Gender Differences in Social Determinants of Hypertension Among Older Brazilian Adults Residing in Urban Areas: A Multilevel Approach from the ELSI-Urbe

Gender Differences in Social Determinants of Hypertension Among Older Brazilian Adults Residing in Urban Areas: A Multilevel Approach from the ELSI-Urbe | 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 Differences in Social Determinants of Hypertension Among Older Brazilian Adults Residing in Urban Areas: A Multilevel Approach from the ELSI-Urbe Débora Moraes Coelho, Amanda Cristina de Souza Andrade, Bruno de Souza Moreira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5176210/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2025 Read the published version in Journal of Urban Health → Version 1 posted 2 You are reading this latest preprint version Abstract Despite growing interest in the social determinants of hypertension, nationally representative studies analyzing the intra-urban effects of individual and contextual socioeconomic conditions within highly unequal urban areas, such as those in Latin America, remain scarce. This paper describes gender disparities in the association of individual and contextual socioeconomic conditions with hypertension among older adults residing in urban areas of Brazil. Data from 6,767 participants from the baseline (2015–2016) of the Brazilian Longitudinal Study of Aging (ELSI-Brazil), a cohort with a nationally representative sample of community-dwelling adults aged ≥ 50 years, were analyzed. Hypertension was self-reported. The measure of individual socioeconomic condition was individual education, and the contextual measure was the Brazilian Deprivation Index (IBP; acronym in Portuguese) of the census tract of the participant’s residence. Multilevel logistic regression models (individuals and census tracts), adjusted for age and stratified by gender, were used. The prevalence of hypertension according to individual education and the IBP differed between men and women. In women, higher individual education (≥ 9 years versus ≤ 4 years of schooling) was associated with a lower chance of hypertension (odds ratio [OR] = 0.62; 95% confidence interval [CI] = 0.52–0.74), and residing in census tracts with higher deprivation was associated with a higher chance of hypertension (OR per standard deviation [SD] = 1.04; 95%CI = 1.01–1.09). In men, there was no significant association between individual education or IBP and hypertension. Our results suggest that public policies addressing this disease’s burden in middle- and low-income countries, such as Brazil, should adopt gender-sensitive strategies and consider the context in which these individuals reside. Neighborhoods Socioeconomic factors Hypertension Multilevel Analysis Figures Figure 1 INTRODUCTION The prevalence of hypertension, the leading cause of cardiovascular diseases and premature death [ 1 ], is decreasing in high-income countries but increasing in low- and middle-income countries [ 2 ]. In these regions, beyond the rapid population aging, urbanization has been recognized as an important determinant of the growing burden of hypertension and its risk factors [ 3 ]. Urban living exposes individuals to higher pollution levels (air, water, light, and noise), increased stress, and reduced access to green spaces [ 3 ]. Moreover, urban environments contribute to nutritional transition, decreased physical activity, and higher consumption of tobacco and alcohol [ 4 ]. Increased exposure to these factors is not evenly distributed across urban populations and tends to be concentrated among the most socioeconomically disadvantaged groups [ 3 ]. Among urban residents, individual socioeconomic conditions are strongly associated with hypertension in high-income countries, particularly among women [ 5 , 6 ]. In low- and middle-income countries, however, studies are limited and suggest the presence of significant heterogeneities in the social patterning of hypertension [ 7 – 9 ]. Socioeconomic characteristics at the neighborhood level — that is, the local context (e.g., census tracts) — are also linked to hypertension [ 9 – 13 ]. A previous study demonstrated that residing in a neighborhood with a lower socioeconomic level was associated with an increase in blood pressure over time and a higher incidence of hypertension over a 9.7-year follow-up period [ 11 ]. Despite this evidence, studies investigating neighborhood-level effects in low- and middle-income countries remain scarce and are predominantly based on local samples, often restricted to a single city [ 14 , 15 ]. Moreover, nationally representative studies have examined the urban environment at broader geographic scales, such as entire cities or larger spatial units like the Brazilian Federative Units [ 8 , 16 ]. Individual and neighborhood socioeconomic conditions can contribute to the development of hypertension through multiple processes. At the individual level, socioeconomic conditions can influence health behaviors and stress-related processes [ 6 ]. Neighborhood socioeconomic conditions, in turn, encompass environmental characteristics