people believe that dealing with overweight and obesity is a personal responsibility. To some degree they are right, but it is also a community responsibility” (U.S. Department of Health and Human Services, 2001). The community is further defined as the environment of the individual, which include settings such as a school, the workplace, and the home. Personal responsibility advocates, on the other hand, focus on the individual’s responsibility for illness due to lifestyle or behavioral choices. Most of the arguments presented are focused on rights-based arguments (e.g. Americans have a right to choose what they wish so long as their actions do not harm others) (Kersh, 2009). Policy responses based on this perspective would consist of, for example, voluntary action from the food industry and national physical education/nutrition guidelines unless externalities are identified that negatively harm others. In recent decades, obesity has replaced tobacco at the forefront of the preventive health policy debate. Food, snack, or beverage taxes have been promoted as a means to deter unhealthy eating habits, and many times, to also fund prevention or public health campaigns (Jacobsen & Brownell, 2000; Brownell & Frieden, 2009). Certain food or snack taxes have been referred to as “fat taxes” and the food industry has “focused on choice and freedom and 24 the danger of the nanny state” in opposing such a category of taxes to encourage positive behavioral changes (Caraher & Cowburn, 2005, p. 1244). Varying degrees of blame metaphors can be used ranging from sinful behavior to a toxic food environment. The former ascribes a high level of blame and the latter represents a low level of blame on the individual (Barry, Brescoll, Brownell, & Schlesinger, 2009). There is a greater focus on the role of the built environment’s contribution to the obesity epidemic in recent years given evidence that low-income communities have fewer healthy options available and more unhealthy food options compared to more affluent communities (Lewis et al., 2005). Environmental responsibility arguments stem from notions of “toxic food” and “obesogenic” environments. These phrases explicitly place causal responsibility for the obesity epidemic on environmental factors. The term toxic environment has been used to describe an environment where inexpensive, energy-dense foods are widely available, and devices and lifestyle lead to increased sedentary behavior. Environmental challenges contributing to the obesity epidemic include an increased demand for and supply of junk and fast foods, increased television watching and computer use time, and decreased physical activity in schools (Brownell & Horgen, 2003). Values and beliefs on self-control and choice appear to undergird decision making and the frames selected within the policy subsystem. A 2005 study found that most Americans “still place the source of obesity in the hands of the individual” while the public health community attributed obesity “to mostly environmental and genetic factors” (Oliver & Lee, 2005, p. 947). A belief in whether obesity is an individual (i.e. self-control) issue or an environmental issue can be related to support for specific obesity policies. In the New York City debate on menu labeling, one of the main arguments identified by the industry coalition 25 was “the U.S. government should not regulate what people eat because food choices are an individual’s personal responsibility” (Roberto, Schwartz, & Brownell, 2009, p. 547). Thus, beliefs about the primary source of obesity are related to support for obesity policies. Table 2.1 summarizes the type of beliefs expected to be presented by each advocacy coalition group in the menu labeling debate. Table 2.1. Belief Categories by Coalition INDUSTRY COALITION PUBLIC HEALTH COALITION BELIEFS Deep Core Beliefs  People are in charge of their decision making process & responsible for the results.  Priority on economic development, educational tools, & private-public relationships.  The environment plays a dominant role in structuring individual decisions therefore it is partially responsible for the results.  Priority on improving the social & physical environments. Policy Core Beliefs  (Most) people are overweight or obese because they make poor decisions about physical exercise or nutrition.  Government should limit their scope to providing educational programs & encouraging physical activity.  Individual rights, consumer freedom, free market.  People are overweight or obese because they have limited access to healthy food or reside in unsafe built environments.  People lack key nutrition information about prepared meals in restaurants.  Government should ensure that all populations have access to healthy food & safe environments through incentives, capacity- building, & learning tools.  Collective good, consumer protection, government intervention. Secondary Beliefs  Favor voluntary actions by industries & individuals to make changes (i.e. voluntary menu labeling).  Favor business incentives.  Limit health education & promotion initiatives to schools, hospitals, & public spaces.  Favor mandates & government regulation of food & beverage industries (i.e. mandatory menu labeling).  Favor health education & promotion in all individual decision making contexts. 26 The Case of Mandatory Menu Labeling in California On September 30, 2008, California became the first state to mandate calorie menu labeling in the country (SB 1420). Legislation was expected to affect an estimated 17,000 fast food and chain restaurants that had 20 or more locations in the state. The bill outlined a two-year phase-in of the law. Beginning on July 1, 2009, brochures listing nutritional information were mandatory upon request. By January 1, 2011, restaurants would have to post calorie information on menus and menu boards (Stein, 2010). Mandatory menu labeling was introduced to California years prior to the passage of the law in 2008. The case of this state level policy illustrates the role of an advocacy coalition in keeping a policy alive in a subsystem and re-introducing a policy into the legislative process. The following sections consider the stakeholders involved in the political debate, the advocacy coalitions, and their corresponding belief systems. Evidence in support of the hypothesized belief systems in the above section or other belief systems will be examined. Key policy-oriented learning events and a legislative timeline are provided and discussed. A Historical Context for Menu Labeling Policy: Emerging Stakeholders and Advocacy Coalitions Understanding the historical context and previous related policy debates is important for getting a sense of the stakeholders involved in the menu labeling debate in California in the 21 st century (Stitch & Miller, 2008). Policy precedence emerged as an important theme, both as a justification for a menu labeling policy and as a basis for the development of advocacy coalitions. This finding is aligned with the notion that policy subsystems “may 27 operate not only at the national level but also at the state, local, and community level” (Weible, Heikkila, deLeon, & Sabatier, 2012, p. 6). The development of coalitions around menu labeling actually began prior to 1999 around a related, but separate issue—ingredient labeling. During the 1980’s, the policy of ingredient labeling for packaged items in grocery stores was introduced and the restaurant industry began to coalesce around a position of opposing the extension of this policy to fast food restaurants. An op-ed written by Ted J. Balestreri (then president and chairman of the National Restaurant Association) cited cost and impracticality as key arguments against ingredient labeling on fast food packaging—“If a fast service pizza parlor offered pizza in three sizes with 10 different products, it would require 300 different packages. Where would you store them? How much time would it take to find the right box? How much would it all cost?” (1986). These economic and practical concerns would percolate as part of the menu labeling policy discussion for decades to come. On the same page in the newspaper, CSPI Director Michael F. Jacobsen presented an argument in favor of labeling the ingredients on fast food packaging. He noted the negative health impacts of certain ingredients, such as saturated fat and sodium, and that their high concentration in some fast food menu items was unbeknownst to most consumers. Jacobsen argued if a restaurant improved the nutrition of their food and provided consumers with ingredient information, “it would prosper while also helping to keep its customers out of the coronary care unit” (1986). He posited restaurants providing this information could reap financial rewards and it could lead to improved nutritional decision making in restaurant settings. Informed decision making would be a key underlying belief of the public health coalition. 28 Thus, broader sets of coalitions were formed and beliefs were publicized in response to the federal ingredient labeling policy debate as early as the 1980’s on a national scale (Boger, 1995). The larger policy change context is valuable and helps us to understand the shorter policy-oriented learning events that occurred in the 2000’s (Jenkins-Smith & Sabatier, 1994). The early leaders in the national policy debate consisted of an industry group (i.e. the National Restaurant Association) and a public health organization (i.e. the CSPI). The initial discussions fomented around two camps. While industry groups were focused on feasibility and cost, public health stakeholders supported the regulation of food products in restaurant settings due to the potential health benefits for consumers. In 1990, national policy precedence for nutritional labeling was set when the Nutrition Labeling and Education Act (NLEA) was enacted, thereby providing the FDA with the authority to mandate accurate nutrition labels on items the agency regulated (Roberto et al., 2009). The restaurant lobby was successful in getting restaurants to be mostly exempt from regulation. The issue would not be raised again until the 21 st century. The federal policy landscape and discussion on obesity policy decades prior to the menu labeling debate provide a valuable historical context for California’s nutrition policy subsystem in the 2000’s. At this time, groups on both sides of the issue were operating on a national level and moved into the state policy level to engage in the menu labeling debate in California. Consider the role of the CSPI, an organization that navigated federal and state politics to advance their policy agenda. The public health organizations involved in the menu labeling debate in California assumed similar stances as the national consumer advocacy nutrition and health organization, CSPI. The CSPI is an advocacy group that began in 1971. Over the years, they contributed to 29 several nutrition and food safety debates, including the passage of the NLEA in 1990. In 2003, the CSPI published “Anyone’s Guess: The Need for Nutrition Labeling at Fast-Food and Other Chain Restaurants” detailing the increased reliance on fast food restaurants and barriers to choosing nutritious alternatives in these settings. They listed studies conducted in the early 1990’s assessing the difficulty in estimating portion sizes and calories and nutritional content for food at restaurants. The CSPI supported requirements for “food- service chains with ten or more units to list on their menus the calorie, saturated and trans fat (combined), and sodium contents of standard menu items” (Center for Science in the Public Interest, 2003, p. 2). The pamphlet contributed to the learning process of the national menu labeling debate and provided a context for state advocacy. The CSPI’s role in the national menu labeling movement incited statewide public health organizations to tackle menu labeling legislation and promoted the public health perspective to address California’s obesity problem. One of the leaders from California’s public health coalition is the California Center for Public Health Advocacy (CCPHA), a nonprofit founded in 1999 and drawing expertise from two state public health associations. This organization gathers epidemiological research and designs health policy solutions for legislators. Since they began, they have focused primarily on childhood obesity in the context of the environment (mainly schools, grocery stores, and restaurants). From 2000 to 2005, CCPHA was involved in legislation to minimize soda and junk food sales in K-12 schools. Their promotion of menu labeling legislation adhered to their belief in the environmental context of obesity and moved legislation from the school context to a more public context (Rudd Center for Food Policy and Obesity, 2008). Moreover, the CSPI provided menu labeling legislative language for the CCPHA to use in California. 30 A Legislative Timeline of Menu Labeling Events (2003-2010) In 2008, California became the first state to mandate nutritional menu labeling with the passage of SB 1420. The policy’s purpose was to indirectly modify individual behavior— namely reduce away-from-home food consumption—in restaurant settings by providing calorie information for menu items. As previously noted, SB 1420 was not the first menu labeling bill introduced in the state’s legislative process. Legislators introduced similar bills as early as 2003 in California when Senator Deborah Ortiz (D) introduced SB 679 mandating menu labeling for restaurants with 10 or more locations in the state. Ortiz’ two bills focused on mandating restaurants to provide a variety of nutritional information upon request. The policy was introduced again in 2007 by Senators Alex Padilla and Carole Migden, who differed in opinion on minor details. They reconciled their bills and Senator Migden was added as a co-author to Senator Padilla’s bill in March 2007. The bill succeeded in the Senate and Assembly, but was vetoed by Governor Schwarzenegger. By 2008, the industry coalition introduced a weaker version of SB 120 known as AB 2572 to compete with a stricter menu labeling bill (SB 1420). AB 2572 reflected the industry group’s decision to amend their position when they introduced a weaker version of the bill, yet it failed. By the time California enacted the policy, mandatory menu labeling policy was already in effect in several counties and one city in the state. San Francisco became the second city and the second county in the nation to approve a menu labeling ordinance in March 2008. San Mateo County and Santa Clara County followed suit in the summer of 2008. In September 2008, Los Angeles County was on the verge of passing a similar ordinance, but held off until state legislation was enacted. Refer to Table 2.2 for a timeline of these events. 31 Table 2.2 Timeline of Menu Labeling Policy in California (2003-2009) LEGISLATIVE EVENTS YEAR POLICY-ORIENTED LEARNING EVENTS Feb 21 st – State Senator Deborah Ortiz (D) introduces SB 679 July 8 th – SB 679 fails passage in the Assembly Committee on Health 2003 FDA convened the Obesity Working Group Feb 3 rd – Senator Ortiz (D) introduces SB 1171 Nov 30 th – SB 1171 from committee without further action 2004 2005 Governor Arnold Schwarzenegger convened the state’s First Summit on Health, Nutrition, and Obesity 2006 CCPHA convened several forums featuring speakers who supported menu labeling Jan 22 nd – Senator Alex Padilla (D) introduces SB 120 Feb 5 th – Senator Carole Migden (D) introduces SB 180 April 9 th – SB 180 amended to focus on labor issues Oct 14 th – Governor Arnold Schwarzenegger (R) vetoes SB 120 2007 March –Menu labeling poll conducted Feb 21 st – Senator Padilla (D) introduces SB 1420 Feb 22 nd – State Assemblywoman Nicole Parra (D) introduces AB 2572 Sept 30 th – Governor Schwarzenegger (R) signs SB 1420 into law Nov 30 th – AB 2572 from Senate without further action 2008 AB = Assembly Bill; SB = Senate Bill; D = Democratic Party; R = Republican Party Stakeholder Analysis The role of the coalition leader was evident in the empirical evidence. Certain organizations invested more resources and time, and may have led intra-coalition coordination efforts between political actors and organizational stakeholders. In sum, certain 32 organizations assumed a leadership role within the coalition in the policy subsystem. Examining the organizational coalition leader and their behavior in a policy subsystem may help to delineate the difference between a coalition motivated by self-interest and one held together by core beliefs, and to answer questions regarding coordination efforts among stakeholders (Weible et al., 2009). Two advocacy coalitions existed in California’s menu labeling debate, a public health coalition and an industry coalition. The number and type of stakeholders involved in each coalition varied by year. The core membership of each coalition was relatively stable from 2003 to 2008. Coalition leadership emerged on two levels—the individual- and the organization- level. Individual stakeholder leaders consist of politicians who introduced and/or sponsored legislative bills in the political stream. Individual stakeholder leaders were more likely to be subject to fixed external events, such as elections and term limits. Senator Deborah Ortiz (D) was the sponsoring politician for a mandatory menu labeling law in both 2003 and 2004. In 2006, she was unable to seek re-election due to the state’s term limits (Quinn, 2006). Other individual level stakeholders (and a brief summary of each bill) are listed as initial bill sponsors in Appendix A. Senator Alex Padilla (D) and Senator Carole Migden (D) re- introduced menu labeling bills in 2007 and jointly introduced SB 1420 in 2008. Assembly member Nicole Parra (D) introduced a weaker version of a menu labeling bill supported by the industry coalition to counter the public health’s Senate version in 2008. Given the state’s governance structure, the governor is a key individual level stakeholder peripherally involved in the process who has differential power access during different legislative phases 33 (Schneider, 1989). Governor Arnold Schwarzenegger (R) was identified as a key individual level stakeholder during his governorship. His role and involvement are discussed elsewhere. Organization level stakeholders consisted of nonprofit groups, professional associations, companies, or other organizations from the private or public sectors identified as formally sponsoring or opposing a bill in the legislative documents. Organizational level coalition leaders appeared more stable throughout the selected time period compared to individual politicians likely because politicians are subject to election rules and term limits. Organizational level leaders were also more likely to provide written content or arguments in support of or in opposition to a bill in the legislative documents analyzed. In the nascent stage of mandatory menu labeling in 2003, a similar number of supporters and opponents were involved in the debate. The original organizational sponsors of SB 679 were the American Heart Association (AHA) and American Cancer Society (ACS). The AHA has previously been identified as an established interest group actor in the obesity policy debate (Kersh, 2009). Two organizations representing health care professionals and public health advocacy efforts joined the coalition leadership and were listed as SB 120 co-sponsors in 2007—the California Center for Public Health Advocacy (CCPHA) and the California Optometric Association (Rudd Center for Food Policy and Obesity, 2008). While the leadership of the public health coalition doubled in size and integrated state level organizational players during the study period, only one organizational level leader was identified for the industry—the California Restaurant Association (CRA), who is a part of the NRA network. The CRA was an early opponent of a state mandatory menu labeling policy in 2003 and the sponsor of AB 2572 in 2008. 34 A brief case study conducted and written by the Rudd Center for Food Policy and Obesity was based on interviews with the CCPHA director and the president of Brown Miller Communications, Inc. who oversaw publicity efforts for the public health coalition’s bill. The initiation of the relationship between political actors and organizational stakeholders consisted of the organizations approaching the politician—“he [Senator Padilla] was looking for a good nutrition bill to sponsor when he was approached by CCPHA and the American Heart Association [AHA].” Senator Padilla was identified due to his public health interest, existing relationship with another coalition leader, and his “rising star” status in the legislature (Rudd Center for Food Policy and Obesity, 2008). Coalition Size and Stability Coalition behavior has been previously identified as an overlooked area of the ACF (Weible, 2007). For this study, the size and stability of each coalition was examined from 2003 to 2008 to identify categories of coalition behavior and to identify fragmentation. Table 2.3 depicts the organization level coalition measures by group. 35 Table 2.3. Organization Level Coalition Measures by Group INDUSTRY COALITION PUBLIC HEALTH COALITION Core Membership (Total # of core participants) 5 6 Average Size (Mean # of participating organizations) 13 28 Average Retention Rate (Mean %) 48% 58% Fragmentation (Total # of defecting organizations) 3 0 During this period, the size of the public health coalition more than tripled, whereas the opposing group only slightly increased in size. On average, the public health coalition had 28 organizational level members and the industry coalition had 13 organizational level members for each bill. Membership was in flux throughout, and the size of the core membership was similar for both coalitions. Core membership was defined as members who were consistently involved throughout the process (i.e. listed at least three times as supporters/opponents from 2003 to 2007). Only six core public health coalition members and five core industry coalition members were involved in 2003 and in 2007, including the leadership. Excluding the leadership, public health coalition core members represented medical and public health associations, whereas industry coalition members consisted of business associations/groups. The public health coalition showed a greater degree of congruence and consistency, particularly in 2007 and 2008 when there was more legislative activity and AB 2572 was introduced by the industry coalition. As such, there is early evidence of the ACF congruent policy core belief hypothesis among the public health coalition which could be tested in another study (Weible, 2005). Retention rates were higher among the public health coalition, 36 potentially resulting from belief congruence and functional overlap (Zafonte & Sabatier, 1998). Retention was defined as the percentage of coalition members who continued in the same coalition group from one bill to the next. From 2003 to 2007, both coalitions had a retention rate of 50%. Their average retention rate was 58% for the public health coalition compared to 48% for the industry coalition. During this period the industry coalition exhibited greater fragmentation. Fragmentation was identified via incidences of coalition defection (Jenkins-Smith et al., 1991). While, none of the public health coalition members defected throughout the various iterations of the bill, three industry coalition members defected and joined the public health coalition in 2007 and 2008. In response to an early version of SB 120, the California Grocers Association opposed the bill and cited concerns around the cost—namely, “the penalty regarding inaccuracy of information” and they expected ”the nutritional analysis to cost several hundred dollars per item.” 2 The California Grocers Association (and the California Independent Grocers Association) who were “previously opponents of this bill are now in support.” 3 The switch was likely due to the SB 120 amendment to exempt grocery stores from menu labeling rules. The second coalition member was a labor organization, the American Federation of State, County, and Municipal Employees (AFL-CIO). The motives for their switch were unclear based on the data. The final member who defected may have done so due to a different belief system. One organization was identified as not sharing the major belief system of the industry coalition (Sabatier, 1988). While the California Alliance for Consumer Protection (CACP) was involved in the first iteration of the bill in 2003 and 2 SB 120. Senate Committee Analyses. March 15, 2007. 3 SB 120. Assembly Floor Analyses. September 4, 2007. 37 was listed as an opponent to the menu labeling bills introduced in 2007 and 2008, they espoused a pro-consumer belief rather than the industry’s policy core beliefs. From 2003 through 2008, the CACP presented policy arguments to require additional nutritional information 4 or to extend the policy to all restaurants in California. In 2007 and 2008, the CACP submitted the following argument—“while in agreement with the intent, [CACP] opposes the bill stating that the measure doesn't go far enough. The group believes the measure should apply to every restaurant in California.” 5 There was no evidence the CACP coordinated or participated with the public health coalition. Qualitative Analysis of Belief Systems Analysis of the legislative documents revealed limited deep core beliefs from the two coalitions. The public health coalition rallied around a policy core belief (namely, the notion that people lack key nutrition information in restaurants and would make better decisions if they were given nutrition information). Meanwhile the industry coalition focused on their policy and secondary beliefs and promoted voluntary actions by restaurants; they pointed to economic and operational barriers as their justification (Almanza, Nelson, & Chai, 1997). Industry Coalition: Beliefs or Self-Interest? Only two statements made by industry coalition members explicitly placed responsibility on individuals. These statements were submitted five years apart and do not appear to be coordinated. The coalition leader, CRA, submitted a statement of opposition to 4 SB 679. Senate Committee Analyses. April 7, 2003. 5 SB 120. Senate Committee Analyses. March 15, 2007. SB 120. Senate Committee Analyses. September 11, 2007. SB 1420. Senate Committee Analyses. March 25, 2008. 38 Ortiz’s menu labeling bill in 2003—“CRA argues the primary reason for high obesity rates is directly related to lifestyle choices people make that are independent of restaurant menus.” 6 After 2003, the CRA submitted statements focused on a less normative claim—namely, the “onerous and costly” nature of a menu labeling bill on restaurants. 7 The CRA mostly presented a perspective relaying their preference to maintain the status quo by allowing restaurants to voluntarily adopt a nutrition labeling policy. This perspective reflected the consumer freedom belief by accepting that some consumers would desire nutrition information. The most popular statement asserted by the CRA from 2003 to 2008 (and the basis for AB 2572) was “that most restaurants already make this information available through their websites, which they believe is sufficient.” 8 The justification for AB 2572 further elaborated on attempts to put the focus on the consumer and voluntary efforts in the free market: “[the CRA] note that many of the state's restaurants already provide this information, ranging from hanging poster charts with nutritional content near the cash registers to including it in their menus, and, in doing so, have responded to consumer demand or chosen to be proactive with regard to public health.” 9 Self-interested statements emerged in 2007 as primary arguments in opposition of a menu labeling bill. In 2007, the CRA pointed to the “impractical” nature of the bill in implementation and the lack of flexibility and “creativity” afforded to restaurants. Cost and 6 SB 679. Assembly Committee Analyses. July 9, 2003. 7 SB 679. Senate Committee Analyses. April 7, 2008. SB 679. Assembly Committee Analyses. July 9, 2003. 8 SB 679. Senate Committee Analyses. April 7, 2003. SB 1171. Bill Introduced. February 3, 2004. 9 AB 2572. Assembly Committee Analyses. April 7, 2008. SB 1420. Assembly Committee Analyses. June 16, 2008. 