University of Southern California Dissertations and Theses

The disease burden among aging people living with HIV/AIDS

(USC Thesis Other)

The disease burden among aging people living with HIV/AIDS

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content The Disease Burden Among Aging People Living with HIV/AIDS by Hsin-Yun Yang A Dissertation Presented to the FACULTY OF THE USC SCHOOL OF PHARMACY UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (HEALTH ECONOMICS ) December 2020 Copyright 2020 Hsin-Yun Yang DEDICATION This dissertation is dedicated to my family, my friends, and all the beautiful souls who have kindly lent a helping hand in my hard times. ii ACKNOWLEDGEMENTS First of all, I would like to thank Ministry of Education of Taiwan for the USC-Taiwan fellowship and the Education Division of Taipei Economic and Cultural Office in Los Angeles for their help and support throughout this PhD. I would like to say thank you to Dr. Sze-chuan Suen a thousand times for all the guidance and patience she gave me throughout this PhD. I would also like to say thank you to Dr. William Padula and Dr. Seth Seabury for serving on my committee. Special thanks to Dr. Jeff McCombs for his valuable advice and help throughout my time at the University of Southern California. I would also like to offer my thanks to Bryan Tysinger, Patricia St. Clair, Patricia Ferido, and Jillian Wallis for providing technical support in my research. I am very grateful for the unconditional love received from my family. Without them, I would have never made it this far. I am also very grateful for the support system kindly provided by our alumni: Chia-Wei, Cho-Han, and Wendy. It helped me survive PhD life and the city lockdown due to the pandemic. Lastly, my thanks go to my kendo senseis and dojo members. It was their fighting spirit that inspired me and their encouragement that held me up through the hardest miles of this PhD marathon. iii Table of Contents DEDICATION ........................................................................................................................... ii ACKNOWLEDGEMENTS .......................................................................................................... iii List of Tables .......................................................................................................................... v List of Figures ......................................................................................................................... vi ABSTRACT ............................................................................................................................. vii INTRODUCTION ...................................................................................................................... 1 CHAPTER 1: CHRONIC DISEASE ONSET AMONG PEPOLE LIVING WITH HIV AND AIDS IN A LARGE PRIVATE INSURANCE CLAIMS DATASET ....................................................................... 3 INTRODUCTION ............................................................................................................................... 3 METHODS ........................................................................................................................................ 5 RESULTS ........................................................................................................................................... 8 DISCUSSION ................................................................................................................................... 11 CHAPTER 2: THE EXCESS COSTS ASSOCIATED WITH HIV INFECTION IN CHRONIC CONDITIONS MANGAEMENT AMONG PEOPLE LIVING WITH HIV AND AIDS (PLWHA) AGED 50 AND OLDER21 INTRODUCTION ............................................................................................................................. 21 METHODS ...................................................................................................................................... 24 RESULTS ......................................................................................................................................... 27 DISCUSSION ................................................................................................................................... 30 CHAPTER 3: THE COST-EFFECTIVENESS ANALYSIS OF REGULAR DEMENTIA SCREENING AMONG PEOPLE LIVING WITH HIV/AIDS AGED 50 AND OLDER ........................................................... 44 INTRODUCTION ............................................................................................................................. 44 METHODS ...................................................................................................................................... 47 RESULTS ......................................................................................................................................... 52 DISCCUSION ................................................................................................................................... 54 REFERENCES ......................................................................................................................... 65 APPENDIX ............................................................................................................................. 74 iv List of Tables Table1 . Summary Statistics. Baseline Characteristics at Enrollment. .......................................... 17 Table2 . Logistic Regression Outcomes. 2-Year Odds Ratios Compared with HIV- enrollees.* The HIV variable is defined by Cohort 1, ever diagnosed with HIV .................................................. 18 Table3 . Odds ratios for HIV in logistic regressions for Cohort 1 (ever diagnosed with HIV), using different intervals for chronic disease onset. ....................................................................... 19 Table4 . Odds Ratios for HIV in logistic regressions for Cohort 2 & 3*, using different intervals for chronic disease onset. .............................................................................................................. 20 Table 21. Our modified initial cohort aged 50/51 ......................................................................... 35 Table 22. Relative risks used in the FEM simulation ................................................................... 35 Table 22. Relative risks used in the FEM simulation ................................................................... 36 Table 23. Prevalence of each comorbidity over age ..................................................................... 38 Table 24. Incidence of each comorbidity over age ....................................................................... 39 Table 25. Healthcare expenditure over age ................................................................................... 40 Table 26. Scenario analyses: projected difference in annual average medical expenditures per capita and annual average Medicare expenditure per capita between the PLWHA cohort and the HIV negative cohort at age 55, 65, 75 .......................................................................................... 41 Table 27. Two-way sensitivity analyses: projected difference in annual average medical expenditures per capita at age 65 between the PLWHA cohort and the HIV negative cohort ..... 42 Table 28. Odds ratios reported from other studies ........................................................................ 43 Table 32. Model inputs ................................................................................................................. 58 Table 33. Two-way sensitivity analysis ........................................................................................ 62 Table5 . ICD-9 and ICD-10 codes used in analysis ....................................................................... 74 Table6 . Incidence: New cases per 100 individuals at risk in each cohort* .................................. 77 Table7 . HIV Relative Risks for HIV Cohort 1 (diagnosed with HIV at any point) Compared with HIV- Enrollees .............................................................................................................................. 78 Table8 . HIV Relative Risks for HIV Cohort 2 (diagnosed with HIV prior to two years from enrollment) Compared with HIV- Enrollees ................................................................................. 78 Table9 . HIV Relative Risks for HIV Cohort 3 (diagnosed with HIV after two years from enrollment) Compared with HIV- Enrollees ................................................................................. 79 Table10 . Logistic Regression Outcomes. 2-Year Odds ratios of Hypertension. HIV variable is defined by Cohort 1, ever diagnosed with HIV ............................................................................ 80 Table11 . Logistic Regression Outcomes. 2-Year Odds ratios of Cognitive Impairment/Dementia. HIV variable is defined by Cohort 1, ever diagnosed with HIV. ................................................. 81 Table12 . Logistic Regression Outcomes. 2-Year Odds ratios of Stroke. HIV variable is defined by Cohort 1, ever diagnosed with HIV ......................................................................................... 82 Table13 . Logistic Regression Outcomes. 2-Year Odds ratios of Cancer. HIV variable is defined by Cohort 1, ever diagnosed with HIV ......................................................................................... 83 Table14 . Logistic Regression Outcomes. 2-Year Odds ratios of Lung Disease. HIV variable is defined by Cohort 1, ever diagnosed with HIV ............................................................................ 84 Table15 . Logistic Regression Outcomes. 2-Year Odds ratios of Cardiovascular Disease. HIV variable is defined by Cohort 1, ever diagnosed with HIV ........................................................... 85 Table16 . Logistic Regression Outcomes. 2-Year Odds ratios of Diabetes. HIV variable is defined by Cohort 1, ever diagnosed with HIV ......................................................................................... 86 v Table17 . HIV+ population used: Individuals in Cohort 3 (diagnosed with HIV after two years from enrollment) diagnosed with HIV/AIDS before 2011 ........................................................... 88 Table18 . HIV+ Population Used: Individuals in Cohort 3 (diagnosed with HIV after two years from enrollment) diagnosed with HIV/AIDS in/after 2011 .......................................................... 90 Table19 . Cohort 3 (diagnosed with HIV after two years from enrollment) summary statistics by age group (%) ................................................................................................................................ 92 Table 20 . HIV odds ratios by age group for Cohort 3 (diagnosed with HIV after two years from enrollment) compared with HIV- enrollees .................................................................................. 93 Table 29. Average annual Medicare expenditure per person by gender at age 65 (2019 USD) .. 95 Table 30. Average annual Medicare expenditure per person by gender among the general US population (2019 USD) ................................................................................................................. 96 Table 31. Average annual Medicare expenditure per person by race/ethnicity at age 65 (2019 USD) ............................................................................................................................................. 97 Table 34. Comparison of lifetime treatment cost (2019 USD) ..................................................... 98 Table 35. Life expectancy ............................................................................................................. 98 Table 36. Scenario 1: both strategies incur the same annual healthcare cost ............................. 100 Table 37. Scenario 2: Annual healthcare cost of Strategy B is 1.5 times as Strategy A ............ 101 Table 38.Scenario 3: Annual healthcare cost of Strategy B is 1.5 times as Strategy A ............. 102 List of Figures Figure 1. Flowchart of inclusion ................................................................................................... 16 Figure 2. Projection of population size by age ............................................................................. 37 Figure 3. Markov model ............................................................................................................... 57 Figure 4. Incremental Net Monetary Benefit ................................................................................ 60 Figure 5. Change in ICERs: Regular Screening + Aducanumab vs No regular screening + Neurotransmitters .......................................................................................................................... 61 Figure 6. Cost-effectiveness Acceptability Curve ........................................................................ 63 Figure 7. Cost-effectiveness Plane ................................................................................................ 64 vi ABSTRACT CHAPTER 1: CHRONIC DISEASE ONSET AMONG PEPOLE LIVING WITH HIV AND AIDS IN A LARGE PRIVATE INSURANCE CLAIMS DATASET People living with HIV/AIDS (PLWHA) have a growing life expectancy in the US due to early provision of effective antiretroviral treatment. This has resulted in increasing exposure to age- related chronic illness that may be exacerbated by HIV/AIDS or antiretroviral treatment. Prior work has suggested that PLWHA may be subject to accelerated aging, with earlier onset and higher risk of acquiring many chronic illnesses. However, the magnitude of these effects, controlling for chronic co-morbidities, has not been fully quantified. We evaluate the magnitude of impact of HIV infection on developing chronic conditions while controlling for demographics, behavioral risk factors, and chronic comorbidities. We compare chronic disease risks of diabetes, hypertension, stroke, cancers, lung diseases, cardiovascular diseases, and cognitive impairment between PLWHA and HIV- individuals in a large, de-identified private insurance claims dataset (~24,000 PLWHA) using logistic regressions. HIV status is statistically significantly associated with higher levels for all chronic illnesses examined, a result which is robust to multiple model specifications and duration of analysis (2, 5, and 10 years from enrollment). Our results suggest that PLWHA may be at elevated risk for a wide variety of chronic illnesses and may require additional care as the aging PLWHA population grows. vii CHAPTER 2: THE EXCESS COSTS ASSOCIATED WITH HIV INFECTION IN CHRONIC CONDITIONS MANGAEMENT AMONG PEOPLE LIVING WITH HIV AND AIDS (PLWHA) AGED 50 AND OLDER People living with HIV/AIDS (PLWHA) are at higher risks of developing aging-related chronic comorbidities because of HIV infection. Identifying the financial burden arising from this increased risk within PLWHA is important for projecting costs as the life expectancy of PLWHA increases. This study evaluated the excess healthcare expenditures incurred by HIV infection among PLWHA with aging-related chronic comorbidities. We used the Future Elderly Model (FEM), a well-established microsimulation model, to project the future health trends in health outcomes including prevalence, incidence, life expectancy, and healthcare expenditures for PLWHA aged 50 and older in the United States. We found that PLWHA had higher healthcare expenditures than non-PLWHA in general. Our findings suggest that policymakers should be ready for this upcoming financial challenge in the healthcare system. viii CHAPTER 3: THE COST-EFFECTIVENESS ANALYSIS OF REGULAR DEMENTIA SCREENING AMONG PEOPLE LIVING WITH HIV/AIDS AGED 50 AND OLDER People living with HIV/AIDS (PLWHA) are at higher risk of developing mild cognitive impairment (MCI) or Alzheimer’s Disease (AD). However, they often experience delayed diagnoses of MCI or AD because currently regular cognitive function screenings are not mandatory. The objective of this study is to evaluate the cost-effectiveness of regular cognitive function screenings in the context of the availability of effective αβ-antibody treatment among PLWHA. We built a 6-state Markov model with 1-year cycles to estimate discounted lifetime health outcomes and expenditures for PLWHA aged 50. We evaluate two scenarios: status quo and annual screening. The health outcomes included life expectancy and quality of life. The expenditures included both direct costs and indirect costs. All costs were reported in 2019 US dollars. A 3% annual discount rate was applied. We used a lifetime time horizon and a healthcare sector perspective. We found that in the status quo scenario, the discounted life expectancy was 21.44 with a discounted total QALY of 10.99. The total lifetime expenditure was estimated to be $126,130.8. In the regular screening scenario, the discounted life expectancy was 21.53, the discounted total QALY was 11.06, and the total lifetime expenditure was $139.032.0. The ICER was $186,920.5 per QALY gained, which was not considered cost-effective at $150,000 willingness-to-pay threshold. ix INTRODUCTION As the development of the new generation of antiretroviral agents (ART), an HIV/AIDS diagnosis is no longer a death sentence. People living with HIV/AIDS (PLWHA) who are treated with ARTs now have a good chance to live into their 70s or even have a life expectancy that is very close to the life expectancy of people without HIV infections. On one hand, this is indeed good news to all of PLWHA, but on the other hand, this also means a new challenge to the capacity of our healthcare system – could our healthcare system accommodate the health needs of these “new” group of older customers? How much more funding does the healthcare system need to take care of these new customers? The studies presented in this dissertation centered around this new challenge and aimed to provide a clearer understanding of the challenge and how we could more efficiently allocate our resource when facing this challenge. In Chapter 1, we aimed to measure how HIV/AIDS would impact the aging process of PLWHA. We measured the impact via estimations of the risks of developing aging-related chronic comorbidities compared to people without HIV/AIDS so that we know the distributions of the incidence and the prevalence of these aging-related chronic comorbidities among aging PLWHA compared to people without HIV/AIDS. This piece of information is the cornerstone of the formation of policy to better allocate our healthcare resources and accommodate aging PLWHA’s health needs. Chapter 2 focused on the future healthcare expenditure landscape of aging PLWHA. We applied a microsimulation model to project the prevalence and the incidence of aging-related chronic comorbidities, the life expectancy, and healthcare expenditures for both PLWHA and people 1 without HIV/AIDS. In this chapter, we could learn how much more aging PLWHA would spend compared to people without HIV/AIDS. In Chapter 3, we conducted a cost-effectiveness analysis to evaluate the cost-effectiveness of implementing of a regular cognitive function screening among PLWHA as a healthcare strategy in managing of aging PLWHA who are at risk of developing mild cognitive impairment or dementia. 2 CHAPTER 1: CHRONIC DISEASE ONSET AMONG PEPOLE LIVING WITH HIV AND AIDS IN A LARGE PRIVATE INSURANCE CLAIMS DATASET Yang, H.-Y., Beymer, M. R., & Suen, S.-C. (2019). Chronic Disease Onset Among People Living with HIV and AIDS in a Large Private Insurance Claims Dataset. Scientific Reports, 9(1), 18514. INTRODUCTION The life expectancy of people living with HIV/AIDS (PLWHA) has significantly increased due to the development of antiretroviral therapy (ART) in the mid-1990s. PLWHA who are treated with ART can now expect to live into their seventies, provided they are adherent to their ART regimen. 1–3 According to the US Centers for Disease Control and Prevention, almost half (47%) of PLWHA in the US were aged 50 and older in 2015; 18% were aged 60-64 and 16% were aged 65 and older. 4 The age distribution of PLWHA suggests that the US now faces the challenge of a growing population of aging PLWHA. 5 As the aging population of PLWHA increases, chronic diseases that are common among elderly populations play a more important role than ever before in the health care management of PLWHA. Moreover, these chronic diseases, including diabetes, hypertension, stroke, lung diseases, cancers, cardiovascular diseases, and neurocognitive disorders, occur more often in PLWHA than in people without HIV/AIDS. 6–10 Although the mechanism by which HIV/AIDS is related with chronic diseases is not fully understood, researchers have found some possible explanations for the high prevalence of the chronic diseases seen in PLWHA. Among the seven aforementioned chronic diseases, diabetes, hypertension, stroke, and cardiovascular diseases are closely associated with metabolic syndrome – which includes abdominal obesity, atherogenic dyslipidemia, raised blood sugar, insulin resistance, proinflammatory and prothrombotic states 11–13 – which is one of the adverse 3 side effects of antiretroviral therapy. 14–19 Other chronic diseases may be linked to long-term inflammation due to persistent low-level viremia that may occur even with ART. 20 Inflammation caused by HIV, independent of ART status, is associated with obstructive lung disease and cancer. 20 It is also associated with HIV-associated neurocognitive disorder (HAND), 21 which is a collective term for HIV-associated dementia, HIV-associated mild neurocognitive disorder, and asymptomatic neuropsychological impairment. The high prevalence of these chronic diseases in PLWHA not only complicates the health management of PLWHA but may also pose a burden to the healthcare system. 22–27 Existing studies have previously examined chronic illness prevalence among PLWHA in Medicare and younger cohorts in the United States, 9,20,28,29 as well as in populations in other countries. Here, we examine a population starting at age 50 using a private insurance claims dataset. It is important to control for chronic comorbidities – a feature that prior studies have typically not examined – as many chronic conditions may interact. For instance, diabetes may elevate the risk of cardiovascular disease; chronic illnesses can exacerbate cognitive impairment or vice versa. 