Gender, Marital Status, and Community Determinants of Cardiovascular Risk in South Africa: An Epidemiological and Sociological Analysis

Gender, Marital Status, and Community Determinants of Cardiovascular Risk in South Africa: An Epidemiological and Sociological Analysis | 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 Gender, Marital Status, and Community Determinants of Cardiovascular Risk in South Africa: An Epidemiological and Sociological Analysis Winfred Avogo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7045465/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 Though social determinants are considered to shape health outcomes, only a few studies have examined how sex, marital status, and community context intersect to influence the risk of cardiovascular diseases (CVDs) in highly unequal societies. This study investigates these dynamics in South Africa using pooled data from seven nationally representative surveys, including the South African Demographic and Health Survey (2003), the National Income Dynamics Study (2008–2017), and the South African National Health and Nutrition Examination Study (2012). Multilevel logistic regression models were used to assess both individual- and community-level predictors of CVD. These findings challenge dominant assumptions of gendered resource theory, particularly the notion that marriage is universally protective. Contrary to expectations, married men exhibited elevated CVD risk, likely reflecting role strains under economic precarity and constrained access to care. This study introduced the Gendered Widowhood Vulnerability Hypothesis, showing that CVD risk among widowed or divorced men is contingent on community-education levels and is significantly higher in moderately educated communities, contexts in which aspirations may outpace opportunities. Higher levels of community education were associated with an increased CVD risk among men, which is consistent with the Community-Level Educational Paradox Hypothesis. This finding complicates the presumed protective role of education and demands reconceptualization of its health impact in resource-constrained settings. By integrating life course, gender, and place-based frameworks, this study advances the sociological understanding of how intersecting social structures produce health disparities. These insights can inform gender-sensitive health strategies targeting structurally marginalized men in unequal political economies. Cardiovascular disease (CVD) Gender and health Marital status Community effects Social stress theory Multilevel modeling Figures Figure 1 Introduction and Background Noncommunicable diseases (NCDs) account for 74% of global mortality, marking a profound shift in global health patterns (Global Burden of Disease [GBD], 2024). Despite decline of death from communicable, maternal, neonatal, and nutritional diseases, cardiovascular diseases (CVDs), particularly ischemic heart disease, have emerged as the leading causes of death worldwide. These trends are driven by risk factors, such as hypertension, tobacco use, elevated blood sugar, and air pollution. This global shift reflects a broader epidemiological transition; as societies develop, infectious diseases decline and chronic lifestyle-related illnesses increase (Omran, 1971, 1998). However, this transition is complex and consequential in Sub-Saharan Africa (SSA). CVDs are increasingly affecting younger adults and socioeconomically disadvantaged populations. Between 1990 and 2019, the percentage of death from NCDs in SSA increased from 18.6% to 30%, with CVDs accounting for 38% of these deaths (Gouda et al., 2019; Keates et al., 2017). Unlike in high-income countries (HICs), where older men face the consequence of CVD, the burden in SSA is more sex-diverse and affects adults in their 40s and 50s, including women and low-income groups (Yuyun et al., 2020). This rising burden is fueled by rapid, unplanned urbanization, changing dietary habits, reduced physical activity, and entrenched structural inequalities. These factors have contributed to the increasing rates of hypertension, obesity, and diabetes, which are major risk factors of CVD in SSA (Belue et al., 2009). While these determinants are well documented, there is a critical gap in understanding how sex moderates the relationship between marital status and cardiovascular risk, particularly within the context of community-level social conditions. Many existing studies assume uniform health benefits of marriage, overlooking complex ways in which sex, structural inequality, and community characteristics interact to shape cardiovascular health in low- and middle-income countries (LMICs). South Africa provides a compelling case study. The country faces a “quadruple burden”: HIV/AIDS, tuberculosis, rising NCDs, and the high rates of violence and injury (Pillay-van Wyk et al., 2016). These health challenges are deeply intertwined with socioeconomic inequalities rooted in colonialism and apartheid (Mayosi et al., 2009; Mooney & McIntyre, 2008). NCDs, particularly CVD, diabetes, and hypertension, represent a growing public-health and economic crisis that contributes to premature morbidity and mortality, reduces labor productivity, and strains healthcare systems (Mayosi et al., 2009; Pillay-van Wyk et al., 2016). The economic burden of NCDs disproportionately affects poor households, which often face out-of-pocket costs, lost wages, and long-term care expenses (Hofman et al., 2014; Kabajulizi & Darko, 2021). These economic pressures may shape how marital status influences access to care, emotional support, and exposure to health risks, which may differ according to sex. Such patterns underscore the need to move beyond individual-level analyses to examine how gendered social roles and community contexts jointly shape cardiovascular risk. This study addresses three critical gaps in the literature: (1) it investigates how sex and marital status intersect to influence cardiovascular risk, (2) it explores how community-level socioeconomic conditions shape these associations, and (3) it critically examines the applicability of dominant theories developed in high-income contexts, such as the gendered resource theory and the protective marriage hypothesis, to a low-resource, high-inequality setting. Positioned at the intersection of biosocial theory, gender analysis, and place-based health inequality, this study draws on theories on gendered resources and social stress to argue that the health effects of marriage are socially contingent, vary by gender, and are shaped by community-level poverty, employment, and education. To address this gap, this study integrates sociological and epidemiological perspectives to investigate how sex, marital status, and community-level socioeconomic conditions shape cardiovascular risk in South Africa—thereby reframing CVD vulnerability as a gendered, place-based social phenomenon rather than solely an individual health outcome. CVD Transitions and Demographic Shifts in SSA Understanding the demographic and epidemiological transitions in SSA is essential to address the shifting disease burden in the region. While communicable diseases, such as malaria, HIV/AIDS, and tuberculosis, remain prevalent, NCDs, particularly CVDs, are rising sharply (Defo, 2014; Omran, 1971; Yuyun et al., 2020). South Africa exemplifies this dual burden, though the emergence of this trend is comparable to other parts of the region. For instance, in Ghana, NCDs account for nearly half of all deaths, with hypertension affecting over 50% of urban residents (Konkor, Waqar, & Kuuire, 2025). In Nigeria, nearly 30% of adults suffer from hypertension, and obesity is increasing, especially among urban women (Chukwuonye et al., 2019). Kenya has adopted national strategies targeting both the behavioral and structural drivers of CVDs (Ministry of Health, 2015). These trends reflect the convergence of infectious and chronic disease burdens, necessitating integrated and context-sensitive public-health responses. The incidence of CVD in SSA differs considerably from that in HICs. It disproportionately affects younger adults during peak economic productivity, with nearly half of all CVD deaths occurring before the age of 70 years owing to late diagnosis, limited access to care, and systemic health inequities (Mensah & Roth, 2015; Moran et al., 2014). These trends threaten the demographic dividends of the region and impose substantial socio-economic costs. Despite the rising rates of hypertension and obesity, few studies in SSA have examined how sex and marital status jointly shape CVD risk in specific contexts. This gap limits our understanding of how gendered social roles and household structures influence cardiovascular vulnerability in transitional societies. Structural and Social Determinants of CVD Risk Social determinants, including poverty, employment, and education, are the well-established predictors of cardiovascular risk (Acquah et al., 2023; Jilani et al., 2021). Individuals in low-income households face barriers to healthcare, nutritious food, and safe environments for physical activity (Shen et al., 2023). Unemployment and informal work contribute to chronic stress and financial insecurity, compounding CVD risks (Elfassy et al., 2019). Education is crucial in this regard. Numerous studies have demonstrated an inverse relationship between educational attainment and the incidence of cardiovascular disease (CVD) (Kubota et al., 2017; Khaing et al., 2017). Education enhances health literacy, improves individuals’ understanding of risks, and increases their access to preventive services. It also ensures their long-term financial stability and employment opportunities, indirectly reducing their vulnerability to chronic diseases. In South Africa, apartheid created structural inequalities that remain central to the disparities in CVD. Spatial and economic segregation limit individuals’ access to essential health-promoting resources, including quality education, employment opportunities, healthy food environments, and safe infrastructure for physical activity (Ataguba et al., 2011; Mayosi & Benatar, 2014). These conditions disproportionately affect Black South Africans and low-income populations, leading to an increased exposure to behavioral risk factors and chronic psychosocial stress (Adjaye-Gbewonyo et al., 2018). The dual health system has compounded inequality. A well-funded private sector, accessible primarily to affluent White South Africans and the segments of the black and colored middle class, coexists with a severely under-resourced public sector that serves the majority (Naidoo, 2012; Tanser et al., 2006). This divide exacerbates disparities between preventive and curative services, contributing to delayed diagnosis, suboptimal treatment, and an elevated CVD risk (Ataguba et al., 2011). These conditions necessitate moving beyond individual-level explanations of CVD toward a broader focus on structural determinants, such as community-level poverty, income inequality, disparities in education and employment, and institutional disinvestment, as the root causes of inequities related to cardiovascular health (Link & Phelan, 1995; Mayosi & Benatar, 2014). Marriage, Gender, and Biosocial Pathways to Cardiovascular Health Biosocial and epidemiological studies have identified marital status as a key social determinant of cardiovascular health. Meta-analyses and longitudinal studies have consistently shown that unmarried individuals, including never-married, divorced, and widowed individuals, have a greater risk of CVD, coronary heart disease, stroke, and mortality than married individuals (Wong et al., 2018; Otto, 2018). Manfredini et al. (2017) reported the increased odds of CVD (OR = 1.42), CHD mortality (OR = 1.43), and stroke mortality (OR = 1.55) among unmarried individuals. However, this association was neither uniform nor unidirectional. Marital status is associated with divergent health profiles that vary according to the life stage, sex, and culture. The higher rates of obesity, hypertension, and type 2 diabetes are often reported in married older individuals, possibly because of their long-standing dietary routines and reduced physical activities (Averett et al., 2013). Conversely, single men, particularly younger ones, are more likely to smoke, drink excessively, eat poorly, and become obese (Molloy et al., 2009; Robards et al., 2012). High levels of stress and risky behaviors that increase their CVD risk are frequently reported in separated or divorced individuals (Umberson et al., 2009). Some studies have found that married men have worse lipid profiles (Liu & Umberson, 2008), indicating the complexity of the marriage-health link. Sex was a critical moderating factor. While early literature emphasized male advantage, newer evidence suggests that the health benefits of marriage may be greater for women in specific settings. For example, Rabiaza et al. (2024) found that married women in Italy had significantly lower CHD and all-cause mortality rates, whereas men showed no significant benefits. Similar patterns have been observed across studies, which highlights poor cardiovascular outcomes among men (Manfredini et al., 2017). Research from SSA underscores the need for context-specific analyses. In Ghana Tuoyire & Ayetey, 2018 found that married, cohabiting, and previously married women had higher odds of hypertension than never-married women, even after adjusting for confounders, whereas marriage was protective for men. In Nigeria and Tanzania, access to care for married women is often limited by unequal decision-making power and gender constraints (Sserwanja et al., 2018). Gendered cultural expectations significantly shape men's health behaviors across sub-Saharan Africa. In Ghana, Nigeria, Uganda, South Africa, and Lesotho, masculinity norms emphasize stoicism, self-reliance, and concern for social reputation, which deters men from seeking preventive and routine care (Gómez-Olivé et al., 2017, 2018; Siu et al., 2014; Skovdal et al., 2011). Clinics are often perceived as feminine spaces that reinforce avoidance and contribute to men’s delayed health-seeking (Dovel et al., 2020; Sileo et al., 2018). These behaviors mirror trends in CVD risk, as men are less likely to engage in screening or early treatment, underscoring how structural gender barriers exacerbate CVD vulnerability across the region. Collectively, these findings confirm that marital status is a gendered and context-contingent determinant of cardiovascular health. Research must adopt intersectional biosocial models that consider interactions among gender norms, structural conditions, and access to care. Neighborhood Context and Community-Level Moderators of CVD An extensive body of research links residences in socioeconomically disadvantaged neighborhoods with an increased prevalence of CVD-risk factors, including physical inactivity, obesity, smoking, hypertension, hypercholesterolemia, and type 2 diabetes (Diez Roux & Mair, 2010; Mujahid et al., 2008; Brown et al., 2004). Studies found that these relationships persist in individuals even after adjusting for individual socioeconomic status, underscoring the distinct impact of contextual environment (Sundquist et al., 1999). Key neighborhood-level disadvantages include low income, low education, and high welfare dependency, each of which is consistently associated with elevated CVD incidence (Diez Roux, 2016). These structural barriers limit access to health-promoting infrastructures, quality services, and safe recreational spaces (Cubbin et al., 2001a). The interplay between individual and contextual risks is particularly pronounced in LMICs. Individuals with limited income, education, or autonomy are at a heightened risk when they reside in structurally marginalized environments characterized by inadequate healthcare infrastructure, food deserts, substandard housing, and physical insecurity (Ameh et al., 2019). These multilevel exposures produce cumulative stress and behavioral constraints that undermine individuals’ cardiovascular health, regardless of their intentions or knowledge (Bevan et al., 2022). The gendered norms add another layer to the conundrum. In patriarchal societies, women often lack the mobility or decision-making power to access health services (Tuoyire & Ayetey, 2018). By contrast, norms of masculinity discourage men from seeking preventive care (Gómez-Olivé et al., 2017, 2018). Addressing CVDs in LMICs such as South Africa requires integrated multilevel strategies that address both individual behaviors and structural inequalities. Focusing solely on individual choices without improving community conditions risks deepening the existing disparities and limiting the impact of public-health interventions. Revisiting Marital Protection and Gendered Health Theories in SSA Many studies that link marital status with health outcomes rely on two prominent theories on gendered resource and social stress. The gendered resource theory posits that marriage provides individuals with unequal access to health-promoting resources, with men typically gaining more from spousal caregiving and emotional support, whereas women's health benefits depend on economic security and relational equity (Gorman et al. 2010; Springer 2010). Conversely, the social-stress theory emphasizes that marital transitions, particularly divorce, widowhood, or separation, generate psychosocial stress and erode social-support networks, thereby increasing the risk of chronic diseases (Pearlin et al., 1981; Thoits, 2010). However, these theoretical frameworks were developed primarily in HICs, where social protection, access to healthcare, and economic resources mitigated the effects of marital strain and relational inequality. Assumptions based on these frameworks may not fully translate to low- and middle-income contexts such as sub-Saharan Africa, where pervasive structural disadvantages and institutional fragility shape life-course health trajectories in distinctive ways. In SSA, individuals often experience early-life deprivation, widespread unemployment, informal marriage arrangements, and gendered access to healthcare and education. The dual burden of communicable diseases and NCDs, compounded by racialized and spatial inequalities in health infrastructure, as seen in South Africa, creates conditions under which conventional theories of marital protection may not hold. For example, caregiving and material benefits presumed in marriage may be absent or unevenly distributed, especially for women with limited agency, or men whose roles as providers are undermined by structural unemployment. Similarly, weak social safety nets and gendered stigma may intensify psychological stress associated with widowhood or divorce. This study contributes to the literature by critically applying and contextualizing theories on gendered resources and social stress in a high-inequality, low-resource setting. Rather than assuming universal patterns, it investigates how sex and marital status interact with place-based disadvantages to produce context-specific cardiovascular vulnerabilities. In doing so, it helps retheorize the relationship between marriage, gender, and health in settings where structural constraints mediate the meaning and impact of intimate relationships. These findings reveal three persistent gaps. First, the existing research has not adequately examined how marital status and sex intersect to shape cardiovascular risk across diverse life contexts (gap 1). Second, the literature has insufficiently explored how community-level disadvantages condition these effects, particularly in settings with stark structural inequalities (Gap 2). Third, the dominant theoretical models developed in high-income contexts have rarely been critically assessed for their relevance for or adaptability to the Global South (Gap 3). By addressing these conceptual and empirical limitations, this study advances a more intersectional, place-sensitive, and globally relevant understanding of cardiovascular risk in SSA. Conceptual Framework: Gender, Marital Status, and Community Moderators of Cardiovascular Risk in SSA This conceptual framework (above) extends the traditional models of the social determinants of CVDs by integrating marital status, sex, and community-level socioeconomic disadvantage within the broader structural inequalities of SSA and by distinguishing its epidemiological transitions. It builds on the existing frameworks of health determinants, including biological, behavioral, and psychosocial pathways, to trace how social location and community context jointly shape cardiovascular risk in structurally unequal settings. At its core, the framework posits that sex and marital status interact through socially patterned pathways to influence individuals’ exposure to CVD-risk factors, such as unhealthy diet, physical inactivity, tobacco and alcohol use, chronic stress, and mental-health challenges. These factors are further shaped by power dynamics within households, particularly in terms of decision-making autonomy, caregiving burdens, and financial control. For example, married women in SSA may experience constrained decision-making autonomy, which limits their access to healthcare, whereas unmarried men are more likely to be exposed to social isolation and adopt riskier health behaviors shaped by dominant masculinity norms (Gómez-Olivé et al., 2018; Gómez-Olivé et al., 2017; Sserwanja et al., 2018). Significantly, the effects of gender and marital status are not fixed. Community-level socioeconomic characteristics include neighborhood poverty, employment patterns, and educational-infrastructure conditions. Individuals who reside in socioeconomically deprived neighborhoods are exposed to intersecting stressors, including food insecurity, environmental hazards, limited healthcare access, and disinvestment in services, which compound individuals’ health risks and increase their vulnerability to CVDs (Cubbin et al., 2001b; Sundquist et al., 1999). These community-level moderators are particularly important in SSA, where urban-rural divides, informal housing, and fragmented health systems further compound personal vulnerability. This conceptual framework critically engages with the assumptions embedded in gendered resource theory and social stress theory, both of which originate in high-income settings and presume relatively equitable access to resources and healthcare (Pearlin et al., 1981; Springer, 2010; Gorman & Read, 2006). While these models offer valuable insights, such as the benefits of marriage through resource pooling and social support, they are grounded in assumptions that reflect the institutional conditions of HICs, including access to formal employment, functioning welfare states, and relatively equitable gender norms (Pearlin et al., 1981; Springer, 2010). However, these conditions are rarely encountered in SSA. Structural unemployment is widespread, particularly among young adults and women, eroding the economic benefits typically attributed to marriage (Posel & Rogan, 2019). Public health systems are often fragmented and under-resourced, which limits the availability of preventive services and undermines the continuity of care (Ataguba et al., 2011; Pillay-van Wyk et al., 2016). Furthermore, deeply entrenched gender hierarchies constrain women’s decision-making autonomy and access to household resources, limiting the protective potential of marital unions for many women (Tuoyire & Ayetey, 2018). These structural conditions challenge the universal applicability of the theories of gendered resources and social stress, underscoring the need for contextual adaptations. Rather than viewing marriage as uniformly protective, this study conceptualizes it as a relational institution embedded in local political economies and gender regimes whose health effects are contingent upon individuals’ access to resources, their social roles, and institutional support that vary significantly across settings. By situating these theories in SSA, the framework recasts marital protection as contingent rather than universal and conceptualizes marriage not simply as a resource but as a relational institution embedded in unequal social and economic systems. Similarly, it understands stress exposure not solely as a product of marital disruption but as a chronic condition shaped by poverty, inequality, and exclusion from institutional support. Thus, the conceptual framework of this study contributes to the decentering of HIC assumptions about gender roles, marriage, and household functioning, thereby bridging individual and contextual levels of analysis to understand cardiovascular vulnerability and highlighting the need for intersectional, multilevel interventions that address structural inequality rather than just personal behavior. Ultimately, the framework provides a scaffold for empirical analysis of how gender, marital status, and place interact to shape the CVD risk in SSA and invites rethinking of how global health theories must evolve to reflect regional realities. Theoretical Hypotheses Emerging from the Framework Drawing on the conceptual framework outlined above, this study identifies a set of theoretically grounded but empirically untested hypotheses that challenge the universality of the gendered-resource theory and social-stress theory. The following hypotheses serve as analytical propositions that extend, adapt, or contest these theories, considering local realities: The Marriage-Paradox Hypothesis Contrary to the widely held assumption that marriage is uniformly protective, married men in high-unemployment and high-stress contexts may face elevated cardiovascular risk. This paradox arises from structural pressures to serve as economic providers, masculine norms that discourage health seeking, and limited access to preventive care, which may override the relational benefits of spousal support. Gendered-Widowhood-Vulnerability Hypothesis The loss of a spouse through divorce or widowhood may disproportionately increase the CVD risk among men in disadvantaged communities. Unlike women, who maintain stronger social networks or caregiving ties, men in patriarchal systems may experience more severe social isolation and stress when marital ties dissolve. Community-Level-Educational-Paradox Hypothesis Contrary to conventional expectations, higher levels of community education may be associated with an increased CVD risk in men, particularly if educational attainment does not translate into economic mobility. This may reflect psychosocial stress stemming from misaligned aspirations, occupational strain, and structural under-employment in South Africa's segmented labor market. Masked-Disadvantage-for-Married-Women Hypothesis Marriage may not protect women in highly gender-unequal settings where asymmetries related to economic dependency, restricted autonomy, and relational power limit their ability to access health services and make preventive health decisions. Thus, married women may face hidden or under-recognized CVD vulnerabilities, particularly in male-dominated households. Moderation-by-Community-Disadvantage Hypothesis The relationship between marital status and CVD risk is moderated by community-level deprivation such that the health penalties for being widowed, divorced, or never married are intensified in neighborhoods characterized by poverty, unemployment, and limited-service infrastructure. These hypotheses underscore the need to reassess the presumed protective effects of social relationships, particularly marriage, in contexts characterized by structural inequality and social stratification. They call for multilevel intersectional approaches that could capture how sex, place, and life-course transitions interact to produce cardiovascular disparities. To empirically evaluate these propositions and extend the global health theory in contextually grounded ways, this study draws on nationally representative data from South Africa. It applies multilevel modeling to examine how sex, marital status, and community-level disadvantages jointly shape CVD risk. In the following section, data sources, sample characteristics, and analytical strategies are discussed. DATA AND METHODS Data Harmonization and Ethics This study used a harmonized dataset developed through the ExPoSE Project ( https://www.exposeproject.net ), a collaboration between the University of Greenwich (UK) and Stellenbosch