39 liability issues were also raised by the CRA and core members. 10 These types of arguments persisted throughout the policy debate over SB 120. When AB 2572 and SB 1420 were introduced in 2008, the industry coalition had backed away from opposing a mandate and went as far as to say the two coalitions had a shared goal in providing nutrition information to customers. 11 The policy debate in 2008 was focused on whether the information would be mandated at the point of sale or could be available in a variety of formats at the discretion of the restaurants. The “overly-restrictive one-size-fits-all” point of sale issue was at the center of the debate. 12 Again, several non-core industry coalition members presented arguments focused on self-interest—citing impracticality, lack of flexibility, and cost as key issues. 13 Industry coalition members believed AB 2572 afforded them the flexibility needed to “provide information to their consumers in the ways they believe work best for both their customers and the restaurant.” 14 In 2008, an industry core coalition member made a statement placing responsibility of weight gain on individuals, but this was the only statement made to this effect. All other opposing arguments focused on economic and implementation concerns. 15 Public Health Coalition: Framing a Consistent Message on Informed Decision Making Almost all of the public health coalition’s arguments focused on a clear and consistent message—providing nutritional information in restaurants could lead to informed decision making and the consumption of fewer calories by consumers. Several public health 10 SB 120. Senate Committee Analyses. March 15, 2007. 11 AB 2572. Bill Introduced. February 22, 2008. SB 1420. Senate Committee Analyses. March 25, 2008. 12 SB 1420. Senate Committee Analyses. March 25, 2008. 13 AB 2572. Assembly Committee Analyses. April 7, 2008. AB 2572. Assembly Committee Analyses. May 28, 2008. 14 SB 1420. Senate Committee Analyses. March 25, 2008. 15 SB 1420. Senate Committee Analyses. March 25, 2008. 40 coalition members, particularly the leaders, repeatedly presented this “informed decision” justification (Rudd Center for Food Policy, 2008). 16 In 2008, the ACS added that if the policy were to be effective in reducing caloric consumption among consumers, “the information must be available with other information—menu options and prices—that contribute to a consumer’s choice.” 17 Three of the public health coalition leaders (the ACS, the AHA, and the CCPHA) consistently submitted similar, informed decision arguments and pointed to the importance of eliminating barriers to information and addressing calorie underestimation among consumers. Understanding the Policy Landscape Policy precedence was a code that emerged early on during the pilot coding process. Policy precedence was invoked in legislative documents to establish the relevance of the proposed policy and to locate the specific policy within a broader, well-established policy context. All of the legislative documents referred to related existing or competing nutritional policies, and some mentioned ongoing legislative activity. The importance of policy precedence in the legislative documents may be an offshoot of judicial precedence, whereby judgements in cases carry precedential value that similar future cases rely on in the U.S. legal system (Sabatier, 1988). Several levels of policy precedence were identified:  Current Federal or State Laws  Competing and Prior State Legislation 16 SB 679. Senate Committee Analyses. April 7, 2003. SB 1171. Bill Introduced. February 3, 2004. SB 120. Bill Introduced. January 22, 2007. AB 2572. Bill Introduced. February 22, 2008. SB 1420. Senate Committee Analyses. March 25, 2008. 17 AB 2572. Senate Committee Analyses. June 25, 2008. 41  Local Policies in the State: Ordinances (passed or introduced) in local jurisdictions in California or in other states  Local Policies in Other States: Ordinances (passed or introduced) in local jurisdictions in California or in other states Federal policy precedence was invoked several times in the menu labeling legislative documents mainly through references to the NLEA as a basis for menu labeling regulation. The NLEA was cited as existing federal policy that “recognized the need for accuracy in nutrition information statements” that also “addressed the challenges of determining restaurant food product nutrition information by permitting nutrition information for such products to be determined with ‘reasonable basis.’” 18 The NLEA required standard nutrition labeling rules among food manufacturers and gave the FDA regulatory authority. While restaurants were largely exempt, they were “required to provide nutritional content information on food items for which the restaurant makes a nutrient or health-related claim, such as ‘low fat’ or ‘heart healthy.’” 19 Competing bills and previous legislative outcomes at the state level were also coded as policy precedence. For instance, competing SB 1420, Governor Schwarzenegger’s prior veto of SB 120, and the outcomes of previous Senate and Assembly bills were mentioned in the analyses documents. 20 Local city policies in California, as well as those from other parts of the country, were mentioned in the legislative documents, lending support to the ACF hypothesis “that 18 SB 679. Bill Amended. April 7, 2003. AB 2572. Bill Introduced. February 22, 2008. SB 120. Senate Floor Analyses. April 17, 2007. SB 1420. Senate Committee Analyses. March 25, 2008. 19 AB 2572. Assembly Committee Analyses. April 7, 2008. SB 120. Senate Floor Analyses. April 17, 2007. 20 AB 2572. Assembly Committee Analyses. April 7, 2008. SB 120. Senate Committee Analyses. April 17, 2007. SB 1420. Senate Committee Analyses. March 25, 2008. 42 adoption of similar policies by other government jurisdictions will precipitate policy change” (Heikkila et al., 2014, p. 69). In 2008, there was a high level of local legislative activity in support of menu labeling policies within the state. Between February and August 2008, three large counties in California approved local menu labeling ordinances requiring restaurants to post calorie information on menu boards, albeit with different definitions of a chain restaurant. 21 Local policies from other states, such as New York City’s menu labeling policy debate, were also mentioned as implementation examples. The year prior, King County in Washington State had also enacted a menu labeling requirement for restaurants with 10 or more national locations. Various other cities (3), counties (2), and states (15) were also in the midst of reviewing menu labeling requirements at the time, suggesting momentum in favor of menu labeling. 22 Policy-Oriented Learning Events One of the most valuable components of the ACF approach is the focus on the integration of learning and technical analyses. Identifying policy-oriented learning across coalitions is a valuable component of the ACF where different coalitions may engage in learning processes and modify their policy positions. Professionalized forums are one place where such learning can occur and reinforce coalitions’ primary beliefs and/or modify secondary beliefs (Albright, 2011; Jenkins-Smith & Sabatier, 1994). While measuring the act of individual learning is challenging and difficult in real world political settings, identifying 21 While San Francisco County’s ordinance required restaurants with 20 or more locations in the state to post calorie information, Santa Clara County’s ordinance required chains with 15 or more locations to post similar information. Interestingly, the City of San Francisco passed a local ordinance in 2008 requiring restaurant chains “with 14 or more outlets in the state to post calorie and other information on menus and menu boards.” Refer to SB 1420. Assembly Committee Analyses. June 16, 2008. AB 2572. Assembly Committee Analyses. April 7, 2008 22 AB 2572. Senate Committee Analyses. June 25, 2008. 43 and describing events where learning occurs is valuable and can be used as a proxy for policy-oriented learning. Tracking the actions of stakeholders following policy-oriented learning events is important and may be associated with learning or beliefs espoused at such an event. Support for obesity policies and the decision making process are affected by an understanding of factors affecting nutrition, metabolism, and weight. As in many public health areas, growing public awareness of the causal factors (and their magnitude) leading to a disease or condition contributes to the policy process. National and state level policy events were identified as key policy learning events where, broadly, stakeholders involved in the nascent obesity issue regime (Kersh, 2009) were convened to learn about the topic and discuss potential avenues to address the issue. National Policy Events A key national policy discussion forum that motivated and encouraged the menu labeling discussion consisted of the Obesity Working Group (OWG). In 2003, the FDA organized an Obesity Working Group (OWG) where professionals from the agency, public health field, food industry, and other fields discussed dimensions of the obesity problem and proposed recommendations. With respect to restaurant menu labeling, the OWG recommended urging “the restaurant industry to launch a nation-wide, voluntary, and point- of-sale nutrition information campaign for consumers” (U.S. Food and Drug Administration, 2004). Although some participants supported more stringent requirements, the participation of the food and beverage industries led to support for voluntary measures. 44 State Level Events In California, three key policy-oriented learning events were identified. Two of these events directly involved the governor. First, the state’s public health focus on obesity was explicitly communicated in 2005. Governors “are chief executives" of the state and they “can use the high visibility of their position in state governments to focus attention on important problems” (Schneider, 1989, p. 916). Governors can “act as catalysts, bringing governmental and nongovernmental forces to find solutions for pressing health care concerns” (Ibid.). The first Governor’s Summit on Health, Nutrition, and Obesity in 2005 brought together various participants from different coalitions to discuss strategies and policy and was an important event in the policy-oriented learning process. The summit qualified as a forum that, due to the governor’s role, may have strongly compelled different stakeholders to participate (Sabatier, 1988). At the summit, Governor Arnold Schwarzenegger described California’s “battle with the bulge” as a cross-sectorial movement engaging business, government, academia, and public health leaders and experts. The California Endowment Foundation (a private health foundation established in 1996) structured the agenda of the conference to focus on the environmental causes of obesity. At the event, the Foundation’s President and CEO, Robert Moss, commented: our social and physical environments have a direct impact on whether we get to exercise and eat right. We need to move beyond what has been so far an exclusive reliance on individual behavior and responsibility - it hasn't worked (Office of the Governor, 2005). The restaurant lobby initially opposed menu labeling because it would impose additional costs on the industry and it would place the responsibility of nutrition on restaurants (Rudd Center for Food Policy and Obesity, 2008). 45 A bill’s legislative outcome is another policy-oriented learning opportunity for advocacy coalition groups. On October 14, 2007, Governor Arnold Schwarzenegger vetoed menu labeling bill SB 120. After two years of promoting healthy living and active living as part of his policy agenda and bringing together various stakeholders from each coalition, he vetoed the menu labeling bill. SB 120 supporters perceived the veto as a “retreat from his [Schwarzenegger’s] own obesity prevention plan” (Padilla, 2007). Governor Schwarzenegger responded by pointing to the industry’s arguments against menu labeling—the lack of flexibility and cost. He denounced “inflexible mandates applied sporadically” and vouched for the voluntary uptake of menu labeling by the restaurant industry. 23 His health advisor revealed that the Governor’s previous experience as a restaurant owner made him sympathetic to additional regulation on the food industry, and his political ideology as a Republican also likely contributed to his stance against industry regulation. Ultimately, Governor Schwarzenegger signed SB 1420 into law in 2008 in part due to closed-door negotiations, but public pressures likely also influenced his decision. 24 Coalition Coordination and Resources Several policy-oriented learning events led to the industry’s re-evaluation of their stance against menu labeling. One of the public health coalition leaders was integral in organizing and hosting policy-oriented learning events for political actors, stakeholders, and the public. CCPHA’s actions spanned policy advocacy strategies and included “developing deep knowledge; building networks; and participating for extended periods of time” in the policy subsystem—three strategies to influence the policy process (Weible et al., 2012, p. 1). 23 AB 2572. Senate Committee Analyses. June 25, 2008. 24 SB 1420. Assembly Floor Analyses. August 22, 2008. 46 Several coordinated efforts occurred from 2006 to 2008 on behalf of the public health coalition leaders. In 2006, the CCPHA developed a “series of forums” where speakers (such as CSPI) presented on the importance of menu labeling and encouraged different avenues of support. These forums were held in districts with legislators who had not communicated their support for mandatory menu labeling (Rudd Center for Food Policy and Obesity, 2008). Beyond exhibiting deep knowledge about the policy subsystem, CCPHA contributed to the development of knowledge in this subsystem. In March 2007, the “CCPHA with assistance from Brown Miller Communications and the other bill sponsors commissioned the Field Research Corporation to add menu labeling questions to its poll” (Rudd Center for Food Policy and Obesity, 2008). The poll’s findings were used to support their policy position. The results supported many of the policy arguments the public health coalition was promoting—such as a high level of support for menu labeling and the low level of nutrition knowledge among the public regarding food items sold in restaurants. The poll results showed that most people—regardless of income or education—were not able to accurately compare nutritional content among available choices based on description alone (Ibid.). The well-publicized findings served as a focusing event where uncertainty about the level of misinformation among restaurant consumers was reduced. The use of a public opinion poll demonstrates the inclusion of the public’s voice and preferences into the policy discussion (Jones & Jenkins-Smith, 2009). The use of a field poll by the public health coalition supports the modification to the ACF to make public opinion a coalition resource (Sabatier & Weible, 2007) and to invest in research to defend their policy argument in the political realm (Sabatier, 1988; Weible, 2008). The use of a field poll by the public health coalition leader(s)—the investment to conduct and to disseminate the findings 47 using a press release—expanded the definition of how a coalition can use public opinion polls as information to support their policy arguments (Weible, 2007). The poll specifically challenged assertions and policies set out by the industry coalition. Previously, the CRA asserted people did not need or desire nutrition information at restaurants since they already knew how to make healthy selections. After poll results were released, industry coalition members shifted their position from no menu labeling policies to voluntary menu labeling agreements within the industry. In 2008, the coalition also responded by presenting their own menu labeling bill, Assembly Bill 2572 which was a mandatory labeling bill that provided restaurants flexibility to provide nutritional information via any format (from brochures to trays). The introduction of a competing menu labeling bill reflected a modified understanding of the policy issue. In response, the public health coalition used evidence to refine their arguments in 2008. The findings from a field poll conducted by CCPHA in March 2007 were included as part of the analysis of legislative documents in April 2008 in opposition to AB 2572: “CCPHA states that the disclosure practices allowed under this bill are ineffective strategies for combating poor nutrition and cites a March 2007 Field Poll that found that 84% of Californians surveyed want this information to be provided on menus.” Evidence indicating a high demand for point-of-order calorie information, and that the problem of underestimation existed, were provided as arguments in support of a menu labeling policy. Use of Technical Knowledge Previous ACF studies have focused on the role of agencies to participate in the policy debate. A hypothesis about administrative agency’s propensity for taking a more moderate 48 position compared to interest group allies has developed as part of the ACF discussion (Jenkins-Smith & Sabatier, 1994). The role of administrative agencies producing technical knowledge (or summarizing and re-packaging academic and scientific studies) has not been clearly identified and discussed. Yet, national and state level agency reports play a critical role in disseminating technical knowledge used by the political actors in terms of providing expert-based information (Weible, 2008). Most of the technical knowledge presented in the legislative documents focused on the magnitude of the obesity epidemic and the associated cost. The evidence also addressed, to a lesser extent, national and state trends in out-of-home food consumption. The most frequently mentioned statistic was the well-known adult prevalence rate attributed to the CDC and the U.S. Department of Health and Human Services in legislative documents— “two-thirds of American adults are overweight or obese.” National and state-specific data focused on the obesity issue among adults and adolescents. Cited survey sources included the National Health and Nutrition Examination Survey (NHANES) and the Youth Risk Behavior Survey (YRBS). In the case of menu labeling in California, agency reports were the primary source of technical knowledge used as evidence in the legislative process. Federal government agencies were the primary authorities listed as sources of technical knowledge. Two former Surgeon Generals—Tommy Thompson and David Satcher—were each cited once. State- specific government agencies and officials were mentioned as well, but to a lesser degree. Interest groups involved in menu labeling advocacy coalitions were also included. For instance, the NRA was cited as an authority source on national and state trends in eating out behaviors. In contrast, CCPHA was presented as a public health expert in California (e.g. 49 “CCPHA found that statewide, approximately 28 of every 100 children are overweight and unfit”), which potentially contributed to their decision to join the coalition (Weible, 2008). Other interest groups were cited less compared to coalition leaders. Several citations referred to specific reports or studies, although the majority of citations were generally attributed to a group or agency instead of a specific source. No specific scientific studies were identified. The authority sources are separated into categories in Table 2.4, which lists specific government agencies and officials, and interest groups who were mentioned in the menu labeling legislative documents. Table 2.4. Sources of Technical Knowledge in the Menu Labeling Debate Government Agencies & Officials Nonprofit Organizations National  Centers for Disease Control & Prevention  Food & Drug Administration  U.S. Department of Agriculture  U.S. Department of Health and Human Services  American Cancer Society  American Diabetes Association  National Restaurant Association  Center for Science in the Public Interest State- Specific (California)  California Department of Public Health  Los Angeles County Department of Public Health  Senator Alex Padilla  California Center for Public Health Advocacy Other  New York City Department of Health and Mental Hygiene  RAND Corporation Overall, minimal evidence was provided in a majority of legislative documents regarding the effectiveness of a menu labeling policy. A reference alluded to the effectiveness of the NLEA and was included in five of the bills (SB 679, SB 1171, AB 2572, SB 120, and SB 1420). 25 Although no specific study is referenced, the NLEA data refers to 25 The specific reference included in each bill consisted of: Three-quarters of American adults report using food labels on packaged foods, which are required by the federal Nutrition Labeling and Education Act of 1990. Using food labels is associated with eating 50 the work of Derby and Levy who assessed trends in food label use (Derby & Levy, 2001); their results were cited in the OWG’s report. High levels of awareness and utilization of NLEA food labels were presented as evidence of the effectiveness of a menu labeling law in restaurants. This may be related in part to the importance of policy precedence and experience with a similar and well-known nutrition policy. The remaining effectiveness statements were included in the analysis documents for bills introduced in 2008. Two other statements were offered as evidence of the potential effectiveness of the policy. Both were based on city health agency findings—from the New York City Department of Health and Mental Hygiene and the Los Angeles County Department of Public Health. In NYC, some restaurants voluntarily provided nutrition information. However, the agency found preliminary evidence that “most of these efforts have failed to inform the vast majority of consumers” and only a very small percentage of consumers (3.1%) had reported seeing calorie information in these restaurants. 26 In Los Angeles, the County’s Department of Public Health conducted a Health Impact Assessment (HIA) in May 2008 and estimated “that a modest 100 calorie reduction by 10% of restaurant patrons would result in a 38% reduction in overall annual weight gain throughout the population for people ages five and up.” The obesity cost savings associated with the policy was estimated at $30 million per year. 27 The HIA was specifically designed to address the lack of studies on the policy effectiveness of menu labeling, specifically SB 120 and SB 1420 (Simon, Jarosz, Kuo, & Fielding, 2008). In mid-2008, the CRA pointed to the lack of more healthful diets, and approximately one-half (48 percent) of people report that the nutrition information on food labels has caused them to change their minds about buying a food product. 26 AB 2572. Senate Committee Analyses. June 25, 2008. SB 1420. Senate Committee Analyses. March 25, 2008. 27 SB 1420. Senate Committee Analyses. June 16, 2008. SB 1420. Senate Committee Analyses. July 1, 2008. 51 evidence (i.e. “there are no studies or data…”) supporting the menu labeling mandate yet that was the only argument presented that pointed to technical knowledge on the effectiveness of a specific policy. 28 Use of technical knowledge is an important component of the policy process. The ACF posits that the role of policy-oriented learning can occur with new information or experience related to the policy. In the case of California, national level events appeared to motivate and initiate the policy discussion, and state level events were spaces to debate specific policy arguments (i.e. the poll) and to present state-specific knowledge. The poll was a critical part of highlighting the public health coalition’s argument regarding a lack of information—consumers apparently did not have an accurate assessment of nutritional information when purchasing food at a restaurant. In sum, the public health coalition was able to couple a clear and consistent message (i.e. lack of nutrition information contributes to the obesity issue) with a political strategy. They exhibited coalition leadership and coordinated efforts and were ultimately successful in their legislative goal. Discussion The ACF is a helpful tool to organize and to understand the events leading up to the adoption of mandatory menu labeling in California. Obesity policy research affords valuable contributions to our understanding of the health policymaking process and other policy processes involving technical knowledge and scientific evidence, such as environmental policy. Future studies should examine this policy subsystem using other policy process theories such as the punctuated equilibrium theory to better understand long-term incremental stability and short-term spikes in policy activity (True, Jones, & Baumgartner, 28 AB 2572, Parra, Senate Committee Analyses. June 25, 2008. 