30,31 We therefore examine diabetes, hypertension, stroke, cancers, lung diseases, cardiovascular diseases, and cognitive impairment/dementia in this population, instead of focusing on a single chronic illness. Our objective in this study is to use a large, de-identified private insurance claims dataset to evaluate the magnitude of impact of HIV infection on developing chronic conditions while 4 controlling for demographics, behavioral risk factors, and chronic comorbidities for patients aged 50 and older. We measure the odds ratios for acquiring chronic illness among PLWHA compared to non-PLWHA, which will further our understanding of what conditions aging PLWHA are most susceptible. In addition, we separately analyze those PLWHA diagnosed later in life, and also examine chronic disease onset after 2, 5, or 10 years from enrollment, to identify a potentially important subgroup within PLWHA which may be at particularly high risk of chronic disease. METHODS Study population This study used data from Optum’s de-identifed Clinformatics® Data Mart Database on enrollees aged 50 and above who had been followed for at least one year from Optum for the period between January 2007 and December 2016. The seven chronic disease groups of interest were diabetes, hypertension, stroke, cancers, lung diseases, cardiovascular diseases, and cognitive impairment and dementia. Cancers included prostate cancer, breast cancer, colorectal cancer, endometrial cancer, and lung cancer. Lung diseases included asthma and chronic obstructive pulmonary disease (COPD). Cardiovascular diseases included atrial fibrillation and ischemic heart diseases. Cognitive impairment and dementia included non-Alzheimer’s dementia, Alzheimer’s dementia, and cognitive impairment. These chronic diseases were identified by ICD-9 codes or ICD-10 codes (given in Appendix Table 5). 5 We applied the Chronic Conditions Data Warehouse algorithm (CCW) 32 to identify new disease onset. This included a washout period, where diagnoses for a disease within the washout period from their enrollment were not considered a “new” diagnosis. In concordance with the CCW, a 2-year washout period was applied to HIV; one-year washout period was applied to hypertension, stroke, cancers, and lung diseases; a two-year washout period was applied to diabetes and cardiovascular diseases; and a three-year washout period was applied to dementia. Using these washout periods increased the probability that the diagnosis observed was truly new and not part of an already-diagnosed condition that started prior to the patient’s enrollment. We cannot observe the time from diagnosis for the majority of patients with HIV in the dataset – 82.5% of those with HIV were diagnosed prior to two years from enrollment, indicating that a sizable proportion were not new diagnoses. However, it is likely that time from diagnosis and exposure to antiretroviral treatment is likely to impact drug toxicity and viral loads and thereby influence the severity of co-morbid chronic conditions in HIV. 33–35 We therefore perform analysis on three groups of PLWHA, which we term cohorts 1, 2, and 3 for convenience. Cohort 1 includes individuals diagnosed with HIV infection or AIDS at any time during their enrollment periods. Cohort 2 was the group of PLWHA who were diagnosed with HIV/AIDS at/before enrollment or within 2 years after enrollment. Cohort 3 was the group of PLWHA that were diagnosed with HIV/AIDS 2 years or more after their enrollment. Groups 2 and 3 are mutually exclusive, and together form Cohort 1. We cannot be certain of the time of HIV acquisition for individuals in any of the three cohorts, but those in Cohort 3 are diagnosed after age 50, as all individuals in our analysis are over age 50. These are therefore individuals who were living with undiagnosed HIV 6 until later in life or acquired HIV after age 50, and we separate out these individuals for subgroup analysis as they may have different patterns of chronic disease acquisition. Analysis Techniques and Outcome Measures We report the percentage of individuals diagnosed with chronic condition for each subgroup for individuals enrolled between 2007-2016. Logistic regressions were used to estimate odds ratios of developing each chronic condition by HIV status – the odds ratio would then provide an estimate of the relative likelihood of that chronic disease for a PLWHA compared to a HIV- individual. We performed this regression for each of our three definitions of having HIV: inclusion in HIV Cohort 1 (diagnosed with HIV at any point), Cohort 2 (diagnosed with HIV prior to two years from enrollment), or Cohort 3 (diagnosed with HIV after two years from enrollment). The dependent variable was whether the individual would develop the chronic disease by 2, 5, or 10 years from baseline (time of enrollment; separate models for each time point); only individuals without the disease at baseline were used in each regression. Other control variables included age at enrollment, sex, race/ethnicity, annual household income level at baseline, behavioral risk factors (including obesity, substance abuse, alcohol-related disease, and smoking), and the other chronic disease groups at baseline not being used as the dependent variable. To explore model sensitivity to independent variables, we also examined different model specifications. We included demographic characteristics in our regressions because age and gender might affect the risk of acquiring chronic conditions; we also chose to include behavioral 7 risk factors including smoking, obesity, drinking, and substance abuse as these may increase the likelihood of particular chronic illnesses. 36–39 We additionally performed additional sensitivity analyses on year of HIV diagnosis (as ART has changed over our analysis period) and age of HIV diagnosis for Cohort 3, where date and age of HIV diagnosis could be observed. An alpha level of 0.05 used applied to determine the statistical significance of results. Data cleaning was performed using SAS and statistical analysis were performed in Stata (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). RESULTS A total of 9,141,867 enrollees aged 50 and above who had been enrolled for at least 1 year were included. 24,636 (0.26%) of them were included in HIV cohort 1 (had been diagnosed with HIV/AIDS prior to enrollment or at any time in enrollment period) (see Fig.1). Table 1 shows the characteristics of the entire study population at enrollment. PLWHA were more likely to be male (74.0% vs 45.8%, p-value <0.05) and Black (28.7% vs 10.4%, p-value <0.05). PLWHA were more likely to earn less compared to people without HIV/AIDS (45.6% had annual household income less than $50k, compared to 33.2% among HIV- individuals). PLWHA were also more likely to be diagnosed with alcohol-related disease (2.0% vs 0.7%) and substance abuse (0.6% vs 0.1%). Smoking was more common among PLWHA (21.5% vs 11%). Individuals diagnosed with HIV after enrollment (Cohort 3) were also more likely to be older during the analysis period, female, obese, abuse alcohol, and have higher prevalence of diabetes, hypertension, cardiovascular disease, and cognitive impairment at enrollment compared to those diagnosed before or soon after enrollment (Cohort 2), as shown in Table 1. 8 The average length of enrollment in the insurance plan was 4.40 years for PLWHA and 4.54 years for people without HIV/AIDS. While this difference of 1.68 months was statistically significant (p<0.05), it is likely too small to be clinically meaningful. PLWHA tended to enroll at younger ages compared to people without HIV/AIDS. The average age at enrollment was 59.2 (95% CI: 59.1- 59.3) for cohort 1 and 64.1 (95% CI: 64.1-64.2) for people without HIV/AIDS. Moreover, 75% of PLWHA were younger than 65 years old at enrollment (78% of cohort 2 and 60% of cohort 3), providing a sizable proportion of relatively younger patients for our study, even in cohort 3. In general, PLWHA (in any HIV cohort) were more likely to have chronic conditions at the time they were enrolled. PLWHA had higher prevalence of diabetes (28.8% vs 24.2%), hypertension (46.9% vs 46.4%), stroke (3.3% vs 2.4%), lung diseases (14.3% vs 11.3%), cardiovascular diseases (22.3% vs 21.2%), and dementia (9.2% vs 8.0%) at enrollment. People living without HIV/AIDS appeared to have higher prevalence of cancer than PLWHA (4.8% vs 5.7%) at enrollment. All differences were statistically significant at an alpha level of 0.05, but not all differences may be clinically meaningful. We ran multivariable logistic regressions for the onset of each chronic condition within two years of enrollment. Results for cognitive impairment/dementia onset when HIV is defined as inclusion in HIV cohort 1 is shown in Table 2 as an example; the outcomes for the remaining diseases are shown in the Appendix (Appendix Tables A2-A7). Table 3 provides a summary of the odds ratios 9 of developing the indicated chronic illness for an individual in HIV Cohort 1 compared to someone without HIV, controlling for all the covariates listed in Table 2. These odds ratios were statistically significant at the 5% level and were positive. This indicates that the risk for the onset of these chronic conditions were higher among this PLWHA group, even controlling for comorbidities, demographics, and behavioral differences. To examine the robustness of these results, we reran the analysis where the dependent variable was changed to the onset of each of these chronic conditions within 5 and 10 years from enrollment; the odds ratios remained very similar in magnitude and remained statistically significant, indicating that disease onset risk may be relatively constant over time (see Table 3). The odds ratios across all chronic conditions were between 1.1 and 1.8, with the highest odds ratio for cognitive impairment and dementia. Given the large sample size in this study, even small differences would be statistically significant, but in general, these odds ratios are similar in size to many of those seen in our outcomes for behavioral risk factors such as smoking, alcohol, or substance use (see tables in Appendix), indicating that the risk due to HIV may be comparable in magnitude to these established risk factors for chronic illness. Table 4 presents the corresponding results when we limit our HIV definition to Cohort 2, PLWHA who were diagnosed with HIV/AIDS at/before two years after enrollment (upper half of table), and Cohort 3, PLWHA who were diagnosed with HIV/AIDS two years or more after enrollment (lower half of table), controlling for all the demographic, disease, and behavioral variables listed above. Here, we find that the odds ratios of developing the indicated chronic illness for Cohort 2 1 0 compared to HIV- individuals was only sometimes statistically significant, depending on the interval on which onset was defined. Notably, the odds ratio of chronic disease onset was higher when HIV status was limited to Cohort 3. The odds ratios were all statistically significant, and many were above 2 (see Table 4). While the odds ratios tended to decrease with longer onset intervals, these values are generally larger than those found when HIV was defined by Cohort 1 or Cohort 2, meaning that individuals diagnosed with HIV after age 50 are generally at higher risk of chronic disease onset compared with HIV- individuals, and even PLWHA who were diagnosed before enrollment. Results from the additional sensitivity analyses on year of HIV diagnosis and age of HIV diagnosis for Cohort 3 are shown in the Appendix and show similar results. Relative risks were also estimated (instead of odds ratios), and these are shown in the Appendix as well. DISCUSSION This study analyzed the risk of developing seven chronic conditions including diabetes, hypertension, stroke, cancers, lung diseases, cardiovascular diseases, and dementia using a large de-identified private insurance claims database for people who were 50 years of age or older, stratified by HIV status. We found that PLWHA were more likely to have chronic conditions even controlling for demographic characteristics, behavioral risk factors, and other chronic comorbidities. Our findings were robust to different follow-up periods of analysis and under different model specifications. 11 HIV infection had an even greater impact on development of these chronic conditions among those who were diagnosed with HIV/AIDS later, as evidenced by our results for individuals diagnosed with HIV two years after enrollment. This could be because these individuals had undiagnosed HIV/AIDS for a longer period (as they were diagnosed after age 50), or had some other unobserved characteristics correlated to acquiring HIV later in life. This finding spurs additional future research on the relationship between HIV diagnosis age and long-term health outcomes. We must acknowledge several limitations of this study. To perform our analysis, we used private insurance claims data, which may not be nationally representative. For instance, individuals in our analysis tend to have higher than average socioeconomic status, as measured through income (see Table 1). However, this would likely mean that this population is, on average, healthier than the national average, leading to conservative estimates of the elevated risk of chronic disease in this population. This population may not be generalizable to the national US population on other characteristics as well, some of which may not be observable. For instance, we could not observe residential areas which might also impact enrollees’ access to medical resources. We tried propensity score matching to address unobservable characteristics, however, we abandoned the idea in that we lost too many enrollees after applying propensity score matching. We chose to limit our analysis to only those aged 50 and older, as these individuals may be at most risk for chronic conditions which tend to be exacerbated by age. Note that these results are 12 also not generalizable to the younger PLWHA population, as those who died younger than 50 could be sicker; our results pertain only to the aging PLWHA population. In general, we were unable to control for competing mortality risk, and this could have potentially led to some counter-intuitive findings (such as higher prevalence of cancer in the HIV- group at enrollment). We also cannot control for the duration an individual lived with HIV, nor what fraction of that time was spent on ARTs, adherence levels on ART, nor what type of ART was used prior to enrollment in this dataset. Unfortunately, many of the PLWHA in our analysis were diagnosed with HIV prior to enrollment (82.5%), and we cannot observe the date of diagnosis nor their treatment. Prior works have identified the duration with HIV and on ART as potentially important in the acquisition of chronic illness among PLWHA. However, even limiting the PLWHA population to those who had been previously diagnosed with HIV (Cohort 2), we found evidence of elevated chronic disease risk. Our data was similarly lacking for behavioral factors; we had to rely on coded diagnoses of smoking and substance abuse – which are likely to only able to identify extreme cases of these risk factors. We additionally note that our analysis does not explicitly account for the timing of diagnosis of HIV relative to chronic illness (instead separating out diagnosis of HIV either before two years from enrollment or afterwards), and we cannot conclude that HIV causes elevated risk for chronic illnesses, merely that the two are associated. Despite these limitations, we believe that our findings shed light on the important issue of chronic disease among aging PLWHA. Even after controlling for co-morbidities and behavioral effects, we found that PLWHA prior to age 65 have elevated risk for a variety of chronic illnesses. While not 13 nationally generalizable, this study includes a much larger PLWHA sample size than in many prior works, and comparable in population size with the largest studies on this topic – many studies use hundreds, or even thousands, of patients, while those with over 24,000 PLWHA are rare. 16,33 We also control, at least in a limited way, for multiple comorbidities and behavior risk factors, which are known to affect the acquisition of further illnesses. We are also able to shed light on the pre-65 PLWHA age group, which may have had less emphasis in the US in prior work. Critically, the results of this analysis suggest that while PLWHA are at higher risk in general for acquiring chronic illness, those PLWHA who are diagnosed after age 50 are at particular risk. The differences in magnitude in the odds ratios between Cohort 2 and Cohort 3 were stark, with odds ratios for chronic illness often above 2 for our Cohort 3 results. In our sample, this population tended to be older and more, female, overweight, and abuse alcohol, but these results persisted even after controlling for these characteristics. This could be due to acquisition of HIV later in life -- other studies have documented that women were more likely to be older than diagnosis than men 40 – or through delayed diagnosis, which could arise for a variety of reasons. These include lack of access to care or HIV stigma and fear of testing. 41 We acknowledge that PLWHA in Cohort 3 tend to be older, and therefore subject to bias – they only enter Cohort 3 if they are still alive to be diagnosed two years after enrollment in the dataset. This study contributes towards our understanding of chronic illness among PLWHA prior to age 65. With an aging PLWHA population, both medical practitioners and health policymakers must understand the risks this growing vulnerable population faces as they are exposed to the natural 14 aging process in addition to unique dangers due to HIV and HIV treatment. Quantification of the magnitude of chronic disease risk will enable more tailored preventative and screening programs for this population. In future work, we hope to explore more finely age-stratified chronic disease risk in this population, although this dataset was not large enough to support finely stratified age- group analysis, particularly given the number of control variables needed. This study’s results also indicate that further work is needed to understand the role of HIV diagnosis age in relationship to chronic disease onset. We hope that the findings in this study will contribute to a growing body of literature which will help our healthcare system prepare for a rapidly increasing population of aging PLWHA. 15 TABLES AND FIGURES Figure 1. Flowchart of inclusion 16 Table1: Summary Statistics. Baseline Characteristics at Enrollment. HIV- Throughout Enrollment HIV+ Cohort 1 ^ HIV+ Cohort 2 ^ HIV+ Cohort 3 ^ Mean age 64.1 59.2* 58.6 62.1** Male (%) 45.8 74.0* 77.0 59.6** Female (%) 54.2 26.0* 23.0 40.4** White (%) 76.5 56.2* 56.2 56.2 Black (%) 10.4 28.7* 29.3 25.4** Hispanic (%) 9.60 13.3* 12.9 15.6** Education level <High School (%) 0.70 0.90* 0.90 1.20 Annual Household Income < 50K (%) 33.2 45.6* 46.4 42.2** Obesity (%) 17.6 17.2* 15.1 27.5** Alcohol-related disease (%) 0.70 2.00* 1.80 3.20** Substance abuse (%) 0.10 0.60* 0.60 0.40 Smoking (%) 11.7 21.5* 21.3 22.4 Diabetes (%) 24.2 28.8* 28.0 32.9** Hypertension (%) 46.4 46.9* 46.4 49.2** Stroke (%) 2.40 3.20* 3.20 3.30 Cancer (%) 5.70 4.80* 4.70 5.10 Lung disease (%) 11.3 14.3* 14.2 14.7 Cardiovascular disease (%) 21.2 22.3* 21.6 25.3** Cognitive impairment and dementia (%) 8.00 9.20* 8.60 11.8** 17 Table2 Logistic Regression Outcomes. 2-Year Odds Ratios Compared with HIV- enrollees.* The HIV variable is defined by Cohort 1, ever diagnosed with HIV Model Hypertension Cog. Impairment/ Dementia Stroke Cancer Lung Disease Cardiovascular Disease Diabetes HIV/AIDS status at enrollment 1.13 1.66 1.28 1.44 1.27 1.30 1.30 (1.06 - 1.20) (1.48 - 1.86) (1.16 - 1.40) (1.31 - 1.58) (1.19 - 1.35) (1.20 - 1.40) (1.19 - 1.42) Age at enrollment 1.03 1.10 1.05 1.05 1.02 1.06 1.02 (1.03 - 1.03) (1.09 - 1.09) (1.04 - 1.04) (1.04 - 1.04) (1.02 - 1.02) (1.05 - 1.05) (1.01 - 1.01) Male 1.21 0.91 1.04 1.24 0.91 1.32 1.20 (1.20 - 1.22) (0.89 - 0.92) (1.02 - 1.05) (1.22 - 1.25) (0.90 - 0.91) (1.31 - 1.33) (1.19 - 1.21) Black 1.13 0.96 1.07 1.04 0.87 0.96 1.28 (1.11 - 1.14) (0.93 - 0.97) (1.05 - 1.09) (1.02 - 1.06) (0.86 - 0.88) (0.95 - 0.97) (1.26 - 1.30) Annual household income < 50K 1.16 1.22 1.18 1.06 1.21 1.18 1.23 (1.14 - 1.16) (1.20 - 1.23) (1.17 - 1.19) (1.04 - 1.06) (1.20 - 1.22) (1.16 - 1.18) (1.21 - 1.23) Diabetes status at enrollment 0.95 1.09 1.22 0.99 1.05 1.22 1.13 (0.94 - 0.96) (1.07 - 1.10) (1.20 - 1.22) (0.98 - 1.00) (1.03 - 1.05) (1.20 - 1.23) (1.11 - 1.13) Stroke status at enrollment 1.01 0.81 1.00 0.79 0.88 1.01 0.97 (0.96 - 1.04) (0.79 - 0.82) (0.98 - 1.00) (0.77 - 0.79) (0.87 - 0.88) (1.00 - 1.02) (0.93 - 1.00) Cancer status at enrollment 0.87 1.41 0.93 0.85 0.93 1.28 0.86 (0.85 - 0.88) (1.36 - 1.45) (0.90 - 0.94) (0.82 - 0.88) (0.90 - 0.95) (1.24 - 1.32) (0.84 - 0.87) Lung disease status at enrollment 0.97 0.92 1.01 1.12 1.01 0.91 1.02 (0.96 - 0.98) (0.89 - 0.93) (1.00 - 1.03) (1.10 - 1.13) (0.99 - 1.02) (0.89 - 0.93) (1.00 - 1.04) Cardiovascular disease status at enrollment 0.82 1.01 1.57 0.99 1.25 1.17 1.22 18 (0.81 - 0.83) (0.99 - 1.02) (1.54 - 1.58) (0.97 - 0.99) (1.23 - 1.25) (1.15 - 1.19) (1.20 - 1.24) Cog. impairment and dementia status at enrollment 1.15 1.25 3.51 1.07 1.33 1.14 1.00 (1.13 - 1.17) (1.23 - 1.26) (3.46 - 3.55) (1.05 - 1.09) (1.30 - 1.34) (1.12 - 1.15) (0.98 - 1.02) Obesity status at enrollment 1.70 1.42 1.33 1.35 1.56 1.98 2.50 (1.68 - 1.70) (1.40 - 1.44) (1.31 - 1.34) (1.32 - 1.36) (1.54 - 1.57) (1.95 - 1.99) (2.47 - 2.53) Alcohol-related disease status at enrollment 1.61 2.90 1.58 1.32 1.70 2.05 1.83 (1.55 - 1.66) (2.77 - 3.04) (1.51 - 1.65) (1.25 - 1.39) (1.64 - 1.75) (1.97 - 2.12) (1.74 - 1.91) Substance abuse status at enrollment 1.27 2.22 1.57 0.73 0.95 1.21 0.98 (1.10 - 1.45) (1.81 - 2.70) (1.33 - 1.85) (0.56 - 0.93) (0.82 - 1.09) (1.02 - 1.42) (0.80 - 1.19) Smoking status at enrollment 1.44 1.72 1.77 1.78 2.64 2.02 1.39 (1.42 - 1.45) (1.69 - 1.74) (1.74 - 1.79) (1.75 - 1.80) (2.62 - 2.66) (2.00 - 2.04) (1.36 - 1.40) Observations 3381256 5907482 6215432 5981854 5658282 5027839 4829266 *Robust 95% CI in parentheses. All values significant at the 5% level unless in italics. Significance of the HIV odds ratio is robust to model specifications (see Appendix) Table3: Odds ratios for HIV in logistic regressions for Cohort 1 (ever diagnosed with HIV), using different intervals for chronic disease onset. Interval Diabetes Hypertension Stroke Cancer Lung Disease Cardiovascular Disease Cognitive Impairment & Dementia 2-year 1.30 1.13 1.28 1.44 1.26 1.30 1.66 (1.19-1.42) (1.06-1.20) (1.16-1.40) (1.31-1.58) (1.19-1.34) (1.20-1.40) (1.48-1.86) 5-year 1.44 1.22 1.43 1.44 1.25 1.38 1.56 (1.33-1.57) (1.13-1.31) (1.30- 1.57) (1.31-1.58) (1.17-1.34) (1.28-1.49) (1.40-1.72) 10-year 1.45 1.36 1.45 1.37 1.33 1.37 1.73 19 (1.24-1.70) (1.17-1.58) (1.22-1.73) (1.15-1.64) (1.16-1.52) (1.19-1.58) (1.44-2.08) 95% CI in Parentheses. All values statistically significantly different from 1 at the 5% level unless in italics. Table4: Odds Ratios for HIV in logistic regressions for Cohort 2 & 3*, using different intervals for chronic disease onset. Interval Diabetes Hypertension Stroke Cancer Lung Disease Cardiovascular Disease Cognitive Impairment & Dementia Cohort 2 2-year 0.88 0.90 1.02 1.17 1.04 0.94 1.18 (0.78-0.99) (0.84-0.97) (0.90-1.15) (1.04-1.31) (0.97-1.13) (0.85-1.04) (1.01-1.38) 5-year 1.09 1.07 1.23 1.34 1.05 1.11 1.33 (0.97-1.22) (0.98- 1.17) (1.08-1.40) (1.19-1.51) (0.96- 1.15) (1.00-1.22) (1.15-1.54) 10-year 1.18 1.23 1.40 1.31 1.14 1.19 1.75 (0.95-1.47) (1.00-1.49) (1.09-1.81) (1.03-1.68) (0.95-1.37) (0.97-1.47) (1.34-2.29) Cohort 3 2-year 2.92 2.10 2.02 2.30 2.04 2.51 2.97 (2.56-3.34) (1.88-2.34) (1.73-2.37) (1.98-2.68) (1.83-2.27) (2.22-2.84) (2.50-3.51) 5-year 2.17 1.55 1.73 1.59 1.63 1.92 1.87 (1.92-2.46) (1.37-1.75) (1.50-1.99) (1.38-1.84) (1.46-1.81) (1.71-2.15) (1.61-2.17) 10-year 1.84 1.57 1.50 1.44 1.58 1.58 1.71 (1.47-2.30) (1.24-1.98) (1.17-1.92) (1.11-1.86) (1.30-1.92) (1.29-1.95) (1.34-2.20) 95% CI in Parentheses. All values statistically significantly different from 1 at the 5% level unless in italics. *Cohort 2 (diagnosed with HIV prior to two years from enrollment); Cohort 3 (diagnosed with HIV after two years from enrollment). Comparison group: HIV- individuals 20 CHAPTER 2: THE EXCESS COSTS ASSOCIATED WITH HIV INFECTION IN CHRONIC CONDITIONS MANGAEMENT AMONG PEOPLE LIVING WITH HIV AND AIDS (PLWHA) AGED 50 AND OLDER INTRODUCTION The statistics from the US Centers for Disease Control and Prevention showed that 1,140,400 people aged 13 and older were living with HIV/AIDS (PLWHA), which could be translated to a prevalence rate of 421.4 per 100,000 people in the United States at the end of 2016. 