such as the availability of physical spaces for purchasing healthy foods, safe places for physical activity, transportation services, and access to healthcare services [ 12 , 13 ]. Additionally, residing in a neighborhood with greater social vulnerability, characterized by poor sanitation, unhealthy housing, high crime rates, violence, and inadequate investment in human capital, can elevate chronic stress levels, thereby contributing to the development of hypertension [ 12 ]. Additionally, research indicates that older individuals [ 10 ] and women [ 17 ] are more susceptible to the characteristics of the environment where they live. Brazil, the most populous country in Latin America and a middle-income nation, has undergone rapid urbanization and population aging in recent years. Currently, over 60% of the population lives in urban areas, and 15.6% are classified as older adults [ 18 ]. This demographic transition has been accompanied by an epidemiological transition characterized by an increasing prevalence of non-communicable chronic diseases and their risk factors, such as hypertension, which are more common in this age group [ 19 ]. This study contributes by using data from a nationally representative survey of Brazilians aged 50 years and over to assess the association between neighborhood characteristics and hypertension. It also addresses a gap in the literature by incorporating intra-urban data, enabling the identification of more vulnerable groups and supporting the planning of more effective public health interventions. Therefore, the goal of this study was to investigate the association of individual and contextual socioeconomic conditions with hypertension by gender, among older adults residing in urban areas of Brazil. We hypothesized that poor individual and contextual socioeconomic conditions would be associated with a higher prevalence of hypertension. In addition, differences by individual and contextual socioeconomic conditions on hypertension would be higher among women than men. METHODS Data source This study used data from the project ELSI-Urbe, "The influence of the physical and social environment on the health of older Brazilian adults: a longitudinal multimethod population-based study,” coordinated by the Belo Horizonte Urban Health Observatory of the Universidade Federal de Minas Gerais (OSUBH-UFMG). The objective of ELSI-Urbe is to identify the attributes of the neighborhood’s physical and social environment that influence the health and well-being of older adults over time. This project is nested within the Brazilian Longitudinal Study of Aging (ELSI-Brazil). The ELSI-Brazil is a population-based longitudinal study, representative of the non-institutionalized Brazilians aged 50 and over residing in 70 municipalities across all five major regions of the country [ 20 ]. The sampling design of ELSI-Brazil utilized a multistage stratified approach, combining stratification of primary sampling units (municipalities), census tracts, and households. Data were collected using a structured questionnaire divided into three main modules: (1) household, (2) individual, and (3) physical measurements (including anthropometry, blood pressure, and physical functioning). The first wave was conducted in 2015–2016, with participation from 9,412 individuals, and the second wave took place in 2019–2021, with 9,949 participants. Detailed information regarding study design and recruitment methods was published elsewhere [ 20 ]. The ELSI-Brazil was approved by the Ethics Committee of the Oswaldo Cruz Foundation – Minas Gerais (CAAE: 34649814.3.0000.5091); all participants or legal guardians have signed an informed consent. The ELSI-Urbe developed a multilevel data structure to integrate individual-level data collected in ELSI-Brazil with contextual data obtained from open-access sources and aggregated across different geographic units of residence (regions, municipalities, neighborhoods, and census tracts). At the individual level, only participants residing in urban areas were included. At the contextual level, the smallest geographic unit used for aggregation was the census tract of the participants’ residence. Each participant was assigned a common geocode to link individual and contextual data. This geocode was used exclusively to construct the multilevel database and was deleted after processing to ensure confidentiality and compliance with data protection guidelines. Non-public geographic identifiers were stored in a secure environment with access restricted to authorized researchers. Study sample For this study, the ELSI-Urbe sample was structured using a two-level multilevel design: individuals, obtained from the first wave of ELSI-Brazil, nested within their residential census tracts. Thus, the eligible population consisted of participants residing in urban areas with available census tract information (n = 7,751 individuals nested within 609 census tracts). Our analyses were restricted to eligible individuals who completed the interview independently — without the assistance of a proxy respondent — and who had complete data on both the outcome and the exposure variables at the individual and contextual levels. Further details on the inclusion and exclusion criteria are provided in Supplementary Fig. 1. Dependent Variable Hypertension Participants were defined as having hypertension if they reported that a physician had told them that they had hypertension. The information was obtained through the following question: “Has a doctor ever told you that you have hypertension (high blood pressure)?” (“no”; “yes”; “yes, only during pregnancy”; “didn’t know/didn’t answer”). Participants who answered “yes, only during pregnancy” or “didn’t know/didn’t answer” were excluded from the analysis. Independent Variables Individual-level education Education was used as a proxy for individual socioeconomic conditions due to its wide availability for both sexes and stability over time [ 21 ]. Beyond its impact on labor market access and income, education is linked to health behaviors [ 21 ] and stands out as the strongest predictor of hypertension compared to income or occupation [ 5 ]. This variable was obtained from the question: “What was the highest grade in school that you completed?”. The question had the following answer options: 1) never studied; 2) 1st grade (Elementary School); 3) 2nd grade (Elementary School); 4) 3rd grade (Elementary School); 5) 4th grade (Elementary School); 6) 5th grade (Elementary School); 7) 6th grade (Middle School); 8) 7th grade (Middle School); 9) 8th grade (Middle School); 10) 9th grade (High School); 11) 10th grade (High School); 12) 11/12th grade (High School); 13) school for adults; 14) some college (incomplete); 15) complete college; 16) specialization/medical residency; 17) Master’s degree; 18) Doctoral degree (Ph.D.). In Brazil, most of the older population has a low level of education [ 22 ]. For this study, education was categorized as follows: ≤ 4 years; 5 to 8 years; ≥ 9 years. These categories better capture educational inequalities within this cohort, as demonstrated in a study that used the same categorization [ 16 ]. Brazilian Deprivation Index (IBP) The Brazilian Deprivation Index (IBP, acronym in Portuguese), an indicator developed using data from the 2010 Demographic Census [ 23 ], was used as a continuous variable to characterize the contextual socioeconomic condition at the census tract level. The IBP is based on three components: (1) the percentage of households with a per capita income ≤ 1/2 minimum wage; (2) the percentage of individuals over 7 years of age who are illiterate; and (3) the average percentage of individuals with inadequate access to basic sanitation services — including sewage, water supply, garbage collection — as well as the absence of a bathroom, bathtub, or shower [ 23 ]. A deprivation score was created by summing the standardized z-scores of these three indicators, weighted by the population of each census tract. It was designed to measure social inequalities at the smallest geographic units in Brazil. Higher IBP scores indicate greater deprivation, whereas lower scores represent less deprived areas [ 23 ]. Other Variables Age and gender were also included in this study. Age (in years) was used as an adjustment variable due to its direct association with the prevalence of hypertension, particularly in older populations [ 19 ]. Gender, defined as sex assigned at birth and categorized as men or women, was considered a potential moderator and used as a stratification variable, based on evidence indicating biological, behavioral, and social differences in the prevalence and determinants of hypertension between men and women [ 17 , 24 , 25 ]. Statistical analysis All analyses were stratified by gender. Initially, descriptive statistics were performed using frequency distributions (%), means, and standard deviations (SD) according to the hypertension status. Posteriorly, age-adjusted hypertension prevalence estimates, and their respective 95% confidence intervals (95% CI) were calculated according to individual education categories (≤ 4, 5–8, ≥ 9 years of education) and the tertiles of the IBP of the census tract (the 1st tertile corresponds to the stratum with the least deprivation and the 3rd tertile to the stratum with the greatest deprivation). In this analysis, the IBP was categorized into tertiles using the subsample of census tracts included in the study. Adjusted prevalence estimates were calculated using a binary logistic regression model. Bivariate and adjusted associations between the outcome and independent variables were analyzed using two-level multilevel logistic regression (individuals and census tracts), including a random effect for the census tract. First, we adjusted a model without independent variables but with a random effect at the census tract level to assess the contribution of variability between census tracts to hypertension (Model 1). Model 2 included individual education and Model 3 included individual education and the IBP. Models 2 and 3 were adjusted for age (in years). Odds ratios (OR) and their respective 95% CI were estimated. We reported the median odds ratio (MOR) for all models to show the residual area-level heterogeneity [ 26 ]. MOR represents the median value of the OR between areas with higher and lower risks when randomly picking two areas. The proportion of change in variance (PCV) was also reported for each model. A sensitivity analysis was conducted using measured blood pressure to assess the robustness of the findings. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg and/or the use of antihypertensive drugs. Additionally, a sensitivity analysis was performed considering only census tracts with at least three eligible older adults, as some census tracts had a small number of older adults in the sample. All estimates accounted for sampling weights and the complex sample design. The survey command (svy) was used for descriptive analysis. Analyses were performed using the statistical software STATA version 17, with a significance level of 5%. RESULTS From the total of 7,751 eligible participants for the present analysis, 6,767 completed the interview independently, without the assistance of a proxy respondent, and provided complete information for all variables of interest. Therefore, the final sample comprised 6,767 participants distributed across 605 census tracts. Details on exclusions are presented in Supplementary Figure S1 . The median number of participants per census tract was 11.2 (minimum = 1; maximum = 24), with 2.9% of census tracts having up to three participants (n = 18) (Table 1 ). Table 1 Distribution of participants by Brazilian census tracts. ¹ Unweighted. Total ¹ Women ¹ Men ¹ Number of participants 6,767 3,876 2,891 Number of census tracts 605 553 590 Participants per census tract Median (Minimum – Maximum) 11.2 (1–24) 7.0 (1–16) 4.9 (1–14) ≤ 3 participants per census tract, n (%) 18 (2.9) 60 (10.8) 104 (17.6) ¹ Unweighted. Table 2 displays the descriptive characteristics and bivariate analysis according to the hypertension status for the total sample and stratified by gender. The mean age of participants was 61.6 years (SD = 9.1), with 54.4% being women, and 44.3% having between 0 and 4 years of education. The mean age was 62.0 years (SD = 9.5) for women and 61.1 years (SD = 8.6) for men. Regarding individual education, 45.6% of women and 42.7% of men had up to 4 years of education. The overall prevalence of hypertension was 51.4% (95% CI = 49.3–53.6%) (data not shown in table), with a significantly higher proportion among women (57.8%; 95% CI = 54.4–61.1%) compared to men (42.2%; 95% CI = 38.9–45.6%). Table 2 Characteristics of study participants and bivariate analysis1 stratified by gender. Characteristics Total Sample Hypertension Yes No OR (95% CI) TOTAL SAMPLE Individual Age, mean (SD) 61.6 (9.1) 63.3 (9.4) 59.7 (8.4) 1.04 (1.03–1.05)*** Gender, % Men 45.6 42.2 49.0 1.00 Women 54.4 57.8 51.0 1.28 (1.15–1.43)*** Individual Education, % ≤ 4 years 44.3 49.7 38.5 1.00 5 to 8 years 23.3 22.6 24.0 0.74 (0.65–0.85)*** ≥ 9 years 32.4 27.7 37.5 0.58 (0.51–0.66)*** Contextual Brazilian Deprivation Index, mean (SD) − 1.21 (1.65) − 1.16 (1.68) − 1.25 (1.62) 1.01 (0.98–1.04) n (unweighted) 6,767 3,581 3,186 WOMEN Individual Age, mean (SD) 62.0 (9.5) 63.9 (9.9) 59.8 (8.5) 1.05 (1.04–1.06)*** Individual Education, % ≤ 4 years 45.6 52.4 37.4 1.00 5 to 8 years 22.5 22.1 23.1 0.71 (0.59–0.85)*** ≥ 9 years 31.9 25.5 39.5 0.48 (0.41–0.56)*** Contextual Brazilian Deprivation Index, mean (SD) − 1.20 (1.67) − 1.10 (1.73) − 1.31 (1.58) 1.06 (1.02–1.10)** n (unweighted) 3,876 2,190 1,686 MEN Individual Age, mean (SD) 61.1 (8.6) 62.6 (8.5) 59.7 (8.4) 1.03 (1.02–1.04)*** Individual Education, % ≤ 4 years 42.7 46.1 39.6 1.00 5 to 8 years 24.2 23.4 25.0 0.82 (0.67–1.01) ≥ 9 years 33.1 30.5 35.4 0.76 (0.62–0.92)** Contextual Brazilian Deprivation Index, mean (SD) − 1.21 (1.62) − 1.23 (1.59) − 1.19 (1.64) 0.96 (0.92–1.01) n (unweighted) 2,891 1,391 1,500 SD: standard deviation. OR: odds ratio. CI: confidence interval. Significance level: **<0.01, ***<0.001. 1 The bivariate model between individual and contextual characteristics and arterial hypertension was carried out using multilevel analysis with a random effect at two levels: individual nested in the census tract. All estimates considered the sampling weights and the complex sample design. The bivariate analysis indicated that women are 28% more likely to report hypertension compared to men. For both genders, age showed a positive association with hypertension. Among women, higher education levels were associated with lower odds of hypertension, while residing in census tracts with higher deprivation was associated with higher odds of this disease. Among men, those with 9 years or more of education exhibited lower odds of hypertension (Table 2 ). Figure 1 depicts the age-adjusted prevalence of hypertension for the total sample and stratified by gender, according to individual education categories and tertiles of the IBP of the census tract. The prevalence of hypertension showed a statistically significant dose-response gradient concerning individual education only among women, with a higher prevalence observed among those with lower education levels (≤ 4 years) (Fig. 1 A). Similarly, a significant dose-response gradient in the prevalence of hypertension was observed across the tertiles of the IBP, also only among women, with a higher prevalence among those residing in census tracts with higher deprivation (3rd IBP tertile) (Fig. 1 B). Table 3 presents the fixed and random parameters between individual education, IBP of the census tract, and hypertension, stratified by gender. Concerning random parameters, the empty model shows neighborhood-level variance in hypertension (Model 1). The MOR at the neighborhood-level was 1.16 in women and 1.55 in men, showing that there are significant differences between neighborhoods. If individuals are selected from two randomly chosen neighborhoods, the odds of reporting hypertension in neighborhoods with more hypertensive individuals are 16% and 55% higher in women and men, respectively. Table 3 Adjusted associations between individual education, Brazilian Deprivation Index (IBP) of the census tract, and arterial hypertension, stratified by gender, for older Brazilian adults residing in urban areas. Model 1 Model 2 Model 3 OR (95% CI) OR (95% CI) OR (95% CI) WOMEN Individual Education ≤ 4 years 1.00 1.00 5 to 8 years 0.85 (0.71–1.02) 0.88 (0.73–1.06) ≥ 9 years 0.59 (0.50–0.70)** 0.62 (0.52–0.74)** Brazilian Deprivation Index -- 1.04 (1.01–1.09)* Random parameters Variance (standard error) 0,024 (0,040) 0,024 (0,042) 0,018 (0,041) PCV Ref. 1% 24% MOR 1,16 1,16 1,13 MEN Individual Education ≤ 4 years 1.00 1.00 5 to 8 years 0.95 (0.77–1.18) 0.92 (0.75–1.14) ≥ 9 years 0.89 (0.73–1.09) 0.84 (0.68–1.04) Brazilian Deprivation Index -- 0.95 (0.90-1.00) Random parameters Variance (standard error) 0,215 (0,074) 0,146 (0,070) 0,141 (0,068) PCV Ref. 32% 32% MOR 1,55 1,43 1,42 OR: odds ratio. CI: confidence interval. Significance level: *<0.05, **<0.01. PCV: Proportion change in variance. MOR: Median odds-ratio. The Brazilian Deprivation Index of the census tract was standardized to a mean of 0 and a standard deviation (SD) of 1. Model 1: Null model (random effect at the census tract level). Model 2: Education, adjusted for age (random effect at the census tract level). Model 3: Education and Brazilian Deprivation Index, adjusted for age (random effect at the census tract level). All estimates considered the sampling weights and the complex sample design. Regarding the fixed parameters, model 3 results show that the odds of hypertension were 38% lower among women with 9 years or more of education compared to those with 4 years or fewer of education. Additionally, IBP was positively associated with hypertension among women, indicating that higher deprivation of the census tract was associated with higher odds of women reporting hypertension. For men, neither individual education nor IBP was significantly associated with hypertension. The sensitivity analyses using measured blood pressure to define hypertension yielded results similar to those of the main analysis for both women and men, maintaining consistency in directionality and association patterns for both exposures in the full model. The results for the random parameters were also consistent with the main analysis (Supplementary Table 1). Similarly, multilevel models that included only census tracts with at least three eligible older adults showed results comparable to those observed in the main analysis (data not shown). DISCUSSION The present study examined the association between socioeconomic conditions at different levels (individual and neighborhood) and hypertension in a nationally representative sample of older Brazilian adults living in urban areas. Our findings revealed a high prevalence of hypertension, particularly among women, aligning with previous literature [ 27 ]. Additionally, we observed an inverse association between individual education and hypertension among women. Furthermore, residing in census tracts with higher levels of deprivation was associated with increased odds of hypertension in women. In contrast, no significant associations were found among men. Research conducted in high-income countries shows a higher prevalence of hypertension among individuals with lower levels of education compared to those with higher education, with this association being more consistent and stronger among women [ 5 ]. Data from low- and middle-income countries, including those from Latin America, show variability in educational gradients [ 8 , 9 , 14 ]. For instance, a recent study analyzing data from eight Latin American countries (Argentina, Brazil, Chile, Colombia, El Salvador, Guatemala, Mexico, and Peru) found that higher education was associated with lower odds of hypertension in women and higher odds in men [ 8 ]. Studies examining the association between education and hypertension among urban residents (aged 18 years and over) in Argentina [ 9 ] and Brazil [ 14 ] also observed an inverse association in women but no association in men. Thus, our findings regarding the association between education and hypertension align with those reported among women in high-income countries and Latin America, and they support previous Latin American studies that reported no association among men. While the reasons for observed gender differences remain unclear, several explanations linking higher education to a decreased risk of hypertension in women warrant consideration. Education may provide greater benefits for women and compensate for potential disparities in access to other resources such as income, power, and authority [ 19 ]. Furthermore, the distribution of risk factors and access to healthcare based on socioeconomic position may differ between men and women [ 24 ]. The chronic stress stemming from work and home environments, such as informal employment, single motherhood, role overload, and experiences of violence, may also affect men and women differently [ 29 , 30 ]. The present study also found that census tract deprivation, as measured by the IBP, is associated with hypertension, but the patterns vary by gender. Among women, residing in deprived census tracts increases the odds of hypertension. However, similar results were observed in a recent study investigating the association between census tract-level education and hypertension in Argentina among participants aged 18 years and older [ 9 ]. There are several plausible pathways through which census tract and/or neighborhood socioeconomic deprivation may contribute to hypertension. These include aspects of the neighborhood’s-built environment, such as the availability of processed food outlets, physical activity facilities, healthcare access, transportation options, and exposure to environmental pollutants [ 12 , 13 ]. Differential psychosocial stress may also explain the gender disparities found in our study regarding the relationship between census tract deprivation and hypertension. Individuals living in areas of higher socioeconomic deprivation report greater daily difficulties and higher exposure to chronic social stressors in terms of both frequency and severity [ 31 ]. These experiences can lead to biological wear and tear, resulting in early health deterioration, and chronic hyperactivity or hypoactivity of allostatic systems [ 32 ], contributing to the development of hypertension [ 33 ]. Therefore, being a woman and residing in census tracts with higher socioeconomic deprivation may indicate greater distress and stress, given that women are more likely than men to experience chronic stress situations [ 29 , 30 ]. Additionally, sex hormones that regulate the hypothalamic-pituitary-adrenal axis response to psychological stress act differently between men and women [ 25 ]. Questionnaires containing self-reported measurements have been widely used in population-based surveys [ 19 ] and other epidemiological studies [ 8 , 9 , 14 ], particularly in low- and middle-income countries, due to their cost-effectiveness and ease of implementation. Previous study comparing self-reported and measured data have demonstrated the reliability of self-reported hypertension as a valid method for population-level estimation [ 34 ]. In the present study, sensitivity analysis using objective blood pressure measurements yielded similar results to those obtained in the main analysis based on self-reported hypertension for both women and men, reinforcing the findings of the previous validation study [ 34 ]. This study has some limitations. First, we acknowledge that the IBP, constructed using data from the 2010 Demographic Census, may not capture changes in deprivation distribution over the past decade. To address this limitation, we used individual-level data from baseline ELSI-Brazil, collected in 2015–2016, shortly after the census. Second, we recognize that other risk factors for hypertension — such as physical inactivity, unhealthy dietary habits, and obesity — were not included in our analyses. However, these factors are likely mediators of the associations investigated [ 12 , 35 ], and we aimed to estimate the overall effect and not the direct association between exposure and outcome. Therefore, residual confounding due to unmeasured or unknown factors cannot be entirely ruled out. On the other hand, this is the first study in Brazil to simultaneously examine the association of individual and neighborhood socioeconomic conditions with hypertension from a gender perspective, using census tracts and a nationally representative sample. The multilevel analysis allows for distinguishing the effects of the environment from individual characteristics [ 26 ], contributing to more effective public policies aimed at reducing health inequalities. Additionally, using a representative sample enhances the generalizability of the findings, making the conclusions applicable to different urban contexts across the country. Additionally, no previous study conducted in Brazil has investigated the relationship of hypertension with census tract socioeconomic deprivation on an urban scale from a gender perspective. In conclusion, our study reinforces evidence that individual and contextual socioeconomic conditions are associated with hypertension and highlights variations by gender. Lower individual education and higher IBP of the census tract (indicative of higher deprivation) were linked to increased odds of hypertension among women, whereas such associations were not observed among men. These findings underscore the importance of addressing social and gender disparities in health planning for controlling hypertension among older Brazilian adults. Therefore, implementing and intensifying public policies aimed at addressing social determinants – such as poverty alleviation and improving access to quality education among socioeconomically disadvantaged groups – is crucial. Furthermore, considering intra-urban disparities, there is a need for more targeted actions to support vulnerable groups, including enhancing access to essential services, promoting conditional cash transfers, and implementing urban interventions that foster healthy environments in both living and working contexts, particularly for women. Abbreviations ELSI-Brazil: Brazilian Longitudinal Study of Aging OSUBH-UFMG: Belo Horizonte Urban Health Observatory of the Universidade Federal de Minas Gerais IBP: Brazilian Deprivation Index CIDACS: Center for Data and Knowledge Integration for Health SD: Standard Deviation CI: Confidence Interval OR: Odds Ratio PCV: Proportion change in variance MOR: Median Odds Ratio Declarations Ethics approval The ELSI-Brazil was approved by the Ethics Committee of the Oswaldo Cruz Foundation – Minas Gerais (CAAE: 34649814.3.0000.5091). All participants or legal guardians have signed an informed consent. Consent for publication Not applicable. Availability of data and material The data that support the findings of this study are available on the ELSI-Brazil homepage after registration (https://elsi.cpqrr.fiocruz.br/en/home-english/en-data-access/). Competing interests We declare no competing interests. Funding ELSI-Brazil was funded by the Ministry of Health: DECIT/SCTIE – Department of Science and Technology of the Secretariat of Science, Technology and Strategic Inputs (Processes: 404965/2012-1 and TED 28/2017); COPID/DECIV/SAPS - Coordination of Health of the Elderly in Primary Care, Department of Life Cycles of the Secretariat of Primary Health Care (Processes: 20836, 22566, 23700, 25560, 25552 and 27510). ELSI-Urbe was funded by FAPEMIG-Foundation for Research Support of the State of Minas Gerais (project APQ-02425-21). The author DMC was also supported by the Coordination of Improvement of Higher Education Personnel (CAPES) through a doctoral scholarship. The authors AALF and WTC are supported by the National Council of Scientific and Technological Development (CNPq) through a research productivity scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. Acknowledgments The authors would like to thank the participants and professionals who collaborated with ELSI-Brazil. Author Contributions DMC, ACSA, and WTC conceived the study. DMC and ACSA performed the statistical analyses. DMC drafted the first version of the manuscript. BSM, LSB and MFLC participated in or supported data collection. All authors participated in the interpretation of results and approved the final version of the manuscript. References GBD Risk Factor Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet . 2020;396(10258):1223–49. NCD Risk Factor Collaboration (NCD-RisC). 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Association between neighborhood-level socioeconomic deprivation and incident hypertension: a longitudinal analysis of data from the Dallas Heart Study. Am Heart J . 2018;204:109–18. Chaix B, Bean K, Leal C, Thomas F, Havard S, Evans D, et al. Individual/neighborhood social factors and blood pressure in the RECORD Cohort Study: which risk factors explain the associations? Hypertension . 2010;55(3):769–75. Lê-Scherban F, Ballester L, Castro JC, Cohen S, Melly S, Moore K, et al. Identifying neighborhood characteristics associated with diabetes and hypertension control in an urban African American population using geo-linked electronic health records. Prev Med Rep . 2019;15:100953. Alves RFS, Faerstein E. Educational inequalities in hypertension: complex patterns in intersections with gender and race in Brazil. Int J Equity Health . 2016;15(1):1–9. Wagner KJP, Boing AF, Subramanian SV, Bastos JL, André C, d’Orsi E. Effects of neighborhood socioeconomic status on blood pressure in older adults. Rev Saude Publica . 2016;50:78. Bento IC, Mambrini JVDM, Peixoto SV. Contextual and individual factors associated with arterial hypertension among Brazilian older adults (National Health Survey–2013). Rev Bras Epidemiol . 2020;23:e200078. Gustafsson PE, Hammarström A, San Sebastian M. Cumulative contextual and individual disadvantages over the life course and adult functional somatic symptoms in Sweden. Eur J Public Health . 2015;25(4):592–7. IBGE – Instituto Brasileiro de Geografia e Estatística. Ministério do Planejamento e Orçamento. Censo Demográfico 2022. População por idade e sexo: pessoas de 60 anos ou mais de idade. Resultados do universo. Brasil, Grandes Regiões e Unidades da Federação. Rio de Janeiro: IBGE; 2023 [cited 2025 Apr 1]. Available from: https://biblioteca.ibge.gov.br/visualizacao/livros/liv102038.pdf Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde. Vigitel Brasil 2016: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Brasília: Ministério da Saúde; 2017. Lima-Costa MF, Andrade FB, Souza PRB Jr, Neri AL, Duarte YAO, Castro-Costa E, et al. The Brazilian Longitudinal Study of Aging (ELSI-BRAZIL): objectives and design. Am J Epidemiol . 2018;187(7):1345–53. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 2). J Epidemiol Community Health. 2006;60(2):95–101. IBGE – Instituto Brasileiro de Geografia e Estatística. Síntese de indicadores sociais: uma análise das condições de vida da população brasileira: 2024. Rio de Janeiro: IBGE; 2024. Allik M, Ramos D, Agranonik M, Pinto Júnior EP, Ichihara MY, Barreto ML, et al. Developing a small-area deprivation measure for Brazil: technical report. National Institute for Health Research ; 2020. Levorato CD, Mello LM, Silva AS, Nunes AA. Factors associated with the demand for health services from a gender-relational perspective. Cien Saude Colet . 2014;19(4):1263–74. Pasquali R. The hypothalamic-pituitary-adrenal axis and sex hormones in chronic stress and obesity: pathophysiological and clinical aspects. Ann N Y Acad Sci . 2012;1264:20–35. Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. J Epidemiol Community Health . 2006;60(4):290–7. Malta DC, Bernal RTI, Prates EJS, Vasconcelos NM, Gomes CS, Stopa SR, et al. Self-reported arterial hypertension, use of health services and guidelines for care in Brazilian population: National Health Survey, 2019. Epidemiol Serv Saude . 2022;31(spe1):e2021369. doi:10.1590/SS2237-9622202200012.especial. Ross CE, Mirowsky J. Gender and the health benefits of education. Sociol Q . 2010;51:1–19. Kandasamy S, Anand SS. Cardiovascular disease among women from vulnerable populations: a review. Can J Cardiol . 2018;34(4):450–7. Vega AP. Pobreza feminina desvendando suas raízes. TEL Temp Esp Linguagem. 2019;10(2):177–200. Bak CK, Andersen PT, Bacher I, Bancila DD. The association between socio-demographic characteristics and perceived stress among residents in a deprived neighbourhood in Denmark. Eur J Public Health . 2012;22(6):787–92. McEwen BS. Protective and damaging effects of stress mediators. N Engl J Med . 1998;338(3):171–9. Keita AD, Judd SE, Howard VJ, Cushman M, Safford MM, McClure LA, et al. Associations of neighborhood area level deprivation with the metabolic syndrome and inflammation among middle- and older-age adults. BMC Public Health . 2014;14:1319. Lima-Costa MF, Peixoto SV, Firmo JOA. Validity of self-reported hypertension and its determinants (the Bambuí study). Rev Saude Publica [Internet]. 2004 [cited 2025 Apr 1];38(5):637–42. Available from: http://www.scielo.br/pdf/rsp/v38n5/21750.pdf Nejatinamini S, Campbell DJ, Godley J, Minaker LM, Sajobi TT, McCormack GR. The contribution of modifiable risk factors to socioeconomic inequities in cardiovascular disease morbidity and mortality: a nationally representative population-based cohort study. Prev Med . 2023;171:107497. Supplementary Files SUPPLEMENTALMATERIALApril3rd.docx SupplementarydatalegendsApril3rd.docx Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2025 Read the published version in Journal of Urban Health → Version 1 posted Editor assigned by journal 07 Apr, 2025 First submitted to journal 04 Apr, 2025 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. 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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-5176210","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439640641,"identity":"503e9568-3b20-49d0-948c-d205fae8efca","order_by":0,"name":"Débora Moraes Coelho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYHACxsMgkr29AUgaWBCnB6yF58wBkBYJUrTcSABRRGjhb28+cLigxiaPR/L51Q0/CiSAIt0JeLVInDmWcHjGsbRiHumcsps9QIdJnDm7Aa8WA4kcg8M8bIcT90vnpN3gAWoxkMglpCX/w2Gef/8TeyTPpN38Q5yWHIbDvG0HEnsk2I/dJsoWoF8MDvP2JSf28OSw3ZYxkOAh6BdgiD18zPPNLrGH/fizm2/+2Mjxt/fi14IEeAzAJLHKQYD9ASmqR8EoGAWjYAQBAPkSSOARHGkMAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0948-0971","institution":"UFMG: Universidade Federal de Minas Gerais","correspondingAuthor":true,"prefix":"","firstName":"Débora","middleName":"Moraes","lastName":"Coelho","suffix":""},{"id":439640642,"identity":"7e1182e8-dc70-421a-b99e-a1594346211c","order_by":1,"name":"Amanda Cristina de Souza Andrade","email":"","orcid":"","institution":"UFMT: Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"Cristina de Souza","lastName":"Andrade","suffix":""},{"id":439640643,"identity":"6a1e62fe-12bb-46c5-a324-714ddd86d4cf","order_by":2,"name":"Bruno de Souza Moreira","email":"","orcid":"","institution":"UFMG: Universidade Federal de Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"de Souza","lastName":"Moreira","suffix":""},{"id":439640644,"identity":"e954696e-53ac-4fed-a2d0-35c06c2199b3","order_by":3,"name":"Luciana de Souza Braga","email":"","orcid":"","institution":"UFMG: Universidade Federal de Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Luciana","middleName":"de Souza","lastName":"Braga","suffix":""},{