52 2007; Givel, 2006). Plausibly, menu labeling is an example of a spike in policy change after years of stability in the policy subsystem at the state level. The definition of obesity in the health policy subsystem has been taking shape in the last few decades. Public health professionals brought the experience of the anti-tobacco campaign into the debate and have taken the obesity discussion from the physician’s office to the legislative realm. In recent years, public health interest groups brought their own beliefs and experiences into the obesity debate and spurred the creation of interest groups functioning at the state level, such as the CCPHA, who can lobby for specific obesity policies. Governor Schwarzenegger’s Health Summit exemplifies macro politics in the state and an increase in obesity policy’s ranking on the list of the Governor’s agenda of priorities. The meeting also introduced new actors into the subsystem. Moreover, conflict arose between participants during times of increased interaction and indicated a shift in institutional attention. The public health coalition leaders, such as CCPHA, may be considered the policy entrepreneurs who took advantage of a “window of opportunity” for policy change (Kingdon, 1995; Mintrom & Vergari, 1996). The findings in the study can help to develop hypotheses and inform a causal theory of the state health policymaking process. When SB 120 legislation was vetoed, one might have argued that the limited attention of government had shifted. Policy failure in the legislative process, however, is potentially a large part of a broader policy change narrative. Menu labeling policy in California demonstrates a process where a policy was introduced and made little progress in the legislative process. The policy was re-introduced a few years later with strong advocacy 53 group support. The process of refinement led to a more parochially focused policy (on calorie information) and a stronger public health coalition in 2008, the year in which the policy was ultimately enacted. This policy change process reflected the “multiple, interacting cycles initiated by actors at different levels of government” and the coalition groups kept the policy debate ongoing (Jenkins-Smith & Sabatier, 1994). How did the public health coalition overcome the transaction costs associated with coordinating efforts and utilizing resources to re-introduce policy strategies (Sabatier & Weible, 2007)? What role did policies in local jurisdictions (i.e. cities and counties) play in the passage of SB 1420? What coalition efforts occurred in those jurisdictions? Remaining questions exist regarding factors or external perturbations influencing the stability of a coalition over time and its membership. Data collection instruments such as interviews and surveys with these actors may help us to better understand the role of meetings, strategic public events, and the translational health policy process. The role of core beliefs among political actors with legislative power is an area ripe for research. This is demonstrated by evidence from a review of state level childhood obesity legislation that found “product and menu labeling or soda and snack taxes” had lower enactment rates compared to other types of obesity prevention policies (Eyler, Nguyen, Kong, Yan, & Brownson, 2012). Studies on the policymaking process may examine these types of regulatory beliefs among politicians in specific policy subsystems. Further research is needed regarding the resources afforded to mobilizing and strategic relationship building that may have occurred between political actors and public health coalition leaders. A model of how to conduct a stakeholder analysis and to measure resources and coalition activities employed by coalition members is available and should be 54 replicated in other subsystems (Elgin & Weible, 2013). This study does not examine the initiation of stakeholder relationships or the reasons for continued participation efforts by long-term coalition members. Policy study researchers should closely examine the development of advocacy coalition networks, particularly the initiation and maintenance of relationships among stakeholders (Gagnon et al., 2007) and the extent of coordination during the policymaking process (Zafonte & Sabatier, 1998; Weible & Sabatier, 2005). Understanding these aspects could help to develop a better understanding of the factors influencing coalitions’ decision making processes. Conclusion In March 2010, the Patient Protection and Affordable Care Act (PPACA) included section 4205 requiring restaurants to provide calorie information for each of their menu items. California enacted this policy in 2008, yet studies on the policymaking process at the state and federal level are nonexistent. It would be interesting to use a similar approach to examine the federal level menu labeling debate. The present study can serve as a model for examining coalition participation and beliefs using legislative documents and social history methods. Practical lessons can also be drawn from the application of the ACF to the case of menu labeling for a variety of audiences from public policy scholars to practitioners (Weible et al., 2012). The case of mandatory menu labeling policy in the state of California is an example of understanding how interest groups coalesce (or failed to coalesce) around specific arguments to promote their core policy beliefs. The question of whether belief or interests motivate industry groups is raised by this case where material self-interest was more highly 55 prioritized in the hierarchy of beliefs for industry coalition members (Jenkins-Smith & Sabatier, 1994). Given the loss of coalition members and the one coalition member espousing a very different set of beliefs, the role of profit and “personal” (or organizational) gain may play a greater role for industry members and their coalition may be more susceptible to fraying at the edges compared to a coalition with a clear message around informed decision making. The ACF contributes toward our understanding of the state policymaking process in California. The approach proposed may be replicated in other policy subsystems. With the growing number of nonprofit organizations and interest groups involved in the policymaking debate, the value of using frameworks and theories to analyze their role in the policymaking process is increasingly important. The personal versus environmental responsibility debate has been fomenting for decades around issues that pose a risk to the population’s health. Before the obesity debate, public health professionals successfully enacted tobacco policies using public health and environmental arguments. This study found technical knowledge plays a key role in the policy process and can be used to buttress a coalition’s message, as was the case with the public health coalition. However, the focus on the policy problem and magnitude of the issue greatly overshadowed the role of evidence of a policy’s effectiveness. Researchers interested in influencing the policy process should be aware of the need to integrate policy effectiveness studies into the policy process as they respond to the call to examine the effectiveness of obesity prevention and reduction policies; policy briefs and similarly concise communication documents may help to promote scientific research in the policy process (McKinnon et al., 2009). 56 This study found other elements that are important factors throughout the policy process, such as coalition leadership and stability among organizational level stakeholders. State level organizational stakeholders played a prominent role in the policy process; coalitions in other states may likely differ compared to those in the political landscape in California. Lastly, the broader historical context of a policy and policy precedence are valuable elements of the policy process that should be included in the ACF as part of an evaluation of external events. The case of menu labeling in California highlights the importance of previously identified elements of the ACF in the state policymaking process while refining certain components. 57 CHAPTER THREE. ASSESSING BARRIERS AND FACILITATORS TO HEALTHY LIVING IN A LOW-INCOME COMMUNITY: HIGH SCHOOL STUDENT PERSPECTIVES ON NEIGHBORHOOD RESOURCE ENVIRONMENTS Introduction Over the past few decades, the United States has experienced a dramatic increase in obesity among the general population. The obesity rate for 12 to 19 year olds in the U.S. grew from 5% to 21% between 1980 and 2012 (Ogden et al., 2014). Low-income Hispanics and African-American adolescents have higher obesity prevalence rates indicating they are at greater risk for poor health outcomes (Wang & Beydoun, 2007), including an increased likelihood of becoming obese adults (Whitaker et al., 1997), of developing type 2 diabetes (Koplan, Liverman, & Krakk, 2004), and numerous physical (Wing, Hui, Pak, Cheun, Li, & Fok, 2003; Sorof, Lai, Turner, Poffenbarger, & Portman, 2004), and mental health issues (American Academy of Child and Adolescent Psychology, 2011). Adolescents make decisions in ecological contexts (Davison & Birch, 2001; Sallis, Owen, & Fisher, 2008) that influence their food choices (Brofenbrenner, 1977; Patrick & Nicklas, 2005; Story, Neumark-Sztainer, & French, 2002; Neumark-Sztainer, Story, Perry, & Casey, 1999) and physical activity levels (King, Stokols, Talen, Brassington, & Killingsworth, 2002; Giles-Corti & Donovan, 2002). Schools—with their high enrollment rates, mandatory physical education curriculum, meal provisions, and after-school programming—have long been recognized as sites for obesity prevention (Davison & Birch, 2001; Story et al., 2002; Centers for Disease Control and Prevention, 2011; Baranowski, Cullen, Nicklas, Thompson, & Baranowski, 2002). In recent years, the investment and 58 development of built environment resources on school campuses has been prioritized to address health disparities in underserved communities throughout the U.S. as depicted by the adoption of School-Based Health Centers (SBHC) (Lofink et al., 2013), community gardens, and after-school programs aimed at promoting healthy eating and active living behaviors among adolescents. Although these resources attempt to address existing access disparities, other types of barriers may persist and prevent at-risk groups from utilizing new resources. Identifying and eliminating these types of barriers and enhancing existing facilitators can help to decrease existing health disparities among youth. Studies have previously explored barriers and facilitators to school-based obesity prevention programs in low-income communities from the perspective of parents and school personnel (Patino-Fernandez, Hernandez, Villa, & Delamater, 2013; Steele et al., 2011; Lucarelli et al., 2014). Goh et al. used a qualitative research design to identify barriers to healthy eating and physical activity based on multiple perspectives, including adolescents, parents, and community residents (2009). However, they concentrated on younger adolescents from a different geography—namely, middle school students from the city of Carson—and did not examine the role of the social environment. Data collected from the vantage point of minority high school adolescents can improve our understanding of the relationship between the built environment and health behaviors. Minority high school student perspectives can improve our understanding of the relationship between the intrapersonal, social, and environmental factors that impact utilization of resources in and around schools. This study was conducted within the context of a broader effort to evaluate a three- year REACH Demonstration Project grant in South Los Angeles to lower health disparities 59 and to improve health conditions among target populations. Three large public high schools located in underserved areas were selected as intervention sites based on specific criterion. All three sites had an on-campus SBHC and two had on-campus community gardens. The purpose of this study was to improve our understanding of the barriers and facilitators to high school participants’ access to healthy food, active living environments, and health care services. This study assessed student participants’ level of awareness of school-based and neighborhood health promoting resources at three high schools for the purpose of evaluating how their environments might influence their behaviors. Methods Focus groups have been used extensively in multiple fields, including public health and health promotion (Morgan, 1996). The method is especially useful to understand the experiences of vulnerable or at-risk groups. The study’s protocols were derived based on existing literature on focus group planning, recruitment, and implementation (Stewart, Shamdasani, & Rook, 2007). Students (<18 years of age) provided written parental consent and assent prior to participation. A purposeful sampling strategy was used to recruit eligible participants who would represent a “typical case” of individuals who would participate in the REACH Demonstration Project activities (Basch, 1987; Patton, 1990). Recruitment has previously been identified as a challenge encountered in focus group research particularly when recruiting low-income populations (Keim, Swanson, & Cann, 1999). Having fewer participants attend than anticipated is a key problem and groups are often smaller than planned (Morgan, 1995). School staff and school-based parent center 60 representatives facilitated the recruitment process to address this potential issue and to ensure diverse representation. Participants Sampling goals were developed prior to recruitment to set group attendance targets. The proposed strategy consisted of conducting three focus groups with approximately 30 high school students (Sandelowski, 1995). The three schools had similar socio-economic profiles. The sites differed in terms of the racial/ethnic composition of their student populations. High school site 1 had a majority of African-American/Black students (68.7%), site 2 had a majority of Hispanic students (87.7%), and site 3 was more evenly divided between these two groups (52.4% African-American/Black and 43.4% Hispanic) (California Longitudinal Pupil Achievement Data System, 2013). Recruitment aimed to reflect these differences. Groups were stratified by school site. All of the groups were conducted in English. Instruments A mixed methods research design was employed to guide the data collection and analyses for this project. Two data collection instruments were used—a pre-focus group questionnaire and a focus group script. Each tool served a distinct purpose and was developed using a different set of procedures (Carey & Smith, 1994). While the focus group transcripts served as the primary source of data, the questionnaire complemented the qualitative data by providing information about participants’ health behaviors and their level of awareness and utilization of resources. 61 The questionnaire topics spanned the health resource environment, individual dietary and physical activity behavior, out-of-school program activity involvement, and access barriers and facilitators. The tool was developed based on existing literature on nutrition, physical activity, and health care resources and adolescent health behavior. Participants anonymously completed the questionnaire after providing consent and prior to the discussion to prevent biases from group dialogue. The self-administered questionnaire had a Flesch- Kincaid reading level of 5.8 (appropriate for a 6 th grade reading level or higher). Refer to Appendix B to see the pre-focus group questionnaire. The focus group script contained open-ended questions about adolescents’ health behaviors, perceptions of health resource environments, and barriers/facilitators to health promotion interventions (see Table 3.1 for an abbreviated table of the focus group questions and Appendix C for the complete focus group script). 62 Table 3.1. List of Focus Group Topic Areas and Selected Questions Topic Area Selected Questions Availability of Healthy Foods What kinds of healthy meals are available in your community? School Nutrition Resource Environment What do you like about your school’s cafeteria? What do you not like about your school’s cafeteria? What are ways to make your school’s cafeteria better? Does your school have any activities or programs to get healthy food outside of school hours (probe: a farmer’s market, healthy cooking or eating class)? [If applicable] What are ways to encourage or to get more students to use the community garden? Off-Campus Nutrition Resource Environment What kinds of food do you buy outside of your school’s campus? How many corner stores or fast food restaurants do you pass on the way to and from school? Availability of & Preferences for Fruits and Vegetables What kinds of fruits and vegetables does your family usually buy? Do you like these fruits and vegetables? Why or why not? School Physical Activity Resource Environment Do you know of any on-campus activities or programs to exercise offered before or after-school? Do you participate in these programs? Why or why not? How did you hear about these programs? Off-Campus Physical Activity Resource Environment For those who don’t walk or ride their bike to school, why don’t you bike/walk to school? Is it safe for you to exercise during the day at all the parks and other places in your neighborhood? School-Based Health Center What have you heard about your school’s wellness center? Have you learned about any physical activity or nutrition programs through the School-Based Wellness Center? Procedure An experienced moderator facilitated each focus group while a minimum of two trained observers took detailed notes. Training sessions were held to role play the scripts, review staff responsibilities, and to reinforce skills, as well as to cover key ethical issues, such as participants’ privacy and the protocol for data transfer to the project manager (Heary & Hennessy, 2002; Smith, 1995). 63 Focus group settings consisted of high school classrooms or parent center rooms to ensure privacy and participants’ comfort (Draper, 2004). Students received a $20 gift card and refreshments. The entire process ranged from 60 to 90 minutes depending on the number of participants and discussion length. A debriefing session occurred immediately after each group to summarize and to discuss the field notes. Field notes included non-verbal aspects of the focus groups—such as participants’ tone or body language—that provided additional context to the transcripts (Carey & Smith, 1994). The University of Southern California’s (USC) Institutional Review Board approved study procedures and data collection instruments. Participants (<18 years of age) provided written parental consent and individual assent prior to participation (see Appendices D-F). Data Analysis Descriptive statistics were calculated from the questionnaires. The data analysis software NVivo was used to organize and review the transcripts; the auto-coding function was utilized to group content by key constructs (NVivo, 2012). Two evaluation members independently reviewed and verified the results. The ecological domains identified were used to organize themes and a grounded theory approach guided the analysis (Corbin, 2014). Thematic analysis techniques identified emerging intrapersonal, social, and environmental barriers across resource environments. Themes were carefully tracked and reviewed using an iterative process. The process was based on analytical induction “in which the researcher moves from observation to generalization,” developing “thick descriptions” of participants’ answers (Geertz, 1973; Draper, 2004). Analysis included comparisons within and across school groups. 64 Results Four focus groups were conducted with 28 high school students between July and October 2014. An average of 7 participants attended each group (range 4-11 participants per group). Table 3.2 provides participants’ socio-demographic characteristics. The racial/ethnic composition varied by school. At site 1, 55.6% were African-American/Black compared to only 9.1% at site 2. A majority from site 2 were Hispanic (81.8%) compared to 22.2% at site 1. At site 3, 55.5% were Hispanic and 33.3% were African-American/Black. Overall, 57% were Hispanic while 32% were African-American/Black. Participants’ average age was 15.9 years (range 14-18 years) evenly distributed by gender (53.6% female). Most participants were 10 th -12 th graders. Only a small portion spoke mostly or only Spanish at home (3.6%) indicating a high level of acculturation. Table 3.2. Socio-Demographic Characteristics of Focus Group Participants (N=28) TOTAL Mean (SD) or % Age 15.9 (1.2) Gender Female 53.6% Male 46.4% Race/Ethnicity African-American or Black 32.1% Hispanic 57.1% White 0% Asian, Native Hawaiian, or Pacific- Islander 3.6% Multi-Ethnic 7.2% Language Spoken at Home English (Only or Mostly) 42.8% Both English & Spanish 53.6% Spanish (Only or Mostly) 3.6% 65 Participants’ average weight was 151 pounds (+/-30). Almost a third (32.1%) of adolescent participants were either overweight or obese (Centers for Disease Control and Prevention, 2009) which is slightly higher than the 30% childhood obesity rate in South Los Angeles (Office of Health Assessment and Epidemiology). Only 2 participants did not provide weight or height data. To limit bias, weight status was also assessed in the questionnaire with a health care diagnosis item that asked, “During the past 12 months, did a doctor or nurse ever say you were overweight or obese?” 32% of adolescents responded in the affirmative which was the same as the obesity/overweight rate calculated by our team using self-reported data. This diagnosis question was perceived as less sensitive compared to the weight and height items given the complete response rate. The results are summarized by the on- and off-campus nutrition environments, physical activity resource environments, and the health care resource environment. On- and Off-Campus Nutrition Resource Environment Fruit and Vegetable Consumption and Preferences Students generally perceived fresh vegetables and fruits to be highly accessible in their communities—75% of participants responded it was somewhat or very easy to get fresh vegetables and fruits. Only 1-2 students from each site responded “somewhat difficult” and none said it was very difficult. In terms of nutrition behavior, 46.4% of students reported “eating enough” which suggests a lack of knowledge of the dietary guidelines. The top barrier to fruit and vegetable consumption was a lack of prioritization—32.1% of the sample reported not eating more fruits and vegetables because “I don’t think about it.” 66 Adolescents identified a wide variety of produce available for consumption in their homes. Almost all of the students reported consuming and enjoying fruits and vegetables in their homes. Some said they did not enjoy eating vegetables as much as fruits or were more selective with vegetables. Two students said they only liked fruits and vegetables mixed with other foods or seasoned: They put it with the food and then it is good. Other than that I wouldn’t eat it. It’s like they give you things they make and put stuff in that makes it taste better, like chili, and when you put it in, it makes it taste good but it isn’t good for you anymore. Parental influence was identified as an important factor: “My mom says, ‘you can’t get up unless you finish your food.’ ” School Food Adolescents were asked about their school’s nutrition resource environment, including food at school (lunches and vending machines) and nutrition education programming. More than half of the participants (53.6%) reported receiving lunch from the school cafeteria on a regular basis although at least 75% of the participants qualified for the free/reduced-lunch program (the remaining 25% did not know whether or not they qualified). Just 17.9% reported bringing lunch from home while 14.2% reported not eating lunch regularly. Several participants only ate occasionally, contingent on the cafeteria menu. Participants were generally aware of recent improvements to the school cafeteria food. One student reported, “I don’t eat over there, but based on what I observe, what they say around me, [the school] changed the menu; it’s gotten better since last year. . . . Everyone’s eating healthier than last year.” Two students perceived similar improvements to the vending machines and school store—“Didn’t they make it healthier? Or like now at the 67 school store, they don’t sell juice, just V8. Or at vending machines they don’t sell pop tarts now.” Respondents did not make any specific positive or negative comments about these changes beyond being aware of them. Participants across school sites reported disliking the cafeteria’s frozen food and preferred fresh food items such as salads. They were also dissatisfied with limited water options and said the water from the fountains was unsavory. Aside from taste preferences, students disliked waiting in long lines to access the cafeteria, school rules that required a pin number to enter the cafeteria (which students may not recall), and the cafeteria running out of the main lunch options and providing alternative food. Students generally believed the alternative food was less desirable compared to the main lunch options. Participants were asked to brainstorm recommendations to improve the cafeteria food and processes (see Table 3.3). 68 Table 3.3. Recommendations to Improve the On-Campus Nutrition Resource Environment Primary Topic Recommendation Supporting Quotes Cafeteria Food Increase the Availability of Fresh Food The food isn’t fresh. Fresh foods. Actually cook the food, not microwave food. Increase the Variety of Food Available Better food selection. Menu Planning Provide a Lunch Meal Calendar They go and get the food and then throw it away because we don’t like it. They should tell us what they’ll serve. They should make a calendar so we know. Yeah, a calendar of the whole month with food. Or tell us for the week. In the cafeteria before we enter. School Lunch Queues Increase Entry Points If they had more lines. Sometimes they close lines. Expand Capacity They should make a two floor cafeteria. Decrease Delays caused by Pin Requirement Yeah, it’s slow, a lot of kids in line…so many kids don’t know their numbers to the thing, so it goes slow. I don’t like the PIN. The PIN takes a long time to put in and kids don’t always remember it. Sometimes if people don’t have their number. It’s waiting in line, like 10-20 minutes. You have to get there early. Water Access Provide Fresh Drinking Water They don’t give us water. Something is wrong with the water fountains. They give a nasty taste. School-Based Community Gardens Improve Access to School Gardens Having a free period or go with your class. Let everyone go. Not every gets to go. Have a sign up so it isn’t chaotic. Then everyone has a chance to go. Nutrition Education Students identified a lack of on-campus opportunities for nutrition education. Only one program was identified at sites 1 and 3—a cooking class and a program facilitated by an after-school provider, respectively. Site 2 had two programs which students were generally 69 aware of. One program combined nutrition education and physical activity components, although the latter was perceived as being more of the dominant focus: They break out the sports and they teach you everything you need to know to be healthy. It’s more physical activity but they incorporate food. The second program at site 2 was a nutrition education program and was associated with the school-based community garden. Participants from the two sites with community gardens were asked regarding their awareness and utilization of this resource. Both community gardens are located on campus and have nutrition education components for students and community residents. Participants from site 2 had a high level of awareness about the garden, yet they said only students from the school’s Small Learning Community (SLC) could visit the garden during the school day. Only 75% of participants at site 1 knew about their garden, as one participant stated, “I wish they told us if they grow things, I didn’t know they grow things, and it’s so far back on campus, I would have never known that if you didn’t tell us.” Other participants disagreed: “There’s a garden in back of the school. It is very available to everyone.” Another participant happened to be a student worker at the garden and exhibited deep knowledge about its activities: I’ve worked back there for two years. We planted rosemary, greens, broccoli, and lots of vegetables…I was in the Sierra Club, and not only did we learn about the liquor stores, and the problems, but also to garden and what else you can do. Conversely, another student reported going only once to the garden as part of a class activity. Evidently students perceive access barriers to the garden during school hours. Table 3.3 provides key quotes illustrating students’ recommendations to increase access to the garden. 70 Off-Campus Food Options A majority of participants reported regularly purchasing food off-campus (before or after-school) where they had a greater range of options—“I find a better selection off campus than here. And I get more of what I have a taste for.” Although only 2 students reported eating lunch off-campus (likely due to closed campus rules), others sometimes skipped lunch to frequent nearby fast food restaurants after-school. The high level of availability of fast food and unhealthy items was mentioned as a barrier to healthy food consumption. Participants from all of the groups described their neighborhoods as being heavily laden with fast food restaurants. They agreed “a lot” of fast food restaurants existed close to their schools and homes. A student said, “It’s crazy, it’s [fast food] on every corner” and another said “We all eat fast food. That is all we eat. Especially around the school because there’s junk food restaurants across the street.” The only student who reported living near fewer fast food restaurants resided outside of the area. Popular junk food included chips, soda, energy drinks, and blended caffeinated drinks. Soda consumption behavior was high (39% drank at least 2 sodas in the past 24 hours). One student contrasted food on- and off-campus, “I prefer to eat junk food than their food. I know it’s healthy, but you’d rather eat good healthy food, not nasty kinds.” Specific food sources varied by school site. At site 2, a favorite mobile food vendor was a “tamale lady” and a local restaurant was a popular hangout spot. At site 3, students purchased junk food from peers, a practice noted by Patino-Fernandez et al. as a challenge in school environments (2013). 