1 Among these PLWHA, about half of them are aged 50 and older. 2 These numbers suggest the life expectancy of PLWHA is increasing. The development and availability of antiretroviral therapy (ART) for HIV treatment plays an important role in elongating the life expectancy of PLWHA. The conditional life expectancy for a 20-year-old treated HIV-infected person had increased from 19 years in 1996 to 53 years in 2011, which means treated PLWHA can now live into their 70s according to a study conducted in California, USA. 3 A cross-country study 4 also found that the expected age at death for PLWHA who had better response to ART treatment, i.e., people who had 350 or more cells per μL 1 year after starting ART, was 78. This number is very close to the expected age at death of the US general population – 78 in men and 82 in women. These findings imply that the gap of the life expectancy between treated PLWHA and people who don’t have HIV/AIDS is closing. However, new challenges arise with the increasing life expectancy among PLWHA. The increased life expectancy among PLWHA indicates that PLWHA will need more healthcare resources to deal with aging-related chronic conditions, such as diabetes, hypertension, stroke, lung diseases, 21 cancers, cardiovascular diseases, and dementia. PLWHA might face a higher risk of developing these aging-related chronic conditions, because chronic inflammation caused by HIV infection might accelerate aging process as well as increase the probability of development of other chronic conditions. 5 In a Canadian study examining PLWHA aged 50 and older, researchers found that incidence of diabetes was 1.39 times higher than the general Canadian population of the similar age. 6 One mechanism by which this may occur is through interaction with ART; ART drugs could increase a PLWHA’s weight and interfere with glucose metabolism by increasing insulin resistance and reducing insulin secretion. 7,8 PLWHA on ART treatment are also at higher risk of developing hypertension than the general population because ART drugs might induce endothelial dysfunction. A systematic review concluded that ART increases systolic blood pressure. 9 Antiretroviral drugs are not the only factors that would affect PLWHA’s physiological system. HIV infection itself can also affect a PLWHA’s physiological system and cause other chronic conditions, such as stroke. HIV infection is also linked to endothelial dysfunction that might accelerate atherosclerosis, elevating stroke risk. 10 An American study conducted among PLWHA also found that PLWHA were at higher risk of developing stroke. 11 HIV-infection induced endothelial dysfunction is also a precursor to cardiovascular disease, 12 and the elevated rates of diabetes, hypertension, and stroke also increase the risk of cardiovascular disease. 22 Cognitive impairment and dementia is another class of chronic conditions that have been observed with higher prevalence among PLWHA than people without HIV/AIDS. HIV could pass the blood-brain barrier and cause HIV encephalitis, which could manifest as dementia. Opportunistic infections are also associated with neurodegenerative diseases among PLWHA. Despite the effectiveness of the new generation ART, neurotoxic HIV proteins can still often be found in PLWHA’s brains. This could be the reason why we are still seeing increase in the cognitive impairment or dementia among PLWHA treated with ART drugs. 13 These risks for cognitive diseases might be exacerbated by the aging-related comorbidities; another recent review argues that cardiovascular diseases, especially the ones that develop during middle-age, is a risk factor for vascular dementia and other types of dementia, such as Alzheimer’s disease. 14 People living with HIV/AIDS are also more likely to have lung diseases and lung cancer in addition to the chronic conditions mentioned above. Chronic systemic inflammation induced by HIV is linked to increased risk of lung infection and chronic lung disease. Antiretroviral drugs can also impact on lung function by causing immune reconstitution inflammatory syndrome to worsen respiratory status. 15 PLWHA also exhibit heightened risk of developing cancers. In a study conducted across the United States using data from 1996 to 2012, researchers found that the standard incidence ratio (SIR) of developing cancers among PLWHA compared to the general population is 1.69 (95% CI 1.67 - 1.72), indicating that PLWHA have an excess of 69% probability of developing cancer compared to the general population. 16 Another study examining US Medicare enrollees using data from 23 2004 to 2011 found that PLWHA are more likely to have lung cancer, liver cancer, non-Hodgkin lymphoma, Hodgkin lymphoma, and anal cancer. 17 It is expected that healthcare expenditures among PLWHA will rise as this population ages, particularly as PLWHA have higher risks of developing chronic conditions. A study estimated that the total lifetime healthcare expenditure for a hypothetical 20-year-old HIV-infected patient who achieved a life expectancy as long as the general population in the United States would exceed 1.6 million. 18 Nevertheless, the excess expenditure caused by the interaction between HIV infection and aging -related comorbidities is not yet well estimated. In this study, we aimed to estimate the excess expenditure linked with HIV infection and aging-related comorbidities among older PLWHA to provide a better understanding of healthcare expenditure landscape of older PLWHA. METHODS We used data from de-identified Clinformatics Data Mart® (OptumInsight, Eden Prairie, MN) from year 2007 to 2016. This claims data contains relatively comprehensive information on millions of enrollees including social economic status, basic demographic information, and medical-related information, such as diagnoses and treatments. We excluded enrollees without complete demographic or medical-related information. We also excluded enrollees who had not been followed for at least one year from their enrollment. The seven therapeutic areas we were interested in were diabetes, hypertension, stroke, cancers, lung diseases, cardiovascular diseases, 24 and cognitive impairment and dementia. Cancers consisted of prostate cancer, breast cancer, colorectal cancer, endometrial cancer, and lung cancer; lung diseases consisted of asthma and chronic obstructive pulmonary disease (COPD); cardiovascular diseases consisted of atrial fibrillation and ischemic heart diseases; cognitive impairment and dementia consisted of non- Alzheimer’s dementia, Alzheimer’s dementia, and cognitive impairment. We identified these diseases using ICD-9 codes, or ICD-10 codes if applicable. We kept all PLWHA that were aged 50 and 51 and still remained in the data pool after exclusion. This group of people was our reference cohort that we would later refer to when creating an initial cohort using the Future Elderly Model (FEM). The FEM is a well-established microsimulation model created by Dr. Goldman and his colleagues. 20 The FEM has three modules: the initial cohort module, the transition module, and the policy outcomes module. The initial cohort module projects the economic and health outcomes of incoming new age 51- and 52- year-old cohorts. This module sources HRS data as well as trends estimated by other sources. This module enables researchers to create study cohort along the way during the simulation process so that economic and health outcomes can be estimated for the cohort aged 51 and older any year. We modified the default initial cohort module of the model so that it could match our reference cohort as closely as possible in terms of the composition of the population, such as gender ratio of the cohort, race/ethnicity distribution, and the prevalence of the 7 disease areas (diabetes, hypertension, stroke, lung diseases, cancers, cardiovascular diseases, and dementia) that we are interested in. The characteristics of our modified initial cohort were 25 presented in Table 21. We conducted a cohort simulation thus we did not have incoming new cohorts. The transition module in the FEM estimates the transition probabilities across a variety of health states and economic outcomes. This module also adjusts for risk factors, including smoking, body weight, age, education, as well as lagged health and financial states. These adjustments allow researchers to account for greater heterogeneity. The default transition probabilities are estimated using the longitudinal data from HRS. We modified these transition probabilities by multiplying them with the relative risk of each chronic condition derived from Chapter 1, 20 except mortality, for which we used the result from the work of Croxford et al. (2017). 21 The relative risks were listed in Table 22. Lastly, the policy outcomes module has aggregated projections of person- level outcomes such as medical care costs. We ran the FEM twice, the first time on the people living with HIV/AIDS cohort and the second on the people without HIV/AIDS cohort (the non-PLWHA population), to examine the magnitude of the effect of HIV infection on the predicted health and economic outcomes. These two cohorts had exactly the same distribution in terms of gender, race/ethnicity, age, and health status. The only thing that was different was their HIV/AIDS status. We did both scenario analyses and two-way sensitivity analyses to evaluate how our results would change as we changed relative risks of aging-related comorbidities and mortality. In scenario analyses, we examined the scenario in which HIV infection is not associated with excess 26 mortality but only aging-related comorbidities, in other words, if a PLWHA faced the same risk of dying as a sex-, age-, and race-matched individual in the non-PLWHA population. Another scenario we examined the opposite situation: HIV infection is not associated with elevated risks in developing any aging-related chronic comorbidities but only excess mortality, that is, PLWHA have the same probabilities of developing chronic conditions as the non-PLWHA population. In two-way sensitivity analyses, we tested different relative risks of HIV-associated mortality and different relative risks of HIV-associated chronic condition to examine the interaction between HIV-associated mortality and HIV-associated risks of developing chronic condition and the effect of the interaction between these two factors on the costs of healthcare management among older PLWHA. All costs were reported in 2019 USD. RESULTS Both PLWHA and the non-PLWHA cohorts started with hypothetical 7.05 million people at age 51 and with the same prevalence and incidence for death and all the seven aging-related chronic conditions we were interested: diabetes, hypertension, stroke, cardiovascular diseases, cognitive impairment and dementia, lung diseases, and cancers in the FEM simulation. They all had the same total annual medical expenditure per capita, total annual Medicare expenditure per capita, and total annual out-of-pocket expenditure per capita as well in the beginning of the simulation. People living with HIV/AIDS had excess risks of death and developing other aging-related chronic comorbidities thus the projected prevalence and incidence and other healthcare outcomes would be different between PLWHA and non-PLWHA. With an excess probability of mortality at 27 460%, the expected age at death was 80.6 for PLWHA and 89.1 for the non-PLWHA. Figure 2 showed the projection of the population size for both cohorts over time. Table 23 showed the projections of prevalence of the same seven chronic conditions respectively. PLWHA cohort did not necessarily have higher prevalence of every chronic condition at every given age. The high mortality among PLWHA could explained why we did not see a consistently higher prevalence in the PLWHA cohort. Table 24 showed the projections of incidence of diabetes, hypertension, stroke, cardiovascular diseases, cognitive impairment and dementia, lung disease, and cancers respectively. In general, the PLWHA cohort had a higher incidence of every chronic condition from age 65 to 70. At age 51, the annual average medical expenditures per capita, including Medicare expenditures and out-of-pocket expenditures, were projected to be $13,378 for both the PLWHA and the non- PLWHA; the annual average Medicare expenditure per capita was $8,068 for the PLWHA cohort and $8,057 for the non-PLWHA. At age 65, when the non-PLWHA cohort starts to join Medicare, the annual average medical expenditures per capita was projected to be $41,335 for the PLWHA cohort and $41,658 for the non-PLWHA while the annual average Medicare expenditure per capita was projected to be $30,730 for the PLWHA cohort and $23,455 for the general population. When the cohorts reach age 79, one year before the expected age of death for the PLWHA cohort, the projected annual average medical expenditures for each person was $7,902 for the PLWHA cohort and $109,434 for the non-PLWHA the projected annual Medicare expenditure was $6,086 for the PLWHA cohort and $44,880 for the general population. Table 25 shows the projected 28 longitudinal trends for the annual average medical expenditures per capita and the annual average Medicare expenditure per capita for both cohorts. In general, the annual medical expenditures on healthcare for the PLWHA cohort were higher than the non-PLWHA cohort before age 65. However, the annual average Medicare expenditure for the PLWHA cohort was consistently higher than that of the general population until age 77 when most of the population in the PLWHA cohort died. This suggested that the PLWHA cohort relied heavily on Medicare to cover their healthcare expenses. In scenario analyses, we examined two scenarios at age 55, 65, and 75 respectively: a scenario in which we only adjusted the transitional module with the HIV-associated mortality so the PLWHA had the same level of the risk of developing all chronic conditions as the general population, and a scenario in which we only adjusted the transitional module with the HIV-associated chronic condition relative risks without modifying mortality – in other words, the PLWHA shared the same mortality rate as the non-PLWHA cohort. The results were presented as the difference in the annual average medical expenditures per capita and the annual average Medicare expenditure in Table 26. The results from the base scenario and the scenario with only HIV- associated mortality adjustment showed similar patterns: the PLWHA cohort had both greater annual average medical expenditures and annual average Medicare expenditure per capita. As the PLWHA cohort aged, the PLWHA cohort still had greater annual average Medicare expenditure per capita than the non-PLWHA. However, the PLWHA cohort no longer spent more than the general population when looked at the annual average medical expenditures. 29 The other scenario, which adjusted only the HIV-associated chronic condition relative risks showed a different story. In this scenario, the PLWHA cohort consistently had greater amount of expenditures in both expenditure categories at all ages. The results from these two scenario analyses suggested that the HIV-associated mortality is the main driver that was driving the healthcare expenditures among the PLWHA population. In two-way sensitivity analyses, we examined the interaction between the HIV-associated mortality relative risks and the HIV-associated chronic condition relative risks at 1, 1.05, and 1.3. These values were chosen arbitrarily. The results are presented as the difference in annual average medical expenditures per capita at age 65 between the PLWHA cohort and the general population cohort (Table 27). The results show that the incremental amount in annual average medical expenditures the PLWHA cohort spent compared to the non-PLWHA cohort increased when increasing the HIV-associated chronic condition relative risks under a fixed level of the HIV- associated mortality. However, the annual average medical expenditures of the PLWHA cohort became less than that of the non-PLWHA cohort as the HIV-associated mortality increased with a fixed level of HIV-associated chronic comorbidity relative risk. DISCUSSION We reported an expected age at death of 80 for PLWHA and around 89 for non-PLWHA. Our estimation of life expectancy for PLWHA was very similar to what other published had reported, 4 As for the estimation of life expectancy for non-PLWHA, it was very close the estimation from the 30 US Social Security Administration’s 2019 Annual Report of the Board of Trustees of the Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds. (89 vs. 84). In general, the PLWHA cohort was projected to have higher incidence of aging-related chronic conditions that we examined before they reached age 65. The prevalence of aging-related chronic comorbidities examined among PLWHA might not be higher, which could be attributed to the high PLWHA mortality. The projected annual average medical expenditures per capita for the PLWHA cohort, which included both annual Medicare expenditures and annual out-of-pocket expenditures, presented a similar pattern in that it is higher than that of the non-PLWHA cohort before age 65, though the difference was not greatly large. However, the PLWHA cohort was projected to have higher Medicare utilization from age 51 to around age 75. At age 55, a person living with HIV/AIDS was projected to spend $6,695 more on average than a person without HIV/AIDS in Medicare every year and $4,962 more in total medical expenditures yearly. When age 65 is reached, those in the PLWHA cohort were predicted to spend $7,274 per person more than the non-PLWHA cohort in Medicare every year, but the annual total medical expenditures per person was predicted to be less than that of the non-PLWHA cohort by $322. By the time they reached age 75, 5 years to PLWHA’s projected age at death, a person living with HIV/AIDS would still spend 4,868 more in Medicare every year (Table 2 5). The HIV-associated mortality among PLWHA played an important role in prevalence projections and healthcare cost projections. However, the uncertainty in mortality of PLWHA made it difficult to project the HIV/AIDS-associated disease burden precisely. Several factors contribute to the 31 uncertainty in the mortality of PLWHA. For instance, it varies by the age that a PLWHA started ART treatment; the life expectancy for a 20-year-old starting ART treatment in the 1990s are likely to be different from that of a 20-year-old starting treatment in the 2000s. HIV/AIDS status when a PLWHA was diagnosed will also change mortality risk – whether it is a delayed diagnosis or not is likely to affect HIV-associated mortality. Other factors such as HCV co-infection and drug addiction may also affect the mortality of PLWHA. 4,21-23 To address this limitation, we ran both scenario analyses and two-way sensitivity analyses to examine how our outcomes would change as HIV-associated mortality of PLWHA varied. Another limitation was that we conducted this projection based on a privately insured population and its aging-associated chronic condition relative risks, which might not be nationally representative. To address this issue, we compared the relative risks used in this study with other studies and found that our relative risks were very similar to what other studies had found. For instance, Mayer et al. (2018) 24 found that PLWHA in the US community were more likely to be diagnosed with diabetes (odds ratio=1.18, 95% CI, 1.22-1.41), hypertension (odds ratio=1.38, 95% CI, 1.31-1.46), cancers other than human papillomavirus (odds ratio=1.25, 95% CI, 1.10-1.42), and stroke (odds ratio=1.32, 95% CI, 1.06-1.63). Friedman et al. (2016) 25 looked into Medicare population and also found that PLWHA aged 65 and older were more likely to have diabetes (odds ratio=2.08 95% CI, 2.02-2.13, adjusted odds ratio=1.51, 95% CI, 1.47-1.55), hypertension (odds ratio=2.08, 95% CI, 2.00-2.15, adjusted odds ratio=2.01, 95% CI, 1.94-2.09), and ischemic heart disease (odds ratio=1.84, 95% CI, 1.79-1.89, adjusted odds ratio=1.82, 95% CI, 1.77-1.86). Bigna et al. (2018) 26 conducted a systematic review and found that PLWHA had greater risk of 32 developing lung disease (pooled odds ratio=1.14, 95% CI, 1.05-1.25). De Ronchi et al. 27 found that lower CD4 + cell count was associated with higher risks of developing cognitive impairment. Another study also reported a prevalence range from 30% to 60% of cognitive impairment among PLWHA (see Table 28). 28 Unable to calculate the 95% Cis for our non-PLWHA is another limitation. Although we were able to calculate the 95% CIs of the outcomes for our PLWHA by applying the 95% CI estimated for the relative risks of diseases we used to modify the FEM transition module from Chapter 1, we were not able to calculate the 95% CIs of the outcomes for our non-PLWHA because it required extensive amount of time to run the bootstrap steps and re-sampling the FEM data. Besides, it also required information which was not accessible. Our results were also subject to selection bias, in that our study was based on a privately insured population who were self-selected to be covered which made our PLWHA population relatively healthier and wealthier than the general PLWHA population. Our sensitivity analyses could also address this limitation in that we examined different HIV/AIDS-associated chronic condition relative risks and mortality, which could be also seen as reflection of different populations with different health statuses. In conclusion, our study showed that the PLWHA cohort had both higher annual average medical expenditures and annual average Medicare expenditure prior to age 65. After age 65, the annual average medical expenditures of the PLWHA cohort was not necessarily projected to exceed that of the non-PLWHA cohort because of high HIV-associated mortality, however the PLWHA cohort was projected to continue to have higher annual average Medicare expenditure after age 65 until the end of life. The results suggest that the PLWHA cohort rely heavily on Medicare, and the 33 Medicare system will be facing a greater economic burden as HIV-associated mortality decreases with the development of more effective HIV/AIDS treatment. Policymakers and other stakeholders should be ready for this upcoming challenge. Re-evaluating the current healthcare system’s capacity to ensure it can efficiently accommodate the older PLWHA’s health needs. The current PLWHA assistance programs such as Ryan White and other state-run programs might also need some changes to better serve the PLWHA population in the era when PLWHA can live into their 70s or older. 