The Relationship Between Household Income and Obesity Among Older Adults: Investigating the Moderating Role of Race

The Relationship Between Household Income and Obesity Among Older Adults: Investigating the Moderating Role of Race | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Relationship Between Household Income and Obesity Among Older Adults: Investigating the Moderating Role of Race Sunkanmi Folorunsho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5754136/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Obesity, a leading global health concern, disproportionately impacts older adults. This study explores the relationship between household income and obesity (BMI ≥ 30) among U.S. adults aged 65 and older, using data from the 2020 wave of the Health and Retirement Study (HRS), a nationally representative dataset. Logistic regression models and predictive margins illustrate disparities, with participants categorized into income quartiles and analyzed across three racial/ethnic groups: Non-Hispanic White (NHW), Non-Hispanic Black (NHB), and Hispanic. Results show that lower-income older adults face significantly higher obesity rates, with those in the lowest income quartile having 1.72 times greater odds of obesity compared to higher-income individuals. NHBs consistently exhibit the highest obesity prevalence across all income levels, followed by Hispanics, while NHWs report the lowest rates. Even among higher-income NHBs, obesity remains elevated, highlighting the role of structural barriers such as food deserts, healthcare disparities, and chronic stress from systemic racism. The income-obesity relationship differs by race/ethnicity. For NHWs, obesity decreases steadily with higher income, while NHBs show persistently high rates regardless of income, and Hispanics display mixed patterns influenced by cultural and environmental factors. These findings suggest that addressing income disparities alone may not suffice to reduce obesity among minority groups, as systemic inequities persist. Targeted interventions are needed to address these structural barriers. Policies promoting access to healthy food, recreational spaces, and preventive healthcare in underserved minority communities are critical to mitigating obesity disparities in older adults. Obesity Older Adults Household Income Race/Ethnicity health Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Obesity is a global health crisis and ranks as the fifth leading cause of mortality worldwide, affecting over 1.9 billion adults (Mamdouh et al., 2023 ; Safaei et al., 2021 ; World Health Organization [WHO], 2016). Defined by the WHO as an abnormal or excessive accumulation of body fat that poses health risks, obesity is commonly measured using body mass index (BMI), with a BMI of 30 or higher indicating obesity. Despite ongoing public health efforts, obesity rates remain alarmingly high. Between 2017 and 2020, 41.9% of the U.S. population was classified as obese, with rates increasing from 30.5–41.9% between 1999–2000 and 2017–2020 (Centers for Disease Control and Prevention [CDC], 2023). This trend is particularly concerning among older adults. From 1988–1994 to 2015–2018, obesity prevalence among U.S. adults aged 65 and older surged from 22–40%, presenting significant challenges for this demographic (Population Reference Bureau [PRB], 2022). Older adults with obesity face elevated risks of chronic conditions, including cardiovascular disease, stroke, hypertension, and type 2 diabetes, as well as functional limitations that reduce life expectancy compared to their non-obese peers (PRB, 2022). While obesity is recognized as a critical health issue among older adults (McKee & Morley, 2021), significant disparities exist within this population. For instance, among women aged 75 and older, 49.4% of non-Hispanic Black women were obese, compared to 30.2% of Hispanic women and 27.5% of non-Hispanic White women in the same age group (CDC, 2023). Furthermore, household income plays a critical role in obesity prevalence (Gong et al., 2022 ; Wang & Beydoun, 2007 ). Individuals from lower-income households bear a disproportionate burden, while 35.6% of individuals in the lowest income bracket are obese, only 15.5% in the highest income bracket face obesity (PRB, 2022). Although previous research has identified the relationships between household income, and obesity among older adults, the moderating role of race/ethnicity in these associations warrants further investigation. Race/ethnicity influences socioeconomic status, with racial minorities, particularly Black and Hispanic individuals, experiencing higher levels of poverty and social disparities (Gong et al., 2022 ). These disparities often limit access to essential resources such as nutritious food, healthcare, and recreational facilities, even among racial minorities with similar income levels as White individuals (Odoms-Young & Bruce, 2018 ). Such inequities exacerbate the effects of low income on obesity within minority groups. Additionally, cultural norms and health behaviors, such as dietary practices and perceptions of body weight, vary across racial and ethnic groups, further shaping obesity risk (Kirby et al., 2012 ). This study uses data from the Health and Retirement Study (HRS) to address gaps in the literature in two significant ways. First, it investigates the relationships between household income and obesity among older adults using logistic regression models. Second, it examines how race/ethnicity moderates these relationships. While existing research explores obesity and its determinants across various age groups, this study focuses on older adults, a demographic with unique health considerations and challenges. By incorporating race/ethnicity as a moderating variable, this study goes beyond simple associations to examine the complex dynamics influencing obesity among older adults. Household Income and Obesity in Older Adults Older adults with lower household incomes are at an increased risk of obesity (Drewnowski, 2009 ; Dinsa et al., 2012 ). Two primary perspectives help explain this relationship. The causation hypothesis suggests that limited financial resources restrict access to nutritious food, healthcare, and opportunities for physical activity, leading to weight gain over time (Reidpath et al., 2002 ). In contrast, the reverse causality hypothesis proposes that obesity precedes and contributes to lower income, as stigma and weight-based discrimination in the workforce reduce economic opportunities for obese individuals (Kim & von dem Knesebeck, 2018). The social determinants of health framework provides additional insight into these dynamics. This model emphasizes how material conditions influence health behaviors, psychosocial stressors, and overall well-being (Hahn, 2021 ). Limited financial resources can lead to unhealthy dietary patterns, reduced physical activity, and heightened stress, all of which contribute to obesity (Warner et al., 2012 ). Moreover, stigma amplifies these challenges, as obese individuals often experience social isolation and reduced self-esteem, further exacerbating their health risks (Kirby et al., 2012 ). Another theoretical framework, Grossman’s health production model (1972), offers valuable understanding into the relationship between income and health, specifically obesity. The model conceptualizes health as a form of capital that individuals accumulate and depreciate over time. People derive utility from good health and, as a result, invest time and resources into health-promoting behaviors to maximize their overall well-being. According to the model, higher income enables individuals to allocate more resources toward maintaining and improving their health. These investments often include better diets, access to healthcare services, regular physical activity, and preventive measures, all of which contribute to a reduced risk of obesity. The model assumes that individuals make rational decisions to balance immediate utility with long-term health benefits. For example, a person with higher income is more likely to afford gym memberships, organic food, or regular health check-ups, which collectively enhance health outcomes. However, this seemingly straightforward relationship between income and health is nuanced by several complexities. Kpelitse et al. ( 2014 ) posited that higher-income individuals often face significant opportunity costs for their time, which may limit their ability to engage in physical activity despite having the financial means. For example, individuals in high-paying professions may work longer hours or prioritize career advancements over leisure or exercise. This trade-off between time and health-promoting activities underscores the role of opportunity costs in moderating the income-health relationship. Additionally, people's differing attitudes toward time, how much they value immediate benefits versus future rewards, add another layer of complexity to the relationship between income and obesity. For instance, individuals who prioritize immediate gratification may indulge in unhealthy eating habits and neglect long-term health investments, regardless of their income level. This highlights that income alone cannot fully explain obesity risk; personal attitudes and time management also play crucial roles. Moreover, these dynamics often differ between genders. Race/Ethnicity, Household Income, and Obesity While household income is a significant predictor of obesity, race and ethnicity play a critical role in shaping this relationship. Minority groups, particularly African Americans and Hispanics, are more likely to experience lower household incomes, live in neighborhoods with limited access to nutritious food, and face environmental stressors that contribute to obesity (Cooksey-Stowers et al., 2017 ; Gordon-Larsen, 2014 ). Minority-dense neighborhoods are often characterized by food deserts, with fewer grocery stores and more fast-food outlets, exacerbating obesity risks (Bell et al., 2019 ). The relationship between income and obesity varies by race. For White women, higher income is typically associated with lower obesity rates (Chang & Lauderdale, 2005 ). However, this association weakens or even reverses for African American men, highlighting that income alone cannot fully explain racial disparities in obesity (Chang & Lauderdale, 2005 ). Additional factors, such as chronic stress, discrimination, and limited access to resources, significantly contribute to these disparities (Williams et al., 2010 ). Even among African Americans with higher income levels, the enduring effects of historical and systemic racism continue to influence health outcomes and perpetuate disparities, including obesity. While higher income theoretically provides greater access to health-promoting resources such as quality food, healthcare, and safe recreational spaces, broader structural barriers rooted in discrimination often undermine these advantages. African Americans are more likely to reside in neighborhoods with fewer grocery stores, limited access to fresh produce, and an overabundance of fast-food outlets, even when income levels are comparable to those of White individuals (Cooksey-Stowers et al., 2017 ; Bell et al., 2019 ). These inequities, shaped by redlining and other discriminatory practices, exacerbate obesity risks. Discrimination also manifests in the healthcare system, where African Americans often encounter implicit bias, reduced quality of care, and limited access to preventive services, regardless of their socioeconomic status (Williams et al., 2010 ). This perpetuates a cycle of unmet health needs and poorer outcomes, including higher obesity rates. Additionally, chronic exposure to racism acts as a significant source of psychosocial stress, triggering physiological responses such as elevated cortisol levels and increased abdominal fat accumulation. These stress-related mechanisms compound obesity risks among African Americans, creating a health burden that cannot be addressed by income improvements alone. Methods Data source Data for this study were drawn from the HRS, a nationally representative longitudinal survey administered by the University of Michigan and funded by the National Institute on Aging (NIA) and the Social Security Administration (SSA). The HRS focuses on U.S. adults aged 50 and older and covers a wide range of topics, including health, income, employment, cognitive function, and genomics ( http://hrsonline.isr.umich.edu ). Participants were recruited using a multistage area probability sampling method designed to ensure a diverse and representative sample of older adults in the United States. The study began in 1992, targeting individuals aged 51 to 61, with their spouses also included. Cohorts were refreshed every six years to maintain the study's representativeness, and new participants were added biennially. At baseline, all participants were community-dwelling individuals, and they were followed over time, even if they transitioned to long-term care settings. Interviews were conducted using a combination of in-person, telephone, and enhanced face-to-face methods. During the baseline year, half of the sample completed in-person interviews, which included physical and biological measures, along with a psychosocial questionnaire. The other half underwent telephone interviews in the baseline year, followed by enhanced face-to-face interviews the subsequent year. Respondents primarily provided self-reports, but proxies were used when necessary. Participants were informed about the study's procedures and provided consent before participation. They were also notified of their rights to decline participation, refuse to answer specific questions, or withdraw at any time. Data collection followed a rigorous protocol, with trained interviewers ensuring privacy and comfort during the interviews. The survey included detailed questions covering multiple domains relevant to aging and was supplemented by linkage to administrative data from Medicare, the Veteran's Administration, and the National Death Index. These linkages enriched the dataset by adding biomarkers and other objective measures. Measures Dependent variable Obesity was the dependent variable in this study, measured using BMI, a widely accepted indicator of weight status. BMI was calculated based on participants’ self-reported height and weight. In alignment with the guidelines established by Zhang and Crimmins (2019) and WHO, obesity was defined as having a BMI of 30 kg/m² or higher. This variable was dichotomized into two categories: 0 = not obese (BMI below 30) and 1 = obese (BMI 30 or higher). HRS enhances the reliability of obesity classification by including both self-reported measures and validated health data collected during face-to-face interviews for a subset of participants. These additional health metrics allow for cross-verification of BMI calculations, minimizing potential biases associated with self-reported data and ensuring a robust assessment of obesity across the study population. Independent Variable Household income was measured as the total annual income reported by participants, encompassing various sources such as earnings from the respondent and their spouse, pensions, annuities, Social Security Disability Insurance, Social Security Retirement benefits, unemployment benefits, workers’ compensation, and capital income (e.g., dividends, interest, and rental income). Other income sources were also included to provide a comprehensive assessment of financial resources. Household income was recoded into quartiles based on its distribution within the dataset. Each income quartile was assigned a numerical code: 0 = less than $ 24,000 annually, 1 = $ 24,000 to $ 49,000, 2 = $ 49,000 to $ 97,000, and 3 = more than $ 97,000 annually. This recoding allowed for a systematic categorization of income disparities across the sample. A count procedure was used to address missing cases, ensuring the validity of the income variable for analysis. This approach aligns with previous research methods, such as those described by Agerbo (2007), and enables a nuanced examination of socioeconomic disparities. Moderating Variable Race/ethnicity was measured as a moderating variable using self-reported data. Participants were allowed to report multiple racial identities, and for those identifying with more than one race, they were asked to specify their primary racial identity. The race/ethnicity variable was recoded into three categories: 0 = non-Hispanic White, 1 = non-Hispanic Black, and 2 = Hispanic. A count procedure was performed to address missing cases, ensuring the completeness of the dataset. This recoding facilitated a systematic analysis of racial and ethnic differences in the relationship between household income and obesity. Covariates Education, marital status, gender, physical activity, alcohol consumption, and self-reported health were included as covariates in the analysis. Marital status was categorized into four groups: married, divorced, widowed, and never married. Education was divided into two categories: those with a college degree and those without. Gender was categorized as male or female. Physical activity was classified into three groups: none, some, and regular. Alcohol consumption was dichotomized into two categories: yes (consumes alcohol) and no (does not consume alcohol). Self-reported health was categorized into three levels: poor, moderate, and good health. Analytical plan This study utilized Wave 15 of the HRS, conducted in 2020, making it a cross-sectional analysis. The analysis focused on older adults aged 65 and above, in accordance with the widely accepted definition of older adults (United Nations, 2019). Binary logistic regression in STATA (Version 18) was applied to examine the relationship between household income, race/ethnicity, and obesity (BMI ≥ 30). Logistic regression is a statistical method that models binary outcomes by estimating the odds of an event occurring compared to a reference category. In this analysis, individuals classified as not obese (BMI < 30) served as the reference group, while those categorized as obese constituted the comparison group. Separate models were estimated for each income level (lower, middle, and upper) to explore how the association between race/ethnicity and obesity varies across income categories. Interaction terms for race/ethnicity and income levels were included to assess differential effects. The primary objective was to evaluate how specific factors influence obesity within each income category, rather than comparing across income levels. All models controlled for a comprehensive set of sociodemographic variables, including age, gender, marital status, and education, as well as health-related factors such as self-reported health status, physical activity, and alcohol consumption. Predictive margins were calculated to estimate the probability of obesity for each racial/ethnic group within each income category. These probabilities, along with 95% confidence intervals, were used to illustrate disparities among Non-Hispanic White (NHW), Non-Hispanic Black (NHB), and Hispanic populations. The results were reported as odds ratios (ORs) with 95% confidence intervals, and statistical significance was determined using p-values (*, **, *** for p < 0.05, 0.01, and 0.001, respectively). Sensitivity analyses were conducted to ensure robustness and interpretability by excluding potential outliers or extreme values. This approach provides a nuanced understanding of how race/ethnicity and household income interact to influence obesity risk among older adults in the U.S. population based on the HRS 2020 wave. Results The findings from Table 1 reveal significant racial and ethnic disparities across key sociodemographic and health-related variables, noting the structural inequities that disproportionately affect Non-Hispanic Blacks (NHBs) and Hispanics compared to Non-Hispanic Whites (NHWs). Obesity (BMI ≥ 30) is most prevalent among NHBs, with nearly half (48.5%) classified as obese, followed by 38.0% of Hispanics, and 32.9% of NHWs. This pattern aligns with disparities in household income, where NHBs and Hispanics are overwhelmingly represented in the lower-income category (62.3% and 70.6%, respectively) compared to 44.9% of NHWs. These income disparities likely contribute to inequities in access to health-promoting resources, further exacerbating obesity rates among minority groups. Educational attainment also varies markedly by race and ethnicity, with NHWs achieving the highest levels of college education (61.7%), compared to 58.5% of NHBs and only 38.9% of Hispanics. This educational gap reflects systemic barriers that likely influence economic stability and access to health resources, perpetuating disparities in health outcomes. Similarly, self-reported health highlights stark differences, with poor health most commonly reported by Hispanics (34.0%) and NHBs (26.9%) and least common among NHWs (16.1%). NHWs are also most likely to report good health (49.3%), while NHBs and Hispanics report significantly lower proportions (30.1% and 28.4%, respectively). These disparities suggest that social determinants, including income, education, and access to care, play a critical role in shaping health perceptions and outcomes. Marital status further illustrates differences, with NHBs having the lowest marriage rates (48.7%) and the highest rates of never being married (11.9%), compared to NHWs and Hispanics, who are more likely to be married (66.8% and 68.1%, respectively). This disparity may reflect broader societal and economic challenges, particularly among NHBs, which can influence social support systems and overall well-being. Alcohol consumption patterns show that NHWs report the highest rates of alcohol use (65.7%), while NHBs report the lowest (53.4%), with Hispanics falling in between (58.6%). Table 1 Descriptive Statistics, Chi-Square Tests, and Summary of Variables by Race/Ethnicity Variable: Sample size : 3,834 Non-Hispanic White (%) Non-Hispanic Black (%) Hispanic (%) Total χ² (df) p- value Mean SD Min Max BMI Categories 59.35 < 0.001 0.63 0.48 0 1 Not Obese 67.1 51.5 62.0 63.3 Obese 32.9 48.5 38.0 36.7 Household Income 177.63 < 0.001 0.59 0.68 0 2 Lower Income 44.9 62.3 70.6 52.1 Middle Income 41.6 32.7 24.9 37.5 Upper Income 13.5 4.9 4.5 10.5 Sex 9.24 0.010 0.60 0.49 0 1 Male 41.6 35.3 39.2 40.0 Female 58.4 64.7 60.8 60.0 Education 97.58 < 0.001 0.58 0.49 0 1 No College 38.3 41.5 61.1 42.2 With College 61.7 58.5 38.9 57.8 Marital Status 159.92 < 0.001 0.64 0.95 0 3 Married 66.8 48.7 68.1 63.6 Divorced 12.0 23.5 14.3 14.6 Widowed 17.4 15.8 11.7 16.3 Never Married 3.7 11.9 5.8 5.6 Self-Reported Health 176.85 < 0.001 1.16 0.74 0 2 Poor Health 16.1 26.9 34.0 20.8 Good Health 49.3 30.1 28.4 42.6 Moderate Health 34.6 43.0 37.6 36.6 Physical Activity 3.57 0.467 0.74 0.81 0 2 None 48.8 52.2 48.6 49.5 Regular 27.8 26.8 26.9 27.5 Some 23.4 21.0 24.5 23.0 Alcohol Consumption 40.52 < 0.001 0.62 0.49 0 1 No 34.3 46.6 41.4 37.7 Yes 65.7 53.4 58.6 62.3 Results in Table 2 reveal significant associations between household income, race/ethnicity, and obesity, with both effect sizes ( b ) and odds ratios (OR) presented to clarify the relationships. Lower household income is strongly linked to higher odds of obesity. Individuals in the lower-income group have b = 0.54 (95% CI: 0.31, 0.78), corresponding to OR = 1.72, p < 0.001, meaning they are 1.72 times more likely to be obese than those in the upper-income group. Similarly, middle-income individuals show b = 0.49 (95% CI: 0.25, 0.73) and OR = 1.63, p < 0.001, indicating a 1.63-fold increase in obesity odds compared to the upper-income category (reference). Racial and ethnic disparities are evident across all income levels. Among lower-income individuals, NHBs exhibit b = 0.34 (95% CI: 0.11, 0.57) and OR = 1.40, p < 0.01OR, indicating they are 1.40 times more likely to be obese compared to NHWs. In the middle-income group, the disparity widens to b = 0.82 (95% CI: 0.51, 1.12) and OR = 2.27, p < 0.001, suggesting NHBs are more than twice as likely to be obese as NHWs. In the upper-income group, NHBs maintain elevated obesity odds ( b = 0.90, 95% CI: 0.15, 1.64; OR = 2.46, p < 0.05). Hispanics also experience disparities, particularly in lower- and upper-income groups. For lower-income Hispanics, the estimates are b = 0.18 (95% CI: 0.07, 0.44) and OR = 1.20, p < 0.05, showing a modest increase in obesity risk compared to NHWs. In the upper-income group, the odds of obesity for Hispanics increase to b = 0.46 (95% CI: 0.46, 1.37) and OR = 1.58, p < 0.05, while no significant differences are observed in the middle-income group. Going to Table 3 , the table shows the results of predictive margins for obesity across race/ethnicity and income categories. NHBs consistently show the highest predicted probability of obesity within each income group, ranging from 43.3% in the lower-income group to 53.0% in the middle-income group. NHWs have the lowest predicted probabilities, with a decreasing trend from 35.5% in the lower-income group to 25.2% in the upper-income group. Hispanics exhibit intermediate probabilities, with some variability across income levels. Table 2 Logistic Regression predicting Obesity based on Stratified Categories of Household Income Variable Model 1 (Household Income & BMI Model 3 (Lower Household Income) Model 4 (Middle Household Income) Model 5 (Upper Household Income) b (95% CI) b (95% CI) b (95% CI) b (95% CI) Household Income Lower Income 0.54*** (0.31, 0.78) Middle Income 0.49*** (0.25, 0.73) Upper Income (ref) Race/Ethnicity Non-Hispanic White (ref) Non-Hispanic Black 0.34** (0.11, 0.57) 0.82*** (0.51, 1.12) 0.90* (0.15, 1.64) Hispanic 0.18* (0.07, 0.44) -0.17* (0.57, 0.23) 0.46* (0.46, 1.37) College Education No College (ref) With College -0.06 (-0.26, 0.14) 0.34** (0.09, 0.59) -0.36 (-1.01, 0.30) Health Status Poor Health (ref) Good Health -0.35** (-0.60, -0.10) -0.53** (-0.89, -0.17) -1.04* (-1.85, -0.22) Moderate Health 0.22 (-0.01, 0.45) 0.13 (-0.23, 0.48) -0.55 (-1.41, 0.31) Alcohol No (ref) Yes -0.14 (-0.33, 0.05) 0.10 (-0.15, 0.35) -0.28 (-0.86, 0.30) Physical Activity None (ref) Regular -0.57*** (-0.82, -0.32) -0.85*** (-1.14, -0.56) -0.52 (-1.11, 0.08) Some -0.31* (-0.56, -0.05) -0.49*** (-0.77, -0.21) 0.06 (-0.53, 0.65) Marital Status Married (ref) Divorced 0.19 (-0.06, 0.44) -0.13 (-0.55, 0.29) -1.05 (-2.65, 0.55) Widowed -0.18 (-0.43, 0.07) -0.66* (-1.21, -0.12) -0.91 (-2.47, 0.66) Never Married 0.17 (-0.18, 0.52) 0.15 (-0.58, 0.88) -1.14 (-3.37, 1.10) Gender Male (ref) Female 0.28* (0.07, 0.49) 0.07 (-0.16, 0.31) -0.36 (-0.84, 0.12) Constant -1.03*** (-1.25, -0.82) -0.49*** (-0.78, -0.20) -0.40 (-0.81, 0.01) 0.67 (-0.39, 1.72) Notes : Reference categories are indicated in parentheses. Odds ratios are presented with 95% confidence intervals in parentheses. *, **, *** indicate statistical significance at the p < 0.05, p < 0.01, and p < 0.001 levels, respectively. The charts (Fig. 1 to 4) visualize how obesity risk varies by income level and race/ethnicity, showing clear patterns of inequality. Across all income levels, individuals with lower household income are more likely to be obese. People in the lowest income group have the highest chance of obesity (~ 40%), which drops to ~ 35% for middle-income earners and ~ 25% for those in the highest income group. This trend shows that higher income can act as a buffer against obesity, likely because of better access to healthy food, exercise opportunities, and healthcare. When race and ethnicity are considered, the disparities become even more striking. NHBs have the highest obesity risk across all income groups. For example, in the lower-income group, nearly 45% of NHBs are likely to be obese, compared to 40% of Hispanics and 35% of NHWs. In the middle-income group, the gap widens—more than half (53%) of NHBs are at risk of obesity, while the rates drop to 34% for NHWs and 31% for Hispanics. Even in the upper-income group, NHBs maintain the highest obesity risk (~ 44%), while NHWs have the lowest (~ 25%), with Hispanics falling in the middle (~ 34%). These patterns suggest that income alone doesn’t fully protect NHBs from obesity. Also for Hispanics, the risk is lower in some cases, but their outcomes are less consistent, potentially reflecting cultural or environmental factors that influence health behaviors. Table 3 Predictive Margins Table Model Race Margin 95% CI p> Model 2 (Lower Income) Non-Hispanic White 0.355 (0.33, 0.38) *** Model 2 (Lower Income) Non-Hispanic Black 0.433 (0.39, 0.48) *** Model 2 (Lower Income) Hispanic 0.396 (0.35, 0.45) *** Model 3 (Middle Income) Non-Hispanic White 0.345 (0.32, 0.37) *** Model 3 (Middle Income) Non-Hispanic Black 0.530 (0.47, 0.59) *** Model 3 (Middle Income) Hispanic 0.309 (0.23, 0.38) *** Model 4 (Upper Income) Non-Hispanic White 0.252 (0.21, 0.30) *** Model 4 (Upper Income) Non-Hispanic Black 0.438 (0.28, 0.60) *** Model 4 (Upper Income) Hispanic 0.341 (0.16, 0.53) *** Discussion The disparities in obesity risk among older adults observed in this study highlight the persistent influence of income and race/ethnicity on health outcomes. Lower-income older adults were more likely to be obese than their higher-income counterparts, a finding consistent with prior research on socioeconomic determinants of health (Lee et al., 2019 ; Reidpath et al., 2002 ). This income-based disparity was particularly pronounced among racial and ethnic minorities, with NHB individuals demonstrating the highest obesity prevalence across all income groups. Hispanic individuals exhibited intermediate obesity rates, NHW individuals consistently reported the lowest prevalence. The relationship between income and obesity among NHWs followed a predictable pattern, with decreasing obesity prevalence as income increased. This finding aligns with the health production model (Grossman, 1972 ), which posits that individuals with greater financial resources can allocate more to health-promoting activities such as access to nutritious food, fitness opportunities, and healthcare. However, this trend did not hold uniformly for NHBs and Hispanics. NHBs in middle-income categories faced the highest obesity risks, suggesting that income alone does not mitigate the structural barriers these populations encounter (Williams et al., 2010 ). For example, NHBs residing in minority-dense neighborhoods often lack access to fresh produce and face an overabundance of fast-food options, exacerbating their risk for obesity regardless of income level (Cooksey-Stowers et al., 2017 ). Hispanic individuals presented a more nuanced pattern, with variability in obesity risk across income groups. Cultural norms regarding body image and dietary practices may partially explain these differences, as these factors shape health behaviors independently of socioeconomic status (Odoms-Young & Bruce, 2018 ). Additionally, structural inequities, such as limited access to healthcare and recreational facilities in predominantly Hispanic neighborhoods, may contribute to these disparities. Even in higher-income groups, Hispanics were more likely to face barriers to health-promoting resources compared to NHWs, underscoring the role of systemic inequities in perpetuating health disparities. The persistent obesity disparities among NHBs, even in upper-income categories, reflect the cumulative effects of historical and systemic racism. Chronic exposure to stressors, including discrimination and implicit bias within healthcare systems, likely undermines the potential health benefits associated with higher income. Elevated cortisol levels from chronic stress have been linked to increased abdominal fat accumulation, further contributing to obesity risk (Kirby et al., 2012 ). These findings indicate that income improvements alone are insufficient to address the health disparities experienced by NHBs and Hispanics. Implications for Interventions The findings of this study point to the need for tailored interventions addressing the interplay of income, race/ethnicity, and obesity among older adults. Structural barriers such as food deserts, which disproportionately affect minority-dense neighborhoods, can be mitigated through initiatives like government-subsidized grocery stores or mobile farmers’ markets that bring affordable fresh produce to underserved areas (Cooksey-Stowers et al., 2017 ; Gordon-Larsen, 2014 ). For instance, programs like the Pennsylvania Fresh Food Financing Initiative have improved access to nutritious food, while mobile markets in North Carolina provide fresh produce directly to communities (Pennsylvania Department of Agriculture, 2023 ). Culturally tailored health promotion programs are equally critical, especially for Hispanic populations, where traditional diets may contribute to obesity risk. Initiatives such as the “Salud y Sabor” program in New Mexico, which combines cooking demonstrations with culturally relevant nutrition education, can encourage healthier eating without disregarding cultural traditions (National Hispanic Cultural Center, 2024 ). Recreational opportunities must also be improved to enhance physical activity in minority communities, where access to safe and affordable recreational spaces is often limited. Investments in public parks, fitness trails, and community centers, along with free exercise programs like “Shape Up NYC,” can provide accessible options for older adults. Addressing income inequities through policy reforms such as increasing minimum wages, expanding tax credits, and providing affordable housing can alleviate financial stress and promote health equity (Hahn, 2021 ; Reidpath et al., 2002 ). Workplace wellness programs and tax benefits like the Earned Income Tax Credit (EITC) have shown promise in supporting low-income families’ health outcomes (Warner et al., 2012 ). Moreover, interventions should focus on mitigating chronic stress and discrimination, which exacerbate obesity risks through physiological pathways like elevated cortisol levels. Community-based mental health services, coupled with anti-racism training for healthcare providers, can help individuals cope with discrimination while reducing implicit bias in healthcare (Cénat et al., 2024 ). Initiatives like “Embrace Race” could be adapted to support older adults facing racial stressors. Community health clinics and mobile health units, such as the “Health Wagon” in rural Appalachia, can also play a vital role by offering free or low-cost preventive care, ensuring better access for underserved populations. Technology offers additional opportunities to address barriers related to access and cultural differences. Digital health platforms like “Vida Health,” which provides bilingual coaching for weight management, can deliver personalized interventions to diverse populations (Silberman et al., 2020 ). Educational campaigns to combat weight stigma, such as the “End Weight Bias” campaign by the Obesity Action Coalition, are equally important in promoting body positivity and encouraging health-promoting behaviors among older adults (Kirby et al., 2012 ). In sum, a comprehensive approach combining policy reforms, community-based programs, culturally tailored interventions, and technology-driven solutions is essential to address obesity disparities effectively. Future research and program evaluations will be critical in refining these interventions and ensuring their success across diverse populations. Limitations and Conclusion This study has several limitations that should be acknowledged. First, the reliance on self-reported data for variables such as height, weight, and household income may introduce reporting bias, potentially affecting the accuracy of obesity classification and income categorization. Although the study utilized robust measures to cross-validate BMI calculations, these biases could still influence the findings. Second, the cross-sectional nature of the analysis limits the ability to establish causal relationships between income, race/ethnicity, and obesity. Future longitudinal research is needed to assess these relationships over time and account for potential reverse causation. Third, the study may not fully capture the complexity of cultural and environmental factors influencing obesity within racial and ethnic groups, as these aspects are challenging to quantify and were not included in the dataset. Finally, while the Health and Retirement Study provides nationally representative data, the findings may not generalize to populations outside the U.S. or to younger age groups, necessitating caution in broader applications of the results. Despite its limitations, this study offers valuable insights into how household income and race/ethnicity intersect to impact obesity among older adults in the United States. The findings shed light on the ongoing disparities in obesity risk, emphasizing the need to address the structural barriers that limit access to healthy food, healthcare, and opportunities for physical activity, particularly for marginalized groups. Effective solutions must go beyond encouraging individual behavior changes and instead focus on tackling the systemic inequalities that drive these disparities. By contributing to the growing evidence on health inequities, this research highlights the importance of comprehensive and equity-focused interventions. It also paves the way for future studies to explore these relationships further and inform policies aimed at creating healthier, more equitable communities. Declarations Funding statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Sunkanmi Folorunsho was solely responsible for the conception and design of the study, data analysis and interpretation, drafting and revising the manuscript, and final approval for submission. The author also verified the integrity and accuracy of the data and its analysis and is accountable for all aspects of the work. References Agerbo, E., Gunnell, D., Bonde, J. P., Mortensen, P. B., & Nordentoft, M. (2007). Suicide and occupation: The impact of socioeconomic, demographic, and psychiatric differences. Psychological Medicine, 37 (8), 1131–1140. https://doi.org/10.1017/S0033291707000487 Bell, C. N., Kerr, J., & Young, J. L. (2019). Associations between obesity, obesogenic environments, and structural racism vary by county-level racial composition. International Journal of Environmental Research and Public Health, 16 (861). https://doi.org/10.3390/ijerph16050861 Cénat, J. M., Broussard, C., Jacob, G., Kogan, C., Corace, K., Ukwu, G., Onesi, O., Furyk, S. E., Bekarkhanechi, F. M., Williams, M., Chomienne, M. H., Grenier, J., & Labelle, P. R. (2024). Antiracist training programs for mental health professionals: A scoping review. Clinical psychology review , 108 , 102373. https://doi.org/10.1016/j.cpr.2023.102373 Centers for Disease Control and Prevention. (2023). Adult obesity facts. Retrieved January 6, 2023, from https://www.cdc.gov/obesity/data/adult.html Chang, V. W., & Lauderdale, D. S. (2005). Income disparities in body mass index and obesity in the United States, 1971–2002. Archives of Internal Medicine, 165 (18), 2122–2128. https://doi.org/10.1001/archinte.165.18.2122 Cooksey-Stowers, K., Schwartz, M. B., & Brownell, K. D. (2017). Food swamps predict obesity rates better than food deserts in the United States. International Journal of Environmental Research and Public Health, 14 (1366). https://doi.org/10.3390/ijerph14111366 Dinsa, G. D., Goryakin, Y., Fumagalli, E., et al. (2012). Obesity and socioeconomic status in developing countries: A systematic review. Obesity Reviews, 13 , 1067–1079. Drewnowski, A. (2009). Obesity, diets, and social inequalities. Nutrition Reviews, 67 (Suppl 1), S36–S39. https://doi.org/10.1111/j.1753-4887.2009.00157.x Gordon-Larsen, P. (2014). Food availability/convenience and obesity. Advances in Nutrition, 5 , 809–817. https://doi.org/10.3945/an.114.007070 Gong, S., Wang, L., Zhou, Z., Wang, K., & Alamian, A. (2022). Income disparities in obesity trends among U.S. adults: An analysis of the 2011-2014 California Health Interview Survey. International Journal of Environmental Research and Public Health, 19 (12), 7188. https://doi.org/10.3390/ijerph19127188 Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80 (2), 223–255. https://doi.org/10.1086/259880 Hahn, R. A. (2021). What is a social determinant of health? Back to basics. Journal of Public Health Research, 10 (4), 2324. https://doi.org/10.4081/jphr.2021.2324 Kim, T. J., & von dem Knesebeck, O. (2018). Income and obesity: What is the direction of the relationship? A systematic review and meta-analysis. BMJ Open, 8 (1), e019862. https://doi.org/10.1136/bmjopen-2017-019862 Kirby, J. B., Liang, L., Chen, H. J., & Wang, Y. (2012). Race, place, and obesity: The complex relationships among community racial/ethnic composition, individual race/ethnicity, and obesity in the United States. American Journal of Public Health, 102 (8), 1572–1578. https://doi.org/10.2105/AJPH.2011.300452 Kpelitse, K. A., Devlin, R. A., & Sarma, S. (2014). The effect of income on obesity among Canadian adults. Canadian Centre for Health Economics Working Paper, C02 . Lee, A., Cardel, M., & Donahoo, W. T. (2019). Social and environmental factors influencing obesity. In K. R. Feingold, B. Anawalt, M. R. Blackman, et al. (Eds.), Endotext . MDText.com, Inc. Available at: https://www.ncbi.nlm.nih.gov/books/NBK278977/ Mamdouh, H., Hussain, H. Y., Ibrahim, G. M., Alawadi, F., Hassanein, M., Zarooni, A. A., Suwaidi, H. A., Hassan, A., Alsheikh-Ali, A., & Alnakhi, W. K. (2023). Prevalence and associated risk factors of overweight and obesity among the adult population in Dubai: A population-based cross-sectional survey in Dubai, the United Arab Emirates. BMJ Open, 13 (1), e062053. https://doi.org/10.1136/bmjopen-2022-062053 McKee, A. M., & Morley, J. E. (2021, September 19). Obesity in the elderly. In K. R. Feingold, B. Anawalt, M. R. Blackman, et al. (Eds.), Endotext . MDText.com, Inc. https://www.ncbi.nlm.nih.gov/books/NBK532533/ National Hispanic Cultural Center. (2024). Salud y Sabor . Retrieved January 2, 2025, from https://events.cityof.com/event/albuquerque/national-hispanic-cultural-center/salud-y-sabor Odoms-Young, A., & Bruce, M. A. (2018). Examining the impact of structural racism on food insecurity: Implications for addressing racial/ethnic disparities. Family & Community Health, 41 (Suppl 2), S3–S6. https://www.pa.gov/services/pda/apply-for-the-pa-fresh-food-financing-initiative.html Pennsylvania Department of Agriculture. (2023). Apply for the PA Fresh Food Financing Initiative . Commonwealth of Pennsylvania. Retrieved from https://www.pa.gov/services/pda/apply-for-the-pa-fresh-food-financing-initiative.html Population Reference Bureau. (2022, July). Obesity in America (Today's Research on Aging No. 45). https://www.prb.org/wp-content/uploads/2022/07/Todays-Research-on-Aging-Obesity-in-America-Final.pdf Reidpath, D. D., Burns, C., Garrard, J., et al. (2002). An ecological study of the relationship between social and environmental determinants of obesity. Health & Place, 8 , 141–145. Safaei, M., Sundararajan, E. A., Driss, M., Boulila, W., & Shapi'i, A. (2021). A systematic literature review on obesity: Understanding the causes and consequences of obesity and reviewing various machine learning approaches used to predict obesity. Computers in Biology and Medicine, 136 , 104754. https://doi.org/10.1016/j.compbiomed.2021.104754 Silberman, J. M., Kaur, M., Sletteland, J., & Venkatesan, A. (2020). Outcomes in a digital weight management intervention with one-on-one health coaching. PloS one , 15 (4), e0232221. https://doi.org/10.1371/journal.pone.0232221 Wang, Y., & Beydoun, M. A. (2007). The obesity epidemic in the United States: Gender, age, socioeconomic, racial/ethnic, and geographic characteristics: A systematic review and meta-regression analysis. Epidemiologic Reviews, 29 , 6–28. https://doi.org/10.1093/epirev/mxm007 Warner, E. T., Wolin, K. Y., Duncan, D. T., Heil, D. P., Askew, S., & Bennett, G. G. (2012). Differential accuracy of physical activity self-report by body mass index. American Journal of Health Behavior, 36 (2), 168–178. https://doi.org/10.5993/AJHB.36.2.3 Williams, D. R., Mohammed, S. A., Leavell, J., & Collins, C. (2010). Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Annals of the New York Academy of Sciences, 1186 , 69–101. https://doi.org/10.1111/j.1749-6632.2009.05339.x World Health Organization. (2016). Obesity and overweight fact sheet. Geneva: WHO. Retrieved from https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight#:~:text=%2Fm2).-,Adults,than%20or%20equal%20to%2030 Zhang, Y. S., Saito, Y., & Crimmins, E. M. (2019). Changing impact of obesity on active life expectancy of older Americans. The Journals of Gerontology: Series A, Biological Sciences and Medical Sciences, 74 (12), 1944–1951. https://doi.org/10.1093/gerona/glz133 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5754136","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":400005967,"identity":"324337cc-87e6-471c-934d-bea61ac2abca","order_by":0,"name":"Sunkanmi Folorunsho","email":"data:image/png;base64,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","orcid":"","institution":"University of Nebraska–Lincoln","correspondingAuthor":true,"prefix":"","firstName":"Sunkanmi","middleName":"","lastName":"Folorunsho","suffix":""}],"badges":[],"createdAt":"2025-01-02 21:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5754136/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5754136/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73782363,"identity":"9b16099a-802f-42f5-a03a-cd6b7a5fada1","added_by":"auto","created_at":"2025-01-14 15:35:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35984,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probabilities for Household Income and Obesity\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5754136/v1/d91d04a6ac4e32af19b3fc3b.png"},{"id":73782366,"identity":"4b5869b2-592b-4e24-b02d-5474c589609f","added_by":"auto","created_at":"2025-01-14 15:35:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40655,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probabilities for Lower Household Income and Obesity (Race/ethnicity as a moderator)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5754136/v1/5113430cfb74010977454237.png"},{"id":73783360,"identity":"6028c958-6642-4918-8b39-dfc39c91c392","added_by":"auto","created_at":"2025-01-14 15:43:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33019,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probabilities for Middle Household\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIncome and Obesity (Race/ethnicity as a Moderator)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5754136/v1/9246e364aa4c1adb310fd8b2.png"},{"id":73782376,"identity":"7c0e1bfe-833b-4ac0-9083-11ef268b35f2","added_by":"auto","created_at":"2025-01-14 15:35:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32806,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted probabilities for Upper Household Income and Obesity (Race/ethnicity as a Moderator)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5754136/v1/e5a7ba5dc48f5ed032dc7e8d.png"},{"id":73784467,"identity":"0820baef-f05e-4f10-ba1d-e68329395871","added_by":"auto","created_at":"2025-01-14 15:59:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1219010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5754136/v1/08d9307c-252d-4acb-b02f-06732bd67170.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship Between Household Income and Obesity Among Older Adults: Investigating the Moderating Role of Race","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a global health crisis and ranks as the fifth leading cause of mortality worldwide, affecting over 1.9\u0026nbsp;billion adults (Mamdouh et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Safaei et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; World Health Organization [WHO], 2016). Defined by the WHO as an abnormal or excessive accumulation of body fat that poses health risks, obesity is commonly measured using body mass index (BMI), with a BMI of 30 or higher indicating obesity. Despite ongoing public health efforts, obesity rates remain alarmingly high. Between 2017 and 2020, 41.9% of the U.S. population was classified as obese, with rates increasing from 30.5\u0026ndash;41.9% between 1999\u0026ndash;2000 and 2017\u0026ndash;2020 (Centers for Disease Control and Prevention [CDC], 2023). This trend is particularly concerning among older adults. From 1988\u0026ndash;1994 to 2015\u0026ndash;2018, obesity prevalence among U.S. adults aged 65 and older surged from 22\u0026ndash;40%, presenting significant challenges for this demographic (Population Reference Bureau [PRB], 2022). Older adults with obesity face elevated risks of chronic conditions, including cardiovascular disease, stroke, hypertension, and type 2 diabetes, as well as functional limitations that reduce life expectancy compared to their non-obese peers (PRB, 2022). While obesity is recognized as a critical health issue among older adults (McKee \u0026amp; Morley, 2021), significant disparities exist within this population. For instance, among women aged 75 and older, 49.4% of non-Hispanic Black women were obese, compared to 30.2% of Hispanic women and 27.5% of non-Hispanic White women in the same age group (CDC, 2023). Furthermore, household income plays a critical role in obesity prevalence (Gong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang \u0026amp; Beydoun, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Individuals from lower-income households bear a disproportionate burden, while 35.6% of individuals in the lowest income bracket are obese, only 15.5% in the highest income bracket face obesity (PRB, 2022).\u003c/p\u003e \u003cp\u003eAlthough previous research has identified the relationships between household income, and obesity among older adults, the moderating role of race/ethnicity in these associations warrants further investigation. Race/ethnicity influences socioeconomic status, with racial minorities, particularly Black and Hispanic individuals, experiencing higher levels of poverty and social disparities (Gong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These disparities often limit access to essential resources such as nutritious food, healthcare, and recreational facilities, even among racial minorities with similar income levels as White individuals (Odoms-Young \u0026amp; Bruce, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Such inequities exacerbate the effects of low income on obesity within minority groups. Additionally, cultural norms and health behaviors, such as dietary practices and perceptions of body weight, vary across racial and ethnic groups, further shaping obesity risk (Kirby et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study uses data from the Health and Retirement Study (HRS) to address gaps in the literature in two significant ways. First, it investigates the relationships between household income and obesity among older adults using logistic regression models. Second, it examines how race/ethnicity moderates these relationships. While existing research explores obesity and its determinants across various age groups, this study focuses on older adults, a demographic with unique health considerations and challenges. By incorporating race/ethnicity as a moderating variable, this study goes beyond simple associations to examine the complex dynamics influencing obesity among older adults.\u003c/p\u003e\n\u003ch3\u003eHousehold Income and Obesity in Older Adults\u003c/h3\u003e\n\u003cp\u003eOlder adults with lower household incomes are at an increased risk of obesity (Drewnowski, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dinsa et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Two primary perspectives help explain this relationship. The causation hypothesis suggests that limited financial resources restrict access to nutritious food, healthcare, and opportunities for physical activity, leading to weight gain over time (Reidpath et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In contrast, the reverse causality hypothesis proposes that obesity precedes and contributes to lower income, as stigma and weight-based discrimination in the workforce reduce economic opportunities for obese individuals (Kim \u0026amp; von dem Knesebeck, 2018). The social determinants of health framework provides additional insight into these dynamics. This model emphasizes how material conditions influence health behaviors, psychosocial stressors, and overall well-being (Hahn, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Limited financial resources can lead to unhealthy dietary patterns, reduced physical activity, and heightened stress, all of which contribute to obesity (Warner et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, stigma amplifies these challenges, as obese individuals often experience social isolation and reduced self-esteem, further exacerbating their health risks (Kirby et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother theoretical framework, Grossman\u0026rsquo;s health production model (1972), offers valuable understanding into the relationship between income and health, specifically obesity. The model conceptualizes health as a form of capital that individuals accumulate and depreciate over time. People derive utility from good health and, as a result, invest time and resources into health-promoting behaviors to maximize their overall well-being. According to the model, higher income enables individuals to allocate more resources toward maintaining and improving their health. These investments often include better diets, access to healthcare services, regular physical activity, and preventive measures, all of which contribute to a reduced risk of obesity. The model assumes that individuals make rational decisions to balance immediate utility with long-term health benefits. For example, a person with higher income is more likely to afford gym memberships, organic food, or regular health check-ups, which collectively enhance health outcomes. However, this seemingly straightforward relationship between income and health is nuanced by several complexities.\u003c/p\u003e \u003cp\u003eKpelitse et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) posited that higher-income individuals often face significant opportunity costs for their time, which may limit their ability to engage in physical activity despite having the financial means. For example, individuals in high-paying professions may work longer hours or prioritize career advancements over leisure or exercise. This trade-off between time and health-promoting activities underscores the role of opportunity costs in moderating the income-health relationship. Additionally, people's differing attitudes toward time, how much they value immediate benefits versus future rewards, add another layer of complexity to the relationship between income and obesity. For instance, individuals who prioritize immediate gratification may indulge in unhealthy eating habits and neglect long-term health investments, regardless of their income level. This highlights that income alone cannot fully explain obesity risk; personal attitudes and time management also play crucial roles. Moreover, these dynamics often differ between genders.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRace/Ethnicity, Household Income, and Obesity\u003c/h2\u003e \u003cp\u003eWhile household income is a significant predictor of obesity, race and ethnicity play a critical role in shaping this relationship. Minority groups, particularly African Americans and Hispanics, are more likely to experience lower household incomes, live in neighborhoods with limited access to nutritious food, and face environmental stressors that contribute to obesity (Cooksey-Stowers et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gordon-Larsen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Minority-dense neighborhoods are often characterized by food deserts, with fewer grocery stores and more fast-food outlets, exacerbating obesity risks (Bell et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The relationship between income and obesity varies by race. For White women, higher income is typically associated with lower obesity rates (Chang \u0026amp; Lauderdale, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, this association weakens or even reverses for African American men, highlighting that income alone cannot fully explain racial disparities in obesity (Chang \u0026amp; Lauderdale, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Additional factors, such as chronic stress, discrimination, and limited access to resources, significantly contribute to these disparities (Williams et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven among African Americans with higher income levels, the enduring effects of historical and systemic racism continue to influence health outcomes and perpetuate disparities, including obesity. While higher income theoretically provides greater access to health-promoting resources such as quality food, healthcare, and safe recreational spaces, broader structural barriers rooted in discrimination often undermine these advantages. African Americans are more likely to reside in neighborhoods with fewer grocery stores, limited access to fresh produce, and an overabundance of fast-food outlets, even when income levels are comparable to those of White individuals (Cooksey-Stowers et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bell et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These inequities, shaped by redlining and other discriminatory practices, exacerbate obesity risks. Discrimination also manifests in the healthcare system, where African Americans often encounter implicit bias, reduced quality of care, and limited access to preventive services, regardless of their socioeconomic status (Williams et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This perpetuates a cycle of unmet health needs and poorer outcomes, including higher obesity rates. Additionally, chronic exposure to racism acts as a significant source of psychosocial stress, triggering physiological responses such as elevated cortisol levels and increased abdominal fat accumulation. These stress-related mechanisms compound obesity risks among African Americans, creating a health burden that cannot be addressed by income improvements alone.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eData for this study were drawn from the HRS, a nationally representative longitudinal survey administered by the University of Michigan and funded by the National Institute on Aging (NIA) and the Social Security Administration (SSA). The HRS focuses on U.S. adults aged 50 and older and covers a wide range of topics, including health, income, employment, cognitive function, and genomics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hrsonline.isr.umich.edu\u003c/span\u003e\u003cspan address=\"http://hrsonline.isr.umich.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Participants were recruited using a multistage area probability sampling method designed to ensure a diverse and representative sample of older adults in the United States. The study began in 1992, targeting individuals aged 51 to 61, with their spouses also included. Cohorts were refreshed every six years to maintain the study's representativeness, and new participants were added biennially. At baseline, all participants were community-dwelling individuals, and they were followed over time, even if they transitioned to long-term care settings.\u003c/p\u003e \u003cp\u003eInterviews were conducted using a combination of in-person, telephone, and enhanced face-to-face methods. During the baseline year, half of the sample completed in-person interviews, which included physical and biological measures, along with a psychosocial questionnaire. The other half underwent telephone interviews in the baseline year, followed by enhanced face-to-face interviews the subsequent year. Respondents primarily provided self-reports, but proxies were used when necessary. Participants were informed about the study's procedures and provided consent before participation. They were also notified of their rights to decline participation, refuse to answer specific questions, or withdraw at any time. Data collection followed a rigorous protocol, with trained interviewers ensuring privacy and comfort during the interviews. The survey included detailed questions covering multiple domains relevant to aging and was supplemented by linkage to administrative data from Medicare, the Veteran's Administration, and the National Death Index. These linkages enriched the dataset by adding biomarkers and other objective measures.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDependent variable\u003c/h2\u003e \u003cp\u003eObesity was the dependent variable in this study, measured using BMI, a widely accepted indicator of weight status. BMI was calculated based on participants\u0026rsquo; self-reported height and weight. In alignment with the guidelines established by Zhang and Crimmins (2019) and WHO, obesity was defined as having a BMI of 30 kg/m\u0026sup2; or higher. This variable was dichotomized into two categories: 0\u0026thinsp;=\u0026thinsp;not obese (BMI below 30) and 1\u0026thinsp;=\u0026thinsp;obese (BMI 30 or higher). HRS enhances the reliability of obesity classification by including both self-reported measures and validated health data collected during face-to-face interviews for a subset of participants. These additional health metrics allow for cross-verification of BMI calculations, minimizing potential biases associated with self-reported data and ensuring a robust assessment of obesity across the study population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Variable\u003c/h2\u003e \u003cp\u003eHousehold income was measured as the total annual income reported by participants, encompassing various sources such as earnings from the respondent and their spouse, pensions, annuities, Social Security Disability Insurance, Social Security Retirement benefits, unemployment benefits, workers\u0026rsquo; compensation, and capital income (e.g., dividends, interest, and rental income). Other income sources were also included to provide a comprehensive assessment of financial resources. Household income was recoded into quartiles based on its distribution within the dataset. Each income quartile was assigned a numerical code: 0\u0026thinsp;=\u0026thinsp;less than \u003cspan\u003e$\u003c/span\u003e24,000 annually, 1 = \u003cspan\u003e$\u003c/span\u003e24,000 to \u003cspan\u003e$\u003c/span\u003e49,000, 2 = \u003cspan\u003e$\u003c/span\u003e49,000 to \u003cspan\u003e$\u003c/span\u003e97,000, and 3\u0026thinsp;=\u0026thinsp;more than \u003cspan\u003e$\u003c/span\u003e97,000 annually. This recoding allowed for a systematic categorization of income disparities across the sample. A count procedure was used to address missing cases, ensuring the validity of the income variable for analysis. This approach aligns with previous research methods, such as those described by Agerbo (2007), and enables a nuanced examination of socioeconomic disparities.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModerating Variable\u003c/h3\u003e\n\u003cp\u003eRace/ethnicity was measured as a moderating variable using self-reported data. Participants were allowed to report multiple racial identities, and for those identifying with more than one race, they were asked to specify their primary racial identity. The race/ethnicity variable was recoded into three categories: 0\u0026thinsp;=\u0026thinsp;non-Hispanic White, 1\u0026thinsp;=\u0026thinsp;non-Hispanic Black, and 2\u0026thinsp;=\u0026thinsp;Hispanic. A count procedure was performed to address missing cases, ensuring the completeness of the dataset. This recoding facilitated a systematic analysis of racial and ethnic differences in the relationship between household income and obesity.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eEducation, marital status, gender, physical activity, alcohol consumption, and self-reported health were included as covariates in the analysis. Marital status was categorized into four groups: married, divorced, widowed, and never married. Education was divided into two categories: those with a college degree and those without. Gender was categorized as male or female. Physical activity was classified into three groups: none, some, and regular. Alcohol consumption was dichotomized into two categories: yes (consumes alcohol) and no (does not consume alcohol). Self-reported health was categorized into three levels: poor, moderate, and good health.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical plan\u003c/h2\u003e \u003cp\u003eThis study utilized Wave 15 of the HRS, conducted in 2020, making it a cross-sectional analysis. The analysis focused on older adults aged 65 and above, in accordance with the widely accepted definition of older adults (United Nations, 2019). Binary logistic regression in STATA (Version 18) was applied to examine the relationship between household income, race/ethnicity, and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30). Logistic regression is a statistical method that models binary outcomes by estimating the odds of an event occurring compared to a reference category. In this analysis, individuals classified as not obese (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30) served as the reference group, while those categorized as obese constituted the comparison group. Separate models were estimated for each income level (lower, middle, and upper) to explore how the association between race/ethnicity and obesity varies across income categories. Interaction terms for race/ethnicity and income levels were included to assess differential effects. The primary objective was to evaluate how specific factors influence obesity within each income category, rather than comparing across income levels. All models controlled for a comprehensive set of sociodemographic variables, including age, gender, marital status, and education, as well as health-related factors such as self-reported health status, physical activity, and alcohol consumption. Predictive margins were calculated to estimate the probability of obesity for each racial/ethnic group within each income category. These probabilities, along with 95% confidence intervals, were used to illustrate disparities among Non-Hispanic White (NHW), Non-Hispanic Black (NHB), and Hispanic populations. The results were reported as odds ratios (ORs) with 95% confidence intervals, and statistical significance was determined using p-values (*, **, *** for p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 0.01, and 0.001, respectively). Sensitivity analyses were conducted to ensure robustness and interpretability by excluding potential outliers or extreme values. This approach provides a nuanced understanding of how race/ethnicity and household income interact to influence obesity risk among older adults in the U.S. population based on the HRS 2020 wave.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe findings from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e reveal significant racial and ethnic disparities across key sociodemographic and health-related variables, noting the structural inequities that disproportionately affect Non-Hispanic Blacks (NHBs) and Hispanics compared to Non-Hispanic Whites (NHWs). Obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) is most prevalent among NHBs, with nearly half (48.5%) classified as obese, followed by 38.0% of Hispanics, and 32.9% of NHWs. This pattern aligns with disparities in household income, where NHBs and Hispanics are overwhelmingly represented in the lower-income category (62.3% and 70.6%, respectively) compared to 44.9% of NHWs. These income disparities likely contribute to inequities in access to health-promoting resources, further exacerbating obesity rates among minority groups. Educational attainment also varies markedly by race and ethnicity, with NHWs achieving the highest levels of college education (61.7%), compared to 58.5% of NHBs and only 38.9% of Hispanics. This educational gap reflects systemic barriers that likely influence economic stability and access to health resources, perpetuating disparities in health outcomes. Similarly, self-reported health highlights stark differences, with poor health most commonly reported by Hispanics (34.0%) and NHBs (26.9%) and least common among NHWs (16.1%). NHWs are also most likely to report good health (49.3%), while NHBs and Hispanics report significantly lower proportions (30.1% and 28.4%, respectively). These disparities suggest that social determinants, including income, education, and access to care, play a critical role in shaping health perceptions and outcomes. Marital status further illustrates differences, with NHBs having the lowest marriage rates (48.7%) and the highest rates of never being married (11.9%), compared to NHWs and Hispanics, who are more likely to be married (66.8% and 68.1%, respectively). This disparity may reflect broader societal and economic challenges, particularly among NHBs, which can influence social support systems and overall well-being. Alcohol consumption patterns show that NHWs report the highest rates of alcohol use (65.7%), while NHBs report the lowest (53.4%), with Hispanics falling in between (58.6%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics, Chi-Square Tests, and Summary of Variables by Race/Ethnicity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSample size\u003c/em\u003e:\u003c/p\u003e\n \u003cp\u003e3,834\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2; (df)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI Categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Reported Health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical Activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eResults in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveal significant associations between household income, race/ethnicity, and obesity, with both effect sizes (\u003cem\u003eb\u003c/em\u003e) and odds ratios (OR) presented to clarify the relationships. Lower household income is strongly linked to higher odds of obesity. Individuals in the lower-income group have \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54 (95% CI: 0.31, 0.78), corresponding to OR\u0026thinsp;=\u0026thinsp;1.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, meaning they are 1.72 times more likely to be obese than those in the upper-income group. Similarly, middle-income individuals show \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49 (95% CI: 0.25, 0.73) and OR\u0026thinsp;=\u0026thinsp;1.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating a 1.63-fold increase in obesity odds compared to the upper-income category (reference). Racial and ethnic disparities are evident across all income levels. Among lower-income individuals, NHBs exhibit b\u0026thinsp;=\u0026thinsp;0.34 (95% CI: 0.11, 0.57) and OR\u0026thinsp;=\u0026thinsp;1.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01OR, indicating they are 1.40 times more likely to be obese compared to NHWs. In the middle-income group, the disparity widens to \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82 (95% CI: 0.51, 1.12) and OR\u0026thinsp;=\u0026thinsp;2.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, suggesting NHBs are more than twice as likely to be obese as NHWs. In the upper-income group, NHBs maintain elevated obesity odds (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90, 95% CI: 0.15, 1.64; OR\u0026thinsp;=\u0026thinsp;2.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Hispanics also experience disparities, particularly in lower- and upper-income groups. For lower-income Hispanics, the estimates are \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18 (95% CI: 0.07, 0.44) and OR\u0026thinsp;=\u0026thinsp;1.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, showing a modest increase in obesity risk compared to NHWs. In the upper-income group, the odds of obesity for Hispanics increase to \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46 (95% CI: 0.46, 1.37) and OR\u0026thinsp;=\u0026thinsp;1.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, while no significant differences are observed in the middle-income group.\u003c/p\u003e\n\u003cp\u003eGoing to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the table shows the results of predictive margins for obesity across race/ethnicity and income categories. NHBs consistently show the highest predicted probability of obesity within each income group, ranging from 43.3% in the lower-income group to 53.0% in the middle-income group. NHWs have the lowest predicted probabilities, with a decreasing trend from 35.5% in the lower-income group to 25.2% in the upper-income group. Hispanics exhibit intermediate probabilities, with some variability across income levels.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic Regression predicting Obesity based on Stratified Categories of Household Income\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1 (Household Income \u0026amp; BMI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3 (Lower Household Income)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 4 (Middle Household Income)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 5 (Upper Household Income)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54*** (0.31, 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle Income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49*** (0.25, 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper Income (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34** (0.11, 0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82*** (0.51, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90* (0.15, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18* (0.07, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17* (0.57, 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46* (0.46, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollege Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo College (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06 (-0.26, 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34** (0.09, 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36 (-1.01, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor Health (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.35** (-0.60, -0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.53** (-0.89, -0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.04* (-1.85, -0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22 (-0.01, 0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (-0.23, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.55 (-1.41, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14 (-0.33, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10 (-0.15, 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28 (-0.86, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical Activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.57*** (-0.82, -0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.85*** (-1.14, -0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52 (-1.11, 0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31* (-0.56, -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.49*** (-0.77, -0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (-0.53, 0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19 (-0.06, 0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.13 (-0.55, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05 (-2.65, 0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18 (-0.43, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.66* (-1.21, -0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.91 (-2.47, 0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (-0.18, 0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (-0.58, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.14 (-3.37, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28* (0.07, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (-0.16, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36 (-0.84, 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.03*** (-1.25, -0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.49*** (-0.78, -0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.40 (-0.81, 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 (-0.39, 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: Reference categories are indicated in parentheses. Odds ratios are presented with 95% confidence intervals in parentheses. *, **, *** indicate statistical significance at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 levels, respectively.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe charts (Fig. 1 to 4) visualize how obesity risk varies by income level and race/ethnicity, showing clear patterns of inequality. Across all income levels, individuals with lower household income are more likely to be obese. People in the lowest income group have the highest chance of obesity (~\u0026thinsp;40%), which drops to ~\u0026thinsp;35% for middle-income earners and ~\u0026thinsp;25% for those in the highest income group. This trend shows that higher income can act as a buffer against obesity, likely because of better access to healthy food, exercise opportunities, and healthcare. When race and ethnicity are considered, the disparities become even more striking. NHBs have the highest obesity risk across all income groups. For example, in the lower-income group, nearly 45% of NHBs are likely to be obese, compared to 40% of Hispanics and 35% of NHWs. In the middle-income group, the gap widens\u0026mdash;more than half (53%) of NHBs are at risk of obesity, while the rates drop to 34% for NHWs and 31% for Hispanics. Even in the upper-income group, NHBs maintain the highest obesity risk (~\u0026thinsp;44%), while NHWs have the lowest (~\u0026thinsp;25%), with Hispanics falling in the middle (~\u0026thinsp;34%). These patterns suggest that income alone doesn\u0026rsquo;t fully protect NHBs from obesity. Also for Hispanics, the risk is lower in some cases, but their outcomes are less consistent, potentially reflecting cultural or environmental factors that influence health behaviors.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredictive Margins Table\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMargin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026gt;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2 (Lower Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.33, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2 (Lower Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.39, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2 (Lower Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.35, 0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3 (Middle Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.32, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3 (Middle Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.47, 0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3 (Middle Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.23, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 4 (Upper Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.21, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 4 (Upper Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.28, 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 4 (Upper Income)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.16, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe disparities in obesity risk among older adults observed in this study highlight the persistent influence of income and race/ethnicity on health outcomes. Lower-income older adults were more likely to be obese than their higher-income counterparts, a finding consistent with prior research on socioeconomic determinants of health (Lee et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Reidpath et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This income-based disparity was particularly pronounced among racial and ethnic minorities, with NHB individuals demonstrating the highest obesity prevalence across all income groups. Hispanic individuals exhibited intermediate obesity rates, NHW individuals consistently reported the lowest prevalence. The relationship between income and obesity among NHWs followed a predictable pattern, with decreasing obesity prevalence as income increased. This finding aligns with the health production model (Grossman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), which posits that individuals with greater financial resources can allocate more to health-promoting activities such as access to nutritious food, fitness opportunities, and healthcare. However, this trend did not hold uniformly for NHBs and Hispanics. NHBs in middle-income categories faced the highest obesity risks, suggesting that income alone does not mitigate the structural barriers these populations encounter (Williams et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For example, NHBs residing in minority-dense neighborhoods often lack access to fresh produce and face an overabundance of fast-food options, exacerbating their risk for obesity regardless of income level (Cooksey-Stowers et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHispanic individuals presented a more nuanced pattern, with variability in obesity risk across income groups. Cultural norms regarding body image and dietary practices may partially explain these differences, as these factors shape health behaviors independently of socioeconomic status (Odoms-Young \u0026amp; Bruce, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, structural inequities, such as limited access to healthcare and recreational facilities in predominantly Hispanic neighborhoods, may contribute to these disparities. Even in higher-income groups, Hispanics were more likely to face barriers to health-promoting resources compared to NHWs, underscoring the role of systemic inequities in perpetuating health disparities. The persistent obesity disparities among NHBs, even in upper-income categories, reflect the cumulative effects of historical and systemic racism. Chronic exposure to stressors, including discrimination and implicit bias within healthcare systems, likely undermines the potential health benefits associated with higher income. Elevated cortisol levels from chronic stress have been linked to increased abdominal fat accumulation, further contributing to obesity risk (Kirby et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These findings indicate that income improvements alone are insufficient to address the health disparities experienced by NHBs and Hispanics.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Interventions\u003c/h2\u003e \u003cp\u003eThe findings of this study point to the need for tailored interventions addressing the interplay of income, race/ethnicity, and obesity among older adults. Structural barriers such as food deserts, which disproportionately affect minority-dense neighborhoods, can be mitigated through initiatives like government-subsidized grocery stores or mobile farmers\u0026rsquo; markets that bring affordable fresh produce to underserved areas (Cooksey-Stowers et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gordon-Larsen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For instance, programs like the Pennsylvania Fresh Food Financing Initiative have improved access to nutritious food, while mobile markets in North Carolina provide fresh produce directly to communities (Pennsylvania Department of Agriculture, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Culturally tailored health promotion programs are equally critical, especially for Hispanic populations, where traditional diets may contribute to obesity risk. Initiatives such as the \u0026ldquo;Salud y Sabor\u0026rdquo; program in New Mexico, which combines cooking demonstrations with culturally relevant nutrition education, can encourage healthier eating without disregarding cultural traditions (National Hispanic Cultural Center, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecreational opportunities must also be improved to enhance physical activity in minority communities, where access to safe and affordable recreational spaces is often limited. Investments in public parks, fitness trails, and community centers, along with free exercise programs like \u0026ldquo;Shape Up NYC,\u0026rdquo; can provide accessible options for older adults. Addressing income inequities through policy reforms such as increasing minimum wages, expanding tax credits, and providing affordable housing can alleviate financial stress and promote health equity (Hahn, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reidpath et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Workplace wellness programs and tax benefits like the Earned Income Tax Credit (EITC) have shown promise in supporting low-income families\u0026rsquo; health outcomes (Warner et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, interventions should focus on mitigating chronic stress and discrimination, which exacerbate obesity risks through physiological pathways like elevated cortisol levels. Community-based mental health services, coupled with anti-racism training for healthcare providers, can help individuals cope with discrimination while reducing implicit bias in healthcare (C\u0026eacute;nat et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Initiatives like \u0026ldquo;Embrace Race\u0026rdquo; could be adapted to support older adults facing racial stressors. Community health clinics and mobile health units, such as the \u0026ldquo;Health Wagon\u0026rdquo; in rural Appalachia, can also play a vital role by offering free or low-cost preventive care, ensuring better access for underserved populations.\u003c/p\u003e \u003cp\u003eTechnology offers additional opportunities to address barriers related to access and cultural differences. Digital health platforms like \u0026ldquo;Vida Health,\u0026rdquo; which provides bilingual coaching for weight management, can deliver personalized interventions to diverse populations (Silberman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Educational campaigns to combat weight stigma, such as the \u0026ldquo;End Weight Bias\u0026rdquo; campaign by the Obesity Action Coalition, are equally important in promoting body positivity and encouraging health-promoting behaviors among older adults (Kirby et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In sum, a comprehensive approach combining policy reforms, community-based programs, culturally tailored interventions, and technology-driven solutions is essential to address obesity disparities effectively. Future research and program evaluations will be critical in refining these interventions and ensuring their success across diverse populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Conclusion\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be acknowledged. First, the reliance on self-reported data for variables such as height, weight, and household income may introduce reporting bias, potentially affecting the accuracy of obesity classification and income categorization. Although the study utilized robust measures to cross-validate BMI calculations, these biases could still influence the findings. Second, the cross-sectional nature of the analysis limits the ability to establish causal relationships between income, race/ethnicity, and obesity. Future longitudinal research is needed to assess these relationships over time and account for potential reverse causation. Third, the study may not fully capture the complexity of cultural and environmental factors influencing obesity within racial and ethnic groups, as these aspects are challenging to quantify and were not included in the dataset. Finally, while the Health and Retirement Study provides nationally representative data, the findings may not generalize to populations outside the U.S. or to younger age groups, necessitating caution in broader applications of the results.\u003c/p\u003e \u003cp\u003eDespite its limitations, this study offers valuable insights into how household income and race/ethnicity intersect to impact obesity among older adults in the United States. The findings shed light on the ongoing disparities in obesity risk, emphasizing the need to address the structural barriers that limit access to healthy food, healthcare, and opportunities for physical activity, particularly for marginalized groups. Effective solutions must go beyond encouraging individual behavior changes and instead focus on tackling the systemic inequalities that drive these disparities. By contributing to the growing evidence on health inequities, this research highlights the importance of comprehensive and equity-focused interventions. It also paves the way for future studies to explore these relationships further and inform policies aimed at creating healthier, more equitable communities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSunkanmi Folorunsho was solely responsible for the conception and design of the study, data analysis and interpretation, drafting and revising the manuscript, and final approval for submission. The author also verified the integrity and accuracy of the data and its analysis and is accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgerbo, E., Gunnell, D., Bonde, J. P., Mortensen, P. B., \u0026amp; Nordentoft, M. 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Changing impact of obesity on active life expectancy of older Americans. \u003cem\u003eThe Journals of Gerontology: Series A, Biological Sciences and Medical Sciences, 74\u003c/em\u003e(12), 1944\u0026ndash;1951. https://doi.org/10.1093/gerona/glz133 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Older Adults, Household Income, Race/Ethnicity, health","lastPublishedDoi":"10.21203/rs.3.rs-5754136/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5754136/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObesity, a leading global health concern, disproportionately impacts older adults. This study explores the relationship between household income and obesity (BMI ≥ 30) among U.S. adults aged 65 and older, using data from the 2020 wave of the Health and Retirement Study (HRS), a nationally representative dataset. Logistic regression models and predictive margins illustrate disparities, with participants categorized into income quartiles and analyzed across three racial/ethnic groups: Non-Hispanic White (NHW), Non-Hispanic Black (NHB), and Hispanic. Results show that lower-income older adults face significantly higher obesity rates, with those in the lowest income quartile having 1.72 times greater odds of obesity compared to higher-income individuals. NHBs consistently exhibit the highest obesity prevalence across all income levels, followed by Hispanics, while NHWs report the lowest rates. Even among higher-income NHBs, obesity remains elevated, highlighting the role of structural barriers such as food deserts, healthcare disparities, and chronic stress from systemic racism. The income-obesity relationship differs by race/ethnicity. For NHWs, obesity decreases steadily with higher income, while NHBs show persistently high rates regardless of income, and Hispanics display mixed patterns influenced by cultural and environmental factors. These findings suggest that addressing income disparities alone may not suffice to reduce obesity among minority groups, as systemic inequities persist. Targeted interventions are needed to address these structural barriers. Policies promoting access to healthy food, recreational spaces, and preventive healthcare in underserved minority communities are critical to mitigating obesity disparities in older adults.\u003c/p\u003e","manuscriptTitle":"The Relationship Between Household Income and Obesity Among Older Adults: Investigating the Moderating Role of Race","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 15:35:28","doi":"10.21203/rs.3.rs-5754136/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"225e67f1-fb6b-4926-8ab4-174351846ffc","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-21T09:23:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 15:35:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5754136","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5754136","identity":"rs-5754136","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}