71 Traditional and Non-Traditional Food Resources Participants reported their families’ shopped mostly at supermarket chains and ethnic (Hispanic) grocery stores. Several participants indicated their parents frequented more than one market depending on the weekly sales circular. A variety of non-traditional, smaller food locations were also identified and emerged during the discussion. A small number of students mentioned the following alternative food sources: the farmer’s market, liquor stores, and the food bank. These resources should not be overlooked when assessing local nutrition resources in low-income communities although they may not be found using formal databases (Odoms-Young, Zenk, & Mason, 2009). Awareness of farmer’s markets varied, with one student stating: “[farmers] come out on Saturdays and sell fresh fruit, fresh stuff,” adding, “I haven’t really seen any around here.” At least one student from each focus group expressed confusion over the notion of a farmer’s market. Two students from site 1 did not know what a farmer’s market was and said they had never been to one; one expressed interest in visiting one (“I want to go to one”). At site 2, one student asked “What’s that?” and a peer replied, “It’s a place where they sell vegetables.” Two students identified two nearby farmer’s markets but had not visited them. Lastly, two students from site 3 said they had been to a farmer’s market, but one actually mentioned purchasing unhealthy food—“I’ve been there like once. I got a bacon wrapped hot dog there, it was awesome.” The potential for unhealthy food available at a farmer’s market should be further explored in similar geographies. The most commonly available fruits (mentioned by students from all three sites) were apples and strawberries. Bananas, grapes, mangoes, oranges, and pineapples were mentioned in at least two groups. Fewer vegetables were mentioned by students although students from 72 two different groups mentioned broccoli, carrots, and lettuce. A small number of students (only 3) from site 1 reported growing fruits and vegetables in their residences. On- and Off-Campus Physical Activity Resource Environment Several participants said they believed they were already engaging in a sufficient amount of physical activity. Although 42.9% said they received enough physical activity on a regular basis in the questionnaire, only 25% reported engaging in the recommended 60 minutes of physical activity per day. A majority of participants (78.6%) exceeded the recommended guidelines of a maximum of one to two hours of screen time per day (American Academy of Pediatrics, 2001). On-Campus Sports and Exercise Programs Overall, participants believed their community offered opportunities to exercise— 78.5% of adolescents reported having access to programs and places to exercise in their community (the remaining 21.5% said they did not know). The most popular physical activity programs were conducted on school campuses. Several participants were aware of numerous on-campus physical activity programs and opportunities to exercise on their campus. Sports programs were the most prevalent type of program mentioned. Several participants reported being involved in specific sports programs or teams. In addition to teams, students from sites 1 and 3 mentioned band and “marching” as exercise. 73 Off-Campus Physical Activity and Exercise Opportunities Similarly, a majority (71.5%) reported someone could easily exercise in their communities, especially in streets and nearby parks compared to 25% who found it difficult. At site 1, a student said, “Yup, the park, your basic block, I think you can find ways to exercise anywhere,” while another reported “You can ride your bike up and down the street.” Private gyms and local fitness classes were identified as part of the physical activity resource environment but to a lesser extent. Site 1 participants mentioned nearby private gyms whereas site 3 participants mentioned a local YMCA. One participant noted, “They have a lot of Zumba classes around here too, free I think, like a big one at Crenshaw Mall.” Although they were aware of these resources, none reported exercising at these locations. Participants were asked to check off barriers to physical activity in the questionnaire and were presented with 11 options. Table 3.4 reports the top three barriers (lack of motivation, lack of transportation, and lack of time). Table 3.4. Top Barriers to Physical Activity Top Barriers to Physical Activity* (N=28) Supporting Quotes Lack of Motivation (39.3%) Easy, you can do it in your house. Some people are lazy. I think it’s easy, but it depends on you. Sometimes I’m lazy. Lack of Transportation (25%) Not mentioned in the focus group discussion. Lack of Time (21.4%) No time. That is the number one answer – no time. *Participants reported barriers in the questionnaire and were instructed to mark all of the barriers that applied. 74 Accepting they had opportunities for physical activity, several students mentioned the role of motivation to engage in physical activity and expressed a belief about their built environment and physical activity behavior—namely, there were various opportunities for physical activity in their neighborhoods but it largely depended on the person and their level of motivation. One student summed up this perspective by stating, “[exercise is] incredibly easily, I just think it depends on them.” Another student combined the barriers of lack of time and laziness to present a more layered view of the challenge—“If you have the time for it [physical activity], like it’s hard if you are a lazy person.” Although participants had not listed safety as a primary barrier to physical activity, it emerged as a barrier to walking, riding bikes, or using neighborhood physical activity resources. Although lack of safety was not identified as a primary barrier to physical activity, lack of safety emerged as a barrier to specific active living behaviors. Public safety was mentioned as a barrier to walking, riding bikes, or using physical activity resources in the community. In the questionnaire, 53.6% reported feeling somewhat safe using physical activity resources in their community compared to 42.9% who said it was somewhat unsafe. Several students in each group mentioned being uncomfortable or feeling unsafe walking or biking in their community. Students from site 3 were particularly concerned about violence and gang activity. Two respondents said bike riding was associated with being a “gang banger” and said they were “mistaken for being a gang banger” when they rode their bike. One student described a recent encounter with violence: When I walk home from school, I won’t say it’s completely safe, ‘cause I did walk home recently and there was a group fight and someone pulled a gun out, and I almost got hit by a car trying to leave. Other than that it is safe. 75 Two other students said their neighborhood was “very dangerous, in mornings and night” or “like a battle zone.” When asked why they did not exercise more, one student responded: It’s hard. Where I live, there’s gang bangers in the apartments; every night there’s helicopters and police cars. Site 3, the most racially balanced of the schools, had the highest perceived threat of violence. For instance, only site 3 participants named specific stabbing and shooting incidences that had occurred within the past year. All groups mentioned the park was a site for potentially unsafe activity (“there’s no safe park”) because of gang activity. Participants from sites 1 and 2 mentioned feeling uncomfortable walking to/from school. Fears of unwanted attention came up: “It’s like old people these days they just be honking at girls for random reasons.” A male participant said, “You see cars following people. My friends, the girls, they have trauma or some bad experiences and you are always with that worry.” They also feared cars for other reason: several participants from sites 1 and 2 witnessed friends or peers get hit by a car or a bike, noting these accidents were reasons to not walk or bike to school. Participants mentioned several adaptive coping strategies, such as “you need to be with somebody” or go out “when there are a lot of people out.” “[W]ho you’re with matters”—“With my dad or brother I’d feel safe, but with my mom I would worry about getting jumped…” Evidently the perceived safety threats stemming from the social and physical environment are key barriers to physical activity outside of school in these neighborhoods. 76 Health Care Resource Environment On-Campus Sources of Health Care Information The school nurse was identified as an important health care resource for students at the school. Over 50% identified the nurse as their primary source of on-campus medical care. None had received information about nutrition or physical activity programs from the SBHC which is a potential active living/healthy eating promotion avenue in the future. Most students said they had heard about such programs or opportunities from teachers (“The PE teacher makes announcements”, “the coaches say stuff”, “Teachers, and the announcements.”) One student noted the role of peers as a source of information as well— “Mostly from teachers and students.” Thus, teachers and peers are key potential facilitators to communicate information about on-campus programs or resources to other students. School-Based Health Centers (SBHC) There was a high level of awareness at all of the schools regarding the SBHC. All of the students said they were aware of a SBHC on their campus. The perception of the SBHC was skewed toward reproductive health care services and, to a lesser extent, vaccines and physical exams. Several students said the SBHC was where you went for “birth control, hormones”, “they have like pregnancy tests, all that stuff, and people you can talk with if you’re having problems.” Slightly under half (42.8%) of participants reported having visited a SBHC. Among those who had visited, many reported a high level of satisfaction embodied by the following quotes from two students: It was great, they actually help you, and it’s really confidential. It’s like they actually care. 77 The potential for increasing utilization of the SBHC was apparent since it seemed to be an underutilized resourced among students who participated in the groups. Discussion Experts have previously called for further research with low-income minority populations at high risk for obesity to inform the obesity policy and health promotion agenda (Sallis, Story, & Lou, 2009). Collecting and examining students’ qualitative perspectives of the on- and off-campus nutrition and physical activity environments can lead to improved environmental and policy interventions to curb and to reduce the obesity rate among adolescents. The findings confirmed previous work that on-campus, the barriers to healthy eating are time constraints and the availability of unhealthy foods (Lucarelli et al., 2014). Food appeal and time considerations have previously been identified as barriers to school lunch consumption with a similar population (Neumark-Sztainer et al., 1999) and in a predominantly White, suburban young adolescent sample (Bauer, Yang, & Austin, 2004) suggesting the need to better understand structural issues around cafeterias. Key recommendations to increase the uptake of school lunches included addressing structural barriers by regularly advertising the lunch menu or increasing access points. School lunches are an important opportunity for students to access and consume healthy food items. Students seem to be aware of recent improvements to their school food environment, which is promising. Positive takeaways are students’ preferences for fresh food and interest in school-based gardens. A previous study conducted with a diverse set of Californian adolescents found most reported fruits and vegetables were important items to be 78 able to purchase at school (Gosliner, Madsen, Woodward-Lopez, & Crawford, 2011). Furthermore, the high level of interest in school-based community gardens can be leveraged by increasing the number of opportunities during school hours to increase awareness and utilization of the garden among students or to promote nutrition education. Indeed, alternative food resources (e.g. community gardens and farmer’s markets) should be considered a source for fruits and vegetables, but the presence of unhealthy food should be further explored in similar geographies (Odoms-Young et al., 2009). The reported high level of unhealthy food surrounding the school campuses is comparable to other studies (Goh et al., 2009; Lucarelli et al., 2014). The presence of unhealthy food options was consistent with a previous study showing fast food comprises a higher percentage of restaurants compared to a more affluent nearby neighborhood (Lewis et al., 2005). Conversely, participants noted only limited healthy food options were available outside of school, confirming a previous study showing significantly lower levels in South Los Angeles compared to more affluent nearby areas (Sloane et al., 2003). Participants did not report the availability of physical activity facilities was a barrier for exercise. Instead, the social environment (especially safety) was critical. In a multilevel longitudinal study with urban adolescents in Chicago, increasing a neighborhood’s safety was estimated to be associated with an increase of 49 minutes of physical activity per week and reducing social disorder was estimated to increase physical activity by 29 minutes per week among those most vulnerable (Molnar, Gortmaker, Bull, & Buka, 2004). Findings around safety concerns were similar to results from qualitative research conducted with low- income groups in urban communities. Crime/violence concerns may be more acutely 79 preventing young female adolescents from engaging in physical activity (Ries, Voorhees, Gittelsohn, Roche, & Astone, 2008). Future studies examining safety concerns in comparable low-income urban communities should seek to understand perceived safety threats at the neighborhood level instead of a larger geography. Community plan areas or service plan areas may be too broad and may underestimate or overlook the nuances of perceived safety threats at the neighborhood level. Feeling unsafe or having prior experience or knowledge about violent incidences or accidents deterred specific physical activity behaviors and active living for adolescents. The relationship between perceived safety threats and specific behaviors should be more closely examined. Moreover, student (and parent) perceptions of pedestrian and bike safety around the school may deter walking/biking to school, whereas safety threats from gangs may deter access to public recreational sites such as parks. While the perception of an unsafe environment may be an interaction effect impeding students’ willingness to engage in off-campus exercise, a lack of motivation has been identified as a barrier to exercise among low-income adolescents and adults (Kubik, Lytle, & Fulkerson, 2005; Goh et al., 2009; Bragg, Tucker, Kaye, & Desmond, 2009). Interventionists aiming to increase physical activity levels need to be aware of this barrier when designing their programs. An opportunity exists to integrate school-based health care resources. SBHCs present a unique opportunity for nutrition education and physical activity engagement since they are located on school campuses and offer a greater focus on primary prevention for adolescents compared to other health care clinics. Nurses are perceived to be a primary source of medical 80 advice, therefore educating nurses about the role of the SBHC and the staff may help to increase utilization of the clinics and alleviate excess student demand on the nurse. The study was focused on exploring barriers and facilitators to specific resources on- and off-campus. Future studies should consider the role of authority and peer influences on dietary, physical activity, and health care seeking behaviors in low-income communities since these are relevant and pertinent factors influencing health behaviors that were not examined in this study (Power, Bindler, Goetz, & Daratha, 2010). Strengths and Limitations The strengths of this study are the inclusion of understudied at-risk high school-aged adolescents and its school focus. Acknowledging and being aware of students’ perspectives and experiences can help to develop more effective interventions, especially since different factors influence high school students’ behaviors compared to younger children. The school level focus provides perspectives across a small, more personal geography. King et al. note the importance of analyzing environment-behavior relationships at the scale where interventions would be implemented (2002). Identifying resources, such as food outlets, from a student’s perspective may provide a more realistic view of a food environment (Odoms-Young et al., 2009). Two potential limitations of using focus groups are the possibility of polarizing perspectives developed through intra-group dialogue/interaction and sampling strategies (Morgan, 1996). Using a brief, literacy appropriate questionnaire proved useful in 1) providing participants with discussion content parameters, 2) collecting quantitative data on 81 participants’ awareness and utilization of resources, and 3) furnishing valuable contextual health behavior data for the qualitative results. Given our non-probability sampling strategy, the findings are not representative of the broader school population or adolescent population residing in South Los Angeles. School gatekeepers invited participants instead of participants being randomly selected. Lastly, 1 to 2 focus groups were conducted per school due to limited project resources therefore the differences identified by school site should be viewed cautiously. Conclusions The results are useful toward developing a better understanding of the barriers and facilitators to accessing health resources in an economically distressed urban community. Our study reveals healthy living resources may be underutilized in low-income urban communities. Participants provided insight into the on- and off-campus barriers that inhibit healthy eating and active living behaviors. The findings should be useful to educational and public health professionals interested in developing obesity prevention or health promotion programs for underserved students (Sallis et al., 2009). The results may also be useful for researchers examining the role of the built environment in promoting physical activity behavior or fruit and vegetable consumption among minority urban populations. Implications for School Health The unhealthy food in South Los Angeles neighborhoods is difficult for students to ignore and competes with healthy foods available on school campuses. School administrators and personnel need to be aware of the off-campus nutrition resource environment since 82 students’ dietary behaviors appear to be influenced by local fast food and chain restaurants, and stores selling unhealthy foods. On-campus, some barriers to healthy food can be addressed using low-cost strategies, such as a prominently displayed lunch menus and minimizing lunchtime queues. Queues might be positively affected by serving school lunches using carts, food trucks, or alternative delivery modes. Surprisingly, participants overwhelmingly believed fruits and vegetables were readily available. While we would treat this finding with caution, taste preferences and the availability of unhealthy food may prove greater barriers to healthy eating. Efforts to increase fruit and vegetable consumption may include creative ways of preparing and presenting healthy foods (e.g. smoothies). Teacher-based interventions are a promising avenue if issues of adequate training and limited time are addressed (Kupolati, MacIntyre, & Gericke, 2014). Out-of-school nutrition education programs that engage students’ family members also offer potential (Williams, Vietch, & Ball, 2011; Berge, Arikian, Doherty, & Neumark-Sztainer, 2012; Patino-Fernandez et al., 2013). Parents from such low-income communities may be receptive to nutrition education programs (Goh et al., 2009; Slusser et al., 2011). Increasing active living barriers requires confronting both intrapersonal and environmental barriers. Marketing after-school sports or physical activity programs as fun and making them more appealing (e.g. by playing music while exercising) might motivate students (O’Dea, 2003; Wilson, Williams, Evans, Mixon, & Rheaume, 2005; Bragg et al., 2009). Power et al. recommend overcoming motivation barriers through new physical activities such as kick-boxing or hip-hop dance (2010). 83 In terms of safety, school personnel may not be able to alter community social environments. Potential strategies to increase such behaviors may build on existing coping strategies by encouraging activities like a walking or bike club. Having safety personnel located at busy intersections near the school and promoting graffiti and vandalism clean-up programs may ameliorate students’ safety concerns. Moreover, expanding after-school programs lowers healthy eating and active living barriers by using the campus to increase physical activity levels and keep students away from the unhealthy food environment (Madsen, Gosliner, Woodward-Lopez, & Crawford, 2009). Moving to the broader school environment, using tools designed to assess the school health climate or adopting recommended school/district level guidelines can promote healthy eating and physical activity (Kolbe et al., 2004). Schools located in low-income neighborhoods may benefit form a coordinated school health program (CSHPs) model in terms of a broader macro level perspective. The CSHP identifies four structures (e.g. a district wellness policy and school health coordinator) and school-specific features to align and coordinate health promotion and prevention services (Stoltz, Coburn, & Knickelbein, 2009). Adopting these recommendations could lead to increased utilization of health promotion resources on high school campuses and improved health for at-risk students in low-income communities. 84 CHAPTER FOUR. FOOD INSECURITY AND BODY MASS INDEX (BMI) AMONG LOW-INCOME WOMEN IN CALIFORNIA IN 2009 Introduction In the past twenty years, there has been a substantial increase in the prevalence of obese and overweight individuals in the United States (Flegal et al., 2010). In 1990, every state in America had an obesity prevalence rate that was less than 14%. By 2010, however, every state had an obesity prevalence rate that was above 20%, with several states in the South reporting rates over 30%. Today, obesity has become one of the nation’s most pressing epidemics and is a pervasive problem with severe health and economic implications. Throughout the last couple of decades, the concept of food security has gained popularity as an important contributing factor to obesity. Several conceptual frameworks have been presented to explore the nuances of the relationship and different facets have been empirically tested. Several studies have shown food insecurity to be associated with overweight status (Townsend, Peerson, Love, Achterberg, & Murphy, 2001) and obesity among women (Larson & Story, 2011), but not among men. Food insecure women have been found to be, on average, two BMI units heavier compared the food secure (Olson, 1999). Another study using BMI as the primary outcome variable found food insecure women of childbearing age were, on average, 0.9 kg/m 2 heavier compared to the food secure (Gooding, Walls, & Richmond, 2012). In 2013, the U.S. had an estimated 14.3% food insecurity rate (Coleman-Jensen, Gregory, & Singh, 2014a). Black and Hispanic individuals exhibited the highest rates of household food insecurity at 28.8% and 25.2%, respectively, compared to 11.3% of non- 85 Hispanic Whites (Coleman-Jensen, Gregory, & Singh, 2014b). Food insecurity is a public health problem since it is associated with a wide range of negative health and social outcomes. Food insecure adults face higher risks of physical and mental health issues (Stuff et al., 2004; Whitaker, Phillips, & Orzol, 2006; Ivers & Cullen, 2011), reduced nutrient intake (Kendall, Olson, & Frongillo, 1996), and higher rates of chronic disease (Stuff et al., 2006; Seligman, Laraia, & Kushel, 2010) such as diabetes (Gucciardi, Vahabi, Norris, Del Monte, & Farnum, 2014). Children who are food insecure also face higher risks of health and behavioral problems (Casey et al., 2004; Kursmark & Weitzman, 2009). Poverty and food insecurity are closely associated. California has a slightly higher food insecurity prevalence rate (15%) compared to the national rate (Coleman-Jensen et al., 2014a). Between 2001 and 2009, the state’s food insecurity rate increased from 28.6% (2.5 million individuals) to 40.5% (3.8 million individuals) among low-income households due to the Great Recession and the rise in the unemployment rate (Langellier et al., 2012). Vulnerable groups (i.e. Spanish-speaking households, households with children, immigrants) experienced the greatest increases of food insecurity (Chaparro, Langellier, Birnbach, Sharp, & Harrison, 2012). Policy responses to address food insecurity consist of government nutrition and food assistance programs for low-income individuals and their families, including CalFresh (also known as the Supplemental Nutrition Assistance Program or SNAP and formerly known as the food stamp program) and the Special Supplemental Nutrition Program for Women, Infants, and Children (popularly known as WIC). Other welfare-promoting programs that serve to supplement low-income households include CalWORKS (also known as Temporary Assistance for Needy Families or TANF) and Medi-Cal (also known as the Medicaid health 86 care program). Local food and nutrition resources (e.g. food banks or soup kitchens) are also available in specific regions depending on the presence of hunger relief organizations, faith- based institutions, or other nonprofit organizations. This study aims to examine the food security literature for the purpose of producing and empirically testing an updated conceptual framework depicting the relationship between food security and BMI. This study’s purpose is to explore the following research question— what is the relationship between food insecurity and BMI among low-income female adults in California? Overall, the primary objectives of the study are to: 1) provide a robust theoretical framework for the relationship between food security and weight status, and 2) explore the relationship between food security and adult BMI, particularly among groups that are disproportionately impacted by obesity such as racial/ethnic minorities and low-income populations. Background The concept of food security has been recognized among international organizations since the 1970’s. Originally, the concept espoused the notion of a consistent food supply in a country or region, amidst economic changes in production or price. In the last couple of decades, a multi-dimensional concept has emerged. Food security is defined as “access by all people at all times to enough food for an active, healthy life” (Campbell, 1991, p. 408-409). This definition encompasses multiple dimensions, including the concepts of local and economic access to non-toxic food that is available in appropriate quantities and containing nutrients for sustenance. This definition considers the local availability of food, access to 87 food defined by political, legal, economic, or social entities, and the process of regulating and distributing safe food to the consumer. In sum, food security occurs when individual or household dietary needs are met consistently over time and are unperturbed by economic or environmental shocks. The concept of food security developed with the globalization of the world economy and trade system. As it became increasingly evident that sufficient amounts of food existed for everyone in the world, the problem of distribution became apparent. The short term health effects of food insecurity depend on the health status of the individual and their coping mechanisms. The long-term health effects of food insecurity can lead to severe malnutrition and poor health outcomes. In terms of global health, most developing countries with high rates of food insecurity are vulnerable to shocks in the economy that lead to diminished local food supplies (i.e. a climate-related agricultural shock) or limited access to food (i.e. political strife). In these cases, food insecurity can lead to dire famine and starvation. Although one might assume that the problem of food insecurity is limited to developing countries facing political and economic instability, residents from developed countries such as the U.S. also suffer from the effects of food insecurity. The United States Department of Agriculture (USDA) Food and Nutrition Service, Office of Analysis, Nutrition, and Evaluation developed a guide to measure household food security which improved upon previous measures. The USDA’s measurements use the following categories: food security, food insecurity with hunger, and food insecurity without hunger (Bickel, Nord, Price, Hamilton, & Cook, 2000). To date, these categories are used in national and statewide surveys to differentiate between states of food security. 