34 TABLES AND FIGURES Table 21: Our modified initial cohort aged 50/51 Percentage Male 63.13% White 31.29% Black 41.64% Hispanic 22.02% Annual household income <50,000 USD 53.58% Education level < High school 5.24% Smoking 39.78% Diabetes 40.05% Hypertension 60.48% Stroke 11.14% Cancer 13.53% Lung Disease 35.54% Cardiovascular Disease 34.22% Dementia/Mild Cognitive Impairment 12.20% Table 6. Relative risks used in the FEM simulation 35 Table 22: Relative risks used in the FEM simulation Relative Risk 95% CI Source Diabetes 1.30 1.19 1.42 Yang et al. (2019) 20 Hypertension 1.10 1.06 1.20 Stroke 1.28 1.16 1.40 Cardiovascular Diseases 1.30 1.20 1.40 Cognitive Impairment and Dementia 1.66 1.48 1.86 Lung Diseases 1.27 1.19 1.35 Cancers 1.44 1.31 1.58 Mortality 5.70 5.50 5.80 Croxford et al. (2017) 21 36 Figure 2. Projection of population size by age 1 10 100 1000 10000 100000 1000000 10000000 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 Population Size Age Non-PLWHA PLWHA 37 Table 23: Prevalence of each comorbidity over age Age HIV Status Diabetes Hypertension Stroke Cancers Lung Diseases Cardiovascular Diseases MCI & AD 55 Non-PLWHA 0.26 0.56 0.08 0.13 0.18 0.22 0.07 PLWHA 0.26 0.54 0.08 0.15 0.14 0.23 0.12 95% CI Lower 0.25 0.44 0.07 0.13 0.14 0.22 0.09 95% CI Upper 0.27 0.64 0.08 0.16 0.14 0.24 0.14 65 Non-PLWHA 0.33 0.73 0.22 0.23 0.16 0.41 0.25 PLWHA 0.27 0.61 0.19 0.19 0.06 0.36 0.34 95% CI Lower 0.26 0.42 0.15 0.16 0.05 0.31 0.27 95% CI Upper 0.38 2.05 0.79 0.40 0.05 1.47 1.54 75 Non-PLWHA 0.32 0.80 0.39 0.26 0.12 0.53 0.59 PLWHA 0.11 0.66 0.24 0.09 0.02 0.43 0.43 95% CI Lower 0.09 0.61 0.20 0.03 0.02 0.32 0.30 95% CI Upper 0.14 0.71 0.28 0.15 0.02 0.53 0.57 38 Table 24: Incidence of each comorbidity over age Age HIV Status Diabetes Hypertension Stroke Cancers Lund Diseases Cardiovascular Diseases MCI & AD 55 Non-PLWHA 0.04 0.12 0.03 0.04 0.02 0.06 0.05 PLWHA 0.05 0.13 0.03 0.06 0.02 0.07 0.08 95% CI Lower 0.05 0.02 0.03 0.05 0.02 0.06 0.07 95% CI Upper 0.06 0.24 0.04 0.07 0.02 0.08 0.09 65 Non-PLWHA 0.03 0.11 0.05 0.03 0.01 0.07 0.15 PLWHA 0.03 0.07 0.05 0.05 0.02 0.07 0.17 95% CI Lower 0.02 -0.01 0.04 0.04 0.02 0.05 0.16 95% CI Upper 1.54 0.01 0.25 0.39 0.16 0.05 0.28 75 Non-PLWHA 0.02 0.10 0.09 0.03 0.01 0.10 0.33 PLWHA 0.00 0.06 0.12 0.03 0.02 0.07 0.28 95% CI Lower 0.00 0.02 0.09 -0.01 0.02 0.04 0.22 95% CI Upper 0.00 0.10 0.14 0.07 0.02 0.10 0.34 39 Table 25: Healthcare expenditure over age Age HIV Status Total annual medical cost (2019 USD) Total annual Medicare cost (2019 USD) 55 Non-PLWHA 21180.71 13502.71 PLWHA 26143.05 20197.83 95% CI Lower 25174.62 19384.97 95% CI Upper 27111.48 21010.69 65 Non-PLWHA 41657.56 23455.05 PLWHA 41335.05 30729.72 95% CI Lower 39280.58 28523.91 95% CI Upper 43389.52 32935.53 75 Non-PLWHA 85401.43 38830.80 PLWHA 61225.69 43698.49 95% CI Lower 48043.49 33364.91 95% CI Upper 74407.89 54032.07 40 Table 26: Scenario analyses: projected difference in annual average medical expenditures per capita and annual average Medicare expenditure per capita between the PLWHA cohort and the HIV negative cohort at age 55, 65, 75 Age 55 Age 65 Age 75 Total Medicare Total Medicare Total Medicare Base 4,551.35 6,695.12 -295.8 7,274.67 -22,173.48 4,267.69 Adjusted for HIV-associated mortality only 3,940.82 5,795.53 -2,836.31 4,687.11 -22,309.57 1,385.68 Adjusted for HIV-associated chronic condition relative risks only 975.69 760.49 2,723.11 2,016.05 4,834 3,086.03 41 Table 27: Two-way sensitivity analyses: projected difference in annual average medical expenditures per capita at age 65 between the PLWHA cohort and the HIV negative cohort HIV-associated Chronic Comorbidities RR 1 1.05 1.3 HIV-associated Mortality RR 1 0 299.33 1,052.89 1.05 -320.33 128.84 2,410.31 1.3 -1,124.04 -680.23 1,557.68 42 Table 28: Odds ratios reported from other studies Mayer et al. (2018) Friedman et al. (2016) Bigna et al. (2018) Odds Ratio 95% CI Odds Ratio 95% CI Odds Ratio 95% CI Diabetes 1.18 1.22-1.41 1.51 1.47-1.55 - - Hypertension 1.38 1.31-1.46 2.01 1.94-2.09 - - Stroke 1.32 1.06-1.63 - - - - Cancers 1.25 1.10-1.42 - - - - Lung Diseases - - - - 1.14 1.05-1.25 Cardiovascular Diseases - - 1.82 1.77-1.86 - - Mild Cognitive Impairment and Dementia - - - - - - 43 CHAPTER 3: THE COST-EFFECTIVENESS ANALYSIS OF REGULAR DEMENTIA SCREENING AMONG PEOPLE LIVING WITH HIV/AIDS AGED 50 AND OLDER INTRODUCTION Approximately 6.08 million people had Alzheimer’s Disease (AD) or mild cognitive impairment (MCI) associated with AD, and 46.7 million people had preclinical AD, in the United States in 2017. It is projected that 15 million Americans will have AD or AD-caused MCI by 2060. 1 People living with HIV/AIDS (PLWHA) are at higher risk of developing AD or MCI because of the linkage of the HIV infection and the increase in the generation of the beta-amyloid peptide (Αβ). 2,3 The accumulation of the beta-amyloid peptide in brain is one of the key characteristics of AD. Another characteristic of AD is the dysfunction of neurotransmitter system, which is believed to be the main driver of neuropsychological and neuropsychiatric symptoms found among AD patients, such as memory loss, learning difficulties, language impairment, depression, aggression, hallucination, delusion, etc. 4,5 There currently exist several options for managing AD for diagnosed patients. Neurotransmitter- based treatments for AD patients have been a popular strategy to manage AD since the advent of the first cholinesterase inhibitor in 1997. 6 Cholinesterase inhibitors prevent the degeneration of acetylcholine, which is an important neurotransmitter involving in memory function. 7 Another common neurotransmitter-based treatment is N-methyl-d-aspartate (NMDA) receptor antagonist, which blocks abnormal neurotransmission caused by a neurotransmitter called glutamate. 8 Moreover, NMDA receptors might play an important role in actions of Αβ on neurotransmission. NMDA receptors might also engage in the generation of Αβ. 9 Common 44 cholinesterase inhibitors prescribed are donepezil, rivastigmine, and galantamine. Cholinesterase inhibitors are mostly used to manage mild to severe dementia, while memantine, an NMDA receptor antagonist, is recommended to be used to manage moderate to severe dementia. Memantine can be used as monotherapy or combination treatment with cholinesterase inhibitors. 10,11 Anti-amyloid treatments that manage AD via reducing the accumulation of beta-amyloid peptide in brain have also gained attention from researchers as a potential focus for therapy. 12 Recently, a previously discontinued Αβ-antibody, aducanumab, had been resurrected because of its clinical effectiveness in the reduction in the deterioration of cognitive function. Aducanumab had shown its capability of reducing Αβ accumulation in its phase 1 clinical trial and hence moved on to phase 2 and phase 3 clinical trials. 13 However, the manufacturer discontinued the phase 3 clinical trials due to its failure in futility tests. Surprisingly, the manufacture later announced that they are applying for US Food and Drug Administration (FDA) market approval for aducanumab after they reanalyzed their data and found some significant and positive findings among a subgroup of patients who received high dose of aducanumab in one of their phase 3 clinical trials. 14 If US FDA grants aducanumab market approval, aducanumab will be the first new AD drug approved in almost 20 years. Despite the availability of neurotransmitter-based treatments that help with the mitigation of AD symptoms, most AD patients are unlikely to receive timely treatment due to delayed diagnoses. It is estimated that AD patients experienced an average of 2.5 years in delay of AD diagnosis. 15 45 Timely treatment is critical in that it allows early interventions that are not only beneficial to AD patients but also cost saving by reducing costs of institutionalization. A variety of reasons might contribute to the delay in AD diagnosis. For example, patients may postpone seeking medical help because of the fear of losing their independence such as losing his or her driver’s license if the results confirm their demented status. Other patients normalize memory difficulty or prioritize physical health conditions thus delaying diagnosis. From a healthcare providers’ perspective, screening patients and treating patients at early stages of dementia might not be an efficient strategy when current available neurotransmitter-based treatments do not manifest dramatic improvement of cognitive function among patients at an early stage of dementia. 16-18 In a presentation at the 2019 Clinical Trials on Alzheimer’s Disease (CTAD) conference in San Diego, California, United States, the manufacturer discussed data from two phase 3 clinical trials that showed that participants in the high-dose arm in one of the two clinical trials had an approximate 23% improvement in cognitive function test scores compared to the placebo arm. 19 In light of the potential availability of aducanumab for the older PLWHA population, we examine the value of implementing regular cognitive function screenings for aging PLWHA to better manage cognitive health and control healthcare expenditures. This study compares the cost- effectiveness between the current standard neurotransmitter-based treatment without regular cognitive function screenings and the hypothetical aducanumab treatment with regular cognitive function screenings. We evaluate the cost-effectiveness of the implementation of regular 46 cognitive function screenings among aging PLWHA under the assumption that aducanumab is accessible. METHODS Overview We built a Markov chain simulation model with six health states to model the healthcare expenditures and clinical effectiveness of the implementation of regular Mini-Mental State Examination (MMSE) 20 when we assume diagnosed cases would be treated with aducanumab among aging PLWHA. The Mini-Mental State Examination is widely used to measure cognitive functions among the elderly. It is composed of two parts: the first part measures attention, memory, and orientation functions; the second part tests language and visuospatial skills. We estimated the incremental cost-effectiveness ratio (ICER), which is the incremental cost incurred per quality-adjusted life-year (QALY) gained, to evaluate the cost-effectiveness of the combination of regular cognitive function screenings and aducanumab. Markov Model States The six health states in the model were normal cognitive function, mild cognitive impairment (MCI), Alzheimer’s Disease (AD), treated MCI, treated AD, and death (Figure 3). Our simulation cohort was 10,000 identical 50-year-old PLWHA whose HIV infection was well-managed (CD4 count above 500) and MMSE score was 30 at age 50. We assumed our simulation cohort did not have any other major underlying health conditions except HIV infection. We reported our model 47 parameters in Table 32. The duration of each simulation cycle was 1 year, and we used a lifetime time horizon. We adopted a US healthcare sector perspective 21 and reported all costs in 2019 US dollars. We applied an annual discount rate of 3% to reflect social time preferences associated with outcomes. 21 All of analyses in this study were performed using Microsoft Excel 2019 version 16.35. Transition Probabilities We drew transition probabilities from the work of Davis et al. 22 in which they estimated age- specific one-year transition probabilities from normal cognitive function to mild cognitive impairment and to AD. They used longitudinal patient-level data from the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) that collected data on all patients enrolled in Alzheimer’s Disease Centers in the United States. Although our base case was younger than their study population (50 years-old vs. 65+ years-old), we believed that it was still reasonable to adopt their values because of the potential impact of HIV infection on accelerating aging process. 23 To calculate death probabilities appropriate for PLWHA in each health state, we multiplied the death probability for the general population with normal cognitive function estimated by Davis et al. 21 by the standardized mortality ratio (SMR) of PLWHA and cognitive health state-specific relative risk of mortality for PLWHA to generate PLWHA-specific MCI death probability and PLWHA-specific AD death probability. 24 The death probabilities for treated states were further modified by the treatment effectiveness. 25,26 Since we did not have mortality data on 48 aducanumab, we assumed it reduced death probability by the same magnitude as observed in neurotransmitter-based treatments. As for other state transition probabilities, we modified transition probabilities by multiplying relative risk that reflected the treatment effectiveness and health state-specific sensitivity of MMSE screening to calculate transition probabilities from MCI to treated MCI and AD to treated AD. 27-30 We assumed that the treatment effectiveness was similar between PLWHA and the general population hence we could apply the treatment effectiveness observed from the general population to PLWHA population. In the status quo scenario, we assumed the average delay in MCI diagnosis and AD diagnosis were the same (2.5 years) 16 We additionally assumed that Aducanumab could lower the Clinical Dementia Rating Scale - Sum of Boxes (CDR-SB) scores by 0.4 points, which could be translated to 23% of the improvement in the cognitive function performance and to about 1 year delay in the progression from mild cognitive impairment to dementia. We then converted this delay to the reduction in the transition probabilities from treated MCI to treated AD in our regular cognitive function screening scenario by applying disease progression model developed by Samtani et al. 31 Costs We extracted both direct and indirect costs from literature. 32-34 We assumed healthcare expenditures incurred in MCI and AD states were the same as in the normal cognitive state 49 because individuals in MCI and AD states were not regularly screened nor treated; therefore no cognitive function treatment-related costs would be generated. Therefore, for normal cognitive function, MCI, and AD states, we only needed expenditures associated with HIV treatment. We applied annual HIV treatment expenditures for those with CD4 counts equal or greater than 500 since we assumed our population had well-controlled HIV/AIDS. 32 We calculated both healthcare expenditures associated with cognitive function treatments and HIV treatments in treated MCI and treated AD states. We assumed people in normal cognitive function, MCI, treated MCI, and AD states did not utilize informal home care, other formal home care, or skilled nursing homes thus no related costs would incur. We included costs of informal home care, other formal home care, or skilled nursing homes in treated AD state. In addition, we also assumed the annual cost of aducanumab was the same as the current standard neurotransmitter-based treatment to give it a most competitive price against the current standard treatments since its price is unlikely to be equal to the current standard treatments, not to mention a price less than the current standard treatments, after it goes to the market. The idea is to test the lowest price possible of aducanumab to see if it is cost-effective. If it is not cost-effective at the potential lowest price, then it is unlikely to be cost-effective compared against the current standard options when it officially goes to the market with a higher price. Quality of Life We used quality-adjusted life-years (QALYs) 35 to measure the quality of life for our PLWHA population. Health state utility values were extracted from literature, shown in Table 32. 36,37 We assumed the aggregated utility associated with each health state for PLWHA was similar to that 50 of the general population since our PLWHA population was relatively healthy. We then calculated quality-adjusted life-years gained (QALYs) by summing up products of utility and life-years gained and discounting the QALYs value by annual discount rate for each scenario. Cost-effectiveness Analysis We calculated the incremental cost-effectiveness ratio (ICER), which is a ratio of difference in costs incurred to the difference in QALYs gained between two scenarios, to evaluate additional life-time cost incurred in order to gained additional benefit. We compared the ICER we estimated to the willingness-to-pay thresholds. Several different ways are available to define a willingness- to-pay threshold. 38-40 In this study, we chose a common willingness-to-pay threshold at $150,000 USD. We also calculated incremental net monetary benefit (NMB) (Figure 4) by multiplying the chosen willingness-to-pay threshold by the difference in QALYs gained and subtracting the difference in costs. A positive incremental NMB suggests cost-effectiveness. Sensitivity Analyses We performed both deterministic, includes both one-way sensitivity analysis and two-way sensitivity analysis, and probabilistic sensitivity analyses to address the uncertainty in our model parameter values. In our deterministic sensitivity analysis, we quantified uncertainty arising from uncertain inputs by varying the value of each parameter by +/- 10% or the range of its 95% confidence interval (if available) to examine how applying lower-bound or upper-bound value of parameters affect ICER value. The results of our one-way sensitivity analysis were presented in a tornado diagram (Figure 5), which allowed us to clearly see the range of changes in the ICER values as well as to identify influential parameters. We also did a scenario analysis to evaluate 51 how ICERs would change as the medical-associated cost or the effectiveness of aducanumab changes (Table 33). In addition to deterministic sensitivity analysis, we also performed probabilistic sensitivity analyses by conducting Monte Carlo simulations to account for the joint uncertainty in all parameters. We first assigned a probability distribution to each model parameter and drew a random value from this distribution (Table 32). We repeated this process 1000 times and to generate 1000 ICER estimations. We then calculated the percentage of ICERs that were cost- effective and plotted these results in a cost-effectiveness acceptability curve (CEAC) (Figure 6). We also plotted all ICERs estimated from the probabilistic sensitivity analyses in a cost- effectiveness plane (Figure 7) to see which quadrant these ICERs fell in. For model validation and budget impact analysis, please see Appendix. RESULTS We found that the total discounted lifetime costs for PLWHA in regular cognitive function screening accompanied by aducanumab was $139,032.0 and the total discounted QALYs gained was 11.06. The total discounted life-years gained was 21.53 and the average age at death was 71.53. For PLWHA in the scenario without regular cognitive function screening and treated with neurotransmitter-based treatments, the total discounted lifetime cost was $126,130.8 and the total discounted QALYs gained was 10.99 The total discounted life-years gained in this scenario was 21.44 and the age at death was 71.44. The estimated ICER was $186,920.5 per QALY gained. 52 The ICER value was greater than the $150,000 willingness-to-pay threshold we designated. As a result, regular cognitive function screening with aducanumab will not be considered cost- effective at $150,000 willingness-to-pay threshold. Our one-way sensitivity analysis showed that treatment effect of current treatment on AD and the relative risk of MCI mortality for PLWHA were the top two most influential factors in our model (Figure 5) because they contributed to the 2 of the widest ranges of changes in ICER values. The results of our two-way sensitivity analysis were presented in Table 33. In Table 33, dark orange-shadowed cells are ICERs considered cost-effective at a willingness-to-pay threshold of $150,000; light orange-shadowed cells are ICERs considered cost-effective at a willingness-to-pay threshold of $200,000. We found that aducanumab needs to reduce transition probability from MCI to AD to about 0.17 (the base case was 0.19) or lower in order to be considered cost-effective with current annual medical cost of $33061.16. If aducanumab were able to reduce the transition probability from MCI to AD to 0.07, then it could still be considered cost-effective with a medical cost of as high as $100,000. The results of our probabilistic sensitivity analysis were presented in Figure 6 and Figure 7. Figure 6 showed at a willingness-to-pay threshold of $150,000, there was only a 50% chance that the implementation of regular cognitive function screening with aducanumab treatment would be cost-effective compared to status quo scenario. 53 DISCCUSION We evaluated quality-adjusted life-years gained and life-time healthcare costs for PLWHA with Alzheimer’s Disease if we implement regular cognitive function screenings and treat people with aducanumab and compared them to the current standard neurotransmitter-based treatment without regular cognitive function screenings. We found that the regular cognitive function screenings and aducanumab treatment strategy was not cost-effective compared to the status quo scenario when the societal willingness-to-pay threshold was set at $150,000. We estimated that discounted total lifetime cost was $126,130.8 and the discounted total life years gained was 21.44 in status quo scenario; as for the regular cognitive function screening scenario, we estimated a $139,032.0 discounted total lifetime cost and 21.53 discounted total life years gained. The US Centers for Disease Control (CDC) and Prevention reported a lifetime cost of $440,414 in 2019 USD for PLWHA, 41 this number is greater than our total discounted lifetime cost, however, in our model, the probability of survival at each simulation was not one, as people could die at each cycle; once they died, they would not have any healthcare expenditures. As a result, the average annual cost decreased gradually as the simulation moved on to later ages, and in the end, we reached a smaller number of lifetime cost. When we assumed everyone in our model could stay alive from the beginning cycle to the last cycle, we estimated that the lifetime cost of HIV treatment after age 50 is be $414,922 in 2019 USD, which was very close to the number reported by CDC mentioned above. A study conducted by Schackman et al. 43 reported a similar lifetime cost as well, with lifetime cost of HIV treatment at age 35 was $326,500 54 in 2012 USD ($502,810 in 2019 USD). They also included non-medication costs and costs dealing with opportunistic infection and therefore their estimation was higher than ours. We also reported the estimated discounted total life years gained of about 21 for both scenarios (21.44 years for status quo scenario, 21.53 for regular cognitive screening scenario). Twenty-one discounted additional survival life years for 50-year-old PLWHA was also reasonable compared to a number reported by the systematic review performed by Ward et al. 44 In the Ward et al. study, they reported a discounted life expectancy of 22.4 years for PLWHA. In addition, our non- discounted life expectancy was also very similar to which Siddiqi et al. 45 reported (about 28 years vs. 30 years). Results from our probabilistic sensitivity analysis showed that most of the simulated ICER values located at the west and northwest side of the $150,000 threshold line; that is, most of the ICERs were not cost-effective. As for the probability of cost-effectiveness, the regular cognitive function screening with aducanumab had only 50% of chance to be deemed cost-effective at a willingness- to-pay threshold less than $150,000. Our analysis was subject to the uncertainty in parameter values, especially insufficient information on the clinical effectiveness of aducanumab and unknown aducanumab treatment cost. As a result, the increase in discounted total QALY gained was only 0.08. We also were not able to account for the interaction between HIV medications and MCI or Alzheimer’s Disease medications and how the interaction affect transition probabilities and survival. In addition, we 55 asl were not able to account for the patient compliance which plays an important role in the clinical effectiveness of medications either. To address these issues, we conducted both deterministic sensitivity analyses and probabilistic sensitivity analyses. We reported an ICER of $186,920.5 per QALY gained for the regular cognitive function screening accompanied by aducanumab treatment if the screening results suggest MCI or Alzheimer’s Disease. This ICER value was above the cost-effectiveness threshold using $150,000 willingness- to-pay threshold. This finding suggests that the implementation of regular cognitive function screening would not be considered cost-effective for PLWHA aged 50 and older when comparing with the status quo strategy with a willingness-to-pay threshold of $150,000. However, this does not mean we should give up aducanumab in that more detailed clinical trial reports on aducanumab might be released in the near future and thus we could learn more about its aducanumab clinical effectiveness, especially mortality. Despite the fact that QALYs gained in the regular cognitive screening with aducanumab scenario was not as large as expected (only 0.07 QALY) , the future cost of aducanumab is very likely to be higher than the cost of neurotransmitter-based treatments, and the combination of regular cognitive function screenings with aducanumab doesn’t seem to be cost-effective in our study, regular cognitive function screenings still offer healthcare providers a chance to identify MCI or Alzheimer’s Disease patients earlier and thus treat patients earlier to better control the progression of the disease. Further research is needed after more information on the clinical effectiveness of aducanumab as well as its treatment costs are available. 