University (South Africa), which investigates epidemiological transitions in cardiovascular disease risk (Adjaye-Gbewonyo & Cois, 2024 ). Data from seven survey waves, originating from three nationally representative South African survey programs, were pooled into a single dataset: the Demographic and Health Survey (SADHS, 2003); the NIDS (Waves 1–5, 2008–2017); and the South African National Health and Nutrition Examination Survey (SANHANES-1, 2011–2012). These combined surveys provided 122,576 observations, of which 71,243 participants had complete data and were included in the final analytic sample. All datasets are publicly available and were accessed through DataFirst at the University of Cape Town (DataFirst, n.d.). Data were drawn from three nationally representative South African surveys: the SADHS (2003) , a two-stage clustered household survey with face-to-face interviews of women (15–49) and a subsample of men (15–59), including biomarker collection (response rates: 95% households, 97% individuals); the NIDS (Waves 1–5, 2008–2017) , a longitudinal two-stage clustered panel survey by the Southern Africa Labour and Development Research Unit (SALDRU) at the University of Cape Town, collecting demographic, health, income, and anthropometric data from individuals aged 15 + via face-to-face interviews with survey weights for non-response; and the SANHANES-1 (2011–2012) , a cross-sectional survey by the Human Sciences Research Council (HSRC) where field teams visited households to administer questionnaires and perform physical and biomarker assessments. The harmonized dataset involved a systematic review and alignment of core demographic, socioeconomic, and health-related variables across surveys to ensure comparability. I applied age restrictions and excluded pregnant women to reduce potential bias. Sampling weights were retained to preserve national representativeness. I conducted descriptive and frequency checks to verify the accuracy of harmonization. Data cleaning and processing were completed using Stata 18. The initial harmonized dataset included 122,576 observations pooled from seven survey waves. I applied an age restriction, excluding 105 participants (0.08%) younger than 15 years. Pregnant women (n = 1,896; 1.55%) were excluded to avoid bias related to physiological changes affecting cardiovascular risk. Subsequently, cases with missing data on key analytic variables were excluded, resulting in a final analytic sample of 71,243 participants. To evaluate the potential bias from missing data, I compared demographic characteristics (age, sex, and urban/rural residence) between included and excluded participants. No significant differences were found, supporting the assumption that data are missing at random (MAR). Additional detailed analyses of missing data patterns, including variable-specific missingness and comparisons, are provided in the supplemental materials. Listwise deletion was used for the final analytic models. Measures Dependent variables Hypertension: I assessed hypertension using systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurements taken at 10-minute intervals using a LIFE SOURCE UA-767 Plus digital oscillometric blood pressure monitor. According to the guidelines of WHO (2003) and American College of Cardiology (2020), blood pressure was categorized into four levels: (1) normotensive (SBP < 120 mmHg and DBP < 80 mmHg, no diagnosis, and not on antihypertensive medication); (2) elevated blood pressure (SBP 120–129 mmHg and DBP < 80 mmHg); (3) stage 1 hypertension or diagnosis (SBP 130–139 mmHg, DBP 80–89 mmHg, or self-reported diagnosis); and (4) stage 2 hypertension or medication (SBP ≥ 140 mmHg, DBP ≥ 90 mmHg, or current use of antihypertensive medication). A binary variable was created for the analysis, with individuals classified as hypertensive (category 2–4 = 1) or normotensive (category 1 = 0). Pregnant women were excluded owing to the risk of gestational hypertension (American College of Obstetricians and Gynecologists, 2020 ). Obesity . Obesity was measured using the body mass index (BMI) calculated from self-reported weight and height (kg/m²). BMI categories were defined as underweight (< 18.5), normal weight (18.5–24.9), overweight (25.0–29.9), and obese (≥ 30.0). A dichotomous variable was constructed for multivariate analysis: healthy weight/underweight (BMI < 25 = 0) and overweight/obese (BMI ≥ 25 = 1). Pregnant women were excluded because of pregnancy-related weight changes. Diabetes . Diabetes was measured as a binary outcome (yes = 1, no = 0) based on the diagnosis of a self-reported physician and/or the current use of diabetes medication. Although self-reported, this measure has been used in population-level studies despite its potential for underdiagnosis or misclassification (Kowall et al., 2024 ; Wang et al., 2014 ). Composite CVD Risk. To capture the overall CVD vulnerability, a composite variable was created to indicate whether the respondent reported any of the three CVD conditions: hypertension, diabetes, or elevated BMI (≥ 25). This variable was used in the supplementary models to assess the cumulative cardiometabolic risk. A binary composite measure of CVD risk was developed to capture the multidimensional and syndemic nature of cardiometabolic diseases in a resource-constrained setting. Consistent with the findings of previous studies, hypertension, diabetes, and elevated BMI were interconnected and mutually reinforcing conditions that significantly increased the risk of adverse cardiovascular events (Gaziano et al., 2013; Reddy et al., 2005 ). Combining these markers with a single binary indicator (presence of any of the three = 1; none = 0) reflects the clustering of risk factors frequently observed in LMICs, where early diagnosis and continuous care are limited (Ataklte et al., 2015 ; Mensah et al., 2015 ). This approach is particularly suitable for multilevel analyses of pooled population-based surveys in which consistent biomarkers and diagnostic measures vary across datasets. Moreover, binary composite risk indicators have been widely used in population-health research to identify individuals at elevated risk and guide public-health prioritization (D'Agostino et al., 2008 ; Sliwa et al., 2021 ). Although this simplification may obscure differences in severity, it provides a parsimonious and policy-relevant measure of CVD vulnerability appropriate for the South African context. Explanatory variables Marital Status: Marital status was coded into three categories: (1) never married/single , (2) currently married or living with a partner , and (3) previously married (widowed, divorced, or separated). Community-Level Characteristics . To assess the neighborhood context, three variables were constructed at the level of the primary sampling unit (PSU): 1. Community education : Proportion of individuals with secondary or higher education categorized into tertiles (low, middle, and high). 2. Community poverty : The proportion of households in the lowest two national income quintiles categorized into tertiles. 3. Community Employment : The proportion of employed adults in each PSU was also categorized into tertiles. These measures reflect the local socioeconomic environment, which may shape individual cardiovascular risks. Covariates Lifestyle Factors: Physical activity was classified as low (no activity or less than once per week), moderate (1–2 times/week), or high (≥ 3 times/week). Smoking status and alcohol use were coded as never , former , or current . Sociodemographic Characteristics . Educational attainment was categorized into three levels: low (no education or some primary education), moderate (completed primary education or some secondary education), and high (completed secondary education or higher). Additional covariates included urban residence (urban = 1, rural = 0), medical insurance (yes/no), employment status (employed/unemployed), household income quintile (1 = poorest to 5 = richest), and self-identified racial classification (Black African, Colored, Indian/Asian, and White). Parity (females only) . Parity was included as a potential biological and social risk factor for CVD in women. They were categorized as nulliparous (0 children), primiparous (1 child), low multiparous (2–3 children), or high multiparous (≥ four children). Multiple pregnancies have been linked to long-term cardiovascular risk owing to gestational hypertension and metabolic strain (Bateman et al., 2015, 2019). Data Analysis I first conducted descriptive statistics, including frequencies, cross-tabulations, and Pearson's chi-square tests, to examine associations between marital status, community characteristics, and CVD outcomes in individuals. These initial bivariate analyses informed the subsequent multivariate modeling. To estimate the relationship between marital status, community-level factors, and CVD risk, I employed multilevel logistic regression models that accounted for the nested structure of the data, with individuals (Level 1) clustered within PSUs (Level 2). This approach enables a more accurate estimation of predictor effects while accounting for unobserved community-level heterogeneity. Sampling weights were used to ensure representativeness and to correct unequal selection probabilities. As recommended by Gabler and Lahiri ( 2009 ), PSU-level weights were derived from individual-level weights, normalized by cluster size, and merged back to consider design effects. Multivariate modeling was implemented separately for each of the three dependent outcomes: hypertension, diabetes, and obesity. A stepwise modeling strategy was employed to systematically assess the contribution of individual and contextual predictors to CVD risk. This approach enables an incremental evaluation of how marital status, community-level characteristics, and individual-level covariates influence outcomes, thereby helping identify the potential confounding or mediating effects. Although only the fully adjusted model (Model 3) is presented in the main results for clarity and parsimony, stepwise progression provides critical insights into the robustness and stability of associations across model specifications. This strategy aligns with best practices in multilevel epidemiological research, in which complex interactions between social and structural determinants are explored in a theoretically informed sequence. These steps included: Step One. Model 0: Unconditional (null) model to assess the baseline variance across PSUs. Step two. Model 1: Only marital status is included. Step Three. Model 2: Adding community-level education, poverty, and employment. Step Four. Model 3: A fully adjusted model including lifestyle (e.g., physical activity, smoking, and alcohol use), demographics (e.g., age, race, education, and employment), and health-system variables (e.g., insurance coverage). I assessed multicollinearity using variance inflation factors (VIFs). None of the variables exceeded a VIF threshold of 5. Analyses were conducted using Stata version 18. The meqrlogit command was chosen for its enhanced convergence properties owing to its QR decomposition solver, and its flexibility in handling numerical integration in large and complex survey datasets (RabeHesketh & Skrondal, 2006 ; Raudenbush & Bryk, 2002 ; StataCorp, 2013 ). Robust standard errors were used to address heteroscedasticity and within-cluster correlations. Adjusted odds ratios (ORs) at 95% confidence intervals (CIs) were reported, and statistical significance was set at p < 0.05. RESULTS Descriptive Results Table 1 presents the weighted frequency distribution of key CVD-risk factors, explanatory variables, and covariates. Elevated blood pressure was observed in 63.1% of the respondents, and 48.3% were classified as overweight or obese, indicating a high CVD risk. Although only 3.7% of patients reported being diagnosed with diabetes, this remains a significant comorbidity. Table 1 Frequency Distribution of Study Variables Variable Category Percentage Hypertension Risk Normotensive 36.90 Elevated BP or Higher 63.10 BMI/Overweight Risk Healthy Weight/Underweight 51.70 Overweight/Obese 48.30 Diabetes Diagnosis Diagnosed/on Medication 3.70 Not Diagnosed 96.30 Sex Female 57.50 Male 42.50 Marital Status Never Married 57.70 Married 31.70 Widowed/Divorced 10.60 Community Education Level Low 23.10 Middle 39.50 High 37.40 Community Poverty Level Low 32.50 Middle 38.50 High 29.00 Community Employment Level Low 27.20 Middle 39.10 High 33.80 Exercise Level Low 76.90 Moderate 11.10 High 12.00 Smoking Status Never Smoker 80.10 Current Smoker 16.90 Former Smoker 3.10 Alcohol Consumption Never Drinker 66.60 Current Drinker 24.40 Former Drinker 9.10 Age Category (10 years) 15–19 16.50 20–29 27.50 30–39 17.90 40–49 13.90 50–59 11.60 60–69 7.30 70–79 3.70 80+ 1.60 Education Level Low 20.90 Moderate 53.20 High 25.90 Race Black African 85.90 Asian 2.30 Colored 9.10 White 2.70 Employment Status Employed 35.90 Unemployed 64.10 Medical Insurance Coverage No 90.70 Yes 9.40 Parity Group Nulliparous 4.50 Primiparous 24.30 Low Multiparous 37.20 High Multiparous 34.00 Household Income Quintile Low (I) 15.90 II 21.60 III 24.00 IV 23.50 High (V) 14.90 Residence Urban 47.20 Rural 52.80 Note . Source: DHS, NIDS, SANHANES (2003–2017). Percentages may not sum to 100% due to rounding. Survey weights are applied to all variables. The sample was predominantly female (57.5%) and largely unmarried (57.7%), with 31.7% married, and 10.6% widowed or divorced. Most respondents resided in communities with medium (39.5%) or high (37.4%) educational levels. Additionally, a similar pattern was observed for community poverty and employment indicators. Analysis of lifestyle-related factors demonstrated that 76.9% of participants performed low physical activity, and 16.9% were current smokers. Two-thirds (66.6%) of the participants had never consumed alcohol, whereas 24.4% reported current or past alcohol consumption The sample was skewed toward young people, with 44% of the participants under the age of 30 years. Only 1.6% of participants were aged 80 years. Education levels were moderate to high for most participants (79.1%), and the racial composition was overwhelmingly Black African (85.9%). Economic hardship was evident, with 64.1% of the participants being unemployed and 9.4% reporting medical-insurance coverage. Parity varied widely, with more than 70% of participants having two or more children. Income was evenly distributed across the quintiles. Urban and rural residences were balanced, with 52.8% of them living in rural areas. These findings provide a demographic and health profile of South African adults, illustrating the co-occurrence of chronic-disease risk, limited access to care, and social disadvantages across multiple domains. [Insert Table 1 about here] Bivariate Associations Table 2 presents the unadjusted prevalence rates of hypertension, obesity, and diabetes across key demographic, socioeconomic, and behavioral variables in South Africa. Except for cases in which F-tests could not be computed because of strata with a single PSU, all associations were statistically significant. Table 2 Bivariate Associations of Sex, Marital Status, Community Characteristics, and Other Covariates with Cardiovascular Disease Risk in South Africa Independent Variables Hypertension (Elevated BP or Higher) Obesity (Overweight/Obese) Diabetes (Diagnosed/On Medication) Sex Female 61.04 62.10 4.36 Male 66.00 29.00 2.70 Marital Status Never Married 49.74 36.63 1.26 Married 74.55 63.55 5.88 Widowed/Divorced 84.85 67.75 10.01 Community-level Education Low 62.35 44.71 3.05 Middle 61.03 46.15 3.45 High 65.89 53.09 4.29 Community-level Poverty Low 68.54 54.11 4.80 Middle 59.80 45.18 3.22 High 61.58 46.51 3.05 Lifestyle Factors Exercise Level Low 62.98 52.34 3.94 Moderate 55.03 37.84 3.07 High 55.82 32.61 2.40 Smoking Status Never Smoker 59.51 52.54 3.88 Current Smoker 67.91 28.12 2.35 Former Smoker 69.97 48.61 5.34 Alcohol Consumption Never Drinker 59.55 51.86 4.06 Current Drinker 66.33 38.82 2.78 Former Drinker 66.35 47.85 4.24 Age Category (10 years) 15–19 35.14 19.26 0.22 20–29 50.79 39.11 0.37 30–39 64.71 55.19 1.38 40–49 74.66 63.11 4.28 50–59 84.81 68.64 9.46 60–69 90.95 65.93 12.56 70–79 92.65 62.14 13.96 80+ 93.42 52.57 11.74 Respondent's Education Low 80.68 53.85 7.02 Moderate 57.51 43.43 2.99 High 59.92 54.16 2.30 Race Black African 61.30 47.58 3.28 Asian 70.09 51.34 9.94 Colored 73.84 50.21 5.14 White 79.92 67.14 6.41 Employment Yes 66.35 54.82 3.25 No 58.87 44.70 3.90 Medical Insurance Coverage No 60.61 46.71 3.45 Yes 67.44 65.11 5.73 Parity Group Nulliparous 41.76 37.10 1.12 Primiparous 46.87 57.50 1.82 Low Multiparous 64.12 71.44 4.64 High Multiparous 81.68 75.01 8.55 Household Income Quintile Low (I) 60.37 42.18 2.43 II 62.96 44.83 3.37 III 62.35 46.66 3.67 IV 62.39 50.10 3.97 High (V) 64.93 60.17 3.67 Residence Urban 64.98 46.18 3.97 Rural 61.11 50.39 3.33 Note. Statistical significance not displayed for clarity. Design-based F-statistics are available upon request. Source: DHS, NIDS, SANHANES 2003–2017 Sex and marital status were strongly associated with CVD outcomes. Men reported a higher prevalence of hypertension (66%) but substantially lower rates of obesity (29%) and diabetes (2.7%) than women (62.1% obese; 4.36% diabetes). Marital status was similarly patterned: never-married individuals had the lowest prevalence of any of the three CVD risks. However, both married and widowed/divorced individuals reported substantially higher rates of risk factors, with hypertension reaching 84.85% and diabetes reaching 10.01%. At the community level, individuals in areas with higher educational attainment exhibited a greater prevalence of hypertension (65.89%) and diabetes (4.29%) than those in less-educated communities. By contrast, obesity was slightly more prevalent in communities with higher education levels (53.09%). Community-poverty gradients were also evident, with the highest prevalence in low-poverty areas (68.54%) and the lowest obesity rates in high-poverty communities (46.51%). Behavioral and lifestyle indicators were associated with all three outcomes. Low physical-activity levels were associated with higher rates of obesity (52.34%) and hypertension (62.98%), whereas individuals with high activity levels had the lowest obesity rates (32.61%). Smoking and alcohol use were also important; current smokers had the highest prevalence of hypertension (67.91%), while current drinkers exhibited elevated hypertension (66.33%) but lower obesity (38.82%). The age gradient was found to be consistent and steep. The prevalence of hypertension increased from 35.14% among those aged 15–19 years to 93.42% among those aged ≥ 80 years, with diabetes following a similar trend. Educational and racial disparities were also notable: hypertension and obesity prevalence were highest among white respondents (79.92% and 67.14%, respectively), and individuals with low formal education had higher hypertension (80.68%) and diabetes (7.02%). Urban residence was associated with a slightly higher prevalence of diabetes (3.97%) than rural settings (3.33%), whereas employment and health-insurance status were positively associated with CVD risk. For example, those with insurance had higher rates of obesity (65.11%) and diabetes (5.73%). These descriptive results provide initial evidence of patterned inequality in cardiovascular-risk factors, particularly in terms of marital status, community context, and age. However, causal inferences and interaction effects, which are central to this study’s theoretical framework, were examined in subsequent multivariate and interaction models. [Insert Table 2 about here] Multivariate Results Table 3 presents the multilevel logistic regression estimates of the risks for hypertension, obesity, and diabetes as functions of individual- and community-level characteristics. Table 3 Multilevel Logistic Regression Models of Hypertension, Obesity, and Diabetes Risk by Gender, Marital Status, and Community-Level Socioeconomic Characteristics Variable Hypertension Obesity Diabetes Odds Ratio SE for Odds Ratio Odds Ratio SE for Odds Ratio Odds Ratio SE for Odds Ratio Sex Female (ref) Male 1.54*** 0.03 0.29*** 0.01 0.85** 0.05 Marital Status Never Married (ref) Married 1.17*** 0.03 1.76*** 0.04 1.19** 0.07 Widowed/Divorced 1.24*** 0.05 1.35*** 0.05 1.26** 0.09 Community-level Education Low (ref) Middle 0.91* 0.04 1.15* 0.06 1.31** 0.12 High 0.95 0.06 1.21** 0.08 1.33* 0.16 Community-level Poverty Low (ref) Middle 0.89* 0.05 0.85** 0.05 0.88 0.1 High 0.93 0.06 1 0.07 0.96 0.13 Community-level Employment Low (ref) Middle 1.07 0.06 0.95 0.06 1.01 0.11 High 1.15* 0.06 0.89 0.06 0.97 0.11 Lifestyle Factors Exercise Level Low (ref) Moderate 0.99 0.03 0.90*** 0.03 1.15 0.09 High 1.02 0.03 0.81*** 0.03 1.04 0.09 Smoking Status Current Smoker (ref) Never Smoker 0.9 0.05 1.98*** 0.11 1.67*** 0.18 Former Smoker 1.19*** 0.03 2.37*** 0.07 1.55*** 0.11 Alcohol Consumption Current Drinker (ref) Never Drinker 0.87*** 0.03 1.12** 0.04 1.23** 0.1 Former Drinker 0.89*** 0.02 1.14*** 0.03 1.27*** 0.08 Socio-Demographic Factors Age Category (10 years) 15–19 (ref) 20–29 1.95*** 0.05 2.45*** 0.08 2.97*** 0.73 30–39 3.29*** 0.11 4.40*** 0.16 8.39*** 1.99 40–49 5.54*** 0.21 5.48*** 0.22 27.71*** 6.4 50–59 10.60*** 0.47 7.18*** 0.31 62.00*** 14.25 60–69 17.23*** 1.02 7.00*** 0.34 77.37*** 17.91 70–79 23.17*** 1.91 5.78*** 0.34 79.94*** 18.85 80+ 25.53*** 3.22 3.72*** 0.3 68.21*** 17.06 Respondent's Education Low (ref) Moderate 0.84*** 0.02 1.38*** 0.04 1.05 0.06 High 0.83*** 0.03 1.65*** 0.06 0.97 0.08 Race Black African (ref) Asian 1.01 0.12 0.64*** 0.08 1.71** 0.36 Colored 1.65*** 0.08 1.01 0.05 1.19 0.12 White 1.28*** 0.1 1.14 0.09 0.69** 0.09 Employment Yes (ref) No 0.95* 0.02 0.80*** 0.02 1.25*** 0.07 Medical Insurance Coverage No (ref) Yes 1 0.04 1.34*** 0.05 1.44*** 0.11 Household Income Quintile Low (I) (ref) II 1.04 0.03 1.05 0.03 1.18* 0.09 III 0.95 0.03 1.15*** 0.03 1.19* 0.09 IV 0.92** 0.03 1.28*** 0.04 1.42*** 0.11 High (V) 0.89** 0.03 1.62*** 0.06 1.41*** 0.13 Residence Rural (ref) Urban 0.99 0.03 1.08* 0.03 1.23** 0.09 Random Effects (PSU) Variance (var(_cons)) 0.08 0.13 0.26 Log-likelihood -39,261.04 -37,378.71 -9,132.86 Sample Information Number of Observations 71,243 67,169 70,363 Note. _cons estimates the baseline odds (conditional on zero random effects). Statistical significance: ***p < .001, **p < .01, *p < .05. Source: DHS, NIDS, SANHANES 2003-2017 Hypertension . Hypertension risk was significantly higher among males (OR = 1.54, 95% CI: 1.48–1.61, p < 0.001), married individuals (OR = 1.17, 95% CI: 1.11–1.22, p < 0.001), and widowed/divorced respondents (OR = 1.24, 95% CI: 1.14–1.35, p < 0.001). Respondents in middle-income (OR = 0.92, 95% CI: 0.86–0.97, p < 0.01) and high-income households (OR = 0.89, 95% CI: 0.83–0.96, p < 0.01) had lower odds of hypertension. Community-level poverty was negatively associated with hypertension (middle vs. low: OR = 0.89, 95% CI: 0.80–0.99, p < 0.05), whereas high community employment was positively associated with hypertension (OR = 1.15, 95% CI: 1.03–1.28, p < 0.05). Racial disparities were observed, with colored (OR = 1.65, 95% CI: 1.50–1.82, p < 0.001) and white (OR = 1.28, 95% CI: 1.11–1.49, p < 0.001) respondents exhibiting elevated risk. Obesity . Males had significantly lower odds of obesity than females (OR = 0.29, 95% CI: 0.28–0.30, p < 0.001). Obesity risk was higher among married (OR = 1.76, 95% CI: 1.67–1.84, p < 0.001) and widowed/divorced individuals (OR = 1.35, 95% CI: 1.26–1.45, p < 0.001). Community education was positively associated with obesity (high vs. low: OR = 1.21, 95% CI: 1.06–1.39, p < 0.01), whereas moderate community poverty was inversely associated (OR = 0.85, 95% CI: 0.75–0.96, p < 0.01). Respondents in the higher-income quintiles had greater odds of obesity (OR = 1.62, 95% CI: 1.50–1.75, p < 0.001). Obesity risk was lower among unemployed individuals (OR = 0.80, 95% CI: 0.77–0.83, p < 0.001) and Asians (OR = 0.64, 95% CI: 0.50–0.82, p < 0.001). Urban residence was associated with a higher obesity risk (OR = 1.08, 95% CI: 1.01–1.14, p < 0.05). Diabetes. Men had lower odds of developing diabetes than women (OR = 0.85, 95% CI: 0.76–0.94, p < 0.01). The risk was elevated among married (OR = 1.19, 95% CI: 1.05–1.34, p < 0.01) and widowed/divorced respondents (OR = 1.26, 95% CI: 1.10–1.44, p < 0.01). Community education was positively associated with diabetes (high vs. low: OR = 1.33, 95% CI: 1.05–1.69, p < 0.05). Asians had higher odds (OR = 1.71, 95% CI: 1.13–2.59, p < 0.01) and Caucasians had lower odds (OR = 0.69, 95% CI: 0.54–0.90, p < 0.01) than Black Africans. The diabetes risk increased with income, with respondents in the top quintile displaying the highest OR at 1.41 (95% CI: 1.18–1.70, p < 0.001). Urban residence (OR = 1.23, 95% CI: 1.06–1.43, p < 0.01), unemployment (OR = 1.25, 95% CI: 1.13–1.39, p < 0.001), and medical insurance coverage (OR = 1.44, 95% CI: 1.23–1.68, p < 0.001) were also associated with increased diabetes risk. Table 3 illustrates that sex, marital status, household income, and community-level factors significantly shaped the risk of hypertension, obesity, and diabetes, with distinct patterns observed across the three conditions. [Insert Table 3 about here] Multilevel Interaction Effects on Composite CVD Risk This section presents the findings of a multilevel logistic regression model that estimates the odds of reporting CVD symptoms by incorporating both individual- and community-level variables (Table 4). For the analysis, five theoretically derived hypotheses concerning the intersection of sex, marital status, and contextual disadvantages in shaping CVD risk in South Africa were evaluated. The marriage-paradox hypothesis. This hypothesis predicts that marriage would not be uniformly protective, particularly among males. The interaction term Male#Married was statistically significant (OR = 2.21, p = 0.034), which indicates that married men had significantly higher odds of reporting CVD symptoms than unmarried men, controlling for other factors This finding supports the marriage paradox as it suggests that in resource-constrained, patriarchal settings, structural stressors tied to economic provision may outweigh the relational health benefits typically associated with marriage. Gendered-widowhood-vulnerability hypothesis . According to this hypothesis, widowed/divorced men are expected to face disproportionate CVD risk owing to social isolation and the lack of support. The interaction term Male #Widowed/Divorced was not statistically significant (OR = 1.09, p = 0.877), suggesting no clear evidence for greater CVD risk for widowed/divorced than for other groups after adjusting for covariates. However, in subgroup analyses by community education (see Hypothesis 3), male #Widowed/divorced #Middle education was significant (OR = 2.47, p = 0.016), indicating that the vulnerability of divorced/widowed men may be contingent on contextual characteristics, which partially supports this hypothesis. Community-level-educational-paradox hypothesis . This hypothesis posits that high community education may increase stress and health risks for men in low-opportunity settings. While high community education alone was not associated with increased CVD risks, the interaction term Male#High community education was positive and marginally nonsignificant (OR = 1.51, p = 0.119). Additionally, the three-way interaction of Male Widowed/Divorced Middle education displayed significantly elevated CVD odds (OR = 2.47, p = 0.016), suggesting that for some subgroups of men, higher educational environments may amplify stress, particularly in the absence of marital support. This finding provides partial empirical support for the hypothesis. Masked-disadvantage-for-married-women Hypothesis . This hypothesis suggests that marriage may conceal health risks for women in unequal households. The baseline odds for married women (reference group) were not significantly different from those who were never married or were separated/divorced (OR = 1.20, p = 0.249), but the lack of a protective effect relative to men and the elevated risk observed among married men highlight a gendered divergence in how marriage shapes health. Although not directly tested, these patterns suggest that married women do not benefit from the health-protective effects of marriage, which is consistent with this hypothesis. Moderation-by-community-disadvantage hypothesis . This hypothesis suggests that the adverse effects of being unmarried, widowed, or divorced are amplified in deprived communities. The three-way interaction terms combining marital status, sex, and community deprivation (education, poverty, or employment) were largely non-significant , except Male #Widowed/Divorced #Middle education (OR = 2.47, p = 0.016). While this supports the idea of contextual moderation , the pattern was not consistent across all the measures of deprivation (poverty and employment exhibited no significant interaction). Thus, this hypothesis received modest support, with educational disadvantage emerging as the most salient moderator. Covariate Findings Several covariates were significantly associated with the CVD risk. Older age was strongly associated with increased risks, with a clear dose-response pattern. Women had substantially lower ORs than men (OR = 0.28, p < 0.001). Never-smokers and those who quit smoking exhibited higher odds than current smokers, indicating complex smoking-related effects. Health-insurance coverage and urban residence were both associated with an elevated CVD risk. Higher household income was positively associated with CVD, whereas tertiary education was protective. Colored individuals had higher, and white individuals had lower odds of CVD than African Black individuals. Employment status, physical activity, and alcohol consumption displayed no significant associations, while a modest increase in risk was observed among never-drinkers. In summary, the multilevel results revealed that married men have a significantly higher CVD risk than unmarried men, supporting the Marriage Paradox in patriarchal, resource-poor contexts. Widowed/divorced men exhibited elevated risk only in communities with moderate education, indicating contextual effects. Higher levels of community education may increase stress-linked CVD risk in unmarried men. Married women do not benefit from marital protection, suggesting gendered health disparities. Community disadvantage moderates these effects mainly through education, highlighting the role of education in shaping CVD risk across sex and marital status. [Insert Table 4 about here] Table 4: Multilevel Logistic Regression Models of CVD Composite Risk: Interaction Between Gender, Marital Status, and Community Characteristics Variable CVD Composite Risk Independent Variables Odds Ratio (95% CI) Sex Female (ref) Male 0.25*** (0.21, 0.29) Marital Status Never Married (ref) Married 1.32*** (1.22, 1.43) Widowed/Divorced 1.17*** (1.06, 1.28) Community-level Education Low (ref) Middle 1.12 (0.99, 1.27) High 1.20** (1.03, 1.40) Community-level Poverty Low (ref) Middle 0.87* (0.76, 0.99) High 0.85 (0.71, 1.01) Community-level Employment Status Low (ref) Middle 0.99 (0.87, 1.14) High 0.97 (0.83, 1.12) Interaction Terms (Two-Way) Male × Married/live with partner 2.21*** (1.65, 2.36) Male × Widowed/divorced/separated 1.09 (1.38, 2.26) Male × Community Education (Middle) 0.59* (0.37, 0.94) Male × Community Education (High) 1.51 (0.90, 2.54) Married × Community Education (Middle) 1.04 (0.85, 1.26) Married × Community Education (High) 1.20 (0.93, 1.54) Widowed/Divorced × Community Education (Middle) 0.98 (0.79, 1.22) Widowed/Divorced × Community Education (High) 1.28 (0.97, 1.68) Interaction Terms (Three-Way) Male × Married × Community Education (Middle) 1.65 (0.98, 2.76) Male × Married × Community Education (High) 0.74 (0.42, 1.33) Male × Widowed/Divorced × Community Education (Middle) 2.47* (1.18, 5.14) Male × Widowed/Divorced × Community Education (High) 1.07 (0.47, 2.44) Male × Married × Community Poverty (Middle) 0.83 (0.50, 1.37) Male × Married × Community Poverty (High) 0.91 (0.47, 1.76) Male × Widowed/Divorced × Community Poverty (Middle) 0.76 (0.38, 1.50) Male × Widowed/Divorced × Community Poverty (High) 1.29 (0.52, 3.19) Lifestyle Factors Exercise Level Low (ref) Moderate 1.07 (0.96, 1.19) High 0.99 (0.89, 1.10) Smoking Status Current Smoker (ref) Never Smoker 1.78*** (1.54, 2.06) Former Smoker 1.72*** (1.56, 1.89) Alcohol Consumption Current Drinker (ref) Never Drinker 1.08 (0.97, 1.20) Former Drinker 1.08* (1.00, 1.17) Socio-Demographic Factors Age Category (10 years) 15-19 (ref) 20-29 5.74*** (4.07, 8.10) 30-39 17.14*** (12.24, 24.01) 40-49 41.24*** (29.50, 57.65) 50-59 80.78*** (57.77, 112.95) 60-69 96.16*** (68.58, 134.82) 70-79 93.80*** (66.50, 132.32) 80+ 55.71*** (38.56, 80.47) Respondent's Education Low (ref) Moderate 1.06 (0.99, 1.17) High 0.84** (0.76, 0.93) Race Black African (ref) Asian 0.96 (0.70, 1.32) Colored 1.15* (1.01, 1.31) White 0.76*** (0.64, 0.90) Employment Yes (ref) No 1.05 (0.99, 1.12) Medical Insurance Coverage No (ref) Yes 1.24*** (1.12, 1.38) Fixed Effects Intercept (_Cons) 0.00*** (0.00, 0.00) Random Effects (PSU) Variance (var(_cons)) 0.12 (0.09, 0.16) Log-likelihood -18,203.69 Sample Information Number of Observations 71,243 Number of Groups (PSU) 400 Source: DHS, NIDS, SANHANES 2003-2017 *Note: _cons estimates the baseline odds (conditional on zero random effects). Statistical significance: ***p < 0.001, **p < 0.01, p < 0.05. Predicted Probabilities Visualization To further illustrate the interaction effects, I computed the predicted probabilities of reporting CVD symptoms across the levels of sex, marital status, and community education. As presented in Fig. 2 , married men in communities with high educational attainment exhibited the highest predicted probability of CVD, supporting both hypotheses regarding marriage- and community-level educational paradox. By contrast, never-married individuals, particularly women, consistently exhibited the lowest predicted probabilities across all community-education levels. These patterns highlight the gendered and contextual nature of cardiovascular risk, visually reinforcing the statistical findings of the multilevel interaction models. [Insert Fig. 2 about here] DISCUSSION AND CONCLUSION This study contributes to theories on demographic and population health by demonstrating that marital status, often treated as a static individual attribute, interacts dynamically with sex and community context to shape CVD risk. The findings challenge the universality of the gendered-resource theory by illustrating that marriage can increase CVD risk for men in patriarchal and economically constrained settings and offer limited protection for women in constrained household power dynamics. By incorporating the social stress theory into intersectional feminist perspectives, I reveal how structural and relational inequalities can shape health vulnerability. In this study, I investigated how marital status, sex, and community-level disadvantages intersect in shaping CVD risk in South Africa. By leveraging a harmonized dataset from the ExPoSE Project, which integrates seven nationally representative surveys spanning nearly 15 years, I examined both the persistent and evolving patterns of cardiovascular risk across diverse social contexts. This harmonized, pooled approach is particularly innovative within the African context, where longitudinal or integrated datasets with consistent measures of relational and community characteristics are exceedingly rare. I initially employed multilevel models to estimate the effects of sex, marital status, and community-level socioeconomic conditions on hypertension, obesity, and diabetes—three key CVD risk factors. I then applied nested multilevel interaction models to capture nuanced, context-sensitive dynamics, revealing how sex-based marital relationships and place-based disadvantages jointly shape cardiometabolic vulnerabilities. This approach is a significant advancement in population-health research, as it offers methodological rigor and theoretical insights into the structural and relational determinants of NCD risk in resource-constrained settings. Altogether, the results critically extend the widely applied frameworks of gendered-resource theory and social stress theory by demonstrating that their core assumptions do not apply uniformly in contexts marked by structural disadvantages. First, contrary to the gendered resource theory, which posits that men typically derive health benefits from marriage owing to spousal support and resource pooling (Gorman et al., 2010; Springer, 2010 ), the findings demonstrate that married men in South Africa face significantly higher odds of composite CVD risk than their unmarried counterparts. This supports the Marriage Paradox Hypothesis and suggests that under the conditions of widespread unemployment and entrenched masculine provider norms, the stress of economic insecurity may negate the relational health advantages of marriage. In patriarchal settings such as those of South Africa, the pressure on men to fulfill culturally prescribed roles as financial providers, despite structural barriers to doing so, may increase psychosocial strain and cardiometabolic risk (Mogano et al., 2025 ). Second, while the main effect of the Male #Widowed/Divorced interaction did not reach statistical significance, the significant three-way interaction involving community education (Male #Widowed/Divorced #Middle education) offers critical insight into the conditional nature of vulnerability. Specifically, the elevated CVD risk among widowed and divorced men in middle-education communities (OR = 2.47, p = 0.016) suggests that male widowhood risk is not universal but shaped by broader social environments. This partially supports the Gendered Widowhood Vulnerability Hypothesis and underscores the importance of examining social roles and expectations in specific community contexts. This finding questions the generalized assumptions in gendered-resource theory, which emphasize the protective effects of marriage for men and the risks associated with its dissolution. While these frameworks predict widowhood as uniformly detrimental for men owing to the loss of spousal caregiving and emotional support, the results indicate that such risks are neither automatic nor evenly distributed. In communities with moderate educational attainment, where aspirations may be rising, economic security remains uncertain, and widowed or divorced men may face intensified role strain and diminished access to informal support networks. Drawing on the status-inconsistency theory (Hughes, 1971 ; Jackson, 1962 ) and insights from social-stress theory (Pearlin et al., 1981 ; Thoits, 1995), the study suggest that middle-aged educational communities may create environments for widowed or divorced men to experience unique psychosocial pressures, such as reduced status, increased stigma, and unmet expectations of masculinity, all of which are tied to provision and stability. Rather than rejecting the widowhood hypothesis, this result underscores the need to reframe it within an interactional and context-sensitive framework, in which the intersection of gender, marital status, and community-level factors influences vulnerability. Third, the hypothesis on the masked disadvantage for married women is supported by the study’s findings. Although married women did not exhibit significantly higher CVD risk than unmarried men, the lack of protective benefit for married men challenges the core tenet of gendered-resource theory, which assumes that marriage provides health advantages through emotional support, shared resources, and spousal caregiving (Ross et al., 1990 ; Umberson, 1992 ; Waite & Gallagher, 2000 ). However, in patriarchal and resource-constrained settings, these assumed benefits may be undermined by gendered power dynamics that limit women's ability to make health-related decisions and access care or prioritize their well-being (Sserwanja et al., 2018 ; Tuoyire & Ayetey, 2018). From the perspective of gendered-resource theory, the absence of benefits for married women suggests that relational status alone is an insufficient proxy for access to resources. Whether women can exercise agency in such relationships matters in this situation. In many South African households, economic dependency, caregiving burdens, and male-dominated decision-making structures constrain women's access to preventive health services and reduce their ability to engage in health-promoting behaviors (Gómez-Olivé et al., 2017 ). Furthermore, insights from intersectional feminist theory deepen this interpretation by emphasizing how structural and relational inequalities compound across gender, class, and place. In disadvantaged communities, even married women may lack social and institutional buffers such as transportation, childcare, or community-based clinics that facilitate access to healthcare. Thus, the association between marriage and CVD among women may be shaped less by marital status than by the intersection of household-level power asymmetries and community-level deprivation (Ataguba et al., 2011 ; Crenshaw, 1991 ). Rather than offering health protection, marriage may obscure vulnerabilities in this context. However, what appears neutral or beneficial at the surface level may be a structurally masked disadvantage. These findings demand a rethinking of marital protection frameworks to consider gendered access to power, autonomy, and care in marriage. Finally, the hypothesis on the moderation by community disadvantage was only partially supported by the study findings, with community education rather than poverty or employment emerging as the key contextual moderators of cardiovascular risk. This underscores the role of educational attainment at the neighborhood level in shaping health behaviors and expectations, which interact with marital and gender roles to influence cardiovascular outcomes (Cubbin et al., 2001a ). Consistent with neighborhood-health theories that emphasize social-ecological processes and collective efficacy, higher community education may reflect stronger social cohesion, health literacy, and normative support for preventive behaviors that mitigate stress-related CVD risks (Diez Roux, 2001; Sampson et al., 2002 ). These findings suggest that policy interventions should address not only economic deprivation but also educational and social resources at the community level to reduce cardiovascular disparities, especially among populations navigating complex social roles. Implications The findings have important implications for research on population-health policy and research based on the South African and broader SSA contexts, where the CVD burden is rising amid persistent structural inequalities. The nuanced interplay between marital status, gender, and community educational context reveals that interventions must go beyond individual behavioral changes to target the social and structural determinants of health. Community-level educational attainment has emerged as a critical lever that shapes health norms, social-support mechanisms, and access to health information. Policy efforts to improve educational resources and strengthen community cohesion may foster environments that support cardiovascular health, particularly among socially vulnerable groups, such as widowed men and married women facing gendered power imbalances. Moreover, the elevated cardiovascular risk among married men in low-resource settings underscores the need for gender-sensitive health-promotion and social-protection policies that address economic insecurity and challenge harmful masculine norms regarding provision and status. Programs incorporating psychosocial support and livelihood security may help mitigate the "marriage paradox" observed in patriarchal and economically constrained environments. Finally, this study highlights the importance of integrating multilevel and intersectional approaches into population-health research to capture the complex ways in which individual, relational, and contextual factors influence health disparities. Demographers and population scientists call for sustained investment in harmonized longitudinal data that incorporate social roles, gender relations, and community contexts to fully understand and address NCD risks in SSA. Limitations While this study advances the understanding of CVD risk in South Africa through a harmonized multilevel approach, several limitations merit consideration. The cross-sectional nature of the pooled surveys limits their ability to make causal inferences and assess changes in marital status, community conditions, or health outcomes over time. Future research could benefit from longitudinal data to better capture the dynamic social and health transitions that occur over time. Additionally, the measurement of community disadvantage using census-tract-level socioeconomic indicators may not fully capture the complexity of neighborhood social environments, including informal support networks, social cohesion, and exposure to violence or discrimination, which are the known determinants of health in SSA. The incorporation of qualitative or mixed methods can enrich our understanding of these contextual factors. Finally, while the harmonized dataset offers broad representativeness, the findings may not be generalizable to other countries in SSA with different social, economic, or health system conditions. Demographers should exercise caution when extrapolating their results and prioritize context-specific investigations by integrating local gender norms and social structures to ensure accuracy and relevance. Conclusion This study underscores the fact that marital status does not offer uniform health benefits in contexts characterized by structural disadvantages. In South Africa, marriage may increase cardiovascular risk for men who navigate provider stress and offer limited protection to women, constrained by gendered power dynamics. These findings demand a revisit of the dominant demographic frameworks that assume marriage as a health-protective factor. For population scientists and policymakers, addressing cardiovascular disparities in SSA requires attention to gendered social roles, community contexts, and broader institutional structures that shape health vulnerabilities. These insights underscore the need for demographers and population-health researchers to adopt multilevel intersectional frameworks that account for the interplay between individual roles, gendered expectations, and community-level disadvantages. The use of harmonized, nationally representative data across multiple countries also demonstrates the value of comparative, context-sensitive approaches in understanding the risk of non-communicable diseases in LMICs. Future research should investigate how relational and structural factors jointly influence health outcomes, particularly in settings characterized by rapid social and epidemiological transitions. Declarations Funding The author received no financial support or funding for the research, authorship, and/or publication of this article. Competing Interests The author declares no conflicts of interest relevant to the content of this manuscript. Ethics Approval This study is based on secondary data analysis of publicly available, de-identified survey datasets. Ethical approval for the original data collection was obtained by the respective implementing institutions. No additional ethical approval was required for this analysis. Consent to Participate Not applicable. This study used secondary data and involved no direct interaction with human participants. Consent to Publish Not applicable. Data Availability The datasets analyzed during the current study are publicly available from the respective institutional repositories. Details can be provided by the author upon reasonable request. Code Availability The analytic code used during the study is available from the author upon reasonable request. Authors’ Contributions The author solely conceptualized the study, conducted the analyses, interpreted the findings, and wrote the manuscript. References Acquah, I., Hagan, K., Javed, Z., Taha, M. B., Valero Elizondo, J., Nwana, N., … Nasir, K. (2023). Social determinants of cardiovascular risk, subclinical cardiovascular disease, and cardiovascular events. Journal of the American Heart Association, 12 (6), e025581. https://doi.org/10.1161/JAHA.122.025581 Adjaye-Gbewonyo, K., Kawachi, I., Subramanian, S. V., & Avendano, M. (2018). Income inequality and cardiovascular disease risk factors in a highly unequal country: A fixed effects analysis from South Africa. International Journal for Equity in Health, 17 , Article 31. https://doi.org/10.1186/s12939-018-0737-9 Adjaye-Gbewonyo, K., & Cois, A. (2024). ExPoSE project: Investigating epidemiological transitions in cardiovascular disease risk in South Africa and England . University of Greenwich & Stellenbosch University. https://www.exposeproject.net Ameh, S., Klipstein-Grobusch, K., Agyemang, C., Owolabi, M., & Peters, R. (2019). Social determinants of hypertension and type 2 diabetes in Nigeria and Ghana: A systematic review. International Journal of Public Health, 64 , 531–546. https://doi.org/10.1007/s00038-019-01256-7 American College of Obstetricians and Gynecologists. (2020). Hypertension in pregnancy (Practice Bulletin No. 222). Obstetrics & Gynecology, 135 (6), e237–e260. https://doi.org/10.1097/AOG.0000000000003891 Ataguba, J. E. O., Akazili, J., & McIntyre, D. (2011). Socioeconomic-related health inequality in South Africa: Evidence from general household surveys. International Journal for Equity in Health, 10 , Article 48. https://doi.org/10.1186/1475-9276-10-48 Ataklte, F., Erqou, S., Kaptoge, S., Taye, B., Echouffo-Tcheugui, J. B., & Kengne, A. P. (2015). Burden of undiagnosed hypertension in sub-Saharan Africa: A systematic review and meta-analysis. Hypertension, 65 (2), 291–298. https://doi.org/10.1161/HYPERTENSIONAHA.114.04394 Averett, S. L., Smith, J. M., & Munnell, A. (2013). Marriage and health: Does marriage affect body weight? Social Science Research, 42 (1), 87–105. https://doi.org/10.1016/j.ssresearch.2012.08.014 Bateman, C., & Bateman, J. (2015). Cardiovascular disease prevention in the developing world. The Lancet, 385 (9981), 1185–1186. https://doi.org/10.1016/S0140-6736(15)60611-0 Bateman, C., & Bateman, J. (2019). Cardiovascular disease prevention strategies in low-resource settings. The Lancet Global Health, 7 (6), e747–e748. https://doi.org/10.1016/S2214-109X(19)30186-1 Belue, R., Linnan, L., & Hovell, M. (2009). Social determinants of cardiovascular disease risk in sub-Saharan Africa: A literature review. Journal of Cardiovascular Nursing, 24 (6), 505–510. https://doi.org/10.1097/JCN.0b013e3181b4c8e2 BMC Cardiovascular Disorders. (2024). Are socioeconomic inequalities related to cardiovascular disease risk? A systematic review and meta-analysis of prospective studies. BMC Cardiovascular Disorders, 24 , Article 685. https://doi.org/10.1186/s12872-024-03721-1 Brown, A. F., Schulz, R., Samuels, S., & Merline, A. (2004). Race, neighborhood, and cardiovascular disease risk factors. American Journal of Public Health, 94 (12), 2160–2167. https://doi.org/10.2105/AJPH.94.12.2160 Chukwuonye, I. I., Ohagwu, K. A., Adelowo, O. O., Chuku, A., Obi, E. C., Onwuchekwa, U., … Oviasu, E. (2019). Prevalence and predictors of chronic kidney disease in a semi-urban community in Lagos. International Journal of Nephrology, 2019 , Article 1625837. https://doi.org/10.1155/2019/1625837 Crenshaw, K. W. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43 (6), 1241–1299. https://doi.org/10.2307/1229039 Cubbin, C., Hadden, W. C., & Winkleby, M. A. (2001)a. Neighborhood context and cardiovascular disease risk factors: The contribution of material deprivation. Ethnicity & Disease, 11 (4), 687–700. Cubbin, C., Winkleby, M. A., & Braveman, P. (2001)b. Socioeconomic status and risk of mortality: The influence of community context. American Journal of Public Health, 91 (3), 434–440. https://doi.org/10.2105/AJPH.91.3.434 D'Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., & Massaro, J. M. (2008). General cardiovascular risk profile for use in primary care. Circulation, 117 (6), 743–753. https://doi.org/10.1161/CIRCULATIONAHA.107.699579 Defo, B. K. (2014). Demographic, epidemiological, and health transitions: Are they relevant to population health patterns in Africa? Global Health Action, 7 (1), 22443. https://doi.org/10.3402/gha.v7.22443 Diez Roux, A. V., & Mair, C. (2010). Neighborhoods and health. Annals of the New York Academy of Sciences, 1186 (1), 125–145. https://doi.org/10.1111/j.1749-6632.2009.05333.x Diez Roux, A. V., Mujahid, M. S., Hirsch, J. A., Moore, K., & Moore, L. V. (2016). The impact of neighborhoods on cardiovascular risk: The MESA Neighborhood Study. Global Heart, 11 (3), 353–363. https://doi.org/10.1016/j.gheart.2016.08.002 Dovel, K., Yeatman, S., Watkins, S., & Poulin, M. (2020). Men's heightened risk of AIDS-related death: The legacy of gendered HIV testing and treatment strategies. AIDS, 34 (Suppl 2), S147–S152. https://doi.org/10.1097/QAD.0000000000002609 Dupre, M., & Beck, A. (2012). Widowhood and cardiovascular disease: A review of risk factors. Journal of Aging and Health, 24 (4), 585–594. https://doi.org/10.1177/0898264311431306 Elfassy, T., Swift, S. L., Glymour, M. M., Calonico, S., Jacobs, D. R., Jr., Mayeda, E. R., Kershaw, K. N., Kiefe, C., & Zeki Al Hazzouri, A. (2019). Associations of income volatility with incident cardiovascular disease and all-cause mortality in a U.S. cohort. Circulation, 139 (7), 850–859. https://doi.org/10.1161/CIRCULATIONAHA.118.035521 Gabler, E., & Lahiri, K. (2009). Statistical methods for the analysis of cardiovascular disease data. Journal of Clinical Epidemiology, 62 (2), 123–130. https://doi.org/10.1016/j.jclinepi.2008.03.008 Gaziano, T. A., & Pagidipati, N. (2013). Scaling up chronic disease prevention interventions in lower- and middle-income countries. Annual Review of Public Health, 34 , 317–335. https://doi.org/10.1146/annurev-publhealth-031912-114402 Global Burden of Disease Collaborative Network. (2024). Global Burden of Disease Study 2021 (GBD 2021) . Institute for Health Metrics and Evaluation (IHME). Gómez-Olivé, F. X., Clark, B. D., Houle, B. C., Thorogood, M., Kabudula, C. W., Clark, S. J., & Tollman, S. M. (2018). Social conditions and health outcomes among older adults in rural South Africa. Global Health Action, 11 (1), 1506565. https://doi.org/10.1080/16549716.2018.1506565 Gómez-Olivé, F. X., Thorogood, M., Bocquier, P., Clark, B. D., Kabudula, C. W., Klipstein-Grobusch, K., … Williams, J. (2017). Cohort profile: Health and Aging in Africa: A longitudinal study of an INDEPTH community in South Africa (HAALSI). International Journal of Epidemiology, 46 (4), 994–1001. https://doi.org/10.1093/ije/dyw225 Gorman, B. K., & Read, J. G. (2006). Gender disparities in adult health: An examination of three measures of morbidity. Journal of Health and Social Behavior, 47 (2), 95–110. https://doi.org/10.1177/002214650604700201 Gouda, H. N., Charlson, F., Sorsdahl, K., Ahmadzada, S., Ferrari, A. J., Erskine, H. E., … Whiteford, H. (2019). Burden of non-communicable diseases in sub-Saharan Africa, 1990–2017: Results from the Global Burden of Disease Study 2017. The Lancet Global Health, 7 (10), e1375–e1387. https://doi.org/10.1016/S2214-109X(19)30374-2 Gorman, E. H., & Kmec, J. A. (2010). Gender and discretionary work effort: Evidence from the United States and Britain. Work and Occupations, 37 (3), 271–293. https://doi.org/10.1177/0730888409352064 Hamad, R., Penko, J., Kazi, D. S., Coxson, P., Guzman, D., Wei, P. C., Mason, A., Wang, E. A., Goldman, L., & Fiscella, K. (2020). Development and validation of the Health Equity Metric: A factor analysis study of health equity using NHANES data. Annals of Internal Medicine, 172 (10), 716–725. https://doi.org/10.7326/M19-1700 Hofman, K. J., Primack, A., Keusch, G. T., & Hrynkow, S. H. (2014). The global impact of non-communicable diseases on healthcare spending and national income: A systematic review. European Journal of Epidemiology, 30 (4), 251–277. https://doi.org/10.1007/s10654-014-9984-2 Holt-Lunstad, J., Smith, T., & Layton, J. (2010). Social relationships and mortality risk: A meta-analytic review. PLoS Medicine, 7 (7), e1000316. https://doi.org/10.1371/journal.pmed.1000316 Hughes, E. C. (1971). The sociological eye: Selected papers . Aldine Publishing Company. Jackson, E. F. (1962). Status consistency and symptoms of stress. American Sociological Review, 27 (4), 469–480. Jilani, M. H., Javed, Z., Yahya, T., Valero Elizondo, J., Khan, S. U., Kash, B., … Nasir, K. (2021). Social determinants of health and cardiovascular disease: Current state and future directions towards healthcare equity. Current Atherosclerosis Reports, 23 (9), Article 55. https://doi.org/10.1007/s11883-021-00949-w Kabajulizi, J., & Darko, F. A. (2021). Do non-communicable diseases influence sustainable development in Sub-Saharan Africa? A panel autoregressive distributed lag model approach. Health Policy and Planning, 37 (3), 337–348. https://doi.org/10.1093/heapol/czab149 Keates, A. K., Tolefac, P. N., & Berridge, C. (2017). The influence of the socioeconomic environment on cardiovascular health in sub-Saharan Africa. Global Health Action, 10 , 1335670. https://doi.org/10.1080/16549716.2017.1335670 Khaing, W., Vallibhakara, S. A., Attia, J., McEvoy, M., & Thakkinstian, A. (2017). Effects of education and income on cardiovascular outcomes: A systematic review and meta-analysis. European Journal of Preventive Cardiology, 24 (10), 1032–1042. https://doi.org/10.1177/2047487317704185 Konkor, I., Waqar, M., & Kuuire, V. (2025). Determinants of chronic non-communicable disease screening among adults in Ghana. Health Promotion International, 40 (3), daaf067. https://doi.org/10.1093/heapro/daaf067 Keates, A. K., Tolefac, P. N., & Berridge, C. (2017). The influence of the socioeconomic environment on cardiovascular health in sub-Saharan Africa. Global Health Action, 10 , 1335670. https://doi.org/10.1080/16549716.2017.1335670 Khaing, W., Vallibhakara, S. A., Attia, J., McEvoy, M., & Thakkinstian, A. (2017). Effects of education and income on cardiovascular outcomes: A systematic review and meta-analysis. European Journal of Preventive Cardiology, 24 (10), 1032–1042. https://doi.org/10.1177/2047487317704185 Kowall, B., Girschik, C., & Stolpe, S. (2024). Inconsistencies in self-reported diabetes in a large panel study: The Survey of Health, Ageing and Retirement in Europe (SHARE). BMC Medical Research Methodology, 24 (1), Article 7. https://doi.org/10.1186/s12874-023-02137-7 Kubota, Y., Heiss, G., MacLehose, R. F., Roetker, N. S., & Folsom, A. R. (2017). Association of educational attainment with lifetime risk of cardiovascular disease: The Atherosclerosis Risk in Communities Study. JAMA Internal Medicine, 177 (8), 1165–1172. https://doi.org/10.1001/jamainternmed.2017.1877 Link, B. G., & Phelan, J. (1995). Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. Journal of Health and Social Behavior , (Extra Issue), 80–94. https://doi.org/10.2307/2626958 Liu, H., & Umberson, D. (2008). The influence of marital status on health and CVD risk in the United States. Health Affairs, 27 (6), 1634–1641. https://doi.org/10.1377/hlthaff.27.6.1634 Maina, L. W., Kimani, E., Azevedo, A., Parkes, A., & Ndinda, L. (2019). Gendered patterns of unpaid care and domestic work in the urban informal settlements of Nairobi, Kenya: Findings from a household care survey. Oxfam Research Reports . https://policy-practice.oxfam.org/resources/gendered-patterns-of-unpaid-care-and-domestic-work-in-the-urban-informal-settlem-620899/ Malik, A., Nyirenda, M., & Soderstrom, M. (2023). Cardiovascular risk and gender in Sub-Saharan Africa. BMC Cardiovascular Disorders, 23 (1), 35. https://doi.org/10.1186/s12872-023-02653-5 Manfredini, R., Gallerani, M., & Cappadona, G. (2017). Marital status and cardiovascular disease risk: A systematic review. Journal of Clinical Medicine, 6 (9), 105. https://doi.org/10.3390/jcm6090105 Marmot, M., Stansfeld, S., & Patel, C. (2012). Social determinants of health and cardiovascular disease risk: A review. The Lancet, 379 (9820), 1720–1731. https://doi.org/10.1016/S0140-6736(11)61738-X Mayosi, B. M., Flisher, A. J., Luntamo, M., & Yach, D. (2009). Cardiovascular diseases in South Africa. The Lancet, 374 (9691), 933–944. https://doi.org/10.1016/S0140-6736(09)61344-3 Mayosi, B. M., & Benatar, S. R. (2014). Health and health care in South Africa—20 years after Mandela. New England Journal of Medicine, 371 (14), 1344–1353. https://doi.org/10.1056/NEJMsr1405012 Mensah, G. A., & Roth, G. A. (2015). Mortality from cardiovascular diseases in sub-Saharan Africa, 1990–2013: A systematic analysis of data from the Global Burden of Disease Study 2013. Cardiovascular Journal of Africa, 26 (2 Suppl 1), S6–S10. https://doi.org/10.5830/CVJA-2015-038 Mensah, G. A., Wei, G. S., Sorlie, P. D., Fine, L. J., Rosenberg, Y. J., Kaufmann, P. G., … the National Heart, Lung, and Blood Institute. (2015). Decline in cardiovascular mortality: Possible causes and implications. Circulation, 132 (11), 1015–1029. https://doi.org/10.1161/CIRCULATIONAHA.115.016596 Ministry of Health. (2015). Kenya national strategy for the prevention and control of non-communicable diseases 2015–2020 . Government of Kenya. https://ncdalliance.org/sites/default/files/resource_files/NCD%20Strategic%20Plan%20Kenya.pdf Mogano, N. T. H., Letsoalo, D. L., & Oduaran, C. A. (2025). Effects of masculine culture on the mental health of Northern Sotho male youth. BMC Psychology, 13 (1), Article 605. https://doi.org/10.1186/s40359-025-02934-3 Molloy, G. J., Stamatakis, E., Randall, G., & Hamer, M. (2009). Marital status, gender, and cardiovascular risk factors in older adults. European Journal of Cardiovascular Prevention & Rehabilitation, 16 (4), 545–550. https://doi.org/10.1097/HJR.0b013e32832c4b7e Mooney, G., & McIntyre, D. (2008). A double burden of disease in sub-Saharan Africa. Health Policy and Planning, 23 (2), 56–63. https://doi.org/10.1093/heapol/czn001 Moran, A. E., Forouzanfar, M. H., Roth, G. A., Mensah, G. A., Ezzati, M., Murray, C. J. L., & Naghavi, M. (2014). Temporal trends in ischemic heart disease mortality in 21 world regions, 1980–2010: The Global Burden of Disease 2010 Study. Circulation, 129 (14), 1483–1492. https://doi.org/10.1161/CIRCULATIONAHA.113.004042 Mujahid, M. S., Diez Roux, A. V., Cooper, R. S., Shea, S., & Williams, D. R. (2008). Neighborhood characteristics and hypertension. Epidemiology, 19 (4), 590–598. https://doi.org/10.1097/EDE.0b013e3181745d67 Naidoo, S. (2012). The South African national health insurance: A revolution in health-care delivery! Journal of Public Health, 34 (1), 149–150. https://doi.org/10.1093/pubmed/fdr089 Omran, A. R. (1971). The epidemiologic transition: A theory of the epidemiology of population change. Milbank Memorial Fund Quarterly, 49 (4), 509–538. https://doi.org/10.2307/3349375 Omran, A. R. (1998). The epidemiologic transition theory revisited thirty years later. World Health Statistics Quarterly, 51 (2–4), 99–119. Otto, C. M. (2018). Marital status and cardiovascular disease risk. Heart, 104 (23), 1893–1894. https://doi.org/10.1136/heartjnl-2018-314336 Oyebode, O., Hill, A., & Gidado, O. (2015). Urbanization and the burden of non-communicable diseases in Nigeria: A systematic review. Journal of the Nigeria Medical Association, 56 (1), 44–49. Pearlin, L. I., Lieberman, M. A., Monaghan, E. G., & Mullan, J. T. (1981). The stress process. Journal of Health and Social Behavior, 22 (4), 337–356. https://doi.org/10.2307/2136676 Pillay-van Wyk, V., Msemburi, W., & Laubscher, R. (2016). The quadruple burden of disease in South Africa. The Lancet, 387 (10015), 2633–2634. Posel, D., & Rogan, M. (2019). The health consequences of marital dissolution in South Africa. Social Science & Medicine, 230 , 174–183. Rabe‐Hesketh, S., & Skrondal, A. (2006). Multilevel modeling of complex survey data. Journal of the Royal Statistical Society: Series A (Statistics in Society, 169 (4), 805–827. Rabiaza, A., Puddu, P. E., Menotti, A., Humbert, X., & RIFLE Research Group. (2024). Marital status and long-term cardiovascular risk in the general population: The Gubbio Population Study. Irish Journal of Medical Science, 193 (5), 2249–2257. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage Publications. Reddy, K. S., Prabhakaran, D., & Chaturvedi, V. (2005). Methods for establishing a surveillance system for cardiovascular diseases in Indian industrial populations. Bulletin of the World Health Organization, 84 (6), 461–469. https://doi.org/10.2471/BLT.05.034031 Regitz-Zagrosek, V., Oertelt-Prigione, S., Prescott, E., Franconi, F., Gerdts, E., Foryst-Ludwig, A., Maas, A. H. E. M., Kautzky-Willer, A., Knappe-Wegner, D., Kintscher, U., Ladwig, K. H., Schenck-Gustafsson, K., & Stangl, V. (2016). Gender in cardiovascular diseases: Impact on clinical manifestations, management, and outcomes. European Heart Journal, 37 (1), 24–34. Robards, J., Evandrou, M., Falkingham, J., & Vlachantoni, A. (2012). Marital status, health, and mortality. Maturitas, 73 (4), 295–299. Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing “neighborhood effects”: Social processes and new directions in research. Annual Review of Sociology, 28 , 443–478. Sartorius, B., & Van der Merwe, A. (2015). Urbanization and non-communicable diseases: A systematic review in sub-Saharan Africa. African Journal of Epidemiology, 32 (3), 21–28. Schatz, E., & Madhavan, S. (2015). Gender, social support, and cardiovascular health in sub-Saharan Africa: A review of the evidence. African Journal of Social Science, 5 (3), 25–35. Shen, Y., Son, H., Zhang, D., Jaysing, A., Zhang, J., Chen, Z., Mu, L., Liu, J., Rajbhandari Thapa, J., Li, Y., & Pagán, J. A. (2023). Social determinants of cardiovascular health: A longitudinal analysis of cardiovascular disease mortality in U.S. counties from 2009 to 2018. Journal of the American Heart Association, 12 (2), e026940. https://doi.org/10.1161/JAHA.122.026940 Sileo, K. M., Fielding-Miller, R., Dworkin, S. L., Fleming, P. J., & Glass, N. (2018). Mechanisms linking masculinity to sexual risk among young Black men who have sex with men. AIDS and Behavior, 22 (3), 844–855. https://doi.org/10.1007/s10461-017-1932-5 Siu, G. E., Seeley, J., & Wight, D. (2014). The role of support groups for people living with HIV in high-HIV prevalence, low-income settings: A systematic review of the evidence. Journal of the International AIDS Society, 17 , 18713. Skovdal, M., Campbell, C., Madanhire, C., Mupambireyi, Z., Nyamukapa, C., & Gregson, S. (2011). Challenges faced by elderly guardians in sustaining adherence to antiretroviral therapy in HIV-infected children in Zimbabwe. AIDS Care, 23 (7), 803–808. Sliwa, K., Wilkinson, D., & Hansen, C. (2021). Promotion of cardiovascular health in Africa. Global Heart, 16 (1), 50. Springer, K. W. (2010). Social stressors and the gendered distribution of psychological distress. Journal of Health and Social Behavior, 51 (1), 24–38. Sserwanja, Q. R., Kasozi, K., & Namusisi, E. (2018). Masculinity norms and health behaviors in Uganda: Insights from the context of hypertension. Health Psychology, 37 (6), 521–527. Sserwanja, Q., Mukunya, D., Kassim, P., & Muhumuza, K. (2018). Social determinants of hypertension among reproductive-aged women in rural Tanzania: A community-based cross-sectional study. Journal of Public Health in Africa, 9 (2), 825. https://doi.org/10.4081/jphia.2018.825 StataCorp. (2013). Stata: Release 13. Statistical software . StataCorp LP. Sundquist, K., & Winkleby, M. A. (1999). Country of birth, acculturation status, and abdominal obesity in a national sample of Mexican-American women and men. International Journal of Epidemiology, 28 (2), 326–333. Tanser, F., Gijsbertsen, B., & Herbst, K. (2006). Modeling and understanding primary health care accessibility and utilization in rural South Africa: An exploration using a geographic information system. Social Science & Medicine, 63 (3), 691–705. Thoits, P. A. (2010). Stress and health: Major findings and policy implications. Journal of Health and Social Behavior, 51 (1_suppl), S41–S53. https://doi.org/10.1177/0022146510383499 Tuoyire, D. A., & Ayetey, H. (2018). Marital status and health outcomes: A study of hypertension risk in Ghana. International Journal of Public Health, 12 (2), 101–109. Umberson, D. (1992). Gender, marital status, and the social control of health behavior. Social Science & Medicine, 34 (8), 907–917. Umberson, D., Williams, K., Powers, D. A., Liu, H., & Needham, B. (2009). Stress, health behaviors, and mortality risk in a national sample: Marital status and health behaviors. Journal of Health and Social Behavior, 50 (3), 267–282. https://doi.org/10.1177/002214650905000301 Waite, L. J., & Gallagher, M. (2000). The case for marriage: Why married people are happier, healthier, and better off financially. Doubleday. Wang, M., Huang, M., Zhao, R., & Li, S. (2014). Self-management behavior in patients with type 2 diabetes: A cross-sectional survey in western urban China. PLoS One, 9 (4), e95138. https://doi.org/10.1371/journal.pone.0095138 Wong, C. W., Kwok, C. S., Narain, A., Gulati, M., Mihalidou, A., Wu, P., … Mamas, M. (2018). Marital status and risk of cardiovascular diseases: A systematic review and meta-analysis. Heart, 104 (1), 1937–1948. World Health Organization. (2003). Prevention of cardiovascular disease: Guidelines for assessment and management of cardiovascular risk . World Health Organization. Yuyun, M. F., Sliwa, K., Kengne, A. P., Mocumbi, A. O., & Bukhman, G. (2020). Cardiovascular diseases in sub-Saharan Africa compared to high-income countries: An epidemiological perspective. Global Heart, 15 (1), Article 15. https://doi.org/10.5334/gh.683 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-7045465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492079140,"identity":"972a7142-df93-448a-92e2-038ce213986a","order_by":0,"name":"Winfred Avogo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYPACGx4+CIOZaC1pPGwQ1cRrOcxAvBaD42cMH36pOC/Dxn/+mARDhXViA0EtZ3KMjWXO3OZhk0hmk2A4k05Yi9mBtDRpyTaQFmY2Cca2w0RoOf8MpOUcDxv/YaCWf8RouZF8TPJj2wFgiAEdxthAhBb7G48PGzOcSQb5xdgi4Vi6MUEtkv2JjQ9/VNjZ8/MffHjjQ421LEEtIMDMA2MlEKMcBBh/EKtyFIyCUTAKRiYAAGRGNvZqyy2KAAAAAElFTkSuQmCC","orcid":"","institution":"Illinois State University","correspondingAuthor":true,"prefix":"","firstName":"Winfred","middleName":"","lastName":"Avogo","suffix":""}],"badges":[],"createdAt":"2025-07-04 09:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7045465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7045465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91450052,"identity":"6c5fb63e-d1e1-49af-b531-f02a9008af64","added_by":"auto","created_at":"2025-09-16 15:20:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePredicted probabilities of cardiovascular disease (CVD) by sex, marital status,\u003c/em\u003e \u003cem\u003eand community-level education in South Africa.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7045465/v1/013fb12c327a031fcec82260.png"},{"id":91451254,"identity":"fa4a5c69-10bf-43a0-9b10-0d8931e97746","added_by":"auto","created_at":"2025-09-16 15:28:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2276093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7045465/v1/ac8c0977-b3ea-42b5-afe3-c3509ba99b1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender, Marital Status, and Community Determinants of Cardiovascular Risk in South Africa: An Epidemiological and Sociological Analysis","fulltext":[{"header":"Introduction and Background","content":"\u003cp\u003eNoncommunicable diseases (NCDs) account for 74% of global mortality, marking a profound shift in global health patterns (Global Burden of Disease [GBD], 2024). Despite decline of death from communicable, maternal, neonatal, and nutritional diseases, cardiovascular diseases (CVDs), particularly ischemic heart disease, have emerged as the leading causes of death worldwide. These trends are driven by risk factors, such as hypertension, tobacco use, elevated blood sugar, and air pollution.\u003c/p\u003e\n\u003cp\u003eThis global shift reflects a broader epidemiological transition; as societies develop, infectious diseases decline and chronic lifestyle-related illnesses increase (Omran, 1971, 1998). However, this transition is complex and consequential in Sub-Saharan Africa (SSA). CVDs are increasingly affecting younger adults and socioeconomically disadvantaged populations. Between 1990 and 2019, the percentage of death from NCDs in SSA increased from 18.6% to 30%, with CVDs accounting for 38% of these deaths (Gouda et al., 2019; Keates et al., 2017). Unlike in high-income countries (HICs), where older men face the consequence of CVD, the burden in SSA is more sex-diverse and affects adults in their 40s and 50s, including women and low-income groups (Yuyun et al., 2020).\u003c/p\u003e\n\u003cp\u003eThis rising burden is fueled by rapid, unplanned urbanization, changing dietary habits, reduced physical activity, and entrenched structural inequalities. These factors have contributed to the increasing rates of hypertension, obesity, and diabetes, which are major risk factors of CVD in SSA (Belue et al., 2009).\u003c/p\u003e\n\u003cp\u003eWhile these determinants are well documented, there is a critical gap in understanding how sex moderates the relationship between marital status and cardiovascular risk, particularly within the context of community-level social conditions. Many existing studies assume uniform health benefits of marriage, overlooking complex ways in which sex, structural inequality, and community characteristics interact to shape cardiovascular health in low- and middle-income countries (LMICs).\u003c/p\u003e\n\u003cp\u003eSouth Africa provides a compelling case study. The country faces a \u0026ldquo;quadruple burden\u0026rdquo;: HIV/AIDS, tuberculosis, rising NCDs, and the high rates of violence and injury (Pillay-van Wyk et al., 2016). These health challenges are deeply intertwined with socioeconomic inequalities rooted in colonialism and apartheid (Mayosi et al., 2009; Mooney \u0026amp; McIntyre, 2008). NCDs, particularly CVD, diabetes, and hypertension, represent a growing public-health and economic crisis that contributes to premature morbidity and mortality, reduces labor productivity, and strains healthcare systems (Mayosi et al., 2009; Pillay-van Wyk et al., 2016). The economic burden of NCDs disproportionately affects poor households, which often face out-of-pocket costs, lost wages, and long-term care expenses (Hofman et al., 2014; Kabajulizi \u0026amp; Darko, 2021).\u003c/p\u003e\n\u003cp\u003eThese economic pressures may shape how marital status influences access to care, emotional support, and exposure to health risks, which may differ according to sex. Such patterns underscore the need to move beyond individual-level analyses to examine how gendered social roles and community contexts jointly shape cardiovascular risk. This study addresses three critical gaps in the literature: (1) it investigates how sex and marital status intersect to influence cardiovascular risk, (2) it explores how community-level socioeconomic conditions shape these associations, and (3) it critically examines the applicability of dominant theories developed in high-income contexts, such as the gendered resource theory and the protective marriage hypothesis, to a low-resource, high-inequality setting. Positioned at the intersection of biosocial theory, gender analysis, and place-based health inequality, this study draws on theories on gendered resources and social stress to argue that the health effects of marriage are socially contingent, vary by gender, and are shaped by community-level poverty, employment, and education.\u003c/p\u003e\n\u003cp\u003eTo address this gap, this study integrates sociological and epidemiological perspectives to investigate how sex, marital status, and community-level socioeconomic conditions shape cardiovascular risk in South Africa\u0026mdash;thereby reframing CVD vulnerability as a gendered, place-based social phenomenon rather than solely an individual health outcome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD Transitions and Demographic Shifts in SSA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding the demographic and epidemiological transitions in SSA is essential to address the shifting disease burden in the region. While communicable diseases, such as malaria, HIV/AIDS, and tuberculosis, remain prevalent, NCDs, particularly CVDs, are rising sharply (Defo, 2014; Omran, 1971; Yuyun et al., 2020).\u003c/p\u003e\n\u003cp\u003eSouth Africa exemplifies this dual burden, though the emergence of this trend is comparable to other parts of the region. For instance, in Ghana, NCDs account for nearly half of all deaths, with hypertension affecting over 50% of urban residents (Konkor, Waqar, \u0026amp; Kuuire, 2025). In Nigeria, nearly 30% of adults suffer from hypertension, and obesity is increasing, especially among urban women (Chukwuonye et al., 2019). Kenya has adopted national strategies targeting both the behavioral and structural drivers of CVDs (Ministry of Health, 2015). These trends reflect the convergence of infectious and chronic disease burdens, necessitating integrated and context-sensitive public-health responses.\u003c/p\u003e\n\u003cp\u003eThe incidence of CVD in SSA differs considerably from that in HICs. It disproportionately affects younger adults during peak economic productivity, with nearly half of all CVD deaths occurring before the age of 70 years owing to late diagnosis, limited access to care, and systemic health inequities (Mensah \u0026amp; Roth, 2015; Moran et al., 2014). These trends threaten the demographic dividends of the region and impose substantial socio-economic costs.\u003c/p\u003e\n\u003cp\u003eDespite the rising rates of hypertension and obesity, few studies in SSA have examined how sex and marital status jointly shape CVD risk in specific contexts. This gap limits our understanding of how gendered social roles and household structures influence cardiovascular vulnerability in transitional societies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural and Social Determinants of CVD Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial determinants, including poverty, employment, and education, are the well-established predictors of cardiovascular risk (Acquah et al., 2023; Jilani et al., 2021). Individuals in low-income households face barriers to healthcare, nutritious food, and safe environments for physical activity (Shen et al., 2023). Unemployment and informal work contribute to chronic stress and financial insecurity, compounding CVD risks (Elfassy et al., 2019).\u003c/p\u003e\n\u003cp\u003eEducation is crucial in this regard. Numerous studies have demonstrated an inverse relationship between educational attainment and the incidence of cardiovascular disease (CVD) (Kubota et al., 2017; Khaing et al., 2017). Education enhances health literacy, improves individuals\u0026rsquo; understanding of risks, and increases their access to preventive services. It also ensures their long-term financial stability and employment opportunities, indirectly reducing their vulnerability to chronic diseases.\u003c/p\u003e\n\u003cp\u003eIn South Africa, apartheid created structural inequalities that remain central to the disparities in CVD. Spatial and economic segregation limit individuals\u0026rsquo; access to essential health-promoting resources, including quality education, employment opportunities, healthy food environments, and safe infrastructure for physical activity (Ataguba et al., 2011; Mayosi \u0026amp; Benatar, 2014). These conditions disproportionately affect Black South Africans and low-income populations, leading to an increased exposure to behavioral risk factors and chronic psychosocial stress (Adjaye-Gbewonyo et al., 2018).\u003c/p\u003e\n\u003cp\u003eThe dual health system has compounded inequality. A well-funded private sector, accessible primarily to affluent White South Africans and the segments of the black and colored middle class, coexists with a severely under-resourced public sector that serves the majority (Naidoo, 2012; Tanser et al., 2006). This divide exacerbates disparities between preventive and curative services, contributing to delayed diagnosis, suboptimal treatment, and an elevated CVD risk (Ataguba et al., 2011).\u003c/p\u003e\n\u003cp\u003eThese conditions necessitate moving beyond individual-level explanations of CVD toward a broader focus on structural determinants, such as community-level poverty, income inequality, disparities in education and employment, and institutional disinvestment, as the root causes of inequities related to cardiovascular health (Link \u0026amp; Phelan, 1995; Mayosi \u0026amp; Benatar, 2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarriage, Gender, and Biosocial Pathways to Cardiovascular Health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiosocial and epidemiological studies have identified marital status as a key social determinant of cardiovascular health. Meta-analyses and longitudinal studies have consistently shown that unmarried individuals, including never-married, divorced, and widowed individuals, have a greater risk of CVD, coronary heart disease, stroke, and mortality than married individuals (Wong et al., 2018; Otto, 2018). Manfredini et al. (2017) reported the increased odds of CVD (OR = 1.42), CHD mortality (OR = 1.43), and stroke mortality (OR = 1.55) among unmarried individuals.\u003c/p\u003e\n\u003cp\u003eHowever, this association was neither uniform nor unidirectional. Marital status is associated with divergent health profiles that vary according to the life stage, sex, and culture. The higher rates of obesity, hypertension, and type 2 diabetes are often reported in married older individuals, possibly because of their long-standing dietary routines and reduced physical activities (Averett et al., 2013). Conversely, single men, particularly younger ones, are more likely to smoke, drink excessively, eat poorly, and become obese (Molloy et al., 2009; Robards et al., 2012). High levels of stress and risky behaviors that increase their CVD risk are frequently reported in separated or divorced individuals (Umberson et al., 2009). Some studies have found that married men have worse lipid profiles (Liu \u0026amp; Umberson, 2008), indicating the complexity of the marriage-health link.\u003c/p\u003e\n\u003cp\u003eSex was a critical moderating factor. While early literature emphasized male advantage, newer evidence suggests that the health benefits of marriage may be greater for women in specific settings. For example, Rabiaza et al. (2024) found that married women in Italy had significantly lower CHD and all-cause mortality rates, whereas men showed no significant benefits. Similar patterns have been observed across studies, which highlights poor cardiovascular outcomes among men (Manfredini et al., 2017).\u003c/p\u003e\n\u003cp\u003eResearch from SSA underscores the need for context-specific analyses. In Ghana\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTuoyire \u0026amp; Ayetey, 2018 found that married, cohabiting, and previously married women had higher odds of hypertension than never-married women, even after adjusting for confounders, whereas marriage was protective for men. In Nigeria and Tanzania, access to care for married women is often limited by unequal decision-making power and gender constraints (Sserwanja et al., 2018).\u003c/p\u003e\n\u003cp\u003eGendered cultural expectations significantly shape men\u0026apos;s health behaviors across sub-Saharan Africa. In Ghana, Nigeria, Uganda, South Africa, and Lesotho, masculinity norms emphasize stoicism, self-reliance, and concern for social reputation, which deters men from seeking preventive and routine care (G\u0026oacute;mez-Oliv\u0026eacute; et al., 2017, 2018; Siu et al., 2014; Skovdal et al., 2011). Clinics are often perceived as feminine spaces that reinforce avoidance and contribute to men\u0026rsquo;s delayed health-seeking (Dovel et al., 2020; Sileo et al., 2018). These behaviors mirror trends in CVD risk, as men are less likely to engage in screening or early treatment, underscoring how structural gender barriers exacerbate CVD vulnerability across the region.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings confirm that marital status is a gendered and context-contingent determinant of cardiovascular health. Research must adopt intersectional biosocial models that consider interactions among gender norms, structural conditions, and access to care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeighborhood Context and Community-Level Moderators of CVD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn extensive body of research links residences in socioeconomically disadvantaged neighborhoods with an increased prevalence of CVD-risk factors, including physical inactivity, obesity, smoking, hypertension, hypercholesterolemia, and type 2 diabetes (Diez Roux \u0026amp; Mair, 2010; Mujahid et al., 2008; Brown et al., 2004). Studies found that these relationships persist in individuals even after adjusting for individual socioeconomic status, underscoring the distinct impact of contextual environment (Sundquist et al., 1999).\u003c/p\u003e\n\u003cp\u003eKey neighborhood-level disadvantages include low income, low education, and high welfare dependency, each of which is consistently associated with elevated CVD incidence (Diez Roux, 2016). These structural barriers limit access to health-promoting infrastructures, quality services, and safe recreational spaces (Cubbin et al., 2001a). The interplay between individual and contextual risks is particularly pronounced in LMICs. Individuals with limited income, education, or autonomy are at a heightened risk when they reside in structurally marginalized environments characterized by inadequate healthcare infrastructure, food deserts, substandard housing, and physical insecurity (Ameh et al., 2019).\u003c/p\u003e\n\u003cp\u003eThese multilevel exposures produce cumulative stress and behavioral constraints that undermine individuals\u0026rsquo; cardiovascular health, regardless of their intentions or knowledge (Bevan et al., 2022). The gendered norms add another layer to the conundrum. In patriarchal societies, women often lack the mobility or decision-making power to access health services (Tuoyire \u0026amp; Ayetey, 2018). By contrast, norms of masculinity discourage men from seeking preventive care (G\u0026oacute;mez-Oliv\u0026eacute; et al., 2017, 2018).\u003c/p\u003e\n\u003cp\u003eAddressing CVDs in LMICs such as South Africa requires integrated multilevel strategies that address both individual behaviors and structural inequalities. Focusing solely on individual choices without improving community conditions risks deepening the existing disparities and limiting the impact of public-health interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRevisiting Marital Protection and Gendered Health Theories in SSA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMany studies that link marital status with health outcomes rely on two prominent theories on gendered resource and social stress. The gendered resource theory posits that marriage provides individuals with unequal access to health-promoting resources, with men typically gaining more from spousal caregiving and emotional support, whereas women\u0026apos;s health benefits depend on economic security and relational equity (Gorman et al. 2010; Springer 2010). Conversely, the social-stress theory emphasizes that marital transitions, particularly divorce, widowhood, or separation, generate psychosocial stress and erode social-support networks, thereby increasing the risk of chronic diseases (Pearlin et al., 1981; Thoits, 2010).\u003c/p\u003e\n\u003cp\u003eHowever, these theoretical frameworks were developed primarily in HICs, where social protection, access to healthcare, and economic resources mitigated the effects of marital strain and relational inequality. Assumptions based on these frameworks may not fully translate to low- and middle-income contexts such as sub-Saharan Africa, where pervasive structural disadvantages and institutional fragility shape life-course health trajectories in distinctive ways.\u003c/p\u003e\n\u003cp\u003eIn SSA, individuals often experience early-life deprivation, widespread unemployment, informal marriage arrangements, and gendered access to healthcare and education. The dual burden of communicable diseases and NCDs, compounded by racialized and spatial inequalities in health infrastructure, as seen in South Africa, creates conditions under which conventional theories of marital protection may not hold. For example, caregiving and material benefits presumed in marriage may be absent or unevenly distributed, especially for women with limited agency, or men whose roles as providers are undermined by structural unemployment. Similarly, weak social safety nets and gendered stigma may intensify psychological stress associated with widowhood or divorce.\u003c/p\u003e\n\u003cp\u003eThis study contributes to the literature by critically applying and contextualizing theories on gendered resources and social stress in a high-inequality, low-resource setting. Rather than assuming universal patterns, it investigates how sex and marital status interact with place-based disadvantages to produce context-specific cardiovascular vulnerabilities. In doing so, it helps retheorize the relationship between marriage, gender, and health in settings where structural constraints mediate the meaning and impact of intimate relationships.\u003c/p\u003e\n\u003cp\u003eThese findings reveal three persistent gaps. First, the existing research has not adequately examined how marital status and sex intersect to shape cardiovascular risk across diverse life contexts (gap 1). Second, the literature has insufficiently explored how community-level disadvantages condition these effects, particularly in settings with stark structural inequalities (Gap 2). Third, the dominant theoretical models developed in high-income contexts have rarely been critically assessed for their relevance for or adaptability to the Global South (Gap 3). By addressing these conceptual and empirical limitations, this study advances a more intersectional, place-sensitive, and globally relevant understanding of cardiovascular risk in SSA.\u003c/p\u003e\n\u003ch3\u003eConceptual Framework: Gender, Marital Status, and Community Moderators of Cardiovascular Risk in SSA\u003c/h3\u003e\n\u003cp\u003eThis conceptual framework (above) extends the traditional models of the social determinants of CVDs by integrating marital status, sex, and community-level socioeconomic disadvantage within the broader structural inequalities of SSA and by distinguishing its epidemiological transitions. It builds on the existing frameworks of health determinants, including biological, behavioral, and psychosocial pathways, to trace how social location and community context jointly shape cardiovascular risk in structurally unequal settings.\u003c/p\u003e\n\u003cp\u003eAt its core, the framework posits that sex and marital status interact through socially patterned pathways to influence individuals\u0026rsquo; exposure to CVD-risk factors, such as unhealthy diet, physical inactivity, tobacco and alcohol use, chronic stress, and mental-health challenges. These factors are further shaped by power dynamics within households, particularly in terms of decision-making autonomy, caregiving burdens, and financial control. For example, married women in SSA may experience constrained decision-making autonomy, which limits their access to healthcare, whereas unmarried men are more likely to be exposed to social isolation and adopt riskier health behaviors shaped by dominant masculinity norms (G\u0026oacute;mez-Oliv\u0026eacute; et al., 2018; G\u0026oacute;mez-Oliv\u0026eacute; et al., 2017; Sserwanja et al., 2018).\u003c/p\u003e\n\u003cp\u003eSignificantly, the effects of gender and marital status are not fixed. Community-level socioeconomic characteristics include neighborhood poverty, employment patterns, and educational-infrastructure conditions. Individuals who reside in socioeconomically deprived neighborhoods are exposed to intersecting stressors, including food insecurity, environmental hazards, limited healthcare access, and disinvestment in services, which compound individuals\u0026rsquo; health risks and increase their vulnerability to CVDs (Cubbin et al., 2001b; Sundquist et al., 1999). These community-level moderators are particularly important in SSA, where urban-rural divides, informal housing, and fragmented health systems further compound personal vulnerability.\u003c/p\u003e\n\u003cp\u003eThis conceptual framework critically engages with the assumptions embedded in gendered resource theory and social stress theory, both of which originate in high-income settings and presume relatively equitable access to resources and healthcare (Pearlin et al., 1981; Springer, 2010; Gorman \u0026amp; Read, 2006). While these models offer valuable insights, such as the benefits of marriage through resource pooling and social support, they are grounded in assumptions that reflect the institutional conditions of HICs, including access to formal employment, functioning welfare states, and relatively equitable gender norms (Pearlin et al., 1981; Springer, 2010). However, these conditions are rarely encountered in SSA. Structural unemployment is widespread, particularly among young adults and women, eroding the economic benefits typically attributed to marriage (Posel \u0026amp; Rogan, 2019). Public health systems are often fragmented and under-resourced, which limits the availability of preventive services and undermines the continuity of care (Ataguba et al., 2011; Pillay-van Wyk et al., 2016). Furthermore, deeply entrenched gender hierarchies constrain women\u0026rsquo;s decision-making autonomy and access to household resources, limiting the protective potential of marital unions for many women (Tuoyire \u0026amp; Ayetey, 2018).\u003c/p\u003e\n\u003cp\u003eThese structural conditions challenge the universal applicability of the theories of gendered resources and social stress, underscoring the need for contextual adaptations. Rather than viewing marriage as uniformly protective, this study conceptualizes it as a relational institution embedded in local political economies and gender regimes whose health effects are contingent upon individuals\u0026rsquo; access to resources, their social roles, and institutional support that vary significantly across settings.\u003c/p\u003e\n\u003cp\u003eBy situating these theories in SSA, the framework recasts marital protection as contingent rather than universal and conceptualizes marriage not simply as a resource but as a relational institution embedded in unequal social and economic systems. Similarly, it understands stress exposure not solely as a product of marital disruption but as a chronic condition shaped by poverty, inequality, and exclusion from institutional support.\u003c/p\u003e\n\u003cp\u003eThus, the conceptual framework of this study contributes to the decentering of HIC assumptions about gender roles, marriage, and household functioning, thereby bridging individual and contextual levels of analysis to understand cardiovascular vulnerability and highlighting the need for intersectional, multilevel interventions that address structural inequality rather than just personal behavior.\u003c/p\u003e\n\u003cp\u003eUltimately, the framework provides a scaffold for empirical analysis of how gender, marital status, and place interact to shape the CVD risk in SSA and invites rethinking of how global health theories must evolve to reflect regional realities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTheoretical Hypotheses Emerging from the Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrawing on the conceptual framework outlined above, this study identifies a set of theoretically grounded but empirically untested hypotheses that challenge the universality of the gendered-resource theory and social-stress theory. The following hypotheses serve as analytical propositions that extend, adapt, or contest these theories, considering local realities:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Marriage-Paradox Hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eContrary to the widely held assumption that marriage is uniformly protective, married men in high-unemployment and high-stress contexts may face elevated cardiovascular risk. This paradox arises from structural pressures to serve as economic providers, masculine norms that discourage health seeking, and limited access to preventive care, which may override the relational benefits of spousal support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGendered-Widowhood-Vulnerability Hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe loss of a spouse through divorce or widowhood may disproportionately increase the CVD risk among men in disadvantaged communities. Unlike women, who maintain stronger social networks or caregiving ties, men in patriarchal systems may experience more severe social isolation and stress when marital ties dissolve.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCommunity-Level-Educational-Paradox Hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eContrary to conventional expectations, higher levels of community education may be associated with an increased CVD risk in men, particularly if educational attainment does not translate into economic mobility. This may reflect psychosocial stress stemming from misaligned aspirations, occupational strain, and structural under-employment in South Africa\u0026apos;s segmented labor market.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMasked-Disadvantage-for-Married-Women Hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMarriage may not protect women in highly gender-unequal settings where asymmetries related to economic dependency, restricted autonomy, and relational power limit their ability to access health services and make preventive health decisions. Thus, married women may face hidden or under-recognized CVD vulnerabilities, particularly in male-dominated households.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModeration-by-Community-Disadvantage Hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between marital status and CVD risk is moderated by community-level deprivation such that the health penalties for being widowed, divorced, or never married are intensified in neighborhoods characterized by poverty, unemployment, and limited-service infrastructure.\u003c/p\u003e\n\u003cp\u003eThese hypotheses underscore the need to reassess the presumed protective effects of social relationships, particularly marriage, in contexts characterized by structural inequality and social stratification. They call for multilevel intersectional approaches that could capture how sex, place, and life-course transitions interact to produce cardiovascular disparities. To empirically evaluate these propositions and extend the global health theory in contextually grounded ways, this study draws on nationally representative data from South Africa. It applies multilevel modeling to examine how sex, marital status, and community-level disadvantages jointly shape CVD risk. In the following section, data sources, sample characteristics, and analytical strategies are discussed.\u003c/p\u003e"},{"header":"DATA AND METHODS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eData Harmonization and Ethics\u003c/h2\u003e\n \u003cp\u003eThis study used a harmonized dataset developed through the ExPoSE Project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.exposeproject.net\u003c/span\u003e\u003c/span\u003e), a collaboration between the University of Greenwich (UK) and Stellenbosch University (South Africa), which investigates epidemiological transitions in cardiovascular disease risk (Adjaye-Gbewonyo \u0026amp; Cois, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Data from seven survey waves, originating from three nationally representative South African survey programs, were pooled into a single dataset: the Demographic and Health Survey (SADHS, 2003); the NIDS (Waves 1\u0026ndash;5, 2008\u0026ndash;2017); and the South African National Health and Nutrition Examination Survey (SANHANES-1, 2011\u0026ndash;2012). These combined surveys provided 122,576 observations, of which 71,243 participants had complete data and were included in the final analytic sample. All datasets are publicly available and were accessed through DataFirst at the University of Cape Town (DataFirst, n.d.).\u003c/p\u003e\n \u003cp\u003eData were drawn from three nationally representative South African surveys: the \u003cem\u003eSADHS (2003)\u003c/em\u003e, a two-stage clustered household survey with face-to-face interviews of women (15\u0026ndash;49) and a subsample of men (15\u0026ndash;59), including biomarker collection (response rates: 95% households, 97% individuals); the \u003cem\u003eNIDS (Waves 1\u0026ndash;5, 2008\u0026ndash;2017)\u003c/em\u003e, a longitudinal two-stage clustered panel survey by the Southern Africa Labour and Development Research Unit (SALDRU) at the University of Cape Town, collecting demographic, health, income, and anthropometric data from individuals aged 15\u0026thinsp;+\u0026thinsp;via face-to-face interviews with survey weights for non-response; and the \u003cem\u003eSANHANES-1 (2011\u0026ndash;2012)\u003c/em\u003e, a cross-sectional survey by the Human Sciences Research Council (HSRC) where field teams visited households to administer questionnaires and perform physical and biomarker assessments.\u003c/p\u003e\n \u003cp\u003eThe harmonized dataset involved a systematic review and alignment of core demographic, socioeconomic, and health-related variables across surveys to ensure comparability. I applied age restrictions and excluded pregnant women to reduce potential bias. Sampling weights were retained to preserve national representativeness. I conducted descriptive and frequency checks to verify the accuracy of harmonization. Data cleaning and processing were completed using Stata 18.\u003c/p\u003e\n \u003cp\u003eThe initial harmonized dataset included 122,576 observations pooled from seven survey waves. I applied an age restriction, excluding 105 participants (0.08%) younger than 15 years. Pregnant women (n\u0026thinsp;=\u0026thinsp;1,896; 1.55%) were excluded to avoid bias related to physiological changes affecting cardiovascular risk. Subsequently, cases with missing data on key analytic variables were excluded, resulting in a final analytic sample of 71,243 participants.\u003c/p\u003e\n \u003cp\u003eTo evaluate the potential bias from missing data, I compared demographic characteristics (age, sex, and urban/rural residence) between included and excluded participants. No significant differences were found, supporting the assumption that data are missing at random (MAR). Additional detailed analyses of missing data patterns, including variable-specific missingness and comparisons, are provided in the supplemental materials.\u003c/p\u003e\n \u003cp\u003eListwise deletion was used for the final analytic models.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasures\u003c/h2\u003e\n \u003cp\u003eDependent variables\u003c/p\u003e\n \u003cp\u003eHypertension: I assessed hypertension using systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurements taken at 10-minute intervals using a LIFE SOURCE UA-767 Plus digital oscillometric blood pressure monitor. According to the guidelines of WHO (2003) and American College of Cardiology (2020), blood pressure was categorized into four levels: (1) \u003cem\u003enormotensive\u003c/em\u003e (SBP\u0026thinsp;\u0026lt;\u0026thinsp;120 mmHg and DBP\u0026thinsp;\u0026lt;\u0026thinsp;80 mmHg, no diagnosis, and not on antihypertensive medication); (2) \u003cem\u003eelevated blood pressure\u003c/em\u003e (SBP 120\u0026ndash;129 mmHg and DBP\u0026thinsp;\u0026lt;\u0026thinsp;80 mmHg); (3) \u003cem\u003estage 1 hypertension or diagnosis\u003c/em\u003e (SBP 130\u0026ndash;139 mmHg, DBP 80\u0026ndash;89 mmHg, or self-reported diagnosis); and (4) \u003cem\u003estage 2 hypertension or medication\u003c/em\u003e (SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive medication). A binary variable was created for the analysis, with individuals classified as hypertensive (category 2\u0026ndash;4\u0026thinsp;=\u0026thinsp;1) or normotensive (category 1\u0026thinsp;=\u0026thinsp;0). Pregnant women were excluded owing to the risk of gestational hypertension (American College of Obstetricians and Gynecologists, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eObesity\u003c/strong\u003e. Obesity was measured using the body mass index (BMI) calculated from self-reported weight and height (kg/m\u0026sup2;). BMI categories were defined as underweight (\u0026lt;\u0026thinsp;18.5), normal weight (18.5\u0026ndash;24.9), overweight (25.0\u0026ndash;29.9), and obese (\u0026ge;\u0026thinsp;30.0). A dichotomous variable was constructed for multivariate analysis: \u003cem\u003ehealthy weight/underweight\u003c/em\u003e (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25\u0026thinsp;=\u0026thinsp;0) and \u003cem\u003eoverweight/obese\u003c/em\u003e (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u0026thinsp;=\u0026thinsp;1). Pregnant women were excluded because of pregnancy-related weight changes.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e. Diabetes was measured as a binary outcome (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0) based on the diagnosis of a self-reported physician and/or the current use of diabetes medication. Although self-reported, this measure has been used in population-level studies despite its potential for underdiagnosis or misclassification (Kowall et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eComposite CVD Risk.\u003c/strong\u003e To capture the overall CVD vulnerability, a composite variable was created to indicate whether the respondent reported \u003cem\u003eany\u003c/em\u003e of the three CVD conditions: hypertension, diabetes, or elevated BMI (\u0026ge;\u0026thinsp;25). This variable was used in the supplementary models to assess the cumulative cardiometabolic risk.\u003c/p\u003e\n \u003cp\u003eA binary composite measure of CVD risk was developed to capture the multidimensional and syndemic nature of cardiometabolic diseases in a resource-constrained setting. Consistent with the findings of previous studies, hypertension, diabetes, and elevated BMI were interconnected and mutually reinforcing conditions that significantly increased the risk of adverse cardiovascular events (Gaziano et al., 2013; Reddy et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Combining these markers with a single binary indicator (presence of any of the three\u0026thinsp;=\u0026thinsp;1; none\u0026thinsp;=\u0026thinsp;0) reflects the clustering of risk factors frequently observed in LMICs, where early diagnosis and continuous care are limited (Ataklte et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mensah et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). This approach is particularly suitable for multilevel analyses of pooled population-based surveys in which consistent biomarkers and diagnostic measures vary across datasets. Moreover, binary composite risk indicators have been widely used in population-health research to identify individuals at elevated risk and guide public-health prioritization (D\u0026apos;Agostino et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sliwa et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although this simplification may obscure differences in severity, it provides a parsimonious and policy-relevant measure of CVD vulnerability appropriate for the South African context.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cp\u003eExplanatory variables\u003c/p\u003e\n \u003cp\u003eMarital Status: Marital status was coded into three categories: (1) \u003cem\u003enever married/single\u003c/em\u003e, (2) \u003cem\u003ecurrently married or living with a partner\u003c/em\u003e, and (3) \u003cem\u003epreviously married\u003c/em\u003e (widowed, divorced, or separated).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCommunity-Level Characteristics\u003c/em\u003e. To assess the neighborhood context, three variables were constructed at the level of the primary sampling unit (PSU):\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. \u003cem\u003eCommunity education\u003c/em\u003e: Proportion of individuals with secondary or higher education categorized into tertiles (low, middle, and high).\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. \u003cem\u003eCommunity poverty\u003c/em\u003e: The proportion of households in the lowest two national income quintiles categorized into tertiles.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. \u003cem\u003eCommunity Employment\u003c/em\u003e: The proportion of employed adults in each PSU was also categorized into tertiles.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThese measures reflect the local socioeconomic environment, which may shape individual cardiovascular risks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003cp\u003eLifestyle Factors: Physical activity was classified as \u003cem\u003elow\u003c/em\u003e (no activity or less than once per week), \u003cem\u003emoderate\u003c/em\u003e (1\u0026ndash;2 times/week), or \u003cem\u003ehigh\u003c/em\u003e (\u0026ge;\u0026thinsp;3 times/week). Smoking status and alcohol use were coded as \u003cem\u003enever\u003c/em\u003e, \u003cem\u003eformer\u003c/em\u003e, or \u003cem\u003ecurrent\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSociodemographic Characteristics\u003c/em\u003e. Educational attainment was categorized into three levels: low (no education or some primary education), moderate (completed primary education or some secondary education), and high (completed secondary education or higher). Additional covariates included \u003cem\u003eurban residence\u003c/em\u003e (urban\u0026thinsp;=\u0026thinsp;1, rural\u0026thinsp;=\u0026thinsp;0), \u003cem\u003emedical insurance\u003c/em\u003e (yes/no), \u003cem\u003eemployment status\u003c/em\u003e (employed/unemployed), \u003cem\u003ehousehold income quintile\u003c/em\u003e (1\u0026thinsp;=\u0026thinsp;poorest to 5\u0026thinsp;=\u0026thinsp;richest), and \u003cem\u003eself-identified racial classification\u003c/em\u003e (Black African, Colored, Indian/Asian, and White).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eParity (females only)\u003c/strong\u003e. Parity was included as a potential biological and social risk factor for CVD in women. They were categorized as \u003cem\u003enulliparous\u003c/em\u003e (0 children), \u003cem\u003eprimiparous\u003c/em\u003e (1 child), \u003cem\u003elow multiparous\u003c/em\u003e (2\u0026ndash;3 children), or \u003cem\u003ehigh multiparous\u003c/em\u003e (\u0026ge;\u0026thinsp;four children). Multiple pregnancies have been linked to long-term cardiovascular risk owing to gestational hypertension and metabolic strain (Bateman et al., 2015, 2019).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003eData Analysis\u003c/p\u003e\n \u003cp\u003eI first conducted descriptive statistics, including frequencies, cross-tabulations, and Pearson\u0026apos;s chi-square tests, to examine associations between marital status, community characteristics, and CVD outcomes in individuals. These initial bivariate analyses informed the subsequent multivariate modeling.\u003c/p\u003e\n \u003cp\u003eTo estimate the relationship between marital status, community-level factors, and CVD risk, I employed multilevel logistic regression models that accounted for the nested structure of the data, with individuals (Level 1) clustered within PSUs (Level 2). This approach enables a more accurate estimation of predictor effects while accounting for unobserved community-level heterogeneity. Sampling weights were used to ensure representativeness and to correct unequal selection probabilities. As recommended by Gabler and Lahiri (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), PSU-level weights were derived from individual-level weights, normalized by cluster size, and merged back to consider design effects.\u003c/p\u003e\n \u003cp\u003eMultivariate modeling was implemented separately for each of the three dependent outcomes: hypertension, diabetes, and obesity. A stepwise modeling strategy was employed to systematically assess the contribution of individual and contextual predictors to CVD risk. This approach enables an incremental evaluation of how marital status, community-level characteristics, and individual-level covariates influence outcomes, thereby helping identify the potential confounding or mediating effects. Although only the fully adjusted model (Model 3) is presented in the main results for clarity and parsimony, stepwise progression provides critical insights into the robustness and stability of associations across model specifications. This strategy aligns with best practices in multilevel epidemiological research, in which complex interactions between social and structural determinants are explored in a theoretically informed sequence.\u003c/p\u003e\n \u003cp\u003eThese steps included:\u003c/p\u003e\n \u003cp\u003eStep One. Model 0: Unconditional (null) model to assess the baseline variance across PSUs.\u003c/p\u003e\n \u003cp\u003eStep two. Model 1: Only marital status is included.\u003c/p\u003e\n \u003cp\u003eStep Three. Model 2: Adding community-level education, poverty, and employment.\u003c/p\u003e\n \u003cp\u003eStep Four. Model 3: A fully adjusted model including lifestyle (e.g., physical activity, smoking, and alcohol use), demographics (e.g., age, race, education, and employment), and health-system variables (e.g., insurance coverage).\u003c/p\u003e\n \u003cp\u003eI assessed multicollinearity using variance inflation factors (VIFs). None of the variables exceeded a VIF threshold of 5. Analyses were conducted using Stata version 18. The \u003cem\u003emeqrlogit\u003c/em\u003e command was chosen for its enhanced convergence properties owing to its QR decomposition solver, and its flexibility in handling numerical integration in large and complex survey datasets (RabeHesketh \u0026amp; Skrondal, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Raudenbush \u0026amp; Bryk, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; StataCorp, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRobust standard errors were used to address heteroscedasticity and within-cluster correlations. Adjusted odds ratios (ORs) at 95% confidence intervals (CIs) were reported, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive Results\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the weighted frequency distribution of key CVD-risk factors, explanatory variables, and covariates. Elevated blood pressure was observed in 63.1% of the respondents, and 48.3% were classified as overweight or obese, indicating a high CVD risk. Although only 3.7% of patients reported being diagnosed with diabetes, this remains a significant comorbidity.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \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\u003eFrequency Distribution of Study Variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\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\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\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\u003eHypertension Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormotensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevated BP or Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI/Overweight Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy Weight/Underweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight/Obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnosed/on Medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Diagnosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Education Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Poverty Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity Employment Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExercise Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge Category (10 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack African\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical Insurance Coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParity Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNulliparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow Multiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Multiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Income Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e. Source: DHS, NIDS, SANHANES (2003\u0026ndash;2017). Percentages may not sum to 100% due to rounding.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eSurvey weights are applied to all variables.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe sample was predominantly female (57.5%) and largely unmarried (57.7%), with 31.7% married, and 10.6% widowed or divorced. Most respondents resided in communities with medium (39.5%) or high (37.4%) educational levels. Additionally, a similar pattern was observed for community poverty and employment indicators.\u003c/p\u003e\n \u003cp\u003eAnalysis of lifestyle-related factors demonstrated that 76.9% of participants performed low physical activity, and 16.9% were current smokers. Two-thirds (66.6%) of the participants had never consumed alcohol, whereas 24.4% reported current or past alcohol consumption The sample was skewed toward young people, with 44% of the participants under the age of 30 years. Only 1.6% of participants were aged 80 years. Education levels were moderate to high for most participants (79.1%), and the racial composition was overwhelmingly Black African (85.9%).\u003c/p\u003e\n \u003cp\u003eEconomic hardship was evident, with 64.1% of the participants being unemployed and 9.4% reporting medical-insurance coverage. Parity varied widely, with more than 70% of participants having two or more children. Income was evenly distributed across the quintiles. Urban and rural residences were balanced, with 52.8% of them living in rural areas.\u003c/p\u003e\n \u003cp\u003eThese findings provide a demographic and health profile of South African adults, illustrating the co-occurrence of chronic-disease risk, limited access to care, and social disadvantages across multiple domains.\u003c/p\u003e\n \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eBivariate Associations\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the unadjusted prevalence rates of hypertension, obesity, and diabetes across key demographic, socioeconomic, and behavioral variables in South Africa. Except for cases in which F-tests could not be computed because of strata with a single PSU, all associations were statistically significant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \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\u003eBivariate Associations of Sex, Marital Status, Community Characteristics, and Other Covariates with Cardiovascular Disease Risk in South Africa\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndependent Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHypertension (Elevated BP or Higher)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObesity (Overweight/Obese)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDiabetes (Diagnosed/On Medication)\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\u003eSex\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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.36\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\u003e66.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status\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\u003eNever Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.26\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\u003e74.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity-level Education\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\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity-level Poverty\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\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLifestyle Factors\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\u003eExercise Level\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\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking Status\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\u003eNever Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol Consumption\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\u003eNever Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge Category (10 years)\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\u003e15\u0026ndash;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespondent\u0026apos;s Education\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\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\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\u003eBlack African\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment\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\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.25\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\u003e58.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical Insurance Coverage\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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \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\u003e67.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParity Group\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\u003eNulliparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow Multiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh Multiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Income Quintile\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\u003eLow (I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\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\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Statistical significance not displayed for clarity. Design-based F-statistics are available upon request.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eSource: DHS, NIDS, SANHANES 2003\u0026ndash;2017\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSex and marital status were strongly associated with CVD outcomes. Men reported a higher prevalence of hypertension (66%) but substantially lower rates of obesity (29%) and diabetes (2.7%) than women (62.1% obese; 4.36% diabetes). Marital status was similarly patterned: never-married individuals had the lowest prevalence of any of the three CVD risks. However, both married and widowed/divorced individuals reported substantially higher rates of risk factors, with hypertension reaching 84.85% and diabetes reaching 10.01%.\u003c/p\u003e\n \u003cp\u003eAt the community level, individuals in areas with higher educational attainment exhibited a greater prevalence of hypertension (65.89%) and diabetes (4.29%) than those in less-educated communities. By contrast, obesity was slightly more prevalent in communities with higher education levels (53.09%). Community-poverty gradients were also evident, with the highest prevalence in low-poverty areas (68.54%) and the lowest obesity rates in high-poverty communities (46.51%).\u003c/p\u003e\n \u003cp\u003eBehavioral and lifestyle indicators were associated with all three outcomes. Low physical-activity levels were associated with higher rates of obesity (52.34%) and hypertension (62.98%), whereas individuals with high activity levels had the lowest obesity rates (32.61%). Smoking and alcohol use were also important; current smokers had the highest prevalence of hypertension (67.91%), while current drinkers exhibited elevated hypertension (66.33%) but lower obesity (38.82%).\u003c/p\u003e\n \u003cp\u003eThe age gradient was found to be consistent and steep. The prevalence of hypertension increased from 35.14% among those aged 15\u0026ndash;19 years to 93.42% among those aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years, with diabetes following a similar trend. Educational and racial disparities were also notable: hypertension and obesity prevalence were highest among white respondents (79.92% and 67.14%, respectively), and individuals with low formal education had higher hypertension (80.68%) and diabetes (7.02%).\u003c/p\u003e\n \u003cp\u003eUrban residence was associated with a slightly higher prevalence of diabetes (3.97%) than rural settings (3.33%), whereas employment and health-insurance status were positively associated with CVD risk. For example, those with insurance had higher rates of obesity (65.11%) and diabetes (5.73%).\u003c/p\u003e\n \u003cp\u003eThese descriptive results provide initial evidence of patterned inequality in cardiovascular-risk factors, particularly in terms of marital status, community context, and age. However, causal inferences and interaction effects, which are central to this study\u0026rsquo;s theoretical framework, were examined in subsequent multivariate and interaction models.\u003c/p\u003e\n \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eMultivariate Results\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the multilevel logistic regression estimates of the risks for hypertension, obesity, and diabetes as functions of individual- and community-level characteristics.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \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\u003eMultilevel Logistic Regression Models of Hypertension, Obesity, and Diabetes Risk by Gender, Marital Status, and Community-Level Socioeconomic Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\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\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiabetes\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\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE for Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE for Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE for Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\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 (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status\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 (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity-level Education\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\u003eLow (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity-level Poverty\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\u003eLow (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunity-level Employment\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\u003eLow (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLifestyle Factors\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\u003eExercise Level\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\u003eLow (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking Status\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\u003eCurrent Smoker (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 \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 Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.98***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol Consumption\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\u003eCurrent Drinker (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 \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 Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocio-Demographic Factors\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\u003eAge Category (10 years)\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\u003e15\u0026ndash;19 (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 \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\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.40***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.39***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.48***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.71***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.60***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.00***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.23***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.94***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespondent\u0026apos;s Education\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\u003eLow (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\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\u003eBlack African (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 \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\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployment\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 (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 \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\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical Insurance Coverage\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\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 \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\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold Income Quintile\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\u003eLow (I) (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 \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\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (V)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence\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\u003eRural (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 \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\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Effects (PSU)\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\u003eVariance (var(_cons))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \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\u003eLog-likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39,261.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-37,378.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9,132.86\u003c/p\u003e\n \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\u003eSample Information\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\u003eNumber of Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71,243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70,363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e _cons estimates the baseline odds (conditional on zero random effects). Statistical significance: ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003eDHS, NIDS, SANHANES 2003-2017\u003cbr\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e. Hypertension risk was significantly higher among males (OR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.48\u0026ndash;1.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), married individuals (OR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.11\u0026ndash;1.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and widowed/divorced respondents (OR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 1.14\u0026ndash;1.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Respondents in middle-income (OR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.86\u0026ndash;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and high-income households (OR\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.83\u0026ndash;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) had lower odds of hypertension. Community-level poverty was negatively associated with hypertension (middle vs. low: OR\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.80\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas high community employment was positively associated with hypertension (OR\u0026thinsp;=\u0026thinsp;1.15, 95% CI: 1.03\u0026ndash;1.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Racial disparities were observed, with colored (OR\u0026thinsp;=\u0026thinsp;1.65, 95% CI: 1.50\u0026ndash;1.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and white (OR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.11\u0026ndash;1.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) respondents exhibiting elevated risk.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eObesity\u003c/strong\u003e. Males had significantly lower odds of obesity than females (OR\u0026thinsp;=\u0026thinsp;0.29, 95% CI: 0.28\u0026ndash;0.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Obesity risk was higher among married (OR\u0026thinsp;=\u0026thinsp;1.76, 95% CI: 1.67\u0026ndash;1.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and widowed/divorced individuals (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.26\u0026ndash;1.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Community education was positively associated with obesity (high vs. low: OR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.06\u0026ndash;1.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas moderate community poverty was inversely associated (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.75\u0026ndash;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Respondents in the higher-income quintiles had greater odds of obesity (OR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 1.50\u0026ndash;1.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Obesity risk was lower among unemployed individuals (OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.77\u0026ndash;0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Asians (OR\u0026thinsp;=\u0026thinsp;0.64, 95% CI: 0.50\u0026ndash;0.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Urban residence was associated with a higher obesity risk (OR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.01\u0026ndash;1.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes.\u003c/strong\u003e Men had lower odds of developing diabetes than women (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.76\u0026ndash;0.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The risk was elevated among married (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 1.05\u0026ndash;1.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and widowed/divorced respondents (OR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.10\u0026ndash;1.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Community education was positively associated with diabetes (high vs. low: OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI: 1.05\u0026ndash;1.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Asians had higher odds (OR\u0026thinsp;=\u0026thinsp;1.71, 95% CI: 1.13\u0026ndash;2.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Caucasians had lower odds (OR\u0026thinsp;=\u0026thinsp;0.69, 95% CI: 0.54\u0026ndash;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) than Black Africans. The diabetes risk increased with income, with respondents in the top quintile displaying the highest OR at 1.41 (95% CI: 1.18\u0026ndash;1.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Urban residence (OR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.06\u0026ndash;1.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), unemployment (OR\u0026thinsp;=\u0026thinsp;1.25, 95% CI: 1.13\u0026ndash;1.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and medical insurance coverage (OR\u0026thinsp;=\u0026thinsp;1.44, 95% CI: 1.23\u0026ndash;1.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also associated with increased diabetes risk.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates that sex, marital status, household income, and community-level factors significantly shaped the risk of hypertension, obesity, and diabetes, with distinct patterns observed across the three conditions.\u003c/p\u003e\n \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e about here]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eMultilevel Interaction Effects on Composite CVD Risk\u003c/h2\u003e\n \u003cp\u003eThis section presents the findings of a multilevel logistic regression model that estimates the odds of reporting CVD symptoms by incorporating both individual- and community-level variables (Table\u0026nbsp;4). For the analysis, five theoretically derived hypotheses concerning the intersection of sex, marital status, and contextual disadvantages in shaping CVD risk in South Africa were evaluated.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe marriage-paradox hypothesis.\u003c/strong\u003e This hypothesis predicts that marriage would not be uniformly protective, particularly among males. The interaction term Male#Married was statistically significant (OR\u0026thinsp;=\u0026thinsp;2.21, p\u0026thinsp;=\u0026thinsp;0.034), which indicates that married men had significantly \u003cem\u003ehigher\u003c/em\u003e odds of reporting CVD symptoms than unmarried men, controlling for other factors This finding supports the \u003cem\u003emarriage paradox\u003c/em\u003e as it suggests that in resource-constrained, patriarchal settings, structural stressors tied to economic provision may outweigh the relational health benefits typically associated with marriage.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGendered-widowhood-vulnerability hypothesis\u003c/strong\u003e. According to this hypothesis, widowed/divorced men are expected to face disproportionate CVD risk owing to social isolation and the lack of support. The interaction term Male #Widowed/Divorced was \u003cem\u003enot statistically significant\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.09, p\u0026thinsp;=\u0026thinsp;0.877), suggesting no clear evidence for greater CVD risk for widowed/divorced than for other groups after adjusting for covariates. However, in subgroup analyses by community education (see Hypothesis 3), male #Widowed/divorced #Middle education was significant (OR\u0026thinsp;=\u0026thinsp;2.47, p\u0026thinsp;=\u0026thinsp;0.016), indicating that the vulnerability of divorced/widowed men may be contingent on contextual characteristics, which partially supports this hypothesis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity-level-educational-paradox hypothesis\u003c/strong\u003e. This hypothesis posits that high community education may increase stress and health risks for men in low-opportunity settings. While high community education alone was not associated with increased CVD risks, the interaction term Male#High community education was positive and marginally nonsignificant (OR\u0026thinsp;=\u0026thinsp;1.51, p\u0026thinsp;=\u0026thinsp;0.119). Additionally, the three-way interaction of Male Widowed/Divorced Middle education displayed significantly elevated CVD odds (OR\u0026thinsp;=\u0026thinsp;2.47, p\u0026thinsp;=\u0026thinsp;0.016), suggesting that for some subgroups of men, higher educational environments may amplify stress, particularly in the absence of marital support. This finding provides partial empirical support for the hypothesis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMasked-disadvantage-for-married-women Hypothesis\u003c/strong\u003e. This hypothesis suggests that marriage may conceal health risks for women in unequal households. The baseline odds for married women (reference group) were not significantly different from those who were never married or were separated/divorced (OR\u0026thinsp;=\u0026thinsp;1.20, p\u0026thinsp;=\u0026thinsp;0.249), but the lack of a protective effect relative to men and the elevated risk observed among married men highlight a gendered divergence in how marriage shapes health. Although not directly tested, these patterns suggest that married women do not benefit from the health-protective effects of marriage, which is consistent with this hypothesis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModeration-by-community-disadvantage hypothesis\u003c/strong\u003e. This hypothesis suggests that the adverse effects of being unmarried, widowed, or divorced are amplified in deprived communities. The three-way interaction terms combining marital status, sex, and community deprivation (education, poverty, or employment) were largely \u003cem\u003enon-significant\u003c/em\u003e, except Male #Widowed/Divorced #Middle education (OR\u0026thinsp;=\u0026thinsp;2.47, p\u0026thinsp;=\u0026thinsp;0.016). While this supports the idea of \u003cem\u003econtextual moderation\u003c/em\u003e, the pattern was not consistent across all the measures of deprivation (poverty and employment exhibited no significant interaction). Thus, this hypothesis received modest support, with educational disadvantage emerging as the most salient moderator.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eCovariate Findings\u003c/h2\u003e\n \u003cp\u003eSeveral covariates were significantly associated with the CVD risk. Older age was strongly associated with increased risks, with a clear dose-response pattern. Women had substantially lower ORs than men (OR\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Never-smokers and those who quit smoking exhibited higher odds than current smokers, indicating complex smoking-related effects. Health-insurance coverage and urban residence were both associated with an elevated CVD risk. Higher household income was positively associated with CVD, whereas tertiary education was protective. Colored individuals had higher, and white individuals had lower odds of CVD than African Black individuals. Employment status, physical activity, and alcohol consumption displayed no significant associations, while a modest increase in risk was observed among never-drinkers.\u003c/p\u003e\n \u003cp\u003eIn summary, the multilevel results revealed that married men have a significantly higher CVD risk than unmarried men, supporting the Marriage Paradox in patriarchal, resource-poor contexts. Widowed/divorced men exhibited elevated risk only in communities with moderate education, indicating contextual effects. Higher levels of community education may increase stress-linked CVD risk in unmarried men. Married women do not benefit from marital protection, suggesting gendered health disparities. Community disadvantage moderates these effects mainly through education, highlighting the role of education in shaping CVD risk across sex and marital status.\u003c/p\u003e\n \u003cp\u003e[Insert Table\u0026nbsp;4 about here]\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 627px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4: Multilevel Logistic Regression Models of CVD Composite Risk: Interaction Between Gender, Marital Status, and Community Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCVD Composite Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eFemale (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.25*** (0.21, 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eNever Married (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.32*** (1.22, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eWidowed/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.17*** (1.06, 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity-level Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eLow (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.12 (0.99, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.20** (1.03, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity-level Poverty\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eLow (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.87* (0.76, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.85 (0.71, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunity-level Employment Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eLow (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.99 (0.87, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.97 (0.83, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction Terms (Two-Way)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Married/live with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e2.21*** (1.65, 2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Widowed/divorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.09 (1.38, 2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Community Education (Middle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.59* (0.37, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Community Education (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.51 (0.90, 2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMarried \u0026times; Community Education (Middle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.04 (0.85, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMarried \u0026times; Community Education (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.20 (0.93, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eWidowed/Divorced \u0026times; Community Education (Middle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.98 (0.79, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eWidowed/Divorced \u0026times; Community Education (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.28 (0.97, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction Terms (Three-Way)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Married \u0026times; Community Education (Middle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.65 (0.98, 2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Married \u0026times; Community Education (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.74 (0.42, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Widowed/Divorced \u0026times; Community Education (Middle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e2.47* (1.18, 5.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Widowed/Divorced \u0026times; Community Education (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.07 (0.47, 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Married \u0026times; Community Poverty (Middle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.83 (0.50, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Married \u0026times; Community Poverty (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.91 (0.47, 1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Widowed/Divorced \u0026times; Community Poverty (Middle)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.76 (0.38, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eMale \u0026times; Widowed/Divorced \u0026times; Community Poverty (High)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.29 (0.52, 3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifestyle Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eLow (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.07 (0.96, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.99 (0.89, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eCurrent Smoker (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eNever Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.78*** (1.54, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eFormer Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.72*** (1.56, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eCurrent Drinker (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eNever Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.08 (0.97, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eFormer Drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.08* (1.00, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocio-Demographic Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Category (10 years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e15-19 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e5.74*** (4.07, 8.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e17.14*** (12.24, 24.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e41.24*** (29.50, 57.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e80.78*** (57.77, 112.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e96.16*** (68.58, 134.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e93.80*** (66.50, 132.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e55.71*** (38.56, 80.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespondent\u0026apos;s Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eLow (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.06 (0.99, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.84** (0.76, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eBlack African (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.96 (0.70, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eColored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.15* (1.01, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.76*** (0.64, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eYes (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.05 (0.99, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical Insurance Coverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eNo (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e1.24*** (1.12, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eIntercept (_Cons)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.00*** (0.00, 0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Effects (PSU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eVariance (var(_cons))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e0.12 (0.09, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eLog-likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e-18,203.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Information\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eNumber of Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e71,243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 353px;\"\u003e\n \u003cp\u003eNumber of Groups (PSU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 353px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource: DHS, NIDS, SANHANES 2003-2017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 273px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 627px;\"\u003e\n \u003cp\u003e*Note: _cons estimates the baseline odds (conditional on zero random effects). Statistical significance: ***p \u0026lt; 0.001, **p \u0026lt; 0.01, \u003cem\u003ep \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003ePredicted Probabilities Visualization\u003c/h2\u003e\n \u003cp\u003eTo further illustrate the interaction effects, I computed the predicted probabilities of reporting CVD symptoms across the levels of sex, marital status, and community education. As presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, married men in communities with high educational attainment exhibited the highest predicted probability of CVD, supporting both hypotheses regarding marriage- and community-level educational paradox. By contrast, never-married individuals, particularly women, consistently exhibited the lowest predicted probabilities across all community-education levels. These patterns highlight the gendered and contextual nature of cardiovascular risk, visually reinforcing the statistical findings of the multilevel interaction models.\u003c/p\u003e\n \u003cp\u003e[Insert Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION AND CONCLUSION","content":"\u003cp\u003eThis study contributes to theories on demographic and population health by demonstrating that marital status, often treated as a static individual attribute, interacts dynamically with sex and community context to shape CVD risk. The findings challenge the universality of the gendered-resource theory by illustrating that marriage can increase CVD risk for men in patriarchal and economically constrained settings and offer limited protection for women in constrained household power dynamics. By incorporating the social stress theory into intersectional feminist perspectives, I reveal how structural and relational inequalities can shape health vulnerability.\u003c/p\u003e\u003cp\u003eIn this study, I investigated how marital status, sex, and community-level disadvantages intersect in shaping CVD risk in South Africa. By leveraging a harmonized dataset from the ExPoSE Project, which integrates seven nationally representative surveys spanning nearly 15 years, I examined both the persistent and evolving patterns of cardiovascular risk across diverse social contexts. This harmonized, pooled approach is particularly innovative within the African context, where longitudinal or integrated datasets with consistent measures of relational and community characteristics are exceedingly rare. I initially employed multilevel models to estimate the effects of sex, marital status, and community-level socioeconomic conditions on hypertension, obesity, and diabetes—three key CVD risk factors. I then applied nested multilevel interaction models to capture nuanced, context-sensitive dynamics, revealing how sex-based marital relationships and place-based disadvantages jointly shape cardiometabolic vulnerabilities. This approach is a significant advancement in population-health research, as it offers methodological rigor and theoretical insights into the structural and relational determinants of NCD risk in resource-constrained settings.\u003c/p\u003e\u003cp\u003eAltogether, the results critically extend the widely applied frameworks of gendered-resource theory and social stress theory by demonstrating that their core assumptions do not apply uniformly in contexts marked by structural disadvantages. First, contrary to the gendered resource theory, which posits that men typically derive health benefits from marriage owing to spousal support and resource pooling (Gorman et al., 2010; Springer, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the findings demonstrate that married men in South Africa face significantly higher odds of composite CVD risk than their unmarried counterparts. This supports the Marriage Paradox Hypothesis and suggests that under the conditions of widespread unemployment and entrenched masculine provider norms, the stress of economic insecurity may negate the relational health advantages of marriage. In patriarchal settings such as those of South Africa, the pressure on men to fulfill culturally prescribed roles as financial providers, despite structural barriers to doing so, may increase psychosocial strain and cardiometabolic risk (Mogano et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, while the main effect of the Male #Widowed/Divorced interaction did not reach statistical significance, the significant three-way interaction involving community education (Male #Widowed/Divorced #Middle education) offers critical insight into the conditional nature of vulnerability. Specifically, the elevated CVD risk among widowed and divorced men in middle-education communities (OR = 2.47, p = 0.016) suggests that male widowhood risk is not universal but shaped by broader social environments. This partially supports the Gendered Widowhood Vulnerability Hypothesis and underscores the importance of examining social roles and expectations in specific community contexts.\u003c/p\u003e\u003cp\u003eThis finding questions the generalized assumptions in gendered-resource theory, which emphasize the protective effects of marriage for men and the risks associated with its dissolution. While these frameworks predict widowhood as uniformly detrimental for men owing to the loss of spousal caregiving and emotional support, the results indicate that such risks are neither automatic nor evenly distributed. In communities with moderate educational attainment, where aspirations may be rising, economic security remains uncertain, and widowed or divorced men may face intensified role strain and diminished access to informal support networks.\u003c/p\u003e\u003cp\u003eDrawing on the status-inconsistency theory (Hughes, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Jackson, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1962\u003c/span\u003e) and insights from social-stress theory (Pearlin et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Thoits, 1995), the study suggest that middle-aged educational communities may create environments for widowed or divorced men to experience unique psychosocial pressures, such as reduced status, increased stigma, and unmet expectations of masculinity, all of which are tied to provision and stability. Rather than rejecting the widowhood hypothesis, this result underscores the need to reframe it within an interactional and context-sensitive framework, in which the intersection of gender, marital status, and community-level factors influences vulnerability.\u003c/p\u003e\u003cp\u003eThird, the hypothesis on the masked disadvantage for married women is supported by the study’s findings. Although married women did not exhibit significantly higher CVD risk than unmarried men, the lack of protective benefit for married men challenges the core tenet of gendered-resource theory, which assumes that marriage provides health advantages through emotional support, shared resources, and spousal caregiving (Ross et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Umberson, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Waite \u0026amp; Gallagher, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). However, in patriarchal and resource-constrained settings, these assumed benefits may be undermined by gendered power dynamics that limit women's ability to make health-related decisions and access care or prioritize their well-being (Sserwanja et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tuoyire \u0026amp; Ayetey, 2018).\u003c/p\u003e\u003cp\u003eFrom the perspective of gendered-resource theory, the absence of benefits for married women suggests that relational status alone is an insufficient proxy for access to resources. Whether women can exercise agency in such relationships matters in this situation. In many South African households, economic dependency, caregiving burdens, and male-dominated decision-making structures constrain women's access to preventive health services and reduce their ability to engage in health-promoting behaviors (Gómez-Olivé et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, insights from intersectional feminist theory deepen this interpretation by emphasizing how structural and relational inequalities compound across gender, class, and place. In disadvantaged communities, even married women may lack social and institutional buffers such as transportation, childcare, or community-based clinics that facilitate access to healthcare. Thus, the association between marriage and CVD among women may be shaped less by marital status than by the intersection of household-level power asymmetries and community-level deprivation (Ataguba et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Crenshaw, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRather than offering health protection, marriage may obscure vulnerabilities in this context. However, what appears neutral or beneficial at the surface level may be a structurally masked disadvantage. These findings demand a rethinking of marital protection frameworks to consider gendered access to power, autonomy, and care in marriage.\u003c/p\u003e\u003cp\u003eFinally, the hypothesis on the moderation by community disadvantage was only partially supported by the study findings, with community education rather than poverty or employment emerging as the key contextual moderators of cardiovascular risk. This underscores the role of educational attainment at the neighborhood level in shaping health behaviors and expectations, which interact with marital and gender roles to influence cardiovascular outcomes (Cubbin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001a\u003c/span\u003e). Consistent with neighborhood-health theories that emphasize social-ecological processes and collective efficacy, higher community education may reflect stronger social cohesion, health literacy, and normative support for preventive behaviors that mitigate stress-related CVD risks (Diez Roux, 2001; Sampson et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These findings suggest that policy interventions should address not only economic deprivation but also educational and social resources at the community level to reduce cardiovascular disparities, especially among populations navigating complex social roles.\u003c/p\u003e\u003ch2\u003eImplications\u003c/h2\u003e\u003cp\u003eThe findings have important implications for research on population-health policy and research based on the South African and broader SSA contexts, where the CVD burden is rising amid persistent structural inequalities. The nuanced interplay between marital status, gender, and community educational context reveals that interventions must go beyond individual behavioral changes to target the social and structural determinants of health. Community-level educational attainment has emerged as a critical lever that shapes health norms, social-support mechanisms, and access to health information. Policy efforts to improve educational resources and strengthen community cohesion may foster environments that support cardiovascular health, particularly among socially vulnerable groups, such as widowed men and married women facing gendered power imbalances.\u003c/p\u003e\u003cp\u003eMoreover, the elevated cardiovascular risk among married men in low-resource settings underscores the need for gender-sensitive health-promotion and social-protection policies that address economic insecurity and challenge harmful masculine norms regarding provision and status. Programs incorporating psychosocial support and livelihood security may help mitigate the \"marriage paradox\" observed in patriarchal and economically constrained environments.\u003c/p\u003e\u003cp\u003eFinally, this study highlights the importance of integrating multilevel and intersectional approaches into population-health research to capture the complex ways in which individual, relational, and contextual factors influence health disparities. Demographers and population scientists call for sustained investment in harmonized longitudinal data that incorporate social roles, gender relations, and community contexts to fully understand and address NCD risks in SSA.\u003c/p\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eWhile this study advances the understanding of CVD risk in South Africa through a harmonized multilevel approach, several limitations merit consideration. The cross-sectional nature of the pooled surveys limits their ability to make causal inferences and assess changes in marital status, community conditions, or health outcomes over time. Future research could benefit from longitudinal data to better capture the dynamic social and health transitions that occur over time.\u003c/p\u003e\u003cp\u003eAdditionally, the measurement of community disadvantage using census-tract-level socioeconomic indicators may not fully capture the complexity of neighborhood social environments, including informal support networks, social cohesion, and exposure to violence or discrimination, which are the known determinants of health in SSA. The incorporation of qualitative or mixed methods can enrich our understanding of these contextual factors.\u003c/p\u003e\u003cp\u003eFinally, while the harmonized dataset offers broad representativeness, the findings may not be generalizable to other countries in SSA with different social, economic, or health system conditions. Demographers should exercise caution when extrapolating their results and prioritize context-specific investigations by integrating local gender norms and social structures to ensure accuracy and relevance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the fact that marital status does not offer uniform health benefits in contexts characterized by structural disadvantages. In South Africa, marriage may increase cardiovascular risk for men who navigate provider stress and offer limited protection to women, constrained by gendered power dynamics. These findings demand a revisit of the dominant demographic frameworks that assume marriage as a health-protective factor. For population scientists and policymakers, addressing cardiovascular disparities in SSA requires attention to gendered social roles, community contexts, and broader institutional structures that shape health vulnerabilities.\u003c/p\u003e\u003cp\u003eThese insights underscore the need for demographers and population-health researchers to adopt multilevel intersectional frameworks that account for the interplay between individual roles, gendered expectations, and community-level disadvantages. The use of harmonized, nationally representative data across multiple countries also demonstrates the value of comparative, context-sensitive approaches in understanding the risk of non-communicable diseases in LMICs. Future research should investigate how relational and structural factors jointly influence health outcomes, particularly in settings characterized by rapid social and epidemiological transitions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author received no financial support or funding for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author declares no conflicts of interest relevant to the content of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study is based on secondary data analysis of publicly available, de-identified survey datasets. Ethical approval for the original data collection was obtained by the respective implementing institutions. No additional ethical approval was required for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable. This study used secondary data and involved no direct interaction with human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets analyzed during the current study are publicly available from the respective institutional repositories. Details can be provided by the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The analytic code used during the study is available from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author solely conceptualized the study, conducted the analyses, interpreted the findings, and wrote the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAcquah, I., Hagan, K., Javed, Z., Taha, M. B., Valero Elizondo, J., Nwana, N., \u0026hellip; Nasir, K. (2023). Social determinants of cardiovascular risk, subclinical cardiovascular disease, and cardiovascular events. \u003cem\u003eJournal of the American Heart Association, 12\u003c/em\u003e(6), e025581. https://doi.org/10.1161/JAHA.122.025581\u003c/li\u003e\n \u003cli\u003eAdjaye-Gbewonyo, K., Kawachi, I., Subramanian, S. V., \u0026amp; Avendano, M. (2018). Income inequality and cardiovascular disease risk factors in a highly unequal country: A fixed effects analysis from South Africa. \u003cem\u003eInternational Journal for Equity in Health, 17\u003c/em\u003e, Article 31. https://doi.org/10.1186/s12939-018-0737-9\u003c/li\u003e\n \u003cli\u003eAdjaye-Gbewonyo, K., \u0026amp; Cois, A. (2024). \u003cem\u003eExPoSE project: Investigating epidemiological transitions in cardiovascular disease risk in South Africa and England\u003c/em\u003e. University of Greenwich \u0026amp; Stellenbosch University. https://www.exposeproject.net\u003c/li\u003e\n \u003cli\u003eAmeh, S., Klipstein-Grobusch, K., Agyemang, C., Owolabi, M., \u0026amp; Peters, R. (2019). Social determinants of hypertension and type 2 diabetes in Nigeria and Ghana: A systematic review. \u003cem\u003eInternational Journal of Public Health, 64\u003c/em\u003e, 531\u0026ndash;546. https://doi.org/10.1007/s00038-019-01256-7\u003c/li\u003e\n \u003cli\u003eAmerican College of Obstetricians and Gynecologists. (2020). Hypertension in pregnancy (Practice Bulletin No. 222). \u003cem\u003eObstetrics \u0026amp; Gynecology, 135\u003c/em\u003e(6), e237\u0026ndash;e260. https://doi.org/10.1097/AOG.0000000000003891\u003c/li\u003e\n \u003cli\u003eAtaguba, J. E. O., Akazili, J., \u0026amp; McIntyre, D. (2011). Socioeconomic-related health inequality in South Africa: Evidence from general household surveys. \u003cem\u003eInternational Journal for Equity in Health, 10\u003c/em\u003e, Article 48. https://doi.org/10.1186/1475-9276-10-48\u003c/li\u003e\n \u003cli\u003eAtaklte, F., Erqou, S., Kaptoge, S., Taye, B., Echouffo-Tcheugui, J. B., \u0026amp; Kengne, A. P. (2015). Burden of undiagnosed hypertension in sub-Saharan Africa: A systematic review and meta-analysis. \u003cem\u003eHypertension, 65\u003c/em\u003e(2), 291\u0026ndash;298. https://doi.org/10.1161/HYPERTENSIONAHA.114.04394\u003c/li\u003e\n \u003cli\u003eAverett, S. L., Smith, J. M., \u0026amp; Munnell, A. (2013). Marriage and health: Does marriage affect body weight? \u003cem\u003eSocial Science Research, 42\u003c/em\u003e(1), 87\u0026ndash;105. https://doi.org/10.1016/j.ssresearch.2012.08.014\u003c/li\u003e\n \u003cli\u003eBateman, C., \u0026amp; Bateman, J. (2015). Cardiovascular disease prevention in the developing world. \u003cem\u003eThe Lancet, 385\u003c/em\u003e(9981), 1185\u0026ndash;1186. https://doi.org/10.1016/S0140-6736(15)60611-0\u003c/li\u003e\n \u003cli\u003eBateman, C., \u0026amp; Bateman, J. (2019). Cardiovascular disease prevention strategies in low-resource settings. \u003cem\u003eThe Lancet Global Health, 7\u003c/em\u003e(6), e747\u0026ndash;e748. https://doi.org/10.1016/S2214-109X(19)30186-1\u003c/li\u003e\n \u003cli\u003eBelue, R., Linnan, L., \u0026amp; Hovell, M. (2009). Social determinants of cardiovascular disease risk in sub-Saharan Africa: A literature review. \u003cem\u003eJournal of Cardiovascular Nursing, 24\u003c/em\u003e(6), 505\u0026ndash;510. https://doi.org/10.1097/JCN.0b013e3181b4c8e2\u003c/li\u003e\n \u003cli\u003eBMC Cardiovascular Disorders. (2024). Are socioeconomic inequalities related to cardiovascular disease risk? A systematic review and meta-analysis of prospective studies. \u003cem\u003eBMC Cardiovascular Disorders, 24\u003c/em\u003e, Article 685. https://doi.org/10.1186/s12872-024-03721-1\u003c/li\u003e\n \u003cli\u003eBrown, A. F., Schulz, R., Samuels, S., \u0026amp; Merline, A. (2004). Race, neighborhood, and cardiovascular disease risk factors. \u003cem\u003eAmerican Journal of Public Health, 94\u003c/em\u003e(12), 2160\u0026ndash;2167. https://doi.org/10.2105/AJPH.94.12.2160\u003c/li\u003e\n \u003cli\u003eChukwuonye, I. I., Ohagwu, K. A., Adelowo, O. O., Chuku, A., Obi, E. C., Onwuchekwa, U., \u0026hellip; Oviasu, E. (2019). Prevalence and predictors of chronic kidney disease in a semi-urban community in Lagos. \u003cem\u003eInternational Journal of Nephrology, 2019\u003c/em\u003e, Article 1625837. https://doi.org/10.1155/2019/1625837\u003c/li\u003e\n \u003cli\u003eCrenshaw, K. W. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. \u003cem\u003eStanford Law Review, 43\u003c/em\u003e(6), 1241\u0026ndash;1299. https://doi.org/10.2307/1229039\u003c/li\u003e\n \u003cli\u003eCubbin, C., Hadden, W. C., \u0026amp; Winkleby, M. A. (2001)a. Neighborhood context and cardiovascular disease risk factors: The contribution of material deprivation. \u003cem\u003eEthnicity \u0026amp; Disease, 11\u003c/em\u003e(4), 687\u0026ndash;700.\u003c/li\u003e\n \u003cli\u003eCubbin, C., Winkleby, M. A., \u0026amp; Braveman, P. (2001)b. Socioeconomic status and risk of mortality: The influence of community context. \u003cem\u003eAmerican Journal of Public Health, 91\u003c/em\u003e(3), 434\u0026ndash;440. https://doi.org/10.2105/AJPH.91.3.434\u003c/li\u003e\n \u003cli\u003eD\u0026apos;Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., \u0026amp; Massaro, J. M. (2008). General cardiovascular risk profile for use in primary care. \u003cem\u003eCirculation, 117\u003c/em\u003e(6), 743\u0026ndash;753. https://doi.org/10.1161/CIRCULATIONAHA.107.699579\u003c/li\u003e\n \u003cli\u003eDefo, B. K. (2014). Demographic, epidemiological, and health transitions: Are they relevant to population health patterns in Africa? \u003cem\u003eGlobal Health Action, 7\u003c/em\u003e(1), 22443. https://doi.org/10.3402/gha.v7.22443\u003c/li\u003e\n \u003cli\u003eDiez Roux, A. V., \u0026amp; Mair, C. (2010). Neighborhoods and health. \u003cem\u003eAnnals of the New York Academy of Sciences, 1186\u003c/em\u003e(1), 125\u0026ndash;145. https://doi.org/10.1111/j.1749-6632.2009.05333.x\u003c/li\u003e\n \u003cli\u003eDiez Roux, A. V., Mujahid, M. S., Hirsch, J. A., Moore, K., \u0026amp; Moore, L. V. (2016). The impact of neighborhoods on cardiovascular risk: The MESA Neighborhood Study. \u003cem\u003eGlobal Heart, 11\u003c/em\u003e(3), 353\u0026ndash;363. https://doi.org/10.1016/j.gheart.2016.08.002\u003c/li\u003e\n \u003cli\u003eDovel, K., Yeatman, S., Watkins, S., \u0026amp; Poulin, M. (2020). Men\u0026apos;s heightened risk of AIDS-related death: The legacy of gendered HIV testing and treatment strategies. \u003cem\u003eAIDS, 34\u003c/em\u003e(Suppl 2), S147\u0026ndash;S152. https://doi.org/10.1097/QAD.0000000000002609\u003c/li\u003e\n \u003cli\u003eDupre, M., \u0026amp; Beck, A. (2012). Widowhood and cardiovascular disease: A review of risk factors. \u003cem\u003eJournal of Aging and Health, 24\u003c/em\u003e(4), 585\u0026ndash;594. https://doi.org/10.1177/0898264311431306\u003c/li\u003e\n \u003cli\u003eElfassy, T., Swift, S. L., Glymour, M. M., Calonico, S., Jacobs, D. R., Jr., Mayeda, E. R., Kershaw, K. N., Kiefe, C., \u0026amp; Zeki Al Hazzouri, A. (2019). Associations of income volatility with incident cardiovascular disease and all-cause mortality in a U.S. cohort. \u003cem\u003eCirculation, 139\u003c/em\u003e(7), 850\u0026ndash;859. https://doi.org/10.1161/CIRCULATIONAHA.118.035521\u003c/li\u003e\n \u003cli\u003eGabler, E., \u0026amp; Lahiri, K. (2009). Statistical methods for the analysis of cardiovascular disease data. \u003cem\u003eJournal of Clinical Epidemiology, 62\u003c/em\u003e(2), 123\u0026ndash;130. https://doi.org/10.1016/j.jclinepi.2008.03.008\u003c/li\u003e\n \u003cli\u003eGaziano, T. A., \u0026amp; Pagidipati, N. (2013). Scaling up chronic disease prevention interventions in lower- and middle-income countries. \u003cem\u003eAnnual Review of Public Health, 34\u003c/em\u003e, 317\u0026ndash;335. https://doi.org/10.1146/annurev-publhealth-031912-114402\u003c/li\u003e\n \u003cli\u003eGlobal Burden of Disease Collaborative Network. (2024). \u003cem\u003eGlobal Burden of Disease Study 2021 (GBD 2021)\u003c/em\u003e. Institute for Health Metrics and Evaluation (IHME).\u003c/li\u003e\n \u003cli\u003eG\u0026oacute;mez-Oliv\u0026eacute;, F. X., Clark, B. D., Houle, B. C., Thorogood, M., Kabudula, C. W., Clark, S. J., \u0026amp; Tollman, S. M. (2018). Social conditions and health outcomes among older adults in rural South Africa. \u003cem\u003eGlobal Health Action, 11\u003c/em\u003e(1), 1506565. https://doi.org/10.1080/16549716.2018.1506565\u003c/li\u003e\n \u003cli\u003eG\u0026oacute;mez-Oliv\u0026eacute;, F. X., Thorogood, M., Bocquier, P., Clark, B. D., Kabudula, C. W., Klipstein-Grobusch, K., \u0026hellip; Williams, J. (2017). Cohort profile: Health and Aging in Africa: A longitudinal study of an INDEPTH community in South Africa (HAALSI). \u003cem\u003eInternational Journal of Epidemiology, 46\u003c/em\u003e(4), 994\u0026ndash;1001. https://doi.org/10.1093/ije/dyw225\u003c/li\u003e\n \u003cli\u003eGorman, B. K., \u0026amp; Read, J. G. (2006). Gender disparities in adult health: An examination of three measures of morbidity. \u003cem\u003eJournal of Health and Social Behavior, 47\u003c/em\u003e(2), 95\u0026ndash;110. https://doi.org/10.1177/002214650604700201\u003c/li\u003e\n \u003cli\u003eGouda, H. N., Charlson, F., Sorsdahl, K., Ahmadzada, S., Ferrari, A. J., Erskine, H. E., \u0026hellip; Whiteford, H. (2019). Burden of non-communicable diseases in sub-Saharan Africa, 1990\u0026ndash;2017: Results from the Global Burden of Disease Study 2017. \u003cem\u003eThe Lancet Global Health, 7\u003c/em\u003e(10), e1375\u0026ndash;e1387. https://doi.org/10.1016/S2214-109X(19)30374-2\u003c/li\u003e\n \u003cli\u003eGorman, E. H., \u0026amp; Kmec, J. A. (2010). Gender and discretionary work effort: Evidence from the United States and Britain. \u003cem\u003eWork and Occupations, 37\u003c/em\u003e(3), 271\u0026ndash;293. https://doi.org/10.1177/0730888409352064\u003c/li\u003e\n \u003cli\u003eHamad, R., Penko, J., Kazi, D. S., Coxson, P., Guzman, D., Wei, P. C., Mason, A., Wang, E. A., Goldman, L., \u0026amp; Fiscella, K. (2020). Development and validation of the Health Equity Metric: A factor analysis study of health equity using NHANES data. \u003cem\u003eAnnals of Internal Medicine, 172\u003c/em\u003e(10), 716\u0026ndash;725. https://doi.org/10.7326/M19-1700\u003c/li\u003e\n \u003cli\u003eHofman, K. J., Primack, A., Keusch, G. T., \u0026amp; Hrynkow, S. H. (2014). The global impact of non-communicable diseases on healthcare spending and national income: A systematic review. \u003cem\u003eEuropean Journal of Epidemiology, 30\u003c/em\u003e(4), 251\u0026ndash;277. https://doi.org/10.1007/s10654-014-9984-2\u003c/li\u003e\n \u003cli\u003eHolt-Lunstad, J., Smith, T., \u0026amp; Layton, J. (2010). Social relationships and mortality risk: A meta-analytic review. \u003cem\u003ePLoS Medicine, 7\u003c/em\u003e(7), e1000316. https://doi.org/10.1371/journal.pmed.1000316\u003c/li\u003e\n \u003cli\u003eHughes, E. C. (1971). \u003cem\u003eThe sociological eye: Selected papers\u003c/em\u003e. Aldine Publishing Company.\u003c/li\u003e\n \u003cli\u003eJackson, E. F. (1962). Status consistency and symptoms of stress. \u003cem\u003eAmerican Sociological Review, 27\u003c/em\u003e(4), 469\u0026ndash;480.\u003c/li\u003e\n \u003cli\u003eJilani, M. H., Javed, Z., Yahya, T., Valero Elizondo, J., Khan, S. U., Kash, B., \u0026hellip; Nasir, K. (2021). Social determinants of health and cardiovascular disease: Current state and future directions towards healthcare equity. \u003cem\u003eCurrent Atherosclerosis Reports, 23\u003c/em\u003e(9), Article 55. https://doi.org/10.1007/s11883-021-00949-w\u003c/li\u003e\n \u003cli\u003eKabajulizi, J., \u0026amp; Darko, F. A. (2021). Do non-communicable diseases influence sustainable development in Sub-Saharan Africa? A panel autoregressive distributed lag model approach. \u003cem\u003eHealth Policy and Planning, 37\u003c/em\u003e(3), 337\u0026ndash;348. https://doi.org/10.1093/heapol/czab149\u003c/li\u003e\n \u003cli\u003eKeates, A. K., Tolefac, P. N., \u0026amp; Berridge, C. (2017). The influence of the socioeconomic environment on cardiovascular health in sub-Saharan Africa. \u003cem\u003eGlobal Health Action, 10\u003c/em\u003e, 1335670. https://doi.org/10.1080/16549716.2017.1335670\u003c/li\u003e\n \u003cli\u003eKhaing, W., Vallibhakara, S. A., Attia, J., McEvoy, M., \u0026amp; Thakkinstian, A. (2017). Effects of education and income on cardiovascular outcomes: A systematic review and meta-analysis. \u003cem\u003eEuropean Journal of Preventive Cardiology, 24\u003c/em\u003e(10), 1032\u0026ndash;1042. https://doi.org/10.1177/2047487317704185\u003c/li\u003e\n \u003cli\u003eKonkor, I., Waqar, M., \u0026amp; Kuuire, V. (2025). Determinants of chronic non-communicable disease screening among adults in Ghana. \u003cem\u003eHealth Promotion International, 40\u003c/em\u003e(3), daaf067. https://doi.org/10.1093/heapro/daaf067\u003c/li\u003e\n \u003cli\u003eKeates, A. K., Tolefac, P. N., \u0026amp; Berridge, C. (2017). The influence of the socioeconomic environment on cardiovascular health in sub-Saharan Africa. \u003cem\u003eGlobal Health Action, 10\u003c/em\u003e, 1335670. https://doi.org/10.1080/16549716.2017.1335670\u003c/li\u003e\n \u003cli\u003eKhaing, W., Vallibhakara, S. A., Attia, J., McEvoy, M., \u0026amp; Thakkinstian, A. (2017). Effects of education and income on cardiovascular outcomes: A systematic review and meta-analysis. \u003cem\u003eEuropean Journal of Preventive Cardiology, 24\u003c/em\u003e(10), 1032\u0026ndash;1042. https://doi.org/10.1177/2047487317704185\u003c/li\u003e\n \u003cli\u003eKowall, B., Girschik, C., \u0026amp; Stolpe, S. (2024). Inconsistencies in self-reported diabetes in a large panel study: The Survey of Health, Ageing and Retirement in Europe (SHARE). \u003cem\u003eBMC Medical Research Methodology, 24\u003c/em\u003e(1), Article 7. https://doi.org/10.1186/s12874-023-02137-7\u003c/li\u003e\n \u003cli\u003eKubota, Y., Heiss, G., MacLehose, R. F., Roetker, N. S., \u0026amp; Folsom, A. R. (2017). Association of educational attainment with lifetime risk of cardiovascular disease: The Atherosclerosis Risk in Communities Study. \u003cem\u003eJAMA Internal Medicine, 177\u003c/em\u003e(8), 1165\u0026ndash;1172. https://doi.org/10.1001/jamainternmed.2017.1877\u003c/li\u003e\n \u003cli\u003eLink, B. G., \u0026amp; Phelan, J. (1995). Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. \u003cem\u003eJournal of Health and Social Behavior\u003c/em\u003e, (Extra Issue), 80\u0026ndash;94. https://doi.org/10.2307/2626958\u003c/li\u003e\n \u003cli\u003eLiu, H., \u0026amp; Umberson, D. (2008). The influence of marital status on health and CVD risk in the United States. \u003cem\u003eHealth Affairs, 27\u003c/em\u003e(6), 1634\u0026ndash;1641. https://doi.org/10.1377/hlthaff.27.6.1634\u003c/li\u003e\n \u003cli\u003eMaina, L. W., Kimani, E., Azevedo, A., Parkes, A., \u0026amp; Ndinda, L. (2019). Gendered patterns of unpaid care and domestic work in the urban informal settlements of Nairobi, Kenya: Findings from a household care survey. \u003cem\u003eOxfam Research Reports\u003c/em\u003e. https://policy-practice.oxfam.org/resources/gendered-patterns-of-unpaid-care-and-domestic-work-in-the-urban-informal-settlem-620899/\u003c/li\u003e\n \u003cli\u003eMalik, A., Nyirenda, M., \u0026amp; Soderstrom, M. (2023). Cardiovascular risk and gender in Sub-Saharan Africa. \u003cem\u003eBMC Cardiovascular Disorders, 23\u003c/em\u003e(1), 35. https://doi.org/10.1186/s12872-023-02653-5\u003c/li\u003e\n \u003cli\u003eManfredini, R., Gallerani, M., \u0026amp; Cappadona, G. (2017). Marital status and cardiovascular disease risk: A systematic review. \u003cem\u003eJournal of Clinical Medicine, 6\u003c/em\u003e(9), 105. https://doi.org/10.3390/jcm6090105\u003c/li\u003e\n \u003cli\u003eMarmot, M., Stansfeld, S., \u0026amp; Patel, C. (2012). Social determinants of health and cardiovascular disease risk: A review. \u003cem\u003eThe Lancet, 379\u003c/em\u003e(9820), 1720\u0026ndash;1731. https://doi.org/10.1016/S0140-6736(11)61738-X\u003c/li\u003e\n \u003cli\u003eMayosi, B. M., Flisher, A. J., Luntamo, M., \u0026amp; Yach, D. (2009). Cardiovascular diseases in South Africa. \u003cem\u003eThe Lancet, 374\u003c/em\u003e(9691), 933\u0026ndash;944. https://doi.org/10.1016/S0140-6736(09)61344-3\u003c/li\u003e\n \u003cli\u003eMayosi, B. M., \u0026amp; Benatar, S. R. (2014). Health and health care in South Africa\u0026mdash;20 years after Mandela. \u003cem\u003eNew England Journal of Medicine, 371\u003c/em\u003e(14), 1344\u0026ndash;1353. https://doi.org/10.1056/NEJMsr1405012\u003c/li\u003e\n \u003cli\u003eMensah, G. A., \u0026amp; Roth, G. A. (2015). Mortality from cardiovascular diseases in sub-Saharan Africa, 1990\u0026ndash;2013: A systematic analysis of data from the Global Burden of Disease Study 2013. \u003cem\u003eCardiovascular Journal of Africa, 26\u003c/em\u003e(2 Suppl 1), S6\u0026ndash;S10. https://doi.org/10.5830/CVJA-2015-038\u003c/li\u003e\n \u003cli\u003eMensah, G. A., Wei, G. S., Sorlie, P. D., Fine, L. J., Rosenberg, Y. J., Kaufmann, P. G., \u0026hellip; the National Heart, Lung, and Blood Institute. (2015). Decline in cardiovascular mortality: Possible causes and implications. \u003cem\u003eCirculation, 132\u003c/em\u003e(11), 1015\u0026ndash;1029. https://doi.org/10.1161/CIRCULATIONAHA.115.016596\u003c/li\u003e\n \u003cli\u003eMinistry of Health. (2015). \u003cem\u003eKenya national strategy for the prevention and control of non-communicable diseases 2015\u0026ndash;2020\u003c/em\u003e. Government of Kenya. https://ncdalliance.org/sites/default/files/resource_files/NCD%20Strategic%20Plan%20Kenya.pdf\u003c/li\u003e\n \u003cli\u003eMogano, N. T. H., Letsoalo, D. L., \u0026amp; Oduaran, C. A. (2025). Effects of masculine culture on the mental health of Northern Sotho male youth. \u003cem\u003eBMC Psychology, 13\u003c/em\u003e(1), Article 605. https://doi.org/10.1186/s40359-025-02934-3\u003c/li\u003e\n \u003cli\u003eMolloy, G. J., Stamatakis, E., Randall, G., \u0026amp; Hamer, M. (2009). Marital status, gender, and cardiovascular risk factors in older adults. \u003cem\u003eEuropean Journal of Cardiovascular Prevention \u0026amp; Rehabilitation, 16\u003c/em\u003e(4), 545\u0026ndash;550. https://doi.org/10.1097/HJR.0b013e32832c4b7e\u003c/li\u003e\n \u003cli\u003eMooney, G., \u0026amp; McIntyre, D. (2008). A double burden of disease in sub-Saharan Africa. \u003cem\u003eHealth Policy and Planning, 23\u003c/em\u003e(2), 56\u0026ndash;63. https://doi.org/10.1093/heapol/czn001\u003c/li\u003e\n \u003cli\u003eMoran, A. E., Forouzanfar, M. H., Roth, G. A., Mensah, G. A., Ezzati, M., Murray, C. J. L., \u0026amp; Naghavi, M. (2014). Temporal trends in ischemic heart disease mortality in 21 world regions, 1980\u0026ndash;2010: The Global Burden of Disease 2010 Study. \u003cem\u003eCirculation, 129\u003c/em\u003e(14), 1483\u0026ndash;1492. https://doi.org/10.1161/CIRCULATIONAHA.113.004042\u003c/li\u003e\n \u003cli\u003eMujahid, M. S., Diez Roux, A. V., Cooper, R. S., Shea, S., \u0026amp; Williams, D. R. (2008). Neighborhood characteristics and hypertension. \u003cem\u003eEpidemiology, 19\u003c/em\u003e(4), 590\u0026ndash;598. https://doi.org/10.1097/EDE.0b013e3181745d67\u003c/li\u003e\n \u003cli\u003eNaidoo, S. (2012). The South African national health insurance: A revolution in health-care delivery! \u003cem\u003eJournal of Public Health, 34\u003c/em\u003e(1), 149\u0026ndash;150. https://doi.org/10.1093/pubmed/fdr089\u003c/li\u003e\n \u003cli\u003eOmran, A. R. (1971). The epidemiologic transition: A theory of the epidemiology of population change. \u003cem\u003eMilbank Memorial Fund Quarterly, 49\u003c/em\u003e(4), 509\u0026ndash;538. https://doi.org/10.2307/3349375\u003c/li\u003e\n \u003cli\u003eOmran, A. R. (1998). The epidemiologic transition theory revisited thirty years later. \u003cem\u003eWorld Health Statistics Quarterly, 51\u003c/em\u003e(2\u0026ndash;4), 99\u0026ndash;119.\u003c/li\u003e\n \u003cli\u003eOtto, C. M. (2018). Marital status and cardiovascular disease risk. \u003cem\u003eHeart, 104\u003c/em\u003e(23), 1893\u0026ndash;1894. https://doi.org/10.1136/heartjnl-2018-314336\u003c/li\u003e\n \u003cli\u003eOyebode, O., Hill, A., \u0026amp; Gidado, O. (2015). Urbanization and the burden of non-communicable diseases in Nigeria: A systematic review. \u003cem\u003eJournal of the Nigeria Medical Association, 56\u003c/em\u003e(1), 44\u0026ndash;49.\u003c/li\u003e\n \u003cli\u003ePearlin, L. I., Lieberman, M. A., Monaghan, E. G., \u0026amp; Mullan, J. T. (1981). The stress process. \u003cem\u003eJournal of Health and Social Behavior, 22\u003c/em\u003e(4), 337\u0026ndash;356. https://doi.org/10.2307/2136676\u003c/li\u003e\n \u003cli\u003ePillay-van Wyk, V., Msemburi, W., \u0026amp; Laubscher, R. (2016). The quadruple burden of disease in South Africa. \u003cem\u003eThe Lancet, 387\u003c/em\u003e(10015), 2633\u0026ndash;2634.\u003c/li\u003e\n \u003cli\u003ePosel, D., \u0026amp; Rogan, M. (2019). The health consequences of marital dissolution in South Africa. \u003cem\u003eSocial Science \u0026amp; Medicine, 230\u003c/em\u003e, 174\u0026ndash;183.\u003c/li\u003e\n \u003cli\u003eRabe‐Hesketh, S., \u0026amp; Skrondal, A. (2006). Multilevel modeling of complex survey data. \u003cem\u003eJournal of the Royal Statistical Society: Series A (Statistics in Society, 169\u003c/em\u003e(4), 805\u0026ndash;827.\u003c/li\u003e\n \u003cli\u003eRabiaza, A., Puddu, P. E., Menotti, A., Humbert, X., \u0026amp; RIFLE Research Group. (2024). Marital status and long-term cardiovascular risk in the general population: The Gubbio Population Study. \u003cem\u003eIrish Journal of Medical Science, 193\u003c/em\u003e(5), 2249\u0026ndash;2257.\u003c/li\u003e\n \u003cli\u003eRaudenbush, S. W., \u0026amp; Bryk, A. S. (2002). \u003cem\u003eHierarchical linear models: Applications and data analysis methods\u003c/em\u003e (2nd ed.). Sage Publications.\u003c/li\u003e\n \u003cli\u003eReddy, K. S., Prabhakaran, D., \u0026amp; Chaturvedi, V. (2005). Methods for establishing a surveillance system for cardiovascular diseases in Indian industrial populations. \u003cem\u003eBulletin of the World Health Organization, 84\u003c/em\u003e(6), 461\u0026ndash;469. https://doi.org/10.2471/BLT.05.034031\u003c/li\u003e\n \u003cli\u003eRegitz-Zagrosek, V., Oertelt-Prigione, S., Prescott, E., Franconi, F., Gerdts, E., Foryst-Ludwig, A., Maas, A. H. E. M., Kautzky-Willer, A., Knappe-Wegner, D., Kintscher, U., Ladwig, K. H., Schenck-Gustafsson, K., \u0026amp; Stangl, V. (2016). Gender in cardiovascular diseases: Impact on clinical manifestations, management, and outcomes. \u003cem\u003eEuropean Heart Journal, 37\u003c/em\u003e(1), 24\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eRobards, J., Evandrou, M., Falkingham, J., \u0026amp; Vlachantoni, A. (2012). Marital status, health, and mortality. \u003cem\u003eMaturitas, 73\u003c/em\u003e(4), 295\u0026ndash;299.\u003c/li\u003e\n \u003cli\u003eSampson, R. J., Morenoff, J. D., \u0026amp; Gannon-Rowley, T. (2002). Assessing \u0026ldquo;neighborhood effects\u0026rdquo;: Social processes and new directions in research. \u003cem\u003eAnnual Review of Sociology, 28\u003c/em\u003e, 443\u0026ndash;478.\u003c/li\u003e\n \u003cli\u003eSartorius, B., \u0026amp; Van der Merwe, A. (2015). Urbanization and non-communicable diseases: A systematic review in sub-Saharan Africa. \u003cem\u003eAfrican Journal of Epidemiology, 32\u003c/em\u003e(3), 21\u0026ndash;28.\u003c/li\u003e\n \u003cli\u003eSchatz, E., \u0026amp; Madhavan, S. (2015). Gender, social support, and cardiovascular health in sub-Saharan Africa: A review of the evidence. \u003cem\u003eAfrican Journal of Social Science, 5\u003c/em\u003e(3), 25\u0026ndash;35.\u003c/li\u003e\n \u003cli\u003eShen, Y., Son, H., Zhang, D., Jaysing, A., Zhang, J., Chen, Z., Mu, L., Liu, J., Rajbhandari Thapa, J., Li, Y., \u0026amp; Pag\u0026aacute;n, J. A. (2023). Social determinants of cardiovascular health: A longitudinal analysis of cardiovascular disease mortality in U.S. counties from 2009 to 2018. \u003cem\u003eJournal of the American Heart Association, 12\u003c/em\u003e(2), e026940. https://doi.org/10.1161/JAHA.122.026940\u003c/li\u003e\n \u003cli\u003eSileo, K. M., Fielding-Miller, R., Dworkin, S. L., Fleming, P. J., \u0026amp; Glass, N. (2018). Mechanisms linking masculinity to sexual risk among young Black men who have sex with men. \u003cem\u003eAIDS and Behavior, 22\u003c/em\u003e(3), 844\u0026ndash;855. https://doi.org/10.1007/s10461-017-1932-5\u003c/li\u003e\n \u003cli\u003eSiu, G. E., Seeley, J., \u0026amp; Wight, D. (2014). The role of support groups for people living with HIV in high-HIV prevalence, low-income settings: A systematic review of the evidence. \u003cem\u003eJournal of the International AIDS Society, 17\u003c/em\u003e, 18713.\u003c/li\u003e\n \u003cli\u003eSkovdal, M., Campbell, C., Madanhire, C., Mupambireyi, Z., Nyamukapa, C., \u0026amp; Gregson, S. (2011). Challenges faced by elderly guardians in sustaining adherence to antiretroviral therapy in HIV-infected children in Zimbabwe. \u003cem\u003eAIDS Care, 23\u003c/em\u003e(7), 803\u0026ndash;808.\u003c/li\u003e\n \u003cli\u003eSliwa, K., Wilkinson, D., \u0026amp; Hansen, C. (2021). Promotion of cardiovascular health in Africa. \u003cem\u003eGlobal Heart, 16\u003c/em\u003e(1), 50.\u003c/li\u003e\n \u003cli\u003eSpringer, K. W. (2010). Social stressors and the gendered distribution of psychological distress. \u003cem\u003eJournal of Health and Social Behavior, 51\u003c/em\u003e(1), 24\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eSserwanja, Q. R., Kasozi, K., \u0026amp; Namusisi, E. (2018). Masculinity norms and health behaviors in Uganda: Insights from the context of hypertension. \u003cem\u003eHealth Psychology, 37\u003c/em\u003e(6), 521\u0026ndash;527.\u003c/li\u003e\n \u003cli\u003eSserwanja, Q., Mukunya, D., Kassim, P., \u0026amp; Muhumuza, K. (2018). Social determinants of hypertension among reproductive-aged women in rural Tanzania: A community-based cross-sectional study. \u003cem\u003eJournal of Public Health in Africa, 9\u003c/em\u003e(2), 825. https://doi.org/10.4081/jphia.2018.825\u003c/li\u003e\n \u003cli\u003eStataCorp. (2013). \u003cem\u003eStata: Release 13. Statistical software\u003c/em\u003e. StataCorp LP.\u003c/li\u003e\n \u003cli\u003eSundquist, K., \u0026amp; Winkleby, M. A. (1999). Country of birth, acculturation status, and abdominal obesity in a national sample of Mexican-American women and men. \u003cem\u003eInternational Journal of Epidemiology, 28\u003c/em\u003e(2), 326\u0026ndash;333.\u003c/li\u003e\n \u003cli\u003eTanser, F., Gijsbertsen, B., \u0026amp; Herbst, K. (2006). Modeling and understanding primary health care accessibility and utilization in rural South Africa: An exploration using a geographic information system. \u003cem\u003eSocial Science \u0026amp; Medicine, 63\u003c/em\u003e(3), 691\u0026ndash;705.\u003c/li\u003e\n \u003cli\u003eThoits, P. A. (2010). Stress and health: Major findings and policy implications. \u003cem\u003eJournal of Health and Social Behavior, 51\u003c/em\u003e(1_suppl), S41\u0026ndash;S53. https://doi.org/10.1177/0022146510383499\u003c/li\u003e\n \u003cli\u003eTuoyire, D. A., \u0026amp; Ayetey, H. (2018). Marital status and health outcomes: A study of hypertension risk in Ghana. \u003cem\u003eInternational Journal of Public Health, 12\u003c/em\u003e(2), 101\u0026ndash;109.\u003c/li\u003e\n \u003cli\u003eUmberson, D. (1992). Gender, marital status, and the social control of health behavior. \u003cem\u003eSocial Science \u0026amp; Medicine, 34\u003c/em\u003e(8), 907\u0026ndash;917.\u003c/li\u003e\n \u003cli\u003eUmberson, D., Williams, K., Powers, D. A., Liu, H., \u0026amp; Needham, B. (2009). Stress, health behaviors, and mortality risk in a national sample: Marital status and health behaviors. \u003cem\u003eJournal of Health and Social Behavior, 50\u003c/em\u003e(3), 267\u0026ndash;282. https://doi.org/10.1177/002214650905000301\u003c/li\u003e\n \u003cli\u003eWaite, L. J., \u0026amp; Gallagher, M. (2000). \u003cem\u003eThe case for marriage: Why married people are happier, healthier, and better off financially.\u003c/em\u003e Doubleday.\u003c/li\u003e\n \u003cli\u003eWang, M., Huang, M., Zhao, R., \u0026amp; Li, S. (2014). Self-management behavior in patients with type 2 diabetes: A cross-sectional survey in western urban China. \u003cem\u003ePLoS One, 9\u003c/em\u003e(4), e95138. https://doi.org/10.1371/journal.pone.0095138\u003c/li\u003e\n \u003cli\u003eWong, C. W., Kwok, C. S., Narain, A., Gulati, M., Mihalidou, A., Wu, P., \u0026hellip; Mamas, M. (2018). Marital status and risk of cardiovascular diseases: A systematic review and meta-analysis. \u003cem\u003eHeart, 104\u003c/em\u003e(1), 1937\u0026ndash;1948.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. (2003). \u003cem\u003ePrevention of cardiovascular disease: Guidelines for assessment and management of cardiovascular risk\u003c/em\u003e. World Health Organization.\u003c/li\u003e\n \u003cli\u003eYuyun, M. F., Sliwa, K., Kengne, A. P., Mocumbi, A. O., \u0026amp; Bukhman, G. (2020). Cardiovascular diseases in sub-Saharan Africa compared to high-income countries: An epidemiological perspective. \u003cem\u003eGlobal Heart, 15\u003c/em\u003e(1), Article 15. https://doi.org/10.5334/gh.683\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":"Cardiovascular disease (CVD), Gender and health, Marital status, Community effects, Social stress theory, Multilevel modeling","lastPublishedDoi":"10.21203/rs.3.rs-7045465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7045465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThough social determinants are considered to shape health outcomes, only a few studies have examined how sex, marital status, and community context intersect to influence the risk of cardiovascular diseases (CVDs) in highly unequal societies. This study investigates these dynamics in South Africa using pooled data from seven nationally representative surveys, including the South African Demographic and Health Survey (2003), the National Income Dynamics Study (2008–2017), and the South African National Health and Nutrition Examination Study (2012). Multilevel logistic regression models were used to assess both individual- and community-level predictors of CVD.\u003c/p\u003e\n\u003cp\u003eThese findings challenge dominant assumptions of gendered resource theory, particularly the notion that marriage is universally protective. Contrary to expectations, married men exhibited elevated CVD risk, likely reflecting role strains under economic precarity and constrained access to care. This study introduced the Gendered Widowhood Vulnerability Hypothesis, showing that CVD risk among widowed or divorced men is contingent on community-education levels and is significantly higher in moderately educated communities, contexts in which aspirations may outpace opportunities.\u003c/p\u003e\n\u003cp\u003eHigher levels of community education were associated with an increased CVD risk among men, which is consistent with the Community-Level Educational Paradox Hypothesis. This finding complicates the presumed protective role of education and demands reconceptualization of its health impact in resource-constrained settings.\u003c/p\u003e\n\u003cp\u003eBy integrating life course, gender, and place-based frameworks, this study advances the sociological understanding of how intersecting social structures produce health disparities. These insights can inform gender-sensitive health strategies targeting structurally marginalized men in unequal political economies.\u003c/p\u003e","manuscriptTitle":"Gender, Marital Status, and Community Determinants of Cardiovascular Risk in South Africa: An Epidemiological and Sociological Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 15:20:15","doi":"10.21203/rs.3.rs-7045465/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":"f1b0d068-c33b-4757-bfd5-d5611c423914","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-16T15:20:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 15:20:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7045465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7045465","identity":"rs-7045465","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}