88 Developing a Revised Conceptual Model for Food Security and Weight Status for Adults Campbell first conceptualized the relationship between food insecurity, the risk factors for food insecurity, and health consequences (1991). In the framework, the risk factors for a poor diet and the risk factors for secondary malnutrition contribute to individual diet (comprised of food insecurity and food intake patterns). In turn, individual diet contribute to nutritional status (including weight category) and feed into the consequences of a poor nutritional state (i.e. physical, social, and mental well-being). In the framework, diet and nutritional status are considered direct indicators of the person’s nutritional state. The figure suggests food insecurity may either play a direct or indirect role in determining a person’s nutritional status. Furthermore, the framework posits food insecurity may play a role in malnutrition and obesity. While Campbell’s work contributed to the initial theories of a relationship between food insecurity and health outcomes, the relationship between food security and weight status is nuanced. While most of the factors in her framework are supported by existing evidence, the differences between food insecurity and food insecurity coupled with hunger are theoretically important when thinking about consumption patterns (Campbell, 1991; Radimer, 2002) and have important rhetorical and historical value in the public policy process (Himmelgreen & Romero-Daza, 2010). The presence of hunger introduces psychological and physical elements of distress, pain, or stress (Jilcott, Wall-Bassett, Burke, & Moore, 2011) which may lead an individual to modify their health behaviors (e.g. hunger may induce stress eating) and thus result in a change in weight status (Townsend et al., 2001). 89 Intuitively, one might believe the relationship between food insecurity and body mass is negative—decreased access to food and increased risk of food deprivation leads to decreased weight. In the U.S., household food insufficiency has been found to be associated with decreased nutrient consumption (Dixon, Winkleby, & Radimer, 2001). In 1995, Dietz published a case study suggesting hunger and obesity may be causally related. The case described an obese 7-year-old and her mother. Dietz noted, “household food shortages significantly impaired her [the mother’s] ability to provide her daughter with the low-caloric- density foods that [the physician] recommended for weight reduction” (p. 766). His analysis included two possible explanations for the child’s obesity: 1) increased fat content consumed to address hunger during periods of deprivation, and/or 2) increased consumption of food as a physiological coping mechanism to “episodic food insufficiency” (Dietz, 1995). Several paths could theoretically link food insecurity and obesity. For instance, food insecurity and obesity could be associated due to the widespread availability and inexpensive nature of high calorie foods which have a longer expiration date than fresh food (e.g. pasta, potato chips, or candy). Consuming calorie-rich items without increasing physical activity could lead to increased weight. Thus, households undergoing food insecurity may resort to purchasing relatively inexpensive high calorie items to survive the food insecure event or to address household hunger, and thereby gain weight (Gibson, 2003). Townsend et al.’s conceptual framework of the relationship between food insecurity and BMI greatly improved Campbell’s model by including other important weight-related factors such as physical activity behavior (2001). This framework was empirically tested using data from the Continuing Survey of Food Intakes by Individuals (CSFI). Food security was found to be a significant predictor of overweight status for females (but not men) 90 controlling for income, education, age, race/ethnic affiliation, occupation, regional residence, urban, household size, physical activity, and TV/video watching behaviors. Below, factors related to food security and obesity are outlined according to the layers of influence outlined in the socio-ecological framework. The socio-ecological framework includes various spheres of influence, such as the individual, interpersonal, institutional/organizational, community, and social structures, policy, and systems (Gregson et al., 2001). In this model, the individual, household, community, and policy levels are considered as they relate to food security and weight status. Individual Level Factors The importance of including relevant socio-demographic variables in the conceptual framework stems from the literature on health disparities. Several national data sets demonstrate large racial/ethnic differences in obesity rates and these differences are more pronounced among women. Higher obesity prevalence rates are seen among Blacks and Hispanics, with the lowest rates present in Whites and Asians (Wang & Beydoun, 2007). According to the CDC, non-Hispanic Blacks had a 51% higher prevalence of obesity compared to Whites; Hispanics had 21% higher obesity prevalence compared to Whites in 2008 (Pan et al., 2009). Cultural differences in eating patterns, differences in environmental conditions and lifestyle, and differing norms of appropriate weight may contribute to existing weight disparities. Different racial/ethnic groups may respond to food insecurity and hunger with different coping mechanisms. A population-based study on California adult women found food insecurity without hunger to be associated with obesity for non-Hispanic Whites 91 whereas food insecurity with hunger was found to be associated with obesity for minorities (Adams, Grummer-Strawn, & Chavez, 2003). Kaiser et al. found a greater risk of obesity among those who were food insecure with hunger in a sample of Latinas from California (2004). Considering both racial/ethnic group and gender when examining the food insecurity- obesity relationship may be useful toward understanding existing disparities. In a sample of Californian adults, very low food security was associated with increased BMI and higher likelihood of obesity among Hispanic men and women, but not among non-Hispanic Whites, African-Americans, Asian men or multi-racial women (Leung, Williams, & Villamor, 2012). Less educated and low-income populations disproportionately face a higher risk of obesity. Socio-economic status (SES) is inversely related to obesity among adults in America (Sobal & Stunkard, 1989; Jeffery & French, 1996). Income has been previously identified as an important predictor of healthy dietary behavior (Diez-Roux, Nieto, Caulfield, Tyroler, Watson, & Szlo, 1999). Research shows the impact of SES status on obesity varies by ethnic group (Wang & Beydoun, 2007). Rutten et al. describe the relationship between poverty, food security, and obesity— “the proposed conceptual framework portrays a cycle of mutual influence among physiological, behavioral, and psycho-social-cultural mechanisms that translate the influences of poverty and food insecurity into obesity and health-related outcomes” (2010, p. 405). Poverty is defined “as the broad environmental, social, and political context for food insecurity” and relevant factors consist of resources, environment, and access to food (Ibid., p. 406). Gender is an important individual level factor for food insecurity (Martin & Ferris, 2007; Leung et al., 2012). The results are mixed on whether the food insecurity and weight 92 status association exists among males whereas numerous studies have found adult females with food insecurity are more likely to be obese or overweight compared to those who are food secure (Larson & Story, 2011; Franklin et al., 2012). Potential interaction effects may exist between gender and partner roles for males that influence the obesity-food security relationship (Hanson, Sobal, & Frongillo, 2007). Health status variables, such as disability or physical limitations, were recently identified as being associated with food insecurity (Brewer et al., 2010) and are important individual level factors to include. Health conditions (e.g. diabetes) can place limitations on the consumption of certain foods or associated health care costs can reduce the amount of money spent on food. Moreover, disabilities or restricted mobility are identified as important factors in a conceptual framework of food security in the elderly based on in-depth qualitative interviews with 41 low-income elderly adults (Wolfe, Olson, Kendall, & Frongillo, 1996). Household Level Factors Given the existing literature, household level factors may include household composition factors. Female-led households (Martin & Ferris, 2007) and households with children under the age of 5 may be at greater risk of food insecurity (Anding, Osborn, & Gorman, 2006) compared to their counterparts. Important interpersonal relationships exist at the household level. The composition of a family and existing interpersonal influences would be important to develop and to measure (Gregson et al., 2001). Constructs such as eating behavior role modeling effects and parent- 93 child relationships are recommended areas of study for those interested in food security research. Community Level Factors Currently, it is not well understood whether individual level factors (e.g. education or occupation) or environment level resources (e.g. neighborhood level economic resources or fast food access) play a greater role in contributing to the obesity epidemic or how multilevel factors interact (Larson & Story, 2011). An important construct that focuses on upstream mechanisms consists of community food security which is defined as “the underlying social, economic, and institutional factors within a community that affect the quantity, quality, and affordability of food” (Kantor, 2001, p. 20). Some of these factors include aspects of the nutrition resource environment. Until recently, there was limited research on the role of food accessibility and the food security-obesity relationship. One study found inaccessibility (i.e. limited grocery store access) was associated with a 1.4 percentage point increase in obesity (Christian, 2010). Another study took into account both individual- and neighborhood level variables and found individuals with neighborhood food access (i.e. high quality stores and self-reported easy access to food) were less likely to report food insecurity (Mayer, Hillier, Bachhuber, & Long, 2014). There is evidence that access to healthy and unhealthy foods are important environmental determinants of diet and obesity in the U.S. (Cummins & Macintyre, 2006). Unhealthy psychological responses to high calorie or fat foods, such as passive eating, may be exacerbated by the availability of these foods (Hill & Peters, 1998). The nutrition resource 94 environment has amassed importance in public health and measures—such as price and availability—are important aspects of the consumer nutrition environment (Glanz et al., 2005). A review of articles on neighborhood disparities in food access found evidence that increased access to supermarkets leads to improved dietary behaviors whereas some evidence suggests limited access to fast food restaurants is associated with improved dietary behaviors (Larson, Story, & Nelson, 2009). Healthy foods and/or grocery stores that sell fresh produce may be less prevalent in low-income neighborhoods relative to unhealthy options (Lewis et al., 2005). Rural residence has also been found to be inversely associated with food insecurity (Coleman-Jensen, 2012; Carter, Dubois, & Tremblay, 2013) and is included in the framework. Policy Level Factors The availability of state and federal welfare programs and policies may potentially help to alleviate health issues related to poverty. Participation in government nutrition assistance programs has previously been examined for its role in the food security-obesity phenomenon. Government nutrition assistance program beneficiaries may be more susceptible to increased weight due to variations in food consumption according to when benefits are received (Wilde & Ranney, 2000). Participation in welfare assistance programs may mitigate the occurrence of food insecurity since increasing welfare eligibility restrictions can increase the number of food insecure households (Borjas, 2004). A relationship between participation in the food stamp program (and SNAP) and obesity has previously been identified for women (Townsend et al., 2001; Gibson, 2003; Leung & Villamor, 2010; Leung, Willet, & Ding, 2012). Food stamp participation may 95 reduce food insecurity rates (Yen, Andrews, Chen, & Eastwood, 2008) although the relationship is believed to be complicated. One study found food insecurity was associated with increased BMI among SNAP female recipients who received a low amount of benefits, thus suggesting participants at the eligibility margin are at greater risk of overweight/obese status (Jilcott et al., 2011). Controlling for socio-demographic characteristics, food stamp participation was found to be associated with increased BMI after controlling for food security, and food stamp participants who participated in the program for more than six months had lower BMI rates compared to those who participated for shorter periods (Webb, Schiff, Currivan, & Villamor, 2008). Findings on this association are mixed. A review of food insecurity and weight status studies found long-term SNAP participation may increase obesity risk among young children and adolescents (Larson & Story, 2011). Dinour, Bergen, and Yeh proposed a conceptual framework examining the role of food stamp program participation in the food security and obesity relationship (2007). The framework focused on the food stamp cycle and the role of psychological and behavioral coping strategies in response to food insecurity. Households or individuals may engage in coping mechanisms such as consuming meals or borrowing money from other people when presented with food insecurity (Olson, Rauschenbach, Frongillo, & Kendall, 1997). Although these strategies are not accounted for in this study’s empirical model, they are included in the conceptual framework since they are theoretically important risk factors for obesity. Based on existing literature reviewed, a revised conceptual framework was developed (see Figure 4.1). Individual, household, community, and policy level factors are included in the conceptual framework. Next, I will empirically test aspects of the revised conceptual framework using a sample representative of a large state population. 96 Figure 4.1. Conceptual Framework of Food Security and Weight Status among Adults Methods Study Population The study conducted a cross-sectional analysis using survey data collected from adults who participated in the 2009 California Health Interview Survey (CHIS). The CHIS is the largest state health survey in the country and began in 2001. In 2009, 47,614 adults participated in the survey; 13,790 of the respondents identified as low-income (defined as belonging to households with <200% of the Federal Poverty Level or FPL). The sub-set 97 database was used for this study given the focus on low-income adults (California Health Interview Survey, 2011a). The survey is a random digit-dial telephone survey and the sampling frame consists of all known California households with a known (landline) telephone number. The 2009 CHIS included robust samples of Latinos, Asians, American Indians, and Pacific Islanders. The variables used for this study came from the 2009 CHIS adult public use files. Primary Health Outcome: BMI The dependent variable is a continuous variable, the BMI score. Adults self-reported their weight and height. A variable named BMI was computed using the BMI formula provided by the CDC [BMI Score = Weight (lb.) / [Height (in)] 2 x 703]. Key Predictor Variable: Household Food Security Status The primary independent variable of interest was food security. In the CHIS 2009, only individuals with household incomes <200% of the FPL were asked questions about their food security status. The six-item Household Food Security Module - Short Form (see Appendix G) was used to measure food security and hunger status (Blumberg, Bialostosky, Hamilton, & Briefel, 1999). The limitation of using this short form (instead of the 18 item module) is that it does not consider the severity of the episode (Radimer, 2002). Three dummy variables were developed—Food Secure, Food Insecure without Hunger, and Food Insecure with Hunger. The category of food security (i.e. <200% FPL and food secure in the last year) was selected as the omitted reference variable. As stated above, 98 adults who identified as having household incomes at or greater than 200% of the FPL were excluded since they were not asked the food security items. Other Independent Variables Several socio-demographic and socio-economic control variables were included in the study. Gender was dichotomized (where 0=male and 1=female). Age was measured as four categories (18-34, 35-49, 50-64, and 65+ years of age where 18-34 was the referent category). The race/ethnicity question was coded as dummy variables with seven categories (White, Latino, African-American, Asian, American Indian or Alaska Native, Pacific- Islander or Native Hawaiian, Other); White was selected as the omitted reference variable. Level of education was measured as four categories (Less than a High School Diploma, High School Degree, Some College, College Degree or more) and coded as dummy variables where High School Degree was the reference variable. Income was dichotomized (where 0=100-199% and 1=0-99% FPL). Employment status was dichotomized where 0=not currently employed and 1=employed; employment included full- or part-time employment status. A married variable was dichotomized (1=married and 0=living with a partner, widowed, separated, divorced, never married). Household size (number of household members) was measured as a continuous variable. A single parent variable was also dichotomized where (1=single parent and 0=single without children, married without children, married with children). Government nutrition assistance program recipients consisted of two dummy variables—SNAP and WIC. An included health covariate was disability. To measure 99 disability, respondents were asked regarding specific physical, mental, and emotional conditions; those who answered in the affirmative to those conditions were identified as disabled (1=disabled and 0=not disabled). Missing variables (either refused, don’t know, or blank answers) were excluded from the analyses. Data Analysis Multivariate linear regression models were employed to evaluate the relationship between food insecurity and adult BMI. The dependent variable selected was adult BMI (a continuous variable). Given prior research results, the model was only run for females. A hierarchical multivariate linear regression was used to run six models which gradually added covariates and predictor variables to examine spuriousness and transmission effects.  Block 1 = Socio-demographic Variables  Block 2 = Socio-economic Variables  Block 3 = Health Variable  Block 4 = Household Variables  Block 5 = Government Nutrition Assistance Variables  Block 6 = Food Security Variables Complex survey weights provided by CHIS were used in the analyses to account for differences in sampling, non-response bias, and post-stratification (California Health Interview Survey, 2011b). All of the data management and statistical analyses conducted for this study were performed using SPSS software, version 22 (IBM Corp, 2013). 100 Results As stated above, the 2009 CHIS data was weighted for the purpose of estimating population-based descriptive statistics for low-income Californian adults. The average age was 42.5 years old (+/- 18.2 years) and the largest age cohort consisted of 18-34 year olds (39%). In terms of gender, 53% were female. The most prevalent racial/ethnic groups consisted of White (24.1%), Latino (55%), Asian (11.3%), and African-American (7%). Compared to the overall California population, Latinos and African-Americans were overrepresented in the low-income group. While Latinos were estimated to comprise more than half of the low-income population, they only represented 32.5% of the total adult population in the state. The average household income was slightly above $20,000 (+/- $12,286). In terms of socio-economic characteristics, 35.8% had less than a high school diploma and 31.5% had a high school degree; slightly under half (49.3%) were employed. Compared to the adult state population, low-income adults had a lower marriage rate (42.7% compared to 53.9%) and a higher percentage of single parent households (9.3% compared to 5.4%). The average household size was 3.3. In terms of government nutrition and food assistance participation, 13.4% self-reported participation in SNAP and more than half (54.5%) were WIC recipients. Over a third of low-income adults were disabled (34.6%). The average BMI was 27.4 kg/m 2 (+/- 6.4 kg/m 2 ). Among those who reported food security, 59.3% reported being food secure, 25.3% experienced food insecurity, and 15% experienced food insecurity with hunger in the past 12 months. See Table 4.1 for weighted variable distributions for selected characteristics. 101 Table 4.1. Weighted Variable Distributions for Selected Characteristics among Low- Income Adults (<200% FPL) in California, CHIS 2009 Mean (SD) or % Age (years) 42.5 (18.2) 18-34 (%) 39% 35-49 (%) 29.6% 50-64 (%) 16.9% 65 or more years (%) 14.5% Gender (%) Female 53% Male 47% Race/Ethnicity (%) White 24.1% Latino 55% African-American 7% Asian 11.3% Other 1.5% Income ($) $20,106 ($12,286) 0-99% FPL (%) 47% 100-199% FPL (%) 53% Education (%) Less Than HS Degree 35.8% HS Degree 31.5% Some College 21.5% College Degree or more 11.1% Employment (%) 49.3% Married (%) 42.7% Household Size (#) 3.3 (2) Single Parent (%) 9.3% Body Mass Index 27.4 (6.4) Disability (%) 34.6% Government Nutrition Assistance Program Recipients (%) SNAP Recipient 13.4% WIC Recipient 54.5% Food Security Status (%) Food Secure 59.3% Food Insecure 25.3% Food Insecure w/ Hunger 15% 102 Hierarchical Regression Models and Variance Table 4.2 shows the results from four models that were tested for low-income adult females. Associations are reported as statistically significant if p< .10, .05, .01, and .001 levels. In the first regression model, the R 2 was 0.062 suggesting the first model explained 6.2% of the variance in the BMI score among low-income female adults. The second model increased by 1.2% which is the largest increase. The second model indicates that 7.4% of the variance in BMI is explained by the inclusion of the second block to the model. There was a small increase in R 2 when food security variables were added in the final model which explains 9.3% of the variance. Overall, this is a small amount of variance explained by the model. Model six included all six blocks in the model. Low-income women who experienced food insecurity with hunger were found to be, on average, associated with an increased 1.23 kg/m 2 BMI (p<0.001) compared to those who were food secure when adjusting for age, race/ethnicity, income, education, employment, disability, marriage, household size, single parent status, and government nutrition assistance program participation. The food insecure were associated with a decreased 0.64 kg/m 2 BMI (p<0.001) compared to the food secure when adjusting for the same variables listed above. The following sections discuss these effects and elaborate on the changes observed across the models. 103 Table 4.2. Hierarchical Linear Regression Model Results Examining the Associations between Food Insecurity and BMI for Low-Income, Female Adults in California (2009) Model 1 β a (S.E.) Model 2 β a (S.E.) Model 3 β a (S.E.) Model 4 β a (S.E.) Model 5 β a (S.E.) Model 6 β a (S.E.) Age 35-49 0.45 (.01)*** 0.56 (.01)*** 0.55 (.01)*** 0.61 (.01)*** 0.74 (.01)*** 0.86 (.01)*** 50-64 1.59 (.04)*** 1.42 (.04)*** 1.21 (.04)*** 1.25 (.04)*** 1.54 (.04)*** 1.29 (.04)*** 65 or more years 17.16 (.08)*** 16.67 (.08)*** 16.69 (.08)*** 16.78 (.08)*** 17.16 (.08)*** 17.35 (.08)*** Race/Ethnicity Latino 1.21 (.02)*** 0.56 (.02)*** 0.75 (.02)*** 0.57 (.02)*** 0.37 (.02)*** 0.30 (.02)*** African-American 2.48 (.03)*** 2.03 (.03)*** 2.07 (.03)*** 2.28 (.03)*** 2.02 (.03)*** 1.99 (.03)*** Asian -3.85 (.03)*** -3.86 (.03)*** -3.68 (.03)*** -3.89 (.03)*** -3.79 (.03)*** -3.82 (.03)*** Other -2.12 (.05)*** -2.22 (.05)*** -2.15 (.05)*** -2.13 (.05)*** -2.37 (.05)*** -2.26 (.05)*** Income 0-99% FPL (%) 0.25 (.01)*** 0.31 (.01)*** 0.35 (.01)*** 0.22 (.01)*** 0.37 (.01)*** Education <HS Degree 0.40 (.01)*** 0.20 (.01)*** 0.17 (.01)*** 0.18 (.01)*** 0.18 (.01)*** Some College 0.20 (.02)*** 0.11 (.02)*** 0.12 (.02)*** 0.12 (.02)*** 0.04 (.02)* College Degree≤ -2.21 (.02)*** -2.09 (.02)*** -2.05 (.02)*** -1.98 (.02)*** -2.03 (.02)*** Employment -0.28 (.01)*** -0.17 (.01)*** -0.17 (.01)*** -0.10 (.01)*** -0.04 (.01)** Disability 1.44 (.01)*** 1.30 (.02)*** 1.36 (.02)*** 1.14 (.02)*** Married -0.68 (.02)*** -0.70 (.02)*** -0.47 (.02)*** Household Size 0.07 (.01)*** 0.04 (.01)*** 0.04 (.01)*** Single Parent -1.10 (.02)*** -1.05 (.02)*** -0.93 (.02)*** Government Nutrition Assistance Program Recipients SNAP Recipient -0.02 (.01) -0.08 (.01)*** WIC Recipient 0.74 (.01)*** 0.80 (.01)*** Food Security Status Food Insecure -0.64 (.01)*** Food Insecure w/ Hunger 1.23 (.02)*** R 2 0.062 0.074 0.081 0.084 0.086 0.093 a The unit is one BMI (kg/m 2 ). Reference groups consisted of 18-34 (age), White (race/ethnicity), 100-199% FPL (income), HS degree (education), and Food Secure. † P< .10 * P < .05 ** P < .01 ***P < .001 104 Socio-Demographic Variables From model one to six, the effect size associated with increased age generally decreased when socio-economic variables and the disability variable were added (statistically significant at the p<0.001) particularly among the middle aged groups compared to the referent 18-34 group. Of note, the elderly (65 years or more) were associated with a high increase in BMI score (16-17 kg/m 2 ) compared to the referent group, across the six models and the relationship was significant at the p<0.001. Moreover, being elderly (compared to the 18-34 referent group) had the greatest effect on BMI based on the standardized beta coefficients (i.e. in model six, the standardized beta coefficient for this variable was 0.172). In terms of race/ethnicity, key racial/ethnic minorities (African-Americans and Latinos) averaged higher BMIs compared to the referent group (White) while Asians had decreased BMI in the unadjusted and adjusted models. These associations were significant at the p<0.001 level. In model one, Latinos averaged an increased 1.21 kg/m 2 compared to Whites, controlling for age among low-income adult females. When socio-economic variables were added, the effect size of being Latino on BMI decreased to 0.56 kg/m 2 indicating that the effect of being Latino was partially spurious in the first model. In other words, socio- economic status variables (i.e. income, education, employment) were in part driving a higher BMI among this group in model one. The effect size further decreased to 0.37 kg/m 2 in model five when household variables (i.e. married, household size, and single parent status) were added suggesting partial spuriousness again. 105 The unstandardized coefficients exhibited a similar pattern for African-Americans but deviated in model four where BMI increased by 0.25 kg/m 2 compared to model two when household variables were added—suggesting partial spuriousness yet in a different direction compared to that of Latinos. Future models may study potential interaction effects between race/ethnicity and household roles to examine these relationships in more detail. Asians were the only group that exhibited a negative sign compared to Whites. On average, Asian female adults had a decreased BMI of 3.85 kg/m 2 compared to Whites in the first model, adjusting for age. The difference decreased to 3.68 kg/m 2 in the third model— suggesting disability status was partially suppressing the effect of Asian race/ethnicity on BMI. These relationships were statistically significant at the p<0.001 level. Socio-Economic Variables Income, education, and employment categories were added as socio-economic variables in models two through six. As expected, increased educational attainment was associated with decreased BMI score for additional degrees compared to those who received less than a high school education in the adjusted models (significant at the p<0.001 level). On average, those with less than a high school diploma had a higher BMI by 0.4 kg/m 2 compared to those with a high school diploma, adjusting for other socio-demographic and SES characteristics. It decreased to 0.2 kg/m 2 in the third model (and for other educational levels as well) when disability was included as a covariate—indicating education was masking part of the effect of disability. The associations were statistically significant at the p<0.001 level. Those below the 100% FPL threshold averaged an increased BMI score compared to those between 100%-200% FPL when controlling for age, race/ethnicity, education, and 106 employment and this effect size changed over the course of the model. The coefficient estimates for income will not be discussed in detail, since they may be more susceptible to small changes in the data given likely multicollinearity between independent variables. In the second model, employed respondents were estimated to have a decreased BMI by 0.28 kg/m 2 compared to the unemployed, adjusting for other SES and socio-demographic variables. However, this effect decreased in models three, four, and five. A plausible explanation is that disability, government nutrition program participation, and food security status suppressed the effect of employment on BMI. The relationship between employment and BMI was statistically significant at the p<0.001 level in the first five models and significant at the p<0.01 level in the sixth model. Health Status Variables In the third model, on average, those who reported disability had an estimated increased BMI of 1.44 kg/m 2 compared to the able bodied, adjusting for SES and socio- demographic variables. Models four and five suggest this association was partially spurious in model three and masked some of the effect of food insecurity. The association was highly statistically significant at the p<0.001 level. Government Nutrition and Food Assistance Programs Model five added SNAP and WIC participation variables. Of all of the models, the association between SNAP recipients and increased BMI was the only one that was not significant at any of the p-value thresholds. The low participation rate in the sample may be an issue and some respondents may have not reported their SNAP participation due to stigma 107 or other perceived barriers. Being a WIC recipient was associated with, on average, an increased 0.74 kg/m 2 and 0.80 kg/m 2 in models five and six, respectively, when adjusting for other variables. The associations were highly statistically significant at the p<0.001 level. Food Security Variables In the final model, the primary independent variables of interest—food security status—were included. On average, low-income female adults who experienced food insecurity were estimated to have decreased BMIs by 0.64 kg/m 2 compared to those who were food secure, adjusting for socio-demographic, SES, disability, household variables, and government nutrition assistance participation variables. Those who experienced food insecurity and hunger in the past year were estimated to have higher BMIs by 1.23 kg/m 2 compared to the food secure, adjusting for other variables. Compared to the referent group of the food secure, the associations were all statistically significant (p<0.001). A stepwise regression model was also conducted to assess changes in the magnitude of the association between food insecurity with hunger and BMI. In the stepwise model, the key predictor variables, food insecurity, were entered first. Among low-income females in California, food insecurity with hunger was associated with an increased BMI of 2.02 kg/m 2 compared to the food secure (data not shown). The magnitude of this relationship was weakened by the addition of socio-economic variables (i.e. controlling for age, race/ethnicity, income, education, and working status) and decreased to 1.78 kg/m 2 . It was further attenuated when disability was added and decreased to 1.4 kg/m 2 . All of these associations were statistically significant at the p<0.001. 108 Discussion The study contributes to the literature by increasing our theoretical understanding of the factors associated with higher BMI among low-income adult females in California. Leung et al. previously empirically examined the food insecurity-obesity relationship with several waves of the CHIS, including the 2009 dataset (2012). However, their study lacked a conceptual framework as a foundation for their modelling and did not closely consider changes between models. The findings are similar to existing evidence that low food insecurity is associated with BMI among low-income adult females (Leung et al., 2012) and food insecure women with hunger are at increased risk of being overweight or obese compared to the food secure (Adams, Grummer-Strawn, & Chavez, 2003). The occurrence of hunger seems to play a unique role given the increased BMI associated with food insecurity and hunger compared to the food secure in model six. As stated earlier, the presence of hunger may be invoking psychological or alternative coping strategies that are risk factors for increased weight among low-income women. Unlike several studies which used logistic regression to examine the food insecurity- weight status relationship, this study used a continuous dependent variable (BMI). The magnitude of the difference between those who were food insecure with hunger and those who were food secure is a meaningful difference in the real world where 1.23 kg/m 2 translates into at least five pounds. Consider a hypothetical adult female who is 5 feet 6 inches and weighs 150 pounds. Her BMI would be in the normal weight category at 24.2 kg/m 2 . Increasing her BMI by 1.23 kg/m 2 would translate into a difference of 7.5 pounds and put her in the overweight category. 109 This difference is significant since increased BMI (even in modest amounts) is associated with greater health risks. An additional weight gain of one BMI unit has been found to be associated with a 25% increased risk of diabetes for women (Colditz, Willett, Rotnitzky, & Manson, 1995). Moreover, weight gain of 11 to 18 pounds for women has been found to be associated with a 25% increased risk of coronary heart disease even among women in the normal BMI range (Willett et al., 1995). Thus, even a modest difference in BMI between two groups constitutes a substantial health risk for vulnerable populations. This study found those who were food insecure (without hunger) had a negative association with BMI compared to the food secure. Although the magnitude of this association was small (0.64 kg/m 2 ), this relationship should be explored further in future studies since food insecure women may be foregoing food when facing household food shortages. This relationship may differ according to parenting or socio-cultural factors. Previous studies have excluded the elderly from their adult samples. Given the high effect size on BMI, using a separate conceptual model adapted for this group may be recommended since they are more susceptible to increased risk of disability, limited mobility, and higher rates of chronic health issues (Wolfe et al., 1996). The elderly may experience different health outcomes from food insecurity and food insecurity coupled with hunger compared to other groups. The associations should be more carefully examined for this population in the future. Limitations There are several limitations to this study. First, a disadvantage from using BMI as a measure for obesity is that fat mass is not differentiated from lean mass. Previous studies 110 have advocated for the use of other measures such as weight circumference (Brewer et al., 2010). There is also a well-known underestimation issue associated with self-reported weight data from females (Rowland, 1990). This study is limited by the cross-sectional study design. A longitudinal or prospective study would be preferable to explore causal relationships (Ivers & Cullen, 2011; Larson & Story, 2011; Frongillo & Bernal, 2014) and to test proposed mechanisms in the conceptual framework (Frongillo, 2003; Rutten et al., 2010; Nord, 2014). Using an instrumental variables or random-effects model would be recommended to address the issue of self-selection if including government assistance programs as covariates (Larson & Story, 2011). Future studies should examine the duration of food insecurity and weight status. The frequency (i.e. acute or prolonged duration) and severity of episodes likely has an effect on an individual’s dietary consumption habits and physical activity therefore a longitudinal database would be more appropriate (Campbell, 1991). Moreover, the length of participation in welfare programs such as SNAP would also likely contribute to the presence of food security (Larson & Story, 2011). Another limitation is a potential for omitted variable bias. Time-varying factors are a likely omitted variable and should be controlled for in future studies (Wilde & Nord, 2005). A few variables included in the conceptual framework were not included in the empirical model since they were not available in the CHIS dataset, such as aspects of the nutrition resource environment or even the availability of local food resources, such as food pantries, which can play a role in emergency food shortages for households (Wolfe et al., 1996). Food insecure individuals who experience hunger and reside in neighborhoods with a higher 111 availability of unhealthy, inexpensive food for longer periods of time may be more susceptible to weight gain compared to those who reside in areas with healthier options. Lastly, the difference between impoverished food secure and food insecure households is not well understood and may be in part explained by specific financial measures, such as liquidity constraint (Chang, Chatterjee, & Kim, 2014), or housing-related factors such as homeownership or housing affordability (De Marco & Thorburn, 2008). Research and Policy Implications The increased understanding of the factors associated with higher BMI in the state of California is relevant to state policymakers and public health professionals because there is a rationale for developing programs and interventions to reduce obesity and food insecurity coupled with hunger for low-income adult females. Broadly speaking, poverty reducing programs and policies can help to ameliorate the prevalence and incidence of food insecurity. Future social welfare programs should target populations with greater rates of food insecurity and hunger. Culturally sensitive and appropriate interventions should be explored for certain groups (e.g. food insecure African- Americans). Health professionals should consider gathering data about patients’ food security status to develop more effective tailored interventions. Nutrition education programs can help to address the obesity-food insecurity issue among females, particularly for those enrolled in WIC (Ivers & Cullen, 2011). Based on this study, low-income populations facing higher rates of food insecurity and hunger are a more nuanced key target population for anti-obesity interventions. The implications of a relationship between food insecurity and hunger with increased BMI 112 provides support for the notion that individuals respond to hunger by consuming readily available, inexpensive higher energy dense food in the U.S. If increased BMI is associated with hunger, then policy responses to prevent or address obesity among populations below the 200% FPL may include nutrient supplementation and increasing the availability of relatively inexpensive healthy food. Local programs encouraging farmer’s markets in low- income neighborhoods or subsidies for healthy items (such as fruits and vegetables) may be appropriate interventions. In terms of future areas of study, conducting food security assessments could identify areas at high risk of food insecurity at the community level (Cohen, Andrews, & Kantor, 2002; Hamm & Bellows, 2003) and is an opportunity for a wide variety of actors to improve their local food environment (Pothukuchi, 2004). Focusing on community food and nutrition resources could shift the response from individual health promotion programs to place-based solutions. Adopting a food systems approach (Rutten, Yaroch, & Story, 2011) that considers the ecological layers influencing food availability could decrease food access and obesity disparities (Rutten, Yaroch, Patrick, & Story, 2012). Moreover, increasing community food security programs for low-income elderly residents may help to reduce the challenge of food insecurity in vulnerable communities. Community food security programs include outreach efforts to increase participation in government nutrition and food assistance programs, and the promotion of non-traditional food resources such as farmer’s markets and community gardens (Kantor, 2001). The results from this study suggest the relationship between weight status and food security is not a paradox, contrary to what previous studies have called it (Dinour et al., 2007; Townsend et al., 2001). The rise in the obesity epidemic has resulted from the 113 increased availability of inexpensively produced food from technological advances in food production (Cutler, Glaeser, & Shapiro, 2003). The relationship between food insecurity and weight is a nuanced one, however, it is not a paradox given the abundance of food in America. As Campbell stated, “in the United States, a person can be obese and undernourished in terms of micronutrients at the same time” (1991, p. 414). We would be remiss to study the obesity epidemic and public policy programs, such as WIC and SNAP, without also examining the role of food security and hunger in low-income communities. The proposed conceptual model can serve future studies that wish to further explore and test this relationship, particularly for priority populations with high obesity prevalence rates. 114 CHAPTER FIVE. CONCLUSION The three essays in this dissertation highlight the importance of studying the multiple levels of the socio-ecological framework to address California’s obesity epidemic. Overall, the dissertation demonstrates the value of examining health behavior within specific settings and environmental contexts. Although many policies related to behavioral modification are indeed contingent on individual action, there is widespread consensus that an ecological framework which considers multiple levels of behavioral influence is more reflective of the real world. The ecological framework purports that both the individual and the sociocultural environment contribute to decisions that affect health behaviors and outcomes (Lancaster & Bermudez, 2011). Interventions that address different factors of an ecological framework—rather than only focusing on individual level factors—are more likely to be effective in the real world and reduce the “victim-blaming ideology” (McLeroy, Bibeau, Steckler, & Glanz, 1988). This dissertation provides several recommendations appropriate for low-income, minority populations. Moreover, this body of work identifies various areas of nutrition policy and behavior research that warrant further investigation. Individuals responsible for developing health promotion programs and policy responses to the obesity epidemic must acknowledge that vulnerable at-risk groups may utilize resources differently or have varying levels of awareness/knowledge compared to other communities. Evidence-based programs or interventions may need to be modified for different groups or neighborhoods to increase their relevance and potential use. For instance, while a farmer’s market may be a source of healthy food and an acceptable social norm for residents in Venice, California, residents from a low-income community in Los Angeles may 115 lack awareness and knowledge about the resource or perceive the available products as not being accessible or culturally relevant, and thus underutilize the resource. Healthy eating practitioners, researchers, and policymakers should aspire to develop place-based and culturally-appropriate interventions and policies to improve dietary behavior among at-risk groups. Lessons Learned Chapter two is distinct from the other studies given the focus on the policymaking process and the behavior of policy elites. The study uses the ACF to examine the role of political and non-political factors that influence healthy policymaking in the 21 st century. The limited use of scientific studies in the state policymaking process is an important finding given the vast increase in policies and funding to address the obesity epidemic. While academics tend to focus on academic journals for dissemination, the case study illustrates the important contribution of government reports in the policy process. Researchers interested in disseminating their findings to policymakers should focus on translating their key findings in accessible and usable products such as policy briefs and agency reports. During a policy window of opportunity or the standard policymaking process, these are the types of products that are relied upon for technical knowledge and information by policymakers. Furthermore, tying specific policies or programs to previous ones can also help to increase the relevance and usability of a policy recommendation given the important role policy precedents play in the legislative process. The second and third studies use health services research methods to examine the factors contributing to the obesity epidemic among low-income populations. Poverty plays an 116 important role in exacerbating disparities in weight and dietary quality between racial/ethnic groups (Wang & Chen, 2011). Chapter three considers how living in a low-income community can impede adolescents’ health behaviors whereas chapter four carefully examines how being impoverished can impact nutrition behavior among low-income women. Chapter three demonstrates the importance of collecting qualitative data to identify and explore target populations’ barriers and facilitators to nutrition and physical activity resources in a localized geography. Focus groups with high school adolescents from a highly impoverished urban community revealed valuable insights. Students exhibited a remarkably high level of awareness of on- and off-campus nutrition and physical activity resources. The role of competing (unhealthy) food resources in the community and lack of safety in the social environment are important impediments to consider when designing school- and community-based health promotion programs for adolescents. Participants also exhibited a high level of awareness of school-based health centers. The findings suggest the school can be leveraged as a place for health promotion activities for adolescents in underserved communities. Success depends on recognizing barriers in the institutional, built, and social environments. Low-cost recommendations to increase access to school lunches and utilization of health care services, after-school programs, and community gardens are provided. Chapter four utilizes a population-based sample of adults to examine the relationship between food insecurity and obesity among adults in California. While much has been written on the food security-obesity “paradox” in the U.S., a conceptual model based on the literature is necessary to empirically test mechanisms and related factors influencing dietary health behaviors. The difference between an individual with food insecurity and an 117 individual with food insecurity and hunger are theoretically important when thinking about consumption patterns, and the latter appear to be at greater risk for obesity. Moreover, the results call attention to the plight of the disabled and the elderly who exhibit higher BMIs and limited mobility. Limited mobility may not only restrict daily living activities, but also restricts these groups’ access to healthy food. The overall findings support the notion that individuals respond to hunger by consuming accessible, inexpensive, higher calorie food. Policy responses to address obesity among low-income populations may include nutrient supplementation or increasing access to relatively inexpensive healthy food. In terms of community and policy levels, local programs encouraging farmer’s markets in low-income neighborhoods or subsidies for healthy items may be appropriate interventions. There is ample opportunity to build on the findings of the three studies included in this dissertation. Using the appropriate nutrition resource assessment measures and validating existing instruments are important future areas of study. The National Collaboration on Childhood Obesity Research developed a registry of nutrition and physical activity measures (http://tools.nccor.org/measures/) to collect data on individual behavior and the environment. 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Comparison of Bills Introduced in California (2003 - 2008) Bill Name SB 679 SB 1171 SB 120 SB 180 AB 2572 SB 1420 Year 2003 2004 2007 2007 2008 2008 Initial Sponsor(s) (Political Party) Senator Deborah Ortiz (D) Senator Ortiz (D) Senator Alex Padilla (D) Senator Carole Migden (D) Assembly Member Nicole Parra (D) Senators Padilla (D) & Migden (D) Bill Summary (initial version) Would require fast food restaurants chains (≥10 locations in the state) to provide nutrition information upon request (i.e. flyer or pamphlet) and to have sign stating information is available upon request Would require restaurant chains (≥10 locations in the state) to post calorie information on menu boards and to provide other nutrition information (# of calories, grams of saturated plus trans- fat, sodium milligrams per serving) in printed menus Would require chain food facilities (refer to amended SB 679 definition) to provide nutritional information (listed in SB 1171 and carbohydrates) for menu items. If only a menu board were available, calorie information would be provided on the board and additional information upon request. Would require menus to state “Recommended limits for a 2,000 calorie daily diet are 20 grams of saturated fat and 2,300 milligrams of sodium. Saturated fat numbers include trans-fat” Would require chain food facilities (i.e. defined as having either 5 facilities in the state or one facility in the state and 10 in other states) to provide nutritional information upon request Would require chains to post or to provide calorie information for each item on a menu or menu board. Would require “covered” food facilities (defined as operating “under the same trade name with ≥20 food facilities in the state, regardless of ownership”) to provide nutrition information for standard food items via at least one of the following formats: menu, standard food item packaging, counter/table tent, tray liner, poster, brochure, or electronic kiosk, among others. If information was not in menu, would require sign stating “nutrition information is available upon request. Would require food facilities with ≥15 locations to provide nutritional information (# of calories, grams of saturated fat, grams of trans fat, sodium milligrams) for each standard menu item on menus and to provide calorie information on menu boards Would require menus to state “Recommended limits for a 2,000 calorie daily diet are 20 grams of saturated fat and 2,300 milligrams of sodium.” Initial Senate or Assembly Committee Senate Committee on Health & Human Services Senate Committee on Health & Human Services Senate Committee on Health Senate Committee on Health Assembly Committee on Health Senate Committee on Health Summary of Key Amendmen ts Fast food restaurant changed to large restaurant chains (≥10 franchises or restaurants in -- Food facility criteria modified to include franchises and facilities (≥15 locations in the state). Available nutritional information would include calories, calories from fat, Nutrition information would be posted on a menu or brochure at the point of sale (defined as a Would require food facilities (≥20 locations) to: a) provide nutritional information (# of calories, # of carbohydrates, 138 the state). Would require menu boards to include the total # of calories per item. Defined standard menu items as items on a menu for ≥6 months. A menu page should include the following statement: “Recommended limits for a 2,000 calorie daily diet are 20 grams of saturated fat and 2,300 milligrams of sodium.” total fat, saturated fat, trans fat, cholesterol, sodium, carbohydrate s, dietary fiber, sugars, protein, vitamin A, vitamin C, calcium, and iron “location where a customer makes an order”) grams of saturated fat, sodium milligrams) in a brochure at the point of sale (applicable to those without sit- down service), OR b) provide nutrition information using one of several methods (facilities with sit-down service), OR c) provide calorie information in a menu (applicable to facilities with menus). Would require facilities with a drive-through and menu board to let consumers know information was available upon request (via a brochure). By January 1, 2011, the food facilities defined above would be required to post calorie information on menu boards and menus. Assembly Floor Vote (Ayes - Noes - Abstain/Ab sent) -- -- 43 - 32 – 4 -- 46 - 15 - 19 46 - 28 – 7 Senate Floor Vote (Ayes - Noes - Abstain/Ab sent) 21 - 15 - 0 -- First Vote: 22 - 17 - 1 Second Vote: 21 - 18 - 1 -- -- First Vote: 21 - 17 - 2 Second Vote: 24 - 13 - 3 Outcome Failed in Assembly Health Committee No further action from Senate Health & Human Services Committee Vetoed Amended to focus on labor issues No further action from Senate Appropriations Committee Signed into law 139 Appendix B. High School Student Focus Group Questionnaire CDC REACH Demonstration Project: High School Student Focus Group Questionnaire Below are some questions about food, exercise, and health care. Please answer honestly. This questionnaire is anonymous. That means you should not write your name. After you finish, we are going to tally the answers and will be using the answers as a whole, not as individual answers. Your answers will remain confidential. Thank you! 1. How easy or difficult is it for you to get fresh fruits and vegetables in your community?  Very Difficult  Somewhat Difficult  Somewhat Easy  Very Easy 2. During the past 24 hours, how many times did you… 0 times 1 time 2 times 3 times 4 times 5+ times a. eat fruit, like an apple, banana, or orange? (Do not count fruit juice.)       b. eat vegetables, like a salad or non-fried potatoes?       c. drink 100% fruit juices, like orange, apple, or grape? (Do not count punch, Kool-Aid, sports drinks, or fruit flavored drinks.)       d. drink soda, like Coke or Pepsi?       e. drink a bottle or glass of plain water, like tap, bottled or unflavored sparkling water?       3. I don’t eat more fruits, vegetables, or drink more 100% juice because… Check all that apply.  This doesn’t apply to me, I eat enough  It’s too expensive  It’s not important  I don’t like the taste  Poor quality  I don’t think about it  It’s not available  I like other foods better  Other:________________ 4. During the past 7 days, how many days did you… 0 days 1 day 2 days 3 days 4 days 5 days 6-7 days a. eat a home cooked dinner?        b. eat fast food or take-out food?        5. When I eat lunch at school, I usually get my food from…  The school cafeteria (a complete lunch or items sold separately)  Food sold off-campus  Fast food sold on campus (like McDonalds, Taco Bell, or KFC)  I bring lunch from home  Food from a school vending machine or snack cart  I don’t usually eat lunch 6. Do you qualify for the free or reduced school lunch program?  Yes  No  Don’t Know 7. Are there places or programs available for you to exercise or be physically active in your community?  Yes  No  Don’t Know 8. During the past 7 days, how many days did you… 0 days 1 day 2 days 3 days 4 days 5 days 6 days 7 days 140 a. exercise or do a physical activity for at least 60 minutes a day?         b. exercise or do a physical activity for at least 20 minutes that made you sweat and breathe hard? (Like basketball, soccer, running, swimming laps, fast bicycling, fast dancing, or similar aerobic activities.)         