56 TABLES AND FIGURES Figure 3. Markov model Diagnosed MCI Diagnosed AD MCI AD Normal Diagnosed MCI Diagnosed AD Death Screening 57 Table 32: Model inputs Parameter Value 95% CI PSA Distribution Source Transition Probabilities Normal to MCI 0.04 beta(304, 7308) Davis et al (2018) MCI to Dementia 0.21 beta(708, 2662) Davis et al (2018) Normal/MCI to Death 0.01 beta(34, 3336) Davis et al (2018) Mortality relative risk (normal HIV+ vs normal HIV-) 14 12.2 16.1 lognormal(2.64, 0.14) Aldax et al. (2011) Mortality relative risk (MCI) (MCI-HIV vs no-MCI HIV) 3.5 1.02 12.1 lognormal(1.25, 0.32) Wilkie et al (1998) Mortality relative risk (Dementia) (Dementia-HIV vs no-Dementia HIV) 6.4 1.3 31.5 lognormal(1.86, 0.41) Wilkie et al (1998) Current treatment effectiveness: relative risk of progression from MCI to AD 0.75 0.66 0.87 normal(0.75, 0.046) Diniz et al (2009) Current treatment effectiveness: relative risk AD to death 0.89 beta(89,11) Meguro et al (2014) Annual probabilty of being diagnosed without regular screening 0.423 uniform Helvik et al (2018) MMSE Screening Sensitivity (MCI) 0.664 beta (664, 336) Breton et al (2019) MMSE Screening Sensitivity (AD) 0.7826 beta (782.6, 217.4) Matías-Guiu et al (2017) Costs MMSE Screening 78 normal (90.48, 4.62) Kalish et al (2016) Medicare expenditure (New Treatment) 33061.16 normal (33061.16, 1686.64) Deb et al (2017) Medicare expenditure (Current Standard Treatment) 33061.16 normal (33061.16, 1686.64) Deb et al (2017) Out-of-pocket 12174.2 normal (12174.2, 621.18) Deb et al (2017) Formal house-health care 6586.48 normal (6586.48, 336.05) Deb et al (2017) Skilled nursing home 7835.8 normal (7835.8, 399.74) Deb et al (2017) Informal care givers 32235.24 normal (32235.24, 1644.65) Deb et al (2017) HIV annual total cost (cd4+>=500) 21265.92 20546.56 21986.56 normal (21265.92, 367.01) Gebo et al (2010) Quality of Life Normal 0.821 beta (82.1, 17.9) Ara et al (2011) MCI 0.69 beta (69, 31) Neumann (1999) Treated MCI 0.73 beta(73, 27) Neumann (1999) 58 Dementia 0.27 beta (27, 73) Neumann (1999) Treated Dementia 0.38 beta (38, 62) Neumann (1999) Informal care giver 0.86 0.88 (SD) beta (0.92, 0.15) Neumann (1999) Death 0 beta Annual discount rate 3% uniform Moore et al (2018) 59 Figure 4. Incremental Net Monetary Benefit -20000 -10000 0 10000 20000 30000 40000 50000 60000 0 200000 400000 600000 800000 1000000 1200000 186920.46 60 Figure 5. Change in ICERs: Regular Screening + Aducanumab vs No regular screening + Neurotransmitters 0 100000 200000 300000 400000 500000 600000 700000 Sensitivity AD Cost: Annual HIV Treatment Cost: Formal Home Care Utility: Informal care givers Cost: Out-of-pocket Cost: Screening Utiity: Treated Dementia TP: Undiagnosed to Diagnosed Cost: Aducanumab Medicare TP: Normal to Death Utility: MCI TP: MCI to AD Mortality RR: MCI vs No MCI TP: transition probability Treatment effect: current AD to Death Treatment effect: Aducanumab AD to Death 61 Table 33: Two-way sensitivity analysis Medical Cost Aducanumab (Person/Year) 40000 50000 60000 70000 80000 90000 100000 Aducanumab Transition Probability From MCI to AD 0.17 126350.00 156540.27 186730.54 216920.81 247111.08 277301.35 307491.61 0.16 114374.67 141589.13 168803.58 196018.04 223232.50 250446.96 277661.41 0.15 104377.29 129107.26 153837.22 178567.18 203297.14 228027.10 252757.06 0.14 95906.09 118530.63 141155.18 163779.72 186404.26 209028.80 231653.35 0.13 88637.18 109454.91 130272.65 151090.39 171908.13 192725.87 213543.61 0.12 82297.11 101545.62 120794.13 140042.64 159291.15 178539.66 197788.17 0.11 76780.79 94657.01 112533.22 130409.43 148285.65 166161.86 184038.08 0.1 71911.72 88576.44 105241.16 121905.88 138570.60 155235.32 171900.04 0.09 67604.59 83193.70 98782.81 114371.91 129961.02 145550.13 161139.23 0.08 63703.80 78327.30 92950.80 107574.30 122197.80 136821.30 151444.80 0.07 60220.95 73976.82 87732.69 101488.56 115244.43 129000.30 142756.17 0.06 57071.03 70041.96 83012.90 95983.83 108954.76 121925.70 134896.63 0.05 54187.29 66443.61 78699.92 90956.24 103212.55 115468.87 127725.19 0.04 51579.16 63184.47 74789.79 86395.10 98000.42 109605.73 121211.05 0.03 49186.05 60195.04 71204.03 82213.02 93222.01 104231.00 115239.99 0.02 46975.45 57435.25 67895.05 78354.85 88814.65 99274.45 109734.26 0.01 44941.25 54894.76 64848.26 74801.76 84755.26 94708.77 104662.27 0 43048.19 52532.94 62017.69 71502.43 80987.18 90471.93 99956.68 $150,000 $200,000 62 Figure 6. Cost-effectiveness Acceptability Curve 0 0.1 0.2 0.3 0.4 0.5 0.6 $0.00 $50,000.00 $150,000.00$250,000.00$350,000.00$450,000.00$550,000.00$650,000.00$750,000.00 Probability Willingness-to-pay threshold 63 Figure 7. Cost-effectiveness Plane -6000 -4000 -2000 0 2000 4000 6000 8000 10000 12000 14000 -1 -0.5 0 0.5 1 1.5 2 2.5 ΔQALY 150K $/QALY ΔCost 64 REFERENCES CHAPTER 1: CHRONIC DISEASE ONSET AMONG PEPOLE LIVING WITH HIV AND AIDS IN A LARGE PRIVATE INSURANCE CLAIMS DATASET 1. Antiretroviral Therapy Cohort Collaboration (ART-CC). Survival of HIV-positive Patients Starting Antiretroviral Therapy Between 1996 and 2013: A Collaborative Analysis of Cohort Studies. Lancet HIV. 2017;3018(17). doi:10.1016/S2352-3018(17)30066-8 2. Marcus JL, Chao CR, Leyden WA, et al. Narrowing the Gap in Life Expectancy between HIV- Infected and HIV-Uninfected Individuals with Access to Care. In: Journal of Acquired Immune Deficiency Syndromes. ; 2016. doi:10.1097/QAI.0000000000001014 3. May MT, Gompels M, Delpech V, et al. Impact on life expectancy of HIV-1 positive individuals of CD4+ cell count and viral load response to antiretroviral therapy. AIDS. 2014;28(8):1193-1202. doi:10.1097/QAD.0000000000000243 4. Centers for Disease Control and Prevention. HIV Among People Aged 50 and Over. Atlanta, CA; 2017. https://www.cdc.gov/hiv/group/age/olderamericans/index.html. 5. Centers for Disease Control and Prevention. HIV Among People Aged 50 and Older. https://www.cdc.gov/hiv/group/age/olderamericans/index.html. Published 2018. 6. Bigna JJ, Kenne AM, Asangbeh SL, Sibetcheu AT. Prevalence of chronic obstructive pulmonary disease in the global population with HIV: a systematic review and meta- analysis. Lancet Glob Heal. 2018. doi:10.1016/S2214-109X(17)30451-5 7. Gelpi M, Afzal S, Lundgren J, et al. Higher Risk of Abdominal Obesity, Elevated Low-Density Lipoprotein Cholesterol, and Hypertriglyceridemia, but not of Hypertension, in People Living With Human Immunodeficiency Virus (HIV): Results From the Copenhagen Comorbidity in HIV Infection Study. Clin Infect Dis. 2018;67(4):579-586. doi:10.1093/cid/ciy146 8. Mayer KH, Loo S, Crawford PM, et al. Excess clinical comorbidity among HIV-infected patients accessing primary care in US community health centers. Public Health Rep. 2018. doi:10.1177/0033354917748670 9. Friedman EE, Duffus WA. Chronic health conditions in Medicare beneficiaries 65 years old, and older with HIV infection. AIDS. 2016;30(16):2529-2536. doi:10.1097/QAD.0000000000001215 10. Schouten J, Wit FW, Stolte IG, et al. Cross-sectional comparison of the prevalence of age- associated comorbidities and their risk factors between HIV-infected and uninfected individuals: the AGEhIV cohort study. Clin Infect Dis. 2014;59(12):1787-1797. doi:10.1093/cid/ciu701 11. Grundy SM, Brewer HB, Cleeman JI, et al. Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Arterioscler Thromb Vasc Biol. 2004;24(2):e13-8. doi:10.1161/01.ATV.0000111245.75752.C6 12. Galassi A, Reynolds K, He J. Metabolic syndrome and risk of cardiovascular disease: a meta- analysis. Am J Med. 2006;119(10):812-819. doi:10.1016/j.amjmed.2006.02.031 65 13. Wilson PWF, D’Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 2005;112(20):3066-3072. doi:10.1161/CIRCULATIONAHA.105.539528 14. Barbaro G, Iacobellis G. Metabolic syndrome associated with HIV and highly active antiretroviral therapy. Curr Diab Rep. 2009. 15. Echecopar-Sabogal J, D’Angelo-Piaggio L, Chanamé-Baca DM, Ugarte-Gil C. Association between the use of protease inhibitors in highly active antiretroviral therapy and incidence of diabetes mellitus and/or metabolic syndrome in HIV-infected patients: A systematic review and meta-analysis. Int J STD AIDS. 2018;29(5):443-452. doi:10.1177/0956462417732226 16. Paula AA, Falcão MCN, Pacheco AG. Metabolic syndrome in HIV-infected individuals: Underlying mechanisms and epidemiological aspects. AIDS Res Ther. 2013. doi:10.1186/1742-6405-10-32 17. van Wijk JPH, Cabezas MC. Hypertriglyceridemia, Metabolic Syndrome, and Cardiovascular Disease in HIV-Infected Patients: Effects of Antiretroviral Therapy and Adipose Tissue Distribution. Int J Vasc Med. 2012;2012:201027. doi:10.1155/2012/201027 18. Wand H, Calmy A, Carey DL, et al. Metabolic syndrome, cardiovascular disease and type 2 diabetes mellitus after initiation of antiretroviral therapy in HIV infection. AIDS. 2007;21(18):2445-2453. doi:10.1097/QAD.0b013e3282efad32 19. Palios J, Kadoglou NPE, Lampropoulos S. The pathophysiology of HIV-/HAART-related metabolic syndrome leading to cardiovascular disorders: the emerging role of adipokines. Exp Diabetes Res. 2012;2012:103063. doi:10.1155/2012/103063 20. Deeks SG, Tracy R, Douek DC. Systemic effects of inflammation on health during chronic HIV infection. Immunity. 2013;39(4):633-645. doi:10.1016/j.immuni.2013.10.001 21. Sheppard DP, Woods SP, Bondi MW, Gilbert PE, Massman PJ, Doyle KL. Does Older Age Confer an Increased Risk of Incident Neurocognitive Disorders among Persons Living with HIV Disease? Clin Neuropsychol. 2015. doi:10.1080/13854046.2015.1077995 22. Brown TT, Tassiopoulos K, Bosch RJ, Shikuma C, McComsey GA. Association between systemic inflammation and incident diabetes in HIV-infected patients after initiation of antiretroviral therapy. Diabetes Care. 2010;33(10):2244-2249. doi:10.2337/dc10-0633 23. Shiels MS, Pfeiffer RM, Gail MH, et al. Cancer burden in the HIV-infected population in the United States. J Natl Cancer Inst. 2011;103(9):753-762. doi:10.1093/jnci/djr076 24. Kirk GD, Merlo C, O’Driscoll P, et al. HIV Infection Is Associated with an Increased Risk for Lung Cancer, Independent of Smoking. Clin Infect Dis. 2007. doi:10.1086/518606 25. Ovbiagele B, Nath A. Increasing incidence of ischemic stroke in patients with HIV infection. Neurology. 2011;76(5):444-450. doi:10.1212/WNL.0b013e31820a0cfc 26. Crothers K, Butt AA, Gibert CL, et al. Increased COPD among HIV-positive compared to HIV- negative veterans. Chest. 2006;130(5):1326-1333. doi:10.1378/chest.130.5.1326 27. Raposo MA, Armiliato GN de A, Guimarães NS, Caram CA, Silveira RD de S, Tupinambás U. Metabolic disorders and cardiovascular risk in people living with HIV/AIDS without the use of antiretroviral therapy. Rev Soc Bras Med Trop. 50(5):598-606. doi:10.1590/0037-8682- 0258-2017 28. Deeks SG, Phillips AN. HIV infection, antiretroviral treatment, ageing, and non-AIDS related morbidity. BMJ. 2009;338(7689):288-292. doi:10.1136/bmj.a3172 66 29. Yanik EL, Katki HA, Engels EA. Cancer risk among the HIV-infected elderly in the United States. Aids. 2016;30(10):1663-1668. doi:10.1097/QAD.0000000000001077 30. Snowden MB, Steinman LE, Bryant LL, et al. Dementia and co-occurring chronic conditions: a systematic literature review to identify what is known and where are the gaps in the evidence? Int J Geriatr Psychiatry. 2017. doi:10.1002/gps.4652 31. Morley JE. Cognition and Chronic Disease. J Am Med Dir Assoc. 2017. doi:10.1016/j.jamda.2017.02.010 32. CMS.gov. CMS Chronic Conditions Data Warehouse ( CCW ) CCW Condition Algorithms. Chronic Cond Algorithms. 2015;72:2015. 33. Odhiambo C, Carolina S. Risk for Cardiovascular Disease in Blacks with HIV/AIDS in America: A Systematic Review and Meta-analysis. J Health Dispar Res Pract. 2017;10(2):121-140. 34. Smith DM, Salters KA, Eyawo O, et al. Mortality among people living with HIV/AIDS with non-small-cell lung cancer in the modern HAART Era. AIDS Care - Psychol Socio-Medical Asp AIDS/HIV. 2018;30(7):936-942. doi:10.1080/09540121.2018.1434120 35. Yideg Yitbarek G, Mossie Ayana A, Bariso Gare M, Garedew Woldeamanuel G. Prevalence of Cognitive Impairment and Its Predictors among HIV/AIDS Patients on Antiretroviral Therapy in Jimma University Medical Center, Southwest Ethiopia. Psychiatry J. 2019;2019:1-7. doi:10.1155/2019/8306823 36. NR A, Connett J, Murray R. Smoking and lung function of Lung Health Study participants after 11 years. Am J Respir Crit Care Med 2002, 166(5):675-9. Am J Respir Crit Care Med. 2002;166(5):675-679. doi:10.1164/rccm.2112096 37. Thomas VS, Rockwood KJ. Alcohol abuse, cognitive impairment, and mortality among older people. J Am Geriatr Soc. 2001. doi:10.1046/j.1532-5415.2001.49085.x 38. Lakier JB. Smoking and Cardiovascular Disease. Am J Med. 1992;93(1A):8S-12S. doi:10.1016/0002-9343(92)90620-q 39. Frishman WH, Del Vecchio A, Sanal S, Ismail A. Cardiovascular manifestations of substance abuse: Part 2: Alcohol, amphetamines, heroin, cannabis, and caffeine. Hear Dis. 2003. doi:10.1097/01.hdx.0000080713.09303.a6 40. Almirol EA, McNulty MC, Schmitt J, et al. Gender Differences in HIV Testing, Diagnosis, and Linkage to Care in Healthcare Settings: Identifying African American Women with HIV in Chicago. AIDS Patient Care STDS. 2018. doi:10.1089/apc.2018.0066 41. Darlington CK, Hutson SP. Understanding HIV-Related Stigma Among Women in the Southern United States: A Literature Review. AIDS Behav. 2017. doi:10.1007/s10461-016- 1504-9 67 CHAPTER 2: THE EXCESS COSTS ASSOCIATED WITH HIV INFECTION IN CHRONIC CONDITIONS MANGAEMENT AMONG PEOPLE LIVING WITH HIV AND AIDS (PLWHA) AGED 50 AND OLDER 1. Centers for Disease Control and Prevention. (2019) Estimated HIV incidence and prevalence in the United States, 2010–2016. HIV Surveillance Supplemental Report 2019;24(1).Retrieved from http://www.cdc.gov/ hiv/library/reports/hiv-surveillance.html 2. Centers for Disease Control and Prevention. (2018) HIV Surveillance Report, 2017;29. Retrieved from http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html. 3. Marcus, J. L., Chao, C. R., Leyden, W. A., Xu, L., Quesenberry, C. P., Jr, Klein, D. B., Towner, W. J., Horberg, M. A., & Silverberg, M. J. (2016). Narrowing the Gap in Life Expectancy Between HIV-Infected and HIV-Uninfected Individuals With Access to Care. Journal of Acquired Immune Deficiency Syndromes, 73(1), 39–46. 4. Antiretroviral Therapy Cohort Collaboration. (2017). Survival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies. The Lancet. HIV, 4(8), e349–e356. 5. Nou, E., Lo, J., & Grinspoon, S. K. (2016). Inflammation, immune activation, and cardiovascular disease in HIV. AIDS , 30(10), 1495–1509. 6. Samad, F., Harris, M., Puskas, C. M., Ye, M., Chia, J., Chacko, S., Bondy, G. P., Lima, V. D., Montaner, J. S., & Guillemi, S. A. (2017). Incidence of diabetes mellitus and factors associated with its development in HIV-positive patients over the age of 50. BMJ Open Diabetes Research & Care, 5(1), e000457. 7. Noubissi, E. C., Katte, J.-C., & Sobngwi, E. (2018). Diabetes and HIV. Current Diabetes Reports, 18(11), 125. 8. Herrin, M., Tate, J. P., Akgün, K. M., Butt, A. A., Crothers, K., Freiberg, M. S., Gibert, C. L., Leaf, D. A., Rimland, D., Rodriguez-Barradas, M. C., Ruser, C. B., Herold, K. C., & Justice, A. C. (2016). Weight Gain and Incident Diabetes Among HIV-Infected Veterans Initiating Antiretroviral Therapy Compared With Uninfected Individuals. Journal of Acquired Immune Deficiency Syndromes, 73(2), 228–236. 9. Nduka, C. U., Stranges, S., Sarki, A. M., Kimani, P. K., & Uthman, O. A. (2016). Evidence of increased blood pressure and hypertension risk among people living with HIV on antiretroviral therapy: a systematic review with meta-analysis. Journal of Human Hypertension, 30(6), 355–362. 10. Benjamin, L., & Khoo, S. (2018). Chapter 15 - HIV infection and stroke. In B. J. Brew (Ed.), Handbook of Clinical Neurology (Vol. 152, pp. 187–200). Elsevier. 11. Chow, F. C., Price, R. W., Hsue, P. Y., & Kim, A. S. (2017). Greater Risk of Stroke of Undetermined Etiology in a Contemporary HIV-Infected Cohort Compared with Uninfected Individuals. Journal of Stroke and Cerebrovascular Diseases: The Official Journal of National Stroke Association, 26(5), 1154–1160. 12. Anand, A. R., Rachel, G., & Parthasarathy, D. (2018). HIV Proteins and Endothelial Dysfunction: Implications in Cardiovascular Disease. Frontiers in Cardiovascular Medicine, 5, 185. 68 13. Mackiewicz, M. M., Overk, C., Achim, C. L., & Masliah, E. (2019). Pathogenesis of age-related HIV neurodegeneration. Journal of Neurovirology, 25(5), 622–633. 14. Cysique, L. A., & Brew, B. J. (2019). Vascular cognitive impairment and HIV-associated neurocognitive disorder: a new paradigm. Journal of Neurovirology, 25(5), 710–721. 15. Cribbs, S. K., Crothers, K., & Morris, A. (2020). Pathogenesis of HIV-Related Lung Disease: Immunity, Infection, and Inflammation. Physiological Reviews, 100(2), 603–632. 16. Hernández-Ramírez, R. U., Shiels, M. S., Dubrow, R., & Engels, E. A. (2017). Cancer risk in HIV- infected people in the USA from 1996 to 2012: a population-based, registry-linkage study. The Lancet. HIV, 4(11), e495–e504. 17. Yanik, E. L., Katki, H. A., & Engels, E. A. (2016). Cancer risk among the HIV-infected elderly in the United States. AIDS , 30(10), 1663–1668. 18. Ward, T., Sugrue, D., Hayward, O., McEwan, P., Anderson, S.-J., Lopes, S., Punekar, Y., & Oglesby, A. (2020). Estimating HIV Management and Comorbidity Costs Among Aging HIV Patients in the United States: A Systematic Review. Journal of Managed Care & Specialty Pharmacy, 26(2), 104–116. 19. Goldman, D. P., Lakdawalla, D., Michaud, P.-C., Eibner, C., Gailey, A., Vaynman, I., Tysinger, B., & Leaf, D. E. (2015). The future elderly model: Technical documentation. https://www.degruyter.com/view/j/fhep.2015.18.issue-1/fhep-2014-0013/suppl/fhep- 2014-0013_TechlApp_AD.docx 20. Yang, H.-Y., Beymer, M. R., & Suen, S.-C. (2019). Chronic Disease Onset Among People Living with HIV and AIDS in a Large Private Insurance Claims Dataset. Scientific Reports, 9(1), 18514. 21. Croxford, S., Kitching, A., Desai, S., Kall, M., Edelstein, M., Skingsley, A., Burns, F., Copas, A., Brown, A. E., Sullivan, A. K., & Delpech, V. (2017). Mortality and causes of death in people diagnosed with HIV in the era of highly active antiretroviral therapy compared with the general population: an analysis of a national observational cohort. The Lancet. Public Health, 2(1), e35–e46. 22. Alejos, B., Hernando, V., Iribarren, J., Gonzalez-García, J., Hernando, A., Santos, J., Asensi, V., Gomez-Berrocal, A., Del Amo, J., Jarrin, I., & CoRIS (Cohort of the Spanish Network on HIVAIDS Research). (2016). Overall and cause-specific excess mortality in HIV-positive persons compared with the general population: Role of HCV coinfection. Medicine, 95(36), e4727. 23. Aldaz, P., Moreno-Iribas, C., Egüés, N., Irisarri, F., Floristan, Y., Sola-Boneta, J., Martínez-Artola, V., Sagredo, M., & Castilla, J. (2011). Mortality by causes in HIV-infected adults: comparison with the general population. BMC Public Health, 11, 300. 24. Mayer, K. H., Loo, S., Crawford, P. M., Crane, H. M., Leo, M., DenOuden, P., Houlberg, M., Schmidt, M., Quach, T., Ruhs, S., Vandermeer, M., Grasso, C., & McBurnie, M. A. (2018). Excess Clinical Comorbidity Among HIV-Infected Patients Accessing Primary Care in US Community Health Centers. Public Health Reports, 133(1), 109–118. 25. Friedman, E. E., & Duffus, W. A. (2016). Chronic health conditions in Medicare beneficiaries 65 years old, and older with HIV infection. AIDS , 30(16), 2529–2536. 26. Bigna, J. J., Kenne, A. M., Asangbeh, S. L., & Sibetcheu, A. T. (2018). Prevalence of chronic obstructive pulmonary disease in the global population with HIV: a systematic review and meta-analysis. The Lancet. Global Health, 6(2), e193–e202. 69 27. De Ronchi, D., Faranca, I., Berardi, D., Scudellari, P., Borderi, M., Manfredi, R., & Fratiglioni, L. (2002). Risk factors for cognitive impairment in HIV-1-infected persons with different risk behaviors. Archives of Neurology, 59(5), 812–818. 28. Cruz, A. L. F., & Fellows, L. K. (2019). Cognitive Impairment in People Living With HIV Infection in the Era of Combination Antiretroviral Therapy. In Reference Module in Neuroscience and Biobehavioral Psychology. Elsevier. CHAPTER 3: THE COST-EFFECTIVENESS ANALYSIS OF REGULAR DEMENTIA SCREENING AMONG PEOPLE LIVING WITH HIV/AIDS AGED 50 AND OLDER INTRODUCTION 1. Brookmeyer, R., Abdalla, N., Kawas, C. H., & Corrada, M. M. (2018). Forecasting the prevalence of preclinical and clinical Alzheimer’s disease in the United States. Alzheimer’s & Dementia: The Journal of the Alzheimer's Association, 14(2), 121–129. 2. Fulop, T., Witkowski, J. M., Larbi, A., Khalil, A., Herbein, G., & Frost, E. H. (2019). Does HIV infection contribute to increased beta-amyloid synthesis and plaque formation leading to neurodegeneration and Alzheimer’s disease? Journal of Neurovirology, 25(5), 634–647. 3. Murphy, M. P., & LeVine, H., 3rd. (2010). Alzheimer’s disease and the amyloid-beta peptide. Journal of Alzheimer’s Disease: JAD, 19(1), 311–323. 4. Lanari, A., Amenta, F., Silvestrelli, G., Tomassoni, D., & Parnetti, L. (2006). Neurotransmitter deficits in behavioural and psychological symptoms of Alzheimer’s disease. Mechanisms of Ageing and Development, 127(2), 158–165. 5. Lyketsos, C. G., Carrillo, M. C., Ryan, J. M., Khachaturian, A. S., Trzepacz, P., Amatniek, J., Cedarbaum, J., Brashear, R., & Miller, D. S. (2011). Neuropsychiatric symptoms in Alzheimer’s disease. Alzheimer’s & Dementia: The Journal of the Alzheimer's Association, 7(5), 532–539. 6. Birks, J. (2006). Cholinesterase inhibitors for Alzheimer’s disease. Cochrane Database of Systematic Reviews , 1, CD005593. 7. Hampel, H., Mesulam, M.-M., Cuello, A. C., Farlow, M. R., Giacobini, E., Grossberg, G. T., Khachaturian, A. S., Vergallo, A., Cavedo, E., Snyder, P. J., & Khachaturian, Z. S. (2018). The cholinergic system in the pathophysiology and treatment of Alzheimer’s disease. Brain: A Journal of Neurology, 141(7), 1917– 1933. 8. Wang, R., & Reddy, P. H. (2017). Role of Glutamate and NMDA Receptors in Alzheimer’s Disease. Journal of Alzheimer’s Disease: JAD, 57(4), 1041–1048. 9. Liu, J., Chang, L., Song, Y., Li, H., & Wu, Y. (2019). The Role of NMDA Receptors in Alzheimer’s Disease. Frontiers in Neuroscience, 13, 43. 10. Alzheimer’s Disease Fact Sheet. (n.d.). National Institute on Aging. Retrieved April 24, 2020, from https://www.nia.nih.gov/health/alzheimers-disease-fact-sheet 11. Parsons, C. G., Danysz, W., Dekundy, A., & Pulte, I. (2013). Memantine and cholinesterase inhibitors: complementary mechanisms in the treatment of Alzheimer’s disease. Neurotoxicity Research, 24(3), 358–369. 12. Sumner, I. L., Edwards, R. A., Asuni, A. A., & Teeling, J. L. (2018). Antibody Engineering for Optimized Immunotherapy in Alzheimer’s Disease. Frontiers in Neuroscience, 12, 254. 70 13. Sevigny, J., Chiao, P., Bussière, T., Weinreb, P. H., Williams, L., Maier, M., Dunstan, R., Salloway, S., Chen, T., Ling, Y., O’Gorman, J., Qian, F., Arastu, M., Li, M., Chollate, S., Brennan, M. S., Quintero- Monzon, O., Scannevin, R. H., Arnold, H. M., … Sandrock, A. (2016). The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature, 537(7618), 50–56. 14. Howard, R., & Liu, K. Y. (2019). Questions EMERGE as Biogen claims aducanumab turnaround. Nature Reviews. Neurology. https://doi.org/10.1038/s41582-019-0295-9 15. Helvik, A.-S., Engedal, K., Šaltytė Benth, J., & Selbæk, G. (2018). Time from Symptom Debut to Dementia Assessment by the Specialist Healthcare Service in Norway. Dementia and Geriatric Cognitive Disorders Extra, 8(1), 117–127. 16. Dubois, B., Padovani, A., Scheltens, P., Rossi, A., & Dell’Agnello, G. (2015). Timely Diagnosis for Alzheimer’s Disease: A Literature Review on Benefits and Challenges. Journal of Alzheimer’s Disease: JAD, 49(3), 617–631. 17. Tricco, A. C., Soobiah, C., Berliner, S., Ho, J. M., Ng, C. H., Ashoor, H. M., Chen, M. H., Hemmelgarn, B., & Straus, S. E. (2013). Efficacy and safety of cognitive enhancers for patients with mild cognitive impairment: a systematic review and meta-analysis. CMAJ: Canadian Medical Association Journal = Journal de l’Association Medicale Canadienne, 185(16), 1393–1401. 18. Doody, R. S., Ferris, S. H., Salloway, S., Sun, Y., Goldman, R., Watkins, W. E., Xu, Y., & Murthy, A. K. (2009). Donepezil treatment of patients with MCI: a 48-week randomized, placebo-controlled trial. Neurology, 72(18), 1555–1561. 19. Haeberlein, S, B. et al. (2019, December). EMERGE and ENGAGE Topline Results: Two Phase 3 Studies to Evaluate Aducanumab in Patients With Early Alzheimer’s Disease Clinical Trials on Alzheimer’s Disease, San Diego, CA https://investors.biogen.com/static-files/ddd45672-9c7e-4c99-8a06- 3b557697c06f 20. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. Journal of psychiatric research, 12(3), 189–198. 21. Neumann, P.J., Sanders, G.D., Russell, L.B., Siegel, J.E., & Ganiats, T.G. (2017) Cost- Effectiveness in Health and Medicine. (2nd ed). New York, US: Oxford University Press 22. Davis, M., O Connell, T., Johnson, S., Cline, S., Merikle, E., Martenyi, F., & Simpson, K. (2018). Estimating Alzheimer’s Disease Progression Rates from Normal Cognition Through Mild Cognitive Impairment and Stages of Dementia. Current Alzheimer Research, 15(8), 777–788. 23. Meir-Shafrir, K., & Pollack, S. (2012). Accelerated Aging in HIV Patients. Rambam Maimonides Medical Journal, 3(4), e0025. 24. Wilkie, F. L., Goodkin, K., Eisdorfer, C., Feaster, D., Morgan, R., Fletcher, M. A., Blaney, N., Baum, M., & Szapocznik, J. (1998). Mild cognitive impairment and risk of mortality in HIV-1 infection. The Journal of Neuropsychiatry and Clinical Neurosciences, 10(2), 125–132. 25. Meguro, K., Kasai, M., Akanuma, K., Meguro, M., Ishii, H., & Yamaguchi, S. (2014). Donepezil and life expectancy in Alzheimer’s disease: a retrospective analysis in the Tajiri Project. BMC Neurology, 14, 83. 26. Diniz, B. S., Pinto, J. A., Jr, Gonzaga, M. L. C., Guimarães, F. M., Gattaz, W. F., & Forlenza, O. V. (2009). To treat or not to treat? A meta-analysis of the use of cholinesterase inhibitors in mild cognitive impairment for delaying progression to Alzheimer’s disease. European Archives of Psychiatry and Clinical Neuroscience, 259(4), 248–256. 27. Gasper, M. C., Ott, B. R., & Lapane, K. L. (2005). Is donepezil therapy associated with reduced mortality in nursing home residents with dementia? The American Journal of Geriatric Pharmacotherapy, 3(1), 1–7. 28. Samtani, M. N., Raghavan, N., Novak, G., Nandy, P., & Narayan, V. A. (2014). Disease progression model for Clinical Dementia Rating-Sum of Boxes in mild cognitive impairment and Alzheimer’s 71 subjects from the Alzheimer's Disease Neuroimaging Initiative. Neuropsychiatric Disease and Treatment, 10, 929–952. 29. Breton, A., Casey, D., & Arnaoutoglou, N. A. (2019). Cognitive tests for the detection of mild cognitive impairment (MCI), the prodromal stage of dementia: Meta-analysis of diagnostic accuracy studies. International Journal of Geriatric Psychiatry, 34(2), 233–242. 30. Matías-Guiu, J. A., Valles-Salgado, M., Rognoni, T., Hamre-Gil, F., Moreno-Ramos, T., & Matías-Guiu, J. (2017). Comparative Diagnostic Accuracy of the ACE-III, MIS, MMSE, MoCA, and RUDAS for Screening of Alzheimer Disease. Dementia and Geriatric Cognitive Disorders, 43(5-6), 237–246. 31. Samtani, M. N., Raghavan, N., Novak, G., Nandy, P., & Narayan, V. A. (2014). Disease progression model for Clinical Dementia Rating-Sum of Boxes in mild cognitive impairment and Alzheimer’s subjects from the Alzheimer's Disease Neuroimaging Initiative. Neuropsychiatric Disease and Treatment, 10, 929–952. 32. Gebo, K. A., Fleishman, J. A., Conviser, R., Hellinger, J., Hellinger, F. J., Josephs, J. S., Keiser, P., Gaist, P., Moore, R. D., & HIV Research Network. (2010). Contemporary costs of HIV healthcare in the HAART era. AIDS, 24(17), 2705–2715. 33. Kalish, V. & Lerner, B. (2016) Mini-Mental State Examination for the Detection of Dementia in Older Patients. Am Fam Physician, 94(11), 880-881. 34. Deb, A., Thornton, J. D., Sambamoorthi, U., & Innes, K. (2017). Direct and indirect cost of managing alzheimer’s disease and related dementias in the United States. Expert Review of Pharmacoeconomics & Outcomes Research, 17(2), 189–202. 35. Glossary | NICE. Retrieved May 3, 2020, from https://www.nice.org.uk/glossary?letter=q 36. Ara, R., & Brazier, J. E. (2011). Using health state utility values from the general population to approximate baselines in decision analytic models when condition-specific data are not available. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 14(4), 539–545. 37. Neumann, P. J., Kuntz, K. M., Leon, J., Araki, S. S., Hermann, R. C., Hsu, M. A., & Weinstein, M. C. (1999). Health utilities in Alzheimer’s disease: a cross-sectional study of patients and caregivers. Medical Care, 37(1), 27–32. 38. Bertram, M. Y., Lauer, J. A., De Joncheere, K., Edejer, T., Hutubessy, R., Kieny, M.-P., & Hill, S. R. (2016). Cost-effectiveness thresholds: pros and cons. Bulletin of the World Health Organization, 94(12), 925– 930. 39. GDP per capita (current US$) - United States | Data. Retrieved May 2, 2020, from https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=US 40. Minacori, R., Geale, K., & Geale, K. (2016). Is there Evidence of a Difference Between Willingness to Pay and Willingness to Accept Thresholds? A Review of Nice Technology Appraisals. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 19(7), A492–A493. 41. HIV Cost-effectiveness | Guidance | Program Resources | HIV/AIDS | CDC. (2019, October 31). https://www.cdc.gov/hiv/programresources/guidance/costeffectiveness/index.html 42. Hurd, M. D., Martorell, P., & Langa, K. M. (2013). Monetary costs of dementia in the United States [Review of Monetary costs of dementia in the United States]. The New England Journal of Medicine, 369(5), 489–490. 43. Schackman, B. R., Fleishman, J. A., Su, A. E., Berkowitz, B. K., Moore, R. D., Walensky, R. P., Becker, J. E., Voss, C., Paltiel, A. D., Weinstein, M. C., Freedberg, K. A., Gebo, K. A., & Losina, E. (2015). The lifetime medical cost savings from preventing HIV in the United States. Medical Care, 53(4), 293–301. 44. Ward, T.M., Sugrue, D., Hayward, O., McEwan, P., Anderson, S-J., Lopes, S., Punekar, Y., & Oglesby, A. (2020). Estimating HIV Management and Comorbidity Costs Among Aging HIV Patients in the United States. Journal of Managed Care & Specialty Pharmacy, 26(2), 104-116. 72 45. Siddiqi, A.-E.-A., Hall, H. I., Hu, X., & Song, R. (2016). Population-Based Estimates of Life Expectancy After HIV Diagnosis: United States 2008-2011. Journal of Acquired Immune Deficiency Syndromes, 72(2), 230–236. 46. Mauskopf, J., Earnshaw, S.R., Brogan, A., Wolowacz, S., & Brodtkorb, T. (2017) Budget-Impact Analysis of Health Care Interventions: A practical guide. Springer International Publishing AG. 73 APPENDIX CHAPTER 1: CHRONIC DISEASE ONSET AMONG PEPOLE LIVING WITH HIV AND AIDS IN A LARGE PRIVATE INSURANCE CLAIMS DATASET Table 5: ICD-9 and ICD-10 codes used in analysis Condition ICD-9 Codes ICD-10 Codes HIV/AIDS 042, 042.0, 042.1, 042.2, 042.9, 043, 043.1, 043.2, 043.3, 043.9, 044, 044.0, 044.9, 079.53, V08 B20-B24, B97.35, Z21 Diabetes 249.00, 249.01, 249.10, 249.11, 249.20, 249.21, 249.30, 249.31, 249.40, 249.41, 249.50, 249.51, 249.60, 249.61, 249.70, 249.71, 249.80, 249.81, 249.90, 249.91, 250.00, 250.01, 250.02, 250.03, 250.10, 250.11, 250.12, 250.13, 250.20, 250.21, 250.22, 250.23, 250.30, 250.31, 250.32, 250.33, 250.40, 250.41, 250.42, 250.43, 250.50, 250.51, 250.52, 250.53, 250.60, 250.61, 250.62, 250.63, 250.70, 250.71, 250.72, 250.73, 250.80, 250.81, 250.82, 250.83, 250.90, 250.91, 250.92, 250.93, 357.2, 362.01, 362.02, 362.03, 362.04, 362.05, 362.06, 366.41 E08.00, E08.01, E08.10, E08.11, E08.21, E08.22, E08.29, E08.311, E08.319, E08.321, E08.329, E08.331, E08.339, E08.341, E08.349, E08.351, E08.359, E08.36, E08.39, E08.40, E08.41, E08.42, E08.43, E08.44, E08.49, E08.51, E08.52, E08.59, E08.610, E08.618, E08.620, E08.621, E08.622, E08.628, E08.630, E08.638, E08.641, E08.649, E08.65, E08.69, E08.8, E08.9, E09.00, E09.01, E09.10, E09.11, E09.21, E09.22, E09.29, E09.311, E09.319, E09.321, E09.329, E09.331, E09.339, E09.341, E09.349, E09.351, E09.359, E09.36, E09.39, E09.40, E09.41, E09.42, E09.43, E09.44, E09.49, E09.51, E09.52, E09.59, E09.610, E09.618, E09.620, E09.621, E09.622, E09.628, E09.630, E09.638, E09.641, E09.649, E09.65, E09.69, E09.8, E09.9, E10.10, E10.11, E10.21, E10.22, E10.29, E10.311, E10.319, E10.321, E10.329, E10.331, E10.339, E10.341, E10.349, E10.351, E10.359, E10.36, E10.39, E10.40, E10.41, E10.42, E10.43, E10.44, E10.49, E10.51, E10.52, E10.59, E10.610, E10.618, E10.620, E10.621, E10.622, E10.628, E10.630, E10.638, E10.641, E10.649, E10.65, E10.69, E10.8, E10.9, E11.00, E11.01, E11.21, E11.22, E11.29, E11.311, E11.319, E11.321, E11.329, E11.331, E11.339, E11.341, E11.349, E11.351, E11.359, E11.36, E11.39, E11.40, E11.41, E11.42, E11.43, E11.44, E11.49, E11.51, E11.52, E11.59, E11.610, E11.618, E11.620, E11.621, E11.622, E11.628, E11.630, E11.638, E11.641, E11.649, E11.65, E11.69, E11.8, E11.9, E13.00, E13.01, E13.10, E13.11, E13.21, E13.22, E13.29, E13.311, E13.319, E13.321, E13.329, E13.331, E13.339, E13.341, E13.349, E13.351, E13.359, E13.36, E13.39, E13.40, E13.41, E13.42, E13.43, E13.44, E13.49, E13.51, E13.52, E13.59, E13.610, E13.618, E13.620, E13.621, E13.622, E13.628, E13.630, E13.638, E13.641, E13.649, E13.65, E13.69, E13.8, E13.9 Hypertension 362.11, 401.0, 401.1, 401.9, 402.00, 402.01, 402.10, 402.11, 402.90, 402.91, 403.00, 403.01, 403.10, 403.11, 403.90, 403.91, 404.00, 404.01, 404.02, 404.03, 404.10, 404.11, 404.12, 404.13, 404.90, 404.91, 404.92, 404.93, 405.01, 405.09, 405.11, 405.19, 405.91, 405.99, 437.2 H35.031, H35.032, H35.033, H35.039, I10, I11.0, I11.9, I12.0, I12.9, I13.0, I13.10, I13.11, I13.2, I15.0, I15.1, I15.2, I15.8, I15.9, I67.4, N26.2 74 Stroke 430, 431, 433.01, 433.11, 433.21, 433.31, 433.81, 433.91, 434.00, 434.01, 434.10, 434.11, 434.90, 434.91, 435.0, 435.1, 435.3, 435.8, 435.9, 436, 997.02 G45.0, G45.1, G45.2, G45.8, G45.9, G46.0, G46.1, G46.2, G97.31, G97.32, I60.00, I60.01, I60.02, I60.10, I60.11, I60.12, I60.20, I60.21, I60.22, I60.30, I60.31, I60.32, I60.4, I60.50, I60.51, I60.52, I60.6, I60.7, I60.8, I60.9, I61.0, I61.1, I61.2, I61.3, I61.4, I61.5, I61.6, I61.8, I61.9, I63.00, I63.02, I63.011, I63.012, I63.019, I63.031, I63.032, I63.039, I63.09, I63.10, I63.111, I63.112, I63.119, I63.12, I63.131, I63.132, I63.139, I63.19, I63.20, I63.211, I63.212, I63.219, I63.22, I63.231, I63.232, I63.239, I63.29, I63.30, I63.311, I63.312, I63.319, I63.321, I63.322, I63.329, I63.331, I63.332, I63.339, I63.341, I63.342, I63.349, I63.39, I63.40, I63.411, I63.412, I63.419, I63.421, I63.422, I63.429, I63.431, I63.432, I63.439, I63.441, I63.442, I63.449, I63.49, I63.50, I63.511, I63.512, I63.519, I63.521, I63.522, I63.529, I63.531, I63.532, I63.539, I63.541, I63.542, I63.549, I63.59, I63.6, I63.8, I63.9, I66.01, I66.02, I66.03, I66.09, I66.11, I66.12, I66.13, I66.19, I66.21, I66.22, I66.23, I66.29, I66.3, I66.8, I66.9, I67.841, I67.848, I67.89, I97.810, I97.811, I97.820, I97.821 Cancers 185, 233.4, V10.46, 162.2, 162.3, 162.4, 162.5, 162.8, 162.9, 231.2, V10.11,174.0, 174.1, 174.2, 174.3, 174.4, 174.5, 174.6, 174.8, 174.9, 175.0, 175.9, 233.0, V10.3, 182.0, 233.2, V10.42, 153.0, 153.1, 153.2, 153.3, 153.4, 153.5, 153.6, 153.7, 153.8, 153.9,154.0,154.1, 230.3, 230.4, V10.05, V10.06 C61, D07.5, Z85.46, C34.00, C34.01, C34.02, C34.10, C34.11, C34.12, C34.2, C34.30, C34.31, C34.32, C34.80, C34.81, C34.82, C34.90, C34.91, C34.92, D02.20, D02.21, D02.22, Z85.118, C50.011, C50.012, C50.019, C50.021, C50.022, C50.029, C50.111, C50.112, C50.119, C50.121, C50.122, C50.129, C50.211, C50.212, C50.219, C50.221, C50.222, C50.229, C50.311, C50.312, C50.319, C50.321, C50.322, C50.329, C50.411, C50.412, C50.419, C50.421, C50.422, C50.429, C50.511, C50.512, C50.519, C50.521, C50.522, C50.529, C50.611, C50.612, C50.619, C50.621, C50.622, C50.629, C50.811, C50.812, C50.819, C50.821, C50.822, C50.829, C50.911, C50.912, C50.919, C50.921, C50.922, C50.929, D05.00, D05.01, C18.0, C18.1, C18.2, C18.3, C18.4, C18.5, C18.6, C18.7, C18.8, C18.9, C19, C20, D01.0, D01.1, D01.2, Z85.038, Z85.048 D05.02, D05.10, D05.11, D05.12, D05.80, D05.81, D05.82, D05.90, D05.91, D05.92, Z85.3, C54.1, C54.2, C54.3, C54.9, D07.0, Z85.42, Lung Diseases 490, 491.0, 491.1, 491.8, 491.9, 492.0, 492.8, 491.20, 491.21, 491.22, 494.0, 494.1, 496, 493.00, 493.01, 493.02, 493.10, 493.11, 493.12, 493.20, 493.21, 493.22, 493.81, 493.82, 493.90, 493.91, 493.92, J40, J41.0, J41.1, J41.8, J42, J43.0, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J44.9, J47.0, J47.1, J47.9, J45.20, J45.21, J45.22, J45.30, J45.31, J45.32, J45.40, J45.41, J45.42, J45.50, J45.51, J45.52, J45.901, J45.902, J45.909, J45.990, J45.991, J45.998 Cardiovascular Diseases 427.31, 410.00, 410.01, 410.02, 410.10, 410.11, 410.12, 410.20, 410.21, 410.22, 410.30, 410.31, 410.32, 410.40, 410.41, 410.42, 410.50, 410.51, 410.52, 410.60, 410.61, 410.62, 410.70, 410.71, 410.72, 410.80, 410.81, 410.82, 410.90, 410.91, 410.92, 411.0, 411.1, 411.81, 411.89, 412, 413.0, 413.1, 413.9, 414.00, 414.01, 414.02, 414.03, 414.04, 414.05, 414.06, 414.07, 414.12, 414.2, 414.3, 414.4, 414.8, 414.9 I48.0, I48.2, I48.91, I20.0, I20.1, I20.8, I20.9, I21.01, I21.02, I21.09, I21.11, I21.19, I21.21, I21.29, I21.3, I21.4, I22.0, I22.1, I22.2, I22.8, I22.9, I24.0, I24.1, I24.8, I24.9, I25.10, I25.110, I25.111, I25.118, I25.119, I25.2, I25.42, I25.5, I25.6, I25.700, I25.701, I25.708, I25.709, I25.710, I25.711, I25.718, I25.719, I25.720, I25.721, I25.728, I25.729, I25.730, I25.731, I25.738, I25.739, I25.750, I25.751, I25.758, I25.759, I25.760, I25.761, I25.768, I25.769, I25.790, I25.791, I25.798, I25.799, I25.810, I25.811, I25.812, I25.82, I25.83, I25.84, I25.89, I25.9 75 Cognitive Impairment & Dementia 331.82, 331.89, 331.9, 290.8, 290.9, 294.9, 780.93, 784.3, 784.69, 331.83, 331.0, 331.11, 331.19, 331.2, 331.7, 797, 290.0, 290.10, 290.11, 290.12, "29013", 290.20, 290.21, 290.3, 290.40, 290.41, 290.42, 290.43, 294.0, 294.10, 294.11, 294.20, 294,21, 294.8 F01.50, F01.51, F02.80, F02.81, F03.90, F03.91, G13.8, F06.1, F06.8, G30.0, G30.1, G30.8, G30.9, G31.1, G31.2, G31.01, G31.09, R41.81, F04, F05, G94, R54, G31.83, G31.89, G31.9, F03.90, F03.90, R41.2, R41.3, R47.01, R48.1, R48.2, R48.8, G31.84 Obesity 278.00 E66.0, E66.01, E66.09. E66.1 E66.2 E66.3 E66.8 E66.9 Alcohol use (abuse/alcohol- related disorder) 305.0 303.0 303.9, 291, 357.5, 425.5, 535.3, 571.0, 571.1, 571.2, 571.3, 655.4, 760.71 F10.0 F10.1 F10.2 F10.9, G62.1, G31.2, G72.1, I42.6, K29.2, K70, Q86, P04.3, O35.4, K86.0 Substance abuse 305.9 F19.10 Smoking 305.1 F17 76 Incidence Summary Statistics Table 6: Incidence: New cases per 100 individuals at risk in each cohort* HIV- HIV+ Cohort 1 HIV+ Cohort 2 HIV+ Cohort 3 Diabetes 7.93 10.96 7.25 29.74 Hypertension 40.69 43.88 38.82 69.01 Stroke 6.22 7.3 5.43 16.13 Cancer 6.05 6.79 5.5 12.9 Lung Diseases 16.49 20.15 16.63 36.88 Cardiovascular Diseases 11.61 13.41 9.06 34.95 Dementia 4.66 4.62 2.69 14.09 *HIV Cohort 1 (diagnosed with HIV at any point), Cohort 2 (diagnosed with HIV prior to two years from enrollment), or Cohort 3 (diagnosed with HIV after two years from enrollment) 77 Relative Risks It may be useful to additionally see report the relative risks associated with HIV (instead of odds ratios). These results, for each of the cohorts, are presented here. HIV Cohort 1 (diagnosed with HIV at any point), Cohort 2 (diagnosed with HIV prior to two years from enrollment), or Cohort 3 (diagnosed with HIV after two years from enrollment). Table 7: HIV Relative Risks for HIV Cohort 1 (diagnosed with HIV at any point) Compared with HIV- Enrollees Interval Diabetes Hypertension Stroke Cancer Lung diseases Cardiovascular diseases Dementia 2-year 1.36 1.10 1.48 1.32 1.26 1.35 1.57 (1.29 - 1.42) (1.07 - 1.12) (1.40 - 1.56) (1.24 - 1.39) (1.22 - 1.31) (1.29 - 1.40) (1.46 - 1.68) 5-year 1.44 1.14 1.46 1.34 1.26 1.39 1.55 (1.36 - 1.52) (1.11 - 1.16) (1.36 - 1.56) (1.25 - 1.43) (1.21 - 1.31) (1.32 - 1.45) (1.43 - 1.68) 10-year 1.37 1.12 1.41 1.27 1.29 1.33 1.60 (1.23 - 1.51) (1.08 - 1.15) (1.21 - 1.60) (1.09 - 1.45) (1.20 - 1.38) (1.23 - 1.44) (1.38 - 1.83) 95% CI in parentheses. All relative risks statistically significantly different from 1 at the 5% level. Table 8: HIV Relative Risks for HIV Cohort 2 (diagnosed with HIV prior to two years from enrollment) Compared with HIV- Enrollees Interval Diabetes Hypertension Stroke Cancer Lung diseases Cardiovascular diseases Dementia 2-year 0.94 0.99 1.14 1.13 1.07 0.97 1.07 (0.88 - 1.01) (0.97 - 1.02) (1.06 - 1.23) (1.05 - 1.20) (1.02 - 1.11) (0.92 - 1.03) (0.97 - 1.18) 5-year 1.44 1.14 1.46 1.34 1.26 1.39 1.55 (1.36 - 1.52) (1.11 - 1.16) (1.36 - 1.56) (1.25 - 1.43) (1.21 - 1.31) (1.32 - 1.45) (1.43 - 1.68) 10-year 1.37 1.12 1.41 1.27 1.29 1.33 1.60 (1.23 - 1.51) (1.08 - 1.15) (1.21 - 1.60) (1.09 - 1.45) (1.20 - 1.38) (1.23 - 1.44) (1.38 - 1.83) 95% CI in parentheses. All relative risks statistically significantly different from 1 at the 5% level. 78 Table 9: HIV Relative Risks for HIV Cohort 3 (diagnosed with HIV after two years from enrollment) Compared with HIV- Enrollees Interval Diabetes Hypertension Stroke Cancer Lung diseases Cardiovascular diseases Dementia 2-year 2.96 1.52 2.47 1.92 1.96 2.69 2.99 (2.75 - 3.16) (1.47 - 1.57) (2.27 - 2.68) (1.74 - 2.09) (1.86 - 2.06) (2.53 - 2.85) (2.69 - 3.29) 5-year 1.44 1.14 1.46 1.34 1.26 1.39 1.55 (1.36 - 1.52) (1.11 - 1.16) (1.36 - 1.56) (1.25 - 1.43) (1.21 - 1.31) (1.32 - 1.45) (1.43 - 1.68) 10-year 1.37 1.12 1.41 1.27 1.29 1.33 1.60 (1.23 - 1.51) (1.08 - 1.15) (1.21 - 1.60) (1.09 - 1.45) (1.20 - 1.38) (1.23 - 1.44) (1.38 - 1.83) 95% CI in parentheses. All relative risks statistically significantly different from 1 at the 5% level. 79 Sensitivity Analyses Robustness to Model Specifications We explore the robustness of our results to model specifications by running logistic regressions with different independent variables. Our results that HIV is associated with higher levels of chronic disease onset is not sensitive to these variations. Table 10: Logistic Regression Outcomes. 2-Year Odds ratios of Hypertension. HIV variable is defined by Cohort 1, ever diagnosed with HIV Model 1 2 3 HIV/AIDS status at enrollment 1.132*** 1.133*** 1.139*** (1.066 - 1.202) (1.067 - 1.203) (1.073 - 1.208) Age at enrollment 1.031*** 1.030*** 1.027*** (1.031 - 1.031) (1.030 - 1.030) (1.027 - 1.028) Male 1.212*** 1.194*** 1.211*** (1.205 - 1.220) (1.187 - 1.202) (1.204 - 1.219) Black 1.131*** 1.131*** 1.155*** (1.118 - 1.144) (1.118 - 1.144) (1.142 - 1.168) Annual household income < 50K 1.155*** 1.152*** 1.205*** (1.146 - 1.163) (1.144 - 1.160) (1.197 - 1.213) Diabetes status at enrollment 0.951*** 1.027*** (0.942 - 0.961) (1.017 - 1.036) Stroke status at enrollment 1.005 1.001 (0.968 - 1.044) (0.964 - 1.039) Cancer status at enrollment 0.871*** 0.865*** (0.857 - 0.885) (0.851 - 0.879) Lung disease status at enrollment 0.972*** 1.046*** (0.960 - 0.984) (1.033 - 1.059) Cardiovascular disease status at enrollment 0.821*** 0.861*** (0.812 - 0.830) (0.852 - 0.870) Cog. impairment and dementia status at enrollment 1.154*** 1.148*** (1.138 - 1.170) (1.132 - 1.164) Obesity status at enrollment 1.695*** 1.668*** (1.682 - 1.709) (1.655 - 1.681) Alcohol-related disease status at enrollment 1.605*** 1.597*** (1.550 - 1.662) (1.542 - 1.654) Substance abuse status at enrollment 1.267*** 1.266*** 80 (1.100 - 1.458) (1.100 - 1.457) Smoking status at enrollment 1.443*** 1.426*** (1.429 - 1.456) (1.413 - 1.439) Observations 3,381,256 3,381,256 3,381,256 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 11: Logistic Regression Outcomes. 2-Year Odds ratios of Cognitive Impairment/Dementia. HIV variable is defined by Cohort 1, ever diagnosed with HIV. Model 1 2 3 HIV/AIDS status at enrollment 1.662*** 1.676*** 1.721*** (1.483 - 1.862) (1.496 - 1.878) (1.537 - 1.928) Age at enrollment 1.095*** 1.096*** 1.088*** (1.094 - 1.096) (1.095 - 1.096) (1.087 - 1.088) Male 0.909*** 0.938*** 0.926*** (0.898 - 0.920) (0.927 - 0.949) (0.915 - 0.938) Black 0.956*** 0.937*** 0.974*** (0.937 - 0.974) (0.919 - 0.955) (0.955 - 0.993) Annual household income < 50K 1.218*** 1.225*** 1.271*** (1.203 - 1.233) (1.210 - 1.240) (1.255 - 1.286) Diabetes status at enrollment 1.094*** 1.138*** (1.079 - 1.109) (1.122 - 1.153) Hypertension status at enrollment 0.810*** 0.831*** (0.799 - 0.821) (0.821 - 0.842) Stroke status at enrollment 1.408*** 1.413*** (1.361 - 1.455) (1.367 - 1.461) Cancer status at enrollment 0.915*** 0.911*** (0.895 - 0.936) (0.891 - 0.932) Lung disease status at enrollment 1.007 1.112*** (0.990 - 1.025) (1.093 - 1.132) Cardiovascular disease status at enrollment 1.248*** 1.297*** (1.231 - 1.266) (1.279 - 1.315) Obesity status at enrollment 1.424*** 1.435*** (1.404 - 1.445) (1.415 - 1.455) Alcohol-related disease status at enrollment 2.904*** 2.935*** 81 (2.770 - 3.045) (2.800 - 3.077) Substance abuse status at enrollment 2.217*** 2.240*** (1.817 - 2.706) (1.836 - 2.734) Smoking status at enrollment 1.719*** 1.743*** (1.691 - 1.748) (1.715 - 1.772) Observations 5,907,482 5,907,482 5,907,482 Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table 12: Logistic Regression Outcomes. 2-Year Odds ratios of Stroke. HIV variable is defined by Cohort 1, ever diagnosed with HIV Model 1 2 3 HIV/AIDS status at enrollment 1.280*** 1.457*** 1.314*** (1.163 - 1.408) (1.326 - 1.602) (1.195 - 1.446) Age at enrollment 1.046*** 1.070*** 1.040*** (1.046 - 1.047) (1.069 - 1.070) (1.039 - 1.040) Male 1.039*** 1.087*** 1.058*** (1.029 - 1.050) (1.076 - 1.098) (1.047 - 1.069) Black 1.073*** 1.103*** 1.093*** (1.055 - 1.090) (1.085 - 1.120) (1.075 - 1.110) Annual household income < 50K 1.183*** 1.238*** 1.237*** (1.171 - 1.196) (1.224 - 1.251) (1.224 - 1.251) Diabetes status at enrollment 1.215*** 1.252*** (1.201 - 1.229) (1.238 - 1.266) Hypertension status at enrollment 0.995 1.021*** (0.983 - 1.006) (1.009 - 1.033) Cancer status at enrollment 0.926*** 0.923*** (0.908 - 0.944) (0.905 - 0.942) Lung disease status at enrollment 1.014* 1.124*** (1.000 - 1.030) (1.108 - 1.140) Cardiovascular disease status at enrollment 1.567*** 1.629*** (1.548 - 1.586) (1.610 - 1.649) Cog. impairment and dementia status at enrollment 3.511*** 3.526*** (3.464 - 3.558) (3.479 - 3.574) Obesity status at enrollment 1.332*** 1.440*** 82 (1.315 - 1.348) (1.423 - 1.458) Alcohol-related disease status at enrollment 1.583*** 1.873*** (1.511 - 1.659) (1.789 - 1.960) Substance abuse status at enrollment 1.572*** 2.014*** (1.334 - 1.852) (1.715 - 2.365) Smoking status at enrollment 1.766*** 1.882*** (1.742 - 1.791) (1.857 - 1.907) Observations 6,215,432 6,215,432 6,215,432 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 13: Logistic Regression Outcomes. 2-Year Odds ratios of Cancer. HIV variable is defined by Cohort 1, ever diagnosed with HIV Model 1 2 3 HIV/AIDS status at enrollment 1.442*** 1.441*** 1.470*** (1.314 - 1.582) (1.313 - 1.580) (1.340 - 1.613) Age at enrollment 1.048*** 1.045*** 1.042*** (1.047 - 1.048) (1.044 - 1.045) (1.041 - 1.042) Male 1.236*** 1.226*** 1.256*** (1.223 - 1.250) (1.213 - 1.239) (1.242 - 1.269) Black 1.042*** 1.005 1.061*** (1.024 - 1.060) (0.987 - 1.022) (1.043 - 1.080) Annual household income < 50K 1.056*** 1.050*** 1.109*** (1.044 - 1.068) (1.038 - 1.062) (1.096 - 1.121) Diabetes status at enrollment 0.994 1.027*** (0.981 - 1.007) (1.014 - 1.040) Hypertension status at enrollment 0.788*** 0.809*** (0.779 - 0.798) (0.800 - 0.819) Stroke status at enrollment 0.853*** 0.855*** (0.824 - 0.884) (0.825 - 0.886) Lung disease status at enrollment 1.121*** 1.244*** (1.104 - 1.139) (1.225 - 1.264) Cardiovascular disease status at enrollment 0.985** 1.025*** 83 (0.972 - 0.998) (1.012 - 1.039) Cog. impairment and dementia status at enrollment 1.074*** 1.072*** (1.054 - 1.094) (1.052 - 1.092) Obesity status at enrollment 1.346*** 1.292*** (1.328 - 1.363) (1.275 - 1.308) Alcohol-related disease status at enrollment 1.321*** 1.315*** (1.252 - 1.394) (1.246 - 1.387) Substance abuse status at enrollment 0.725** 0.716** (0.562 - 0.936) (0.554 - 0.923) Smoking status at enrollment 1.779*** 1.787*** (1.754 - 1.805) (1.762 - 1.812) Observations 5,981,854 5,981,854 5,981,854 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 14: Logistic Regression Outcomes. 2-Year Odds ratios of Lung Disease. HIV variable is defined by Cohort 1, ever diagnosed with HIV Model 1 2 3 HIV/AIDS status at enrollment 1.269*** 1.292*** 1.315*** (1.193 - 1.350) (1.215 - 1.374) (1.237 - 1.397) Age at enrollment 1.021*** 1.024*** 1.012*** (1.020 - 1.021) (1.024 - 1.025) (1.011 - 1.012) Male 0.906*** 0.926*** 0.936*** (0.900 - 0.912) (0.919 - 0.932) (0.929 - 0.942) Black 0.873*** 0.863*** 0.905*** (0.863 - 0.883) (0.854 - 0.873) (0.895 - 0.915) Annual household income < 50K 1.214*** 1.221*** 1.322*** (1.205 - 1.222) (1.212 - 1.230) (1.313 - 1.332) Diabetes status at enrollment 1.045*** 1.100*** (1.036 - 1.053) (1.091 - 1.109) Hypertension status at enrollment 0.880*** 0.922*** (0.874 - 0.887) (0.915 - 0.929) Stroke status at enrollment 0.929*** 0.935*** 84 (0.908 - 0.950) (0.915 - 0.957) Cancer status at enrollment 1.010 1.001 (0.996 - 1.024) (0.987 - 1.015) Cardiovascular disease 1.247*** 1.339*** (1.236 - 1.257) (1.328 - 1.351) Cog. impairment and dementia status at enrollment 1.325*** 1.326*** (1.309 - 1.341) (1.311 - 1.342) Obesity status at enrollment 1.560*** 1.566*** (1.547 - 1.572) (1.554 - 1.578) Alcohol-related disease status at enrollment 1.701*** 1.746*** (1.648 - 1.756) (1.692 - 1.803) Substance abuse status at enrollment 0.951 0.982 (0.828 - 1.092) (0.855 - 1.127) Smoking status at enrollment 2.644*** 2.677*** (2.621 - 2.666) (2.655 - 2.700) Observations 5,658,282 5,658,282 5,658,282 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 15: Logistic Regression Outcomes. 2-Year Odds ratios of Cardiovascular Disease. HIV variable is defined by Cohort 1, ever diagnosed with HIV Model 1 2 3 HIV/AIDS status at enrollment 1.300*** 1.322*** 1.325*** (1.203 - 1.404) (1.225 - 1.428) (1.228 - 1.430) Age at enrollment 1.056*** 1.059*** 1.047*** (1.056 - 1.057) (1.058 - 1.059) (1.047 - 1.048) Male 1.323*** 1.324*** 1.334*** (1.312 - 1.334) (1.313 - 1.335) (1.323 - 1.345) Black 0.964*** 0.985** 0.988* (0.951 - 0.978) (0.972 - 0.999) (0.975 - 1.002) Annual household income < 50K 1.176*** 1.197*** 1.258*** (1.166 - 1.187) (1.187 - 1.208) (1.247 - 1.269) Diabetes status at enrollment 1.221*** 1.327*** 85 (1.209 - 1.233) (1.314 - 1.340) Hypertension status at enrollment 1.012** 1.077*** (1.003 - 1.021) (1.068 - 1.087) Stroke status at enrollment 1.282*** 1.290*** (1.242 - 1.322) (1.250 - 1.330) Cancer status at enrollment 0.913*** 0.909*** (0.898 - 0.930) (0.893 - 0.925) Lung disease status at enrollment 1.174*** 1.354*** (1.159 - 1.190) (1.337 - 1.371) Cog. impairment and dementia status at enrollment 1.138*** 1.136*** (1.120 - 1.156) (1.118 - 1.154) Obesity status at enrollment 1.975*** 2.058*** (1.957 - 1.994) (2.039 - 2.077) Alcohol-related disease status at enrollment 2.046*** 2.100*** (1.971 - 2.125) (2.023 - 2.180) Substance abuse status at enrollment 1.209** 1.253*** (1.028 - 1.421) (1.066 - 1.473) Smoking status at enrollment 2.023*** 2.063*** (2.000 - 2.045) (2.040 - 2.085) Observations 5,027,839 5,027,839 5,027,839 Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table 16: Logistic Regression Outcomes. 2-Year Odds ratios of Diabetes. HIV variable is defined by Cohort 1, ever diagnosed with HIV Model 1 2 3 HIV/AIDS status at enrollment 1.303*** 1.313*** 1.280*** (1.194 - 1.422) (1.203 - 1.433) (1.174 - 1.397) Age at enrollment 1.017*** 1.021*** 1.009*** (1.016 - 1.018) (1.020 - 1.021) (1.008 - 1.009) Male 1.202*** 1.226*** 1.181*** (1.190 - 1.214) (1.214 - 1.238) (1.169 - 1.192) Black 1.283*** 1.300*** 1.329*** 86 (1.263 - 1.303) (1.280 - 1.320) (1.309 - 1.350) Annual household income < 50K 1.225*** 1.236*** 1.287*** (1.212 - 1.239) (1.223 - 1.250) (1.273 - 1.301) Hypertension status at enrollment 1.125*** 1.244*** (1.112 - 1.137) (1.231 - 1.258) Stroke status at enrollment 0.969* 0.949*** (0.936 - 1.003) (0.916 - 0.982) Cancer status at enrollment 0.858*** 0.852*** (0.840 - 0.877) (0.833 - 0.870) Lung disease status at enrollment 1.024*** 1.117*** (1.008 - 1.040) (1.100 - 1.135) Cardiovascular disease status at enrollment 1.224*** 1.305*** (1.208 - 1.240) (1.288 - 1.322) Cog. impairment and dementia status at enrollment 1.000 0.977** (0.980 - 1.020) (0.958 - 0.997) Obesity status at enrollment 2.504*** 2.584*** (2.477 - 2.531) (2.557 - 2.612) Alcohol-related disease status at enrollment 1.826*** 1.861*** (1.744 - 1.912) (1.777 - 1.948) Substance abuse status at enrollment 0.981 1.014 (0.804 - 1.195) (0.832 - 1.235) Smoking status at enrollment 1.387*** 1.422*** (1.368 - 1.406) (1.403 - 1.441) Observations 4,829,266 4,829,266 4,829,266 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 87 Year of HIV diagnosis The regression analysis was repeated for individuals in Cohort 3 diagnosed with HIV/AIDS before and after 2011, as ART has changed dramatically since 2007 and there may be temporal effects. We find that the results are similar to those in the main manuscript. Table 17: HIV+ population used: Individuals in Cohort 3 (diagnosed with HIV after two years from enrollment) diagnosed with HIV/ AIDS before 2011 Diabetes Hypertension Stroke Cancer Lung Diseases Cardiovascular Diseases Dementia HIV/AIDS status at enrollment 3.64 2.75 2.82 2.40 2.76 3.86 3.17 (3.06 - 4.34) (2.30 - 3.29) (2.31 - 3.44) (1.97 - 2.93) (2.38 - 3.20) (3.25 - 4.58) (2.49 - 4.05) Age at enrollment 1.02 1.05 1.05 1.05 1.03 1.07 1.11 (1.02 - 1.02) (1.05 - 1.05) (1.05 - 1.05) (1.05 - 1.05) (1.03 - 1.03) (1.07 - 1.07) (1.11 - 1.11) Male 1.23 1.21 0.99 1.17 0.85 1.38 0.89 (1.22 - 1.24) (1.20 - 1.21) (0.98 - 0.99) (1.17 - 1.18) (0.84 - 0.85) (1.37 - 1.38) (0.88 - 0.89) Black 1.29 1.40 1.11 1.07 0.86 0.95 0.99** (1.28 - 1.30) (1.39 - 1.42) (1.09 - 1.12) (1.06 - 1.08) (0.86 - 0.87) (0.95 - 0.96) (0.98 - 1.00) Annual household income <50,000 1.22 1.22 1.13 0.99 1.21 1.14 1.15 (1.21 - 1.23) (1.21 - 1.22) (1.12 - 1.13) (0.98 - 0.99) (1.20 - 1.21) (1.14 - 1.15) (1.14 - 1.16) Diabetes status at enrollment 2.73 1.22 1.00 1.07 1.22 1.06 (2.71 - 2.75) (1.21 - 1.23) (0.99 - 1.00) (1.06 - 1.07) (1.22 - 1.23) (1.05 - 1.07) Hypertension status at enrollment 1.03 0.91 0.72 0.80 0.93 0.74 (1.02 - 1.03) (0.90 - 0.92) (0.71 - 0.72) (0.80 - 0.81) (0.92 - 0.93) (0.74 - 0.75) Stroke status at enrollment 0.91 0.67 0.80 0.86 1.23 1.38 (0.89 - 0.93) (0.66 - 0.69) (0.78 - 0.82) (0.85 - 0.87) (1.21 - 1.26) (1.35 - 1.41) 88 Cancer status at enrollment 0.82 0.69 0.89 1.01 0.88 0.88 (0.81 - 0.83) (0.69 - 0.70) (0.88 - 0.90) (1.00 - 1.02) (0.87 - 0.89) (0.86 - 0.89) Lung diseases at enrollment 0.96 0.75 0.94 1.04 1.13 0.94 (0.95 - 0.97) (0.74 - 0.76) (0.94 - 0.95) (1.03 - 1.05) (1.12 - 1.14) (0.93 - 0.95) Cardiovascular diseases status at enrollment 1.19 2.64 1.97 1.10 1.55 1.18 (1.18 - 1.20) (2.62 - 2.66) (1.95 - 1.98) (1.09 - 1.10) (1.54 - 1.55) (1.17 - 1.19) Dementia status at enrollment 1.06 1.26 3.01 0.95 1.23 1.45 (1.05 - 1.07) (1.24 - 1.27) (2.99 - 3.04) (0.94 - 0.96) (1.22 - 1.24) (1.43 - 1.46) Obesity at enrollment 3.01 3.05 1.52 1.49 1.86 2.43 1.62 (2.99 - 3.03) (3.03 - 3.07) (1.51 - 1.53) (1.48 - 1.50) (1.85 - 1.87) (2.41 - 2.44) (1.61 - 1.64) Alcohol-related diseases at enrollment 1.95 2.52 1.76 1.36 1.90 2.37 3.14 (1.89 - 2.01) (2.44 - 2.61) (1.71 - 1.82) (1.32 - 1.41) (1.86 - 1.94) (2.31 - 2.44) (3.04 - 3.24) Substance Abuse at enrollment 1.00 1.54 1.76 0.84 1.05 1.29 2.59 (0.88 - 1.14) (1.36 - 1.74) (1.58 - 1.96) (0.73 - 0.97) (0.96 - 1.15) (1.15 - 1.44) (2.28 - 2.95) Smoking status at enrollment 1.46 1.81 1.93 1.90 3.44 2.31 1.85 (1.45 - 1.47) (1.80 - 1.82) (1.91 - 1.95) (1.89 - 1.92) (3.42 - 3.46) (2.30 - 2.33) (1.84 - 1.87) Constant 0.01 0.02 0.00 0.00 0.03 0.00 0.00 (0.01 - 0.01) (0.02 - 0.02) (0.00 - 0.00) (0.00 - 0.00) (0.03 - 0.03) (0.00 - 0.00) (0.00 - 0.00) Observations 5,724,270 4,037,359 7,358,402 7,088,435 6,697,859 5,975,517 7,008,298 Robust 95% CI in parentheses. All OD were statistically significantly different from 1 at the 5% level except those in italics. 89 Table 18: HIV+ Population Used: Individuals in Cohort 3 (diagnosed with HIV after two years from enrollment) diagnosed with HIV/ AIDS in/after 2011 Diabetes Hypertension Stroke Cancer Lung Diseases Cardiovascular Diseases Dementia HIV/AIDS status at enrollment 4.25 3.02 2.68 2.16 2.64 4.00 3.90 (3.81 - 4.73) (2.66 - 3.42) (2.39 - 3.01) (1.92 - 2.44) (2.40 - 2.90) (3.61 - 4.42) (3.44 - 4.43) Age at enrollment 1.02 1.05 1.05 1.05 1.03 1.07 1.11 (1.02 - 1.02) (1.05 - 1.05) (1.05 - 1.05) (1.05 - 1.05) (1.03 - 1.03) (1.07 - 1.07) (1.11 - 1.11) Male 1.23 1.21 0.99 1.17 0.85 1.38 0.89 (1.22 - 1.24) (1.20 - 1.21) (0.98 - 0.99) (1.17 - 1.18) (0.84 - 0.85) (1.37 - 1.38) (0.88 - 0.89) Black 1.29 1.40 1.11 1.07 0.86 0.95 0.99 (1.28 - 1.30) (1.39 - 1.42) (1.09 - 1.12) (1.06 - 1.08) (0.86 - 0.87) (0.95 - 0.96) (0.98 - 1.00) Annual household income <50,000 1.22 1.22 1.13 0.99 1.21 1.14 1.15 (1.21 - 1.23) (1.21 - 1.22) (1.12 - 1.13) (0.98 - 0.99) (1.20 - 1.21) (1.14 - 1.15) (1.14 - 1.16) Diabetes status at enrollment 2.73 1.22 1.00 1.07 1.22 1.06 (2.71 - 2.75) (1.21 - 1.23) (0.99 - 1.00) (1.06 - 1.07) (1.22 - 1.23) (1.05 - 1.07) Hypertension status at enrollment 1.03 0.91 0.72 0.80 0.93 0.74 (1.02 - 1.03) (0.90 - 0.92) (0.71 - 0.72) (0.80 - 0.81) (0.92 - 0.93) (0.74 - 0.75) Stroke status at enrollment 0.91 0.67 0.80 0.86 1.23 1.38 (0.89 - 0.93) (0.66 - 0.69) (0.78 - 0.82) (0.85 - 0.87) (1.21 - 1.26) (1.35 - 1.41) Cancer status at enrollment 0.82 0.69 0.89 1.01 0.88 0.88 (0.81 - 0.83) (0.69 - 0.70) (0.88 - 0.91) (1.00 - 1.02) (0.87 - 0.89) (0.86 - 0.89) Lung diseases at enrollment 0.96 0.75 0.94 1.04 1.13 0.94 (0.95 - 0.97) (0.74 - 0.76) (0.94 - 0.95) (1.03 - 1.05) (1.12 - 1.14) (0.93 - 0.95) Cardiovascular diseases status at enrollment 1.19 2.64 1.97 1.10 1.55 1.18 90 (1.18 - 1.20) (2.62 - 2.66) (1.95 - 1.98) (1.09 - 1.10) (1.54 - 1.55) (1.17 - 1.19) Dementia status at enrollment 1.06 1.26 3.01 0.95 1.23 1.45 (1.05 - 1.07) (1.24 - 1.27) (2.99 - 3.04) (0.94 - 0.96) (1.22 - 1.24) (1.43 - 1.46) Obesity at enrollment 3.01 3.05 1.52 1.49 1.86 2.43 1.62 (2.98 - 3.03) (3.03 - 3.07) (1.50 - 1.53) (1.48 - 1.50) (1.85 - 1.87) (2.41 - 2.44) (1.61 - 1.64) Alcohol-related diseases at enrollment 1.95 2.52 1.76 1.36 1.90 2.38 3.14 (1.89 - 2.01) (2.44 - 2.61) (1.71 - 1.82) (1.32 - 1.41) (1.86 - 1.94) (2.31 - 2.44) (3.05 - 3.24) Substance Abuse at enrollment 1.01 1.55 1.76 0.84 1.05 1.29 2.60 (0.89 - 1.15) (1.37 - 1.75) (1.59 - 1.96) (0.73 - 0.97) (0.96 - 1.16) (1.16 - 1.44) (2.29 - 2.95) Smoking status at enrollment 1.46 1.81 1.93 1.90 3.44 2.31 1.85 (1.45 - 1.47) (1.80 - 1.82) (1.91 - 1.95) (1.89 - 1.92) (3.42 - 3.46) (2.30 - 2.33) (1.84 - 1.87) Constant 0.01 0.02 0.00 0.00 0.03 0.00 0.00 (0.01 - 0.01) (0.02 - 0.02) (0.00 - 0.00) (0.00 - 0.00) (0.03 - 0.03) (0.00 - 0.00) (0.00 - 0.00) Observations 5,725,291 4,038,085 7,360,053 7,090,035 6,699,249 5,976,788 7,009,824 Robust 95% CI in parentheses. All OD were statistically significantly different from 1 at the 5% level except those in italics. 91 Cohort 3 Analysis: Age of HIV diagnosis Cohort 3 HIV+ individuals may be of particular interest, as their dates of HIV diagnosis are observable. Summary statistics for this cohort by age are provided in the table below. Table 19: Cohort 3 (diagnosed with HIV after two years from enrollment) summary statistics by age group (%) Age 50-59 60-69 70-79 >=80 HIV status HIV - HIV + HIV - HIV + HIV - HIV + HIV - HIV + % % % % % % % % Male 48 64.9 46.3 59.9 43.5 49.6 38.6 40.9 White 77.1 56.3 76.2 55.4 75.3 56.7 78.3 58.6 Black 10.3 25.9 10.5 27.4 10.6 23.6 10.1 15.7 Hispanic 9.3 15 9.7 14.9 10.4 16.5 8.6 22.7 Education level < high school 0.5 1.1 0.7 1.2 0.9 1.7 0.7 0 Annual household income < 50000 22.2 35.3 32.8 44.9 46.5 52.8 54.3 59.7 Diabetes status at enrollment 16.2 25.1 26.9 38.5 31.5 40.9 32.7 47.5 Hypertension status at enrollment 31.5 38.3 49.1 55.7 60.3 61 71.1 74.7 Stroke status at enrollment 0.9 2.4 2 3.2 4.1 5.8 6.9 4.5 Cancer status at enrollment 2.7 2.5 5.7 5.5 9.3 9.5 11.1 12.1 Lung diseases status at enrollment 7.9 12.2 11.5 16.1 14.8 17.6 17.4 20.2 Cardiovascular diseases status at enrollment 9.9 15.9 20.1 26.9 33.5 41 48.7 51.5 Dementia status at enrollment 1.9 4.2 4.5 10.6 14.7 24.5 35.1 48.5 Obesity status at enrollment 18.6 27.6 20.1 32.1 15.6 23.5 7.9 13.1 Alcohol-related status at enrollment 0.7 3.8 0.7 3.2 0.6 2.1 0.3 1 Substance abuse status at enrollment 0.1 0.5 0.1 0.4 0 0.1 0 0 Smoking status at enrollment 13.6 26.2 12.6 22.3 9.3 15.6 4.7 8.6 We include the results of a sensitivity analysis of Cohort 3 that includes age of diagnosis in addition to the other covariates below. These results suggest that HIV patients have higher odds ratios of acquiring chronic illness at every age. The table reports the odds ratios associated with the HIV variable. 92 Independent variables used include demographic variables, comorbidities, and behavioral factors. The demographic variables are: HIV status, male, black, and annual household income < 50K. The comorbidity independent variables are: diabetes status at enrollment, stroke status at enrollment, cancer status at enrollment, lung disease status at enrollment, cardiovascular disease status at enrollment, cog. impairment and dementia status at enrollment. The behavioral independent variables are: obesity status at enrollment, alcohol-related disease status at enrollment, substance abuse status at enrollment, and smoking status at enrollment. Model 1 uses all independent variables, model 2 excludes variables on comorbidities, and model 3 excludes behavioral variables. Table 20: HIV odds ratios by age group for Cohort 3 (diagnosed with HIV after two years from enrollment) compared with HIV- enrollees Age group Diabetes Hypertension Stroke Cancer Lung diseases Cardiovascular diseases Dementia Model Specification 1 50-59 3.87 2.39 3.59 2.42 2.78 4.08 4.95 (3.31 - 4.53) (2.06 - 2.76) (2.95 - 4.37) (1.98 - 2.94) (2.42 - 3.20) (3.50 - 4.75) (3.90 - 6.29) 60-69 4.39 3.37 2.75 2.51 2.67 3.73 3.94 (3.76 - 5.13) (2.81 - 4.03) (2.31 - 3.27) (2.12 - 2.98) (2.32 - 3.06) (3.22 - 4.32) (3.27 - 4.76) 70-79 4.24 3.22 2.59 1.92 2.62 4.35 3.14 (3.46 - 5.20) (2.35 - 4.42) (2.16 - 3.12) (1.56 - 2.36) (2.22 - 3.09) (3.60 - 5.26) (2.57 - 3.84) >=80 3.51 8.57 2.92 1.84 2.76 4.39 3.41 (2.64 - 4.67) (4.31 - 17.02) (2.33 - 3.65) (1.40 - 2.41) (2.20 - 3.47) (3.32 - 5.80) (2.61 - 4.46) Model Specification 2 50-59 3.94 2.49 3.38 2.58 2.90 4.31 5.32 (3.39 - 4.58) (2.16 - 2.88) (2.79 - 4.11) (2.13 - 3.14) (2.55 - 3.31) (3.73 - 4.98) (4.21 - 6.72) 60-69 4.87 3.61 2.95 2.78 3.00 4.20 4.50 (4.20 - 5.65) (3.02 - 4.31) (2.49 - 3.50) (2.35 - 3.29) (2.64 - 3.41) (3.66 - 4.83) (3.73 - 5.43) 70-79 4.63 3.58 2.79 2.14 2.96 4.77 3.57 (3.81 - 5.63) (2.62 - 4.89) (2.31 - 3.37) (1.74 - 2.63) (2.52 - 3.47) (3.99 - 5.70) (2.93 - 4.34) >=80 3.53 8.50 2.71 1.86 2.71 4.41 3.51 (2.68 - 4.66) (4.32 - 16.70) (2.12 - 3.45) (1.42 - 2.43) (2.16 - 3.39) (3.34 - 5.80) (2.71 - 4.55) Model Specification 3 50-59 3.84 2.40 3.17 2.42 2.72 3.99 4.92 (3.28 - 4.49) (2.07 - 2.79) (2.60 - 3.87) (1.99 - 2.95) (2.37 - 3.13) (3.42 - 4.64) (3.88 - 6.24) 93 60-69 4.40 3.37 2.63 2.52 2.66 3.68 3.95 94 CHAPTER 2: THE EXCESS COSTS ASSOCIATED WITH HIV INFECTION IN CHRONIC CONDITIONS MANGAEMENT AMONG PEOPLE LIVING WITH HIV AND AIDS (PLWHA) AGED 50 AND OLDER Table 29: Average annual Medicare expenditure per person by gender at age 65 (2019 USD) Male Female Non-PLHWA 23,555.39 23,212.16 PLWHA 31,944.34 29,499.17 95 Table 30: Average annual Medicare expenditure per person by gender among the general US population (2019 USD) Male Female Age 65-84 10,729 10,818 Age 85+ 20,070 18,553 Source: US CDC 96 Table 31: Average annual Medicare expenditure per person by race/ethnicity at age 65 (2019 USD) White Black Hispanic Non-PLWHA 22,502.34 27,669.99 18,755.26 PLWHA 28,558.51 37,370.08 27,270.68 97 CHAPTER 3: THE COST-EFFECTIVENESS ANALYSIS OF REGULAR DEMENTIA SCREENING AMONG PEOPLE LIVING WITH HIV/AIDS AGED 50 AND OLDER INTRODUCTION Model Validation Table 34: Comparison of lifetime treatment cost (2019 USD) Our study US CDC Schackman et al (2015) 43 414,922 440,414 502,810 Lifetime treatment Cost for HIV Table 35: Life expectancy Our Study Ward et al (2020) 44 Siddiqi et al (2016) 45 Age 50 28.42 (not discounted) 22.4 (discounted) 30 (not discounted) 21.53 (discounted) Table 34 and table 35 showed that the outputs generated from our model are validated when compared with other published studies. 98 Budget Impact Analysis In this analysis, we estimated a PLWHA with AD population of 35,569 calculated from the total population of the United States from the US Census Bureau, the total population of AD patients of the United States from US the CDC, the relative risk of AD among PLWHA from Chapter 1, and the total population of PLWHA from the US CDC. We assumed the size of this PLWHA with AD population stay constant for a 3-year period. Here we called the status quo scenario strategy A, and regular cognitive function screening with aducanumab strategy B. We explored how the introduction of strategy B would impact the budget in the next 3 years after the implementation of the strategy by changing the proportion of the population that adopts strategy B (25%, 50%, and 75%) and the assumptive medical cost incurred by strategy B (the same as strategy A, 1.5 times as strategy A, and 2 times as strategy A). Although our cost-effectiveness analysis did not suggest strategy B is a cost-effective option, we found that the scenario where 75% of the PLWHA with AD population adopt strategy A and 25% of the PLWHA with AD population adopt strategy B has the least budget impact across all the three different annual healthcare cost of strategy B scenarios, even in the scenario where we assumed the annual healthcare cost of strategy B was twice as much as strategy A. 99 Table 3 6 : Scenario 1: both strategies incur the same annual healthcare cost Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 50% Strategy A + 50% Strategy B 712.3M 653.1M 567.4M Budget Impact 25M 33.8M 33M Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 25% Strategy A + 75% Strategy B 724.9M 669.9M 583.9M Budget Impact 37.6M 50.7M 49.5M Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 75% Strategy A + 25% Strategy B 699.8M 636.2M 550.8M Budget Impact 12.5M 16.9M 16.5M 100 Table 3 7 : Scenario 2: Annual healthcare cost of Strategy B is 1.5 times as Strategy A Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 50% Strategy A + 50% Strategy B 720.1M 665.3M 580.1M Budget Impact 32.9M 46.0M 45.8M Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 25% Strategy A + 75% Strategy B 736.6M 688.3M 603.0M Budget Impact 49.3M 69.0M 68.6M Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 75% Strategy A + 25% Strategy B 703.7M 642.3M 557.2M Budget Impact 16.4M 23.0M 22.9M 101 Table 38: Scenario 3: Annual healthcare cost of Strategy B is 1.5 times as Strategy A Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 50% Strategy A + 50% Strategy B 727.9M 677.6M 592.9M Budget Impact 40.6M 58.3M 58.5M Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 25% Strategy A + 75% Strategy B 748.3M 706.7M 622.1M Budget Impact 61.0M 87.4M 87.7M Year 1 Year 2 Year 3 Strategy A 687.3M 619.3M 534.4M 75% Strategy A + 25% Strategy B 707.6M 648.4M 563.6M Budget Impact 20.3M 29.1M 29.2M 102

Abstract (if available)

Abstract People living with HIV/AIDS (PLWHA) treated with ARTs now have a good chance to live into their 70s due to the effectiveness of ARTs. This means a new challenge to the capacity of our healthcare system. In this dissertation, we aim to provide a more comprehensive understanding of this new challenge. This dissertation is composed of three chapters. In the first chapter, we measure how HIV/AIDS will impact the aging process of PLWHA by estimating the risks of developing aging-related chronic comorbidities compared to people without HIV/AIDS using a large U.S. commercial claims data set. In the second chapter, we forecast future healthcare expenditure and other health outcomes of aging PLWHA in the United States by incorporating the first chapter's results into a well-established microsimulation model. In the third chapter, we evaluate the cost-effectiveness of implementing regular cognitive function screenings among aging PLWHA in the United States as a healthcare strategy in managing aging PLWHA who are at risk of developing cognitive impairment diseases.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Essays in health economics

PDF

Value in health in the era of vertical integration

PDF

Crohn’s disease: health outcomes and resource utilization in the biologic era

PDF

Assessing value defects in limb preservation care

PDF

Risky health behaviors and neurocognitive function among people living with HIV

PDF

Disease modifying treatments for Alzeimer's disease: modeling, value assessment and eligible patients

PDF

Strategic communication in the fight against HIV/AIDS in the Russian Federation

PDF

Affective neuropsychiatric symptoms and neural connectivity in the early stages of Alzheimer’s disease

PDF

Essays on the economics of infectious diseases

PDF

Stage of readiness and learning styles -- a tailored HIV/AIDS prevention intervention for youth detained in the Los Angeles County Juvenile Justice system

PDF

The value of novel antihyperlipidemic treatments in the U.S. healthcare system: Reducing the burden of cardiovascular diseases and filling the gap of low adherence in statins

PDF

Designing infectious disease models for local level policymakers

PDF

Three essays on estimating the effects of government programs and policies on health care among disadvantaged population

PDF

Essays on work, retirement, and fostering longer working lives

PDF

Outcomes of antibiotic use among children with acute respiratory tract infections

PDF

Calibration uncertainty in model-based analyses for medical decision making with applications for ovarian cancer

PDF

Documenting the impact of under-treatment for chronic disorders

PDF

Neuroinflammation and ApoE4 genotype in at-risk female aging: implications for Alzheimer's disease

PDF

Burden of illness in hemophilia A: taking the patient’s perspective

PDF

From risk mitochondrial and metabolic phenotype towards a precision medicine approach for Alzheimer's disease

Asset Metadata

Creator Yang, Hsin-Yun (author)

Core Title The disease burden among aging people living with HIV/AIDS

School School of Pharmacy

Degree Doctor of Philosophy

Degree Program Health Economics

Publication Date 12/12/2020

Defense Date 05/21/2020

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

Tag aging,chronic diseases,dementia,disease burden,HIV/AIDS,OAI-PMH Harvest,PLWHA

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Padula, William (committee chair), Seabury, Seth (committee member), Suen, Sze-Chuan (committee member)

Creator Email hsinyuny@usc.edu,hsyunyang@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-405143

Unique identifier UC11668419

Identifier etd-YangHsinYu-9034.pdf (filename),usctheses-c89-405143 (legacy record id)

Legacy Identifier etd-YangHsinYu-9034.pdf

Dmrecord 405143

Document Type Dissertation

Rights Yang, Hsin-Yun

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