c. do exercises to strengthen or tone your muscles? (Like push–ups, sit–ups, or weight lifting.)         d. walk or ride your bike in your neighborhood?         9. On an average day, how much time do you spend… None Less than 1 hour 2 hours 3 hours 4+ hours a. watching TV, DVDs, or videos?      b. playing video games or using a computer for something that is not school work (like Xbox, PlayStation, iPad/tablet, smartphone, YouTube, Facebook or the Internet)?      10. During the past 12 months, how many different sports teams OR after-school programs to improve nutrition or physical activity have you participated in at school or in your community?  0 programs/teams  1 program/team  2 programs/teams  3 or more 11. How easy or difficult is it for someone to be physically active or exercise in your community?  Very Difficult  Somewhat Difficult  Somewhat Easy  Very Easy 12. I don’t get more exercise or physical activity because… Check all that apply.  I get enough  I’m physically unable  I don’t have access to safe places  I have too much homework  The weather is too hot  I don’t have transportation  I don’t like to sweat  I get lazy  I don’t have playgrounds, parks, or gyms nearby  It’s uncomfortable  Not enough time  Other:__________________________ 13. How safe is it to walk or to use the parks, playgrounds, and sports fields in your community?  Very Safe  Somewhat Safe  Somewhat Unsafe  Very Unsafe 14. How do you describe your weight?  Very  Slightly  About the  Slightly  Very Underweight Underweight Right Weight Overweight Overweight 15. I am currently trying to…  Lose weight  Gain Weight  Stay the same  None of these 16. At school, where do you usually go for help when you are sick, need medical care or health advice? Mark only one answer.  School-Based Wellness Center  Teacher  Other:________________  School Nurse  P.E. Teacher/Coach  None  Counselor  Friend(s) 17. Is there a School-Based Wellness Center/Health Center on campus?  Yes  No  Don’t Know 141 18. Have you ever seen a doctor, nurse, or health professional at a School-Based Wellness Center?  Yes  No  go to Question 19 19. During the past 12 months, did a doctor or nurse ever say you were overweight or obese?  Yes  No Please tell us more about yourself. These answers will not be shared with other people. 20. I am ________ years old 21. I am in ________ grade 22. I am a…  Male  Female 23. Do you have your own smartphone (a cellphone with Internet access, such as the Apple iPhone, Android phone, or Windows phone)?  Yes  No 24. Which category best describes your race/ethnicity?  Black or African American  Hispanic or Latino  White  Asian  Native Hawaiian or Pacific Islander  American Indian or Alaska Native Other:_________________________ 25. What language(s) do you usually speak at home?  Only English  Both English and Spanish  Only Spanish  Mostly English  Mostly Spanish  Other:_____________ 26. How tall are you without your shoes on? _______ Feet _______ Inches 27. How much do you weigh without your shoes on? _______ Pounds 28. What is your home zip code? ____ ____ ____ ____ ___ a. When you went to the Wellness Center, did the doctor or nurse… Yes No Don’t Know 1. Weigh you?    2. Take your blood pressure?    3. talk to you about physical activity or healthy eating?    4. tell you about a nutrition or physical activity program?    142 Appendix C. High School Student Focus Group Script CDC REACH Demonstration Project: High School Student Focus Group Script Focus Group Leader/Location: _______________________________ Date: ____________ Hi, my name is XXX and I am a staff/faculty member/graduate student at USC. Today we are going to ask you questions about what and where you eat, where you exercise, how you get health care services, and what you think about your community. The purpose of this focus group is to help us understand how community leaders (including you!) might improve your community. The entire focus group session should take about 60 to 90 minutes. We appreciate your honest responses. This session will be audio recorded. If you do not want to be recorded, you cannot participate in the focus group. If we ask a question that you do not want to answer, please do not answer. We will be taking the responses from this group, and those from groups at other schools and in the community, and writing a report to our funder, Community Health Councils. We will keep your answers confidential. Any questions? (1) How would you describe a healthy meal? (List all responses) b) What kinds of healthy meals are available in your community? 2) In the questionnaire, we asked about eating lunch at your school cafeteria during the school year. a) What do you like about your school’s cafeteria? [Probe: It could mean the food that your friends eat there, etc.] b) What are things that you do not like about your school’s cafeteria? For those who said they do not eat there, why don’t you eat lunch at the cafeteria? [Prompt: This could include the food, the cafeteria lines or the amount of time you have to eat lunch] c) What are ways to make your school’s cafeteria better? d) Have you heard messages in the media about school-based meal programs or changes to the program? i) Through which media? (such as TV, radio, newspaper, social media) ii) What did you hear? 3) What kinds of food do you buy outside of your school’s campus? a) Do you prefer to eat food outside of campus rather than the school cafeteria? i) If yes, why? Is the food better? b) Do you sometimes buy food at a store on the way home from a cart, corner store or fast food restaurant? i) What carts, stores or restaurants do you go to? 143 c) How many corner stores or fast food restaurants do you pass on the way to and from school? 4) Where does your family usually buy food? [Prompt: A grocery store, Walmart, or elsewhere] a) Does your family usually buy food near your house or do they go outside of the community? b) Do you or your family ever go to a Farmer’s Market or somewhere that is not a grocery store to buy food in your neighborhood? 5) What kinds of fruits and vegetables does your family usually buy? a) Do you like these fruits and vegetables? Do you eat them when they are available at home? i) Why or why not? 6) Does your school have any activities or programs to get healthy food outside of school hours, like a farmer’s market, healthy cooking or eating class, etc.? a) Do you participate in this program? b) How did you hear about it? [Q7 FOR FREMONT and Crenshaw HS ONLY, otherwise skip to Q8] 7) Have you heard of your school’s community garden? a) Have you used your school’s community garden? i) What types of things have you done there? b) What are ways to encourage or to get more students to use the community garden? Now we want to ask you about programs available in your community to be physically active. 8) Do you know of any on-campus activities or programs to exercise offered before or after school? a) Do you participate in this program? b) How did you hear about it? c) Do you ever do any exercise at your school before or after school or during the weekend? [Probe: like a sports program, running on the track]? i) Why or why not? 9) Are there places and programs available for you to exercise outside of your school? a) If yes, what kinds of places and programs are available? b) How easy is it for someone to be physically active or exercise in your community? Would you say very easy, somewhat easy, somewhat difficult, or very difficult? c) Is it safe for you to exercise during the day at all the parks and other places in your neighborhood? In the evening? i) If not, why not? d) Has the availability of places or programs to exercise in your community increased, decreased or stayed the same since a year ago? 144 10) During the school, do any of you walk or ride your bike to school? a) For those who walk or ride their bike to school, do you like to walk/bike to school? i) Why or why not? b) For those who don’t, why don’t you bike/walk to school? c) Is it safe for you to walk or bike on your way to and from school? i) Have you ever had an accident? 11) Where have you heard information about physical activity or healthy eating? [Probe: Like at a park or at school] 12) What have you heard about your school’s wellness center? [Probe: Have you ever heard of a wellness prescription? What have you heard?] 13) For those of you who have you been to a school based wellness center, what was it like? a) Have you learned about any physical activity or nutrition programs through the School-Based Wellness Center? b) Have you participated in any physical activity or nutrition programs you were referred to through the School-Based Wellness Center? If yes, which programs? 14) Have you heard about Healthy Kid Zones? Do you recognize this picture [show HKZ logo]? a) If yes, how did you hear about HKZ? b) Have you heard messages in local media about HKZs? i) Through which media? (such as TV, radio, newspaper, social media) ii) What did you hear? 15) Have you heard anything about the REACH Demonstration Project (Partners in Health) – a collaboration between the Community Health Councils, Los Angeles County Department of Public Health, and Los Angeles Unified School District to transform the health of two communities? a) If yes, how did you hear about it? What did you hear? That concludes our discussion. Thank you very much for your time and your participation. 145 Appendix D. Informed Consent for Non-Medical Research (Parental Permission - English) University of Southern California Sol Price School of Public Policy 650 Childs Way, Los Angeles, CA 90089 INFORMED CONSENT FOR NON-MEDICAL RESEARCH PARENTAL PERMISSION REACH DEMONSTRATION OBESITY AND HYPERTENSION PROJECT Parental Consent Form – Focus Group Your child is invited to participate in a research study conducted by Dr. David Sloane and Dr. LaVonna Lewis, from the University of Southern California. This study is funded by the Centers for Disease Control and Prevention (CDC). Your child’s participation is voluntary. You should read the information below, and ask questions about anything you do not understand before deciding whether to allow your child to participate. Please take as much time as you need to read the consent form. Your child will also be asked his/her permission and given a form to read, which is called an assent form. Your child can decline to participate, even if you agree to allow him/her. Your child may decide to discuss it with your family or friends. If you give us permission to talk to your child about this study, please sign this form; your child be asked to sign the assent form. You will be given a copy of this form. PURPOSE OF THE STUDY The purpose of this study is to help us understand what students think about the healthiness of their community, and if they think there is anything policymakers and community leaders can do to help you and your family live a better, healthier life. STUDY PROCEDURES If you agree to allow your child to participate, your child will be asked to participate in a focus group. The focus group will contain roughly 10-15 students and will take about 60-90 minutes. The focus group will be audio recorded. If you do not want your child to be audio- recorded, s/he cannot participate in the focus group. The focus groups will take place in the afternoon, evening or on the weekend at local community sites, such as Washington Prep, Fremont and Crenshaw High School, local libraries, community clinics or faith based organization, etc. In the interviews, we will ask your child to discuss where they get their food, the kind of physical activity in which they participate, whether they eat out or at home, and where they get their health care. If you agree to allow your child to participate, your child is free to leave at any time. Your child does not have to answer any question he/she does not want to answer. POTENTIAL RISKS AND DISCOMFORTS There are no potential risks from participating in this study. The focus group will be audio- recorded and only the research team will have access to the audio files. If we ask any question your child does not want to answer, your child is encouraged not to answer. 146 POTENTIAL BENEFITS TO PARTICIPANTS AND/OR TO SOCIETY Information gathered throughout this study has the potential to help policymakers and community leaders develop programs and policies that help you and your family members live a better, healthier life. Your child may not directly benefit from his/her participation. PAYMENT/COMPENSATION FOR PARTICIPATION Your child will receive a $20 gift card for their time; they will receive the card at the end of their participation. CONFIDENTIALITY Any identifiable information obtained in connection with this study will be disclosed only with your permission or as required by law. The members of the research team, the funding agency and the University of Southern California’s Human Subjects Protection Program (HSPP) may access the data. The HSPP reviews and monitors research studies to protect the rights and welfare of research subjects. Due to the nature of the focus group, confidentiality cannot be guaranteed; however, your child is asked not to discuss the focus group with anyone not in the focus group. Neither you nor anyone in the school will have access to your child’s responses. Your child’s responses to the questions will be kept at USC in a hard drive that is password protected to ensure that they are kept confidential. The audio recorded files will be kept in a locked file cabinet USC. The study’s investigators and researchers from USC will be the only ones to access the data. We are not going to record names with the responses so that we ensure no one will be able to connect the two together. The data and recordings will be kept indefinitely for research purposes. We will not disclose any information that can be identified with your child, nor connect your child’s name to any information we present. We will be taking the responses from this group, and those from other students, and write a report for the CDC and the community. When the results of the research are published or discussed in conferences, no identifiable information will be included. PARTICIPATION AND WITHDRAWAL Your child’s participation is voluntary. Your child’s refusal to participate will involve no penalty or loss of benefits to which you, your child, or your child’s school are otherwise entitled. You may withdraw your consent, and your child may withdraw his/her assent, at any time and discontinue participation without penalty. You, or your child, are not waiving any legal claims, rights or remedies because of your child’s participation in this research study. INVESTIGATORS CONTACT INFORMATION 147 If you have any questions or concerns about the research, please contact David Sloane, 313 Lewis Hall, University of Southern California, 213-740-5768 or LaVonna Lewis, 309 Lewis Hall, University of Southern California, 213-740-4280. RIGHTS OF RESEARCH PARTICIPANT – IRB CONTACT INFORMATION If you have questions, concerns, or complaints about your rights as a research participant you may contact the IRB directly at the information provided below. If you have questions, concerns, complaints about the research and are unable to contact the research team, or if you want to talk to someone independent of the research team, please contact the University Park IRB (UPIRB), Office of the Vice Provost for Research Advancement, Credit Union Building, 3720 South Flower Street, CUB # 301 Los Angeles, CA 90089-0702, (213) 821-5272 or upirb@usc.edu. SIGNATURE OF PARENT(S) I have read the information provided above. I have been given a chance to ask questions. My questions have been answered to my satisfaction, and I agree to have my child(ren) participate in this study. I have been given a copy of this form. Name of Participant (child) Name of Parent Signature of Parent Date SIGNATURE OF INVESTIGATOR I have explained the research to the participant and his/her parent(s), and answered all of their questions. I believe that the parent(s) understand the information described in this document and freely consents to participate. Name of Person Obtaining Consent Signature of Person Obtaining Consent Date 148 Appendix E. Informed Consent for Non-Medical Research (Parental Permission - Spanish) University of Southern California Sol Price School of Public Policy 650 Childs Way, Los Angeles, CA 90089 FORMULARIO DE CONSENTIMIENTO INFORMADO PARA UN ESTUDIO DE INVESTIGACIÓN PERMISO DE LOS PADRES REACH DEMONSTRATION OBESITY AND HYPERTENSION PROJECT Formulario de Consentimiento de los Padres– Grupo de Discusión Estamos invitando a su hijo(a) para participar en un proyecto de investigación dirigido por el Dr. David Sloane y la Dra. LaVonna Lewis de la Universidad del Sur de California (USC, por sus siglas en inglés). Este estudio está patrocinado por los Centros para el Control y la Prevención de Enfermedades (CDC, por sus siglas en inglés). La participación de su hijo(a) es voluntario. Usted debe leer la siguiente información y hacer preguntas sobre cualquier cosa que usted no entiende antes de decidir si quiere permitir que su hijo(a) participe. Por favor tómese todo el tiempo que necesita para leer el formulario de consentimiento. También le vamos a preguntar a su hijo(a) para el permiso de el/ella y le vamos a dar una forma, que se llama una forma de asentimiento, para leer. Su hijo(a) puede negar participar aunque usted le dé permiso para participar. Su hijo(a) también puede decidir que quiere discutirlo con su familia o amistades. Si su hijo(a) decide participar, le pediremos que firme esta forma y le pediremos a su hijo(a) que firme la forma de asentimiento. Le daremos una copia de esta forma. PROPÓSITO DEL ESTUDIO El propósito de este estudio es para ayudarnos a entender lo que los estudiantes piensan sobre la salubridad de su comunidad y si ellos piensan que hay algo que los políticos y líderes de la comunidad pueden hacer para ayudar a los estudiantes y a sus familiares vivir una vida mejor y más saludable. PROCEDIMIENTOS DEL ESTUDIO Si usted está de acuerdo, le vamos a invitar a su hijo(a) que participe en un grupo de discusión. El grupo de discusión va tener alrededor de 10 a 15 estudiantes y tomará entre 60 a 90 minutos. Audio del grupo de enfoque será grabado. Si Ud. no quiere que la voz de su hijo o hija sea grabada, él o ella no podrá participar en el grupo de enfoque. Los grupos de discusión van a ocurrir en las tardes o durante el fin de semana en sitios en la comunidad, tal como las escuelas de Washington Prep, Fremont, o Crenshaw, bibliotecas locales,clínicas comunitarias u organizaciones basada en la fe. En el grupo, le vamos a pedir a su hijo(a) que hable sobre cuestiones relacionadas con lugares donde los estudiantes consiguen sus alimentos, el tipo de actividad física en la cual los estudiantes participan, si los estudiantes comen fuera de casa o en casa, y donde los estudiantes reciben servicios de salud. Si está de 149 acuerdo, su hijo(a) está libre para irse en cualquier minuto. Si le hacemos una pregunta y su hijo(a) no quiere contestar, le vamos e animar que su hijo(a) no conteste la pregunta. POSIBLES RIESGOS E INCOMODIDADES No hay riesgos previstos si participa en el estudio. El grupo de enfoque será grabado en audio y solamente el equipo de investigaciones tendrá acceso a los archivos de audio. Si le hacemos una pregunta a su hijo(a) y no quiere contestar, le vamos e animar que no conteste. POSIBLES BENEFICIOS PARA LOS PARTICIPANTES Y/O LA SOCIEDAD La información que estamos obteniendo durante este estudio tiene el potencial de ayudar a políticos y líderes de la comunidad a desarrollar programas y leyes que le ayudarán a usted y a su familia vivir una vida mejor y más saludable. Es posible que si hijo no se beneficie directamente con su participación. PAGO/COMPENSACIÓN POR SU PARTICIPACIÓN Para compensar a los estudiantes por su tiempo, su hijo(a) va a recibir una tarjeta de regalo de $20 al completar su participación. CONFIDENCIALIDAD Cualquier información obtenida en relación con este estudio será compartida solamente con su permiso o si es requerido por la ley. Los miembros de este equipo de investigación y el Programa Para Proteger a Los Derechos Humanos (HSPP, por sus siglas en inglés) en la Universidad del Sur de California tendrán acceso a los datos. HSPP revisa los proyectos de investigación para proteger a los derechos y el bienestar de los participantes en los estudios. Debido a la naturaleza de los grupos de discusión, no podemos garantizar la confidencialidad; sin embargo, se le va pedir a su hijo(a) que no hable sobre el contenido del grupo de discusión con otros que no asistieron el grupo de discusión. Ni usted ni nadie en la escuela tendrán acceso a las respuestas de su hijo(a). Mantendremos las respuestas de su hijo(a) en USC en un disco duro que está protegido con contraseña para garantizar que se mantengan confidenciales. Las grabaciones de audio se mantendrán bajo llave en un archivero en USC. Los investigadores del estudio y los investigadores de USC serán los únicos que pueden acceder a los datos. Vamos a separar a los nombres de los participantes y las respuestas para garantizar que nadie pueda conectarlos. Los datos y el audio serán conservados un tiempo indefinido para fines de investigación. No revelaremos ninguna información que pueda ser identificada con su hijo(a), ni conectar el nombre de su hijo(a) con la información que vamos a presentar. Tomaremos todas las repuestas de este grupo, y las de los otros estudiantes, y escribiremos un reporte para el CDC y la comunidad. Cuando los resultados del estudio sean publicados y discutidos en conferencias, ninguna información de identificación será incluida. PARTICIPACIÓN Y RETIRO 150 La participación de su hijo(a) es voluntaria. Si su hijo(a) niega participar, ningún penal o pérdida de beneficios va ocurrirle a usted, a su hijo(a), o a la escuela de su hijo(a) a la cual tiene derecho. Usted puede retirar su consentimiento, y su hijo(a) puede retirar su asentimiento, en cualquier momento y pueden discontinuar su participación sin consecuencia negativa. Usted, o su hijo(a), no están renunciando cualquier reclamación, derechos, o recursos legales si participa su hijo(a) en este estudio de investigación. INFORMACIÓN PARA CONTACTAR A LOS INVESTIGADORES Si usted tiene alguna pregunta o inquietud acerca de este estudio, por favor póngase en contacto con David Sloane, 313 Lewis Hall, University of Southern California, 213-740- 5768 o LaVonna Lewis, 309 Lewis Hall, University of Southern California, 213-740-4280. DERECHOS DE LOS PARTICIPANTES EN EL ESTUDIO – INFORMACIÓN PARA CONTACTAR EL IRB Si usted tiene preguntas, inquietudes, o quejas acerca de sus derechos como un participante en el estudio, puede comunicarse con el IRB directamente usando la información proveída a su continuación. Si usted tiene preguntas, inquietudes, o quejas acerca del estudio y no se puede poner en contacto con el equipo de investigación, o si usted quiere hablar con alguien independiente del equipo de investigación, por favor comuníquese con el University Park IRB (UPIRB), Office of the Vice Provost for Research Advancement, Credit Union Building, 3720 South Flower Street, CUB # 301 Los Angeles, CA 90089-0702, (213) 821-5272 o upirb@usc.edu. FIRMA DEL(DE LOS) PADRE(S) He leído la información proporcionada anteriormente. Se me ha dado la oportunidad de hacer preguntas. Mis preguntas han sido contestadas a mi satisfacción, y estoy de acuerdo de que mi hijo(a) participe en este estudio. Me han dado una copia de este formulario. Nombre del Participante Nombre del Padre Firma del Padre Fecha FIRMA DEL INVESTIGADOR Le he explicado el estudio al participante y a su padre(s), y he contestado todas sus preguntas. Yo creo que el/los padre(s) entienden la información descrita en este documento y dieron su consentimiento libremente para participar. 151 Nombre de la Persona Obteniendo el Consentimiento Firma de la Persona Obteniendo el Consentimiento Fecha 152 Appendix F. Child Assent to Participate in Research University of Southern California Sol Price School of Public Policy 650 Childs Way, Los Angeles, CA 90089 ASSENT FORM TO PARTICIPATE IN RESEARCH CDC REACH Demonstration Project Dr. David Sloane and Dr. LaVonna Lewis, from the University of Southern California want to learn about what you eat, where you exercise, how you get your health care, and what you think about your community. The purpose of this research study is to help us understand how policymakers and community leaders might improve your community to make it healthier. One way to learn about it is to do a research study; the people doing the study are called researchers. Your mom/dad/Legally Authorized Representative (LAR) have told us we can talk to you about the study. You also can talk this over with your mom, dad or LAR. It’s up to you if you want to take part, you can say “yes” or “no”. No one will be upset with you if you don’t want to take part. If you do want to take part, you will be asked to answer some questions on a survey and then take part in a focus group. A focus group is an interview of more than two people talking about a specific topic. In this case the focus group will include 10-15 children in your school and will take take about 60 to 90 minutes. The focus group will be audio recorded; if you don’t want to be audio-recorded, you can’t participate in the focus group. You will be asked questions about your activities around food, physical activity and health care. Focus groups will take place in the afternoon, evening or on the weekend at Washington Prep, Fremont and Crenshaw High School, at local libraries or other community sites, such as churches, etc. Researchers don’t always know what will happen to people in a research study. We don’t expect anything to happen to you. The focus group will be audio-recorded and only the research team will have access to the audio files. You don’t have to answer any question you don’t want to and no one outside of the focus group will know your responses. You will receive a $20 gift card for your time. You will receive the gift card at the end of your participation. Your answers will not be graded. Only the researchers will see your answers, your parents or teachers will not see your answers. You are asked not to discuss the focus group with your people who are not in the focus group. If you have any questions, you can ask the researchers. 153 If you want to take part in the study, please write and then sign your name at the bottom. You can change your mind if you want to, just tell the researchers. _________________________________ Name of Participant ____________________________________ ____________________ Participant’s Signature Date ___________________________________ Name of person obtaining consent ___________________________________ ____________________ Signature of person obtaining consent Date 154 Appendix G. CHIS 2009 Six Items to Measure Food Security Status and Hunger (from the U.S. Household Food Security Survey Module) Instructions: These next questions are about the food eaten in your household in the last 12 months and whether you were able to afford food. I'm going to read two statements that people have made about their food situation. For each, please tell me whether the statement describes something that was often true, sometimes true, or never true for you and your household in the last 12 months. Food Security Items (4) 1. The first statement is: “The food that {I/we} bought just didn't last, and {I/we} didn't have money to get more.” Was that often true, sometimes true, or never true for you and your household in the last 12 months?” 2. The second statement is: “{I/We} couldn't afford to eat balanced meals.” Was that often true, sometimes true, or never true for you and your household in the last 12 months?” 3. “Please tell me yes or no. In the last 12 months, did you or other adults in your household ever cut the size of your meals or skip meals because there wasn't enough money for food?” 4. “How often did this happen -- almost every month, some months but not every month, or only in 1 or 2 months?” Hunger Items (2) 1. “In the last 12 months, did you ever eat less than you felt you should because there wasn't enough money to buy food?” 2. “In the last 12 months, were you ever hungry but didn't eat because you couldn't afford enough food?”

Abstract (if available)

Abstract This dissertation consists of three studies examining facets of the obesity epidemic and related policies in California. Chapter two uses the advocacy coalition framework (ACF) to analyze the health policymaking process from 1999 to 2009 and the role of policy‐oriented learning in California. During that period, six menu labeling bills were introduced in the state legislature, which resulted in the enactment of SB 1420 in 2008. The study examines how advocacy coalitions engaged in a policy debate within a policy subsystem and used technical knowledge to promote their agendas. Chapter three aims to understand adolescents’ perceptions of barriers and facilitators to healthy eating, active living, and well‐being. This qualitative study identifies prevailing beliefs, attitudes, perceptions, and experiences among high school students in an underserved community. Chapter four examines the role of food security and its contribution to Body Mass Index (BMI) using data from the 2009 California Health Interview Survey (CHIS), and provides a conceptual framework of food security and weight status among adults based on existing literature. The three essays in this dissertation highlight the importance of studying the multiple levels of the socio‐ecological framework to address California’s obesity epidemic. Overall, the dissertation demonstrates the value of examining health behavior within specific settings and environmental contexts.

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Asset Metadata

Creator Payán, Denise Diaz (author)

Core Title Essays examining nutrition behavior and policy in California

School School of Policy, Planning and Development

Degree Doctor of Philosophy

Degree Program Public Policy and Management

Publication Date 07/23/2015

Defense Date 06/17/2015

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

Tag advocacy coalition framework,California,food security,health behaviors,Health policy,low‐income communities,nutrition,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Nichol, Michael B. (committee chair), Cousineau, Michael (committee member), Lewis, LaVonna (committee member)

Creator Email denisedi@usc.edu,twindiaz1@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-604810

Unique identifier UC11299086

Identifier etd-PayanDenis-3681.pdf (filename),usctheses-c3-604810 (legacy record id)

Legacy Identifier etd-PayanDenis-3681.pdf

Dmrecord 604810

Document Type Dissertation

Format application/pdf (imt)

Rights Payán, Denise Diaz; Payan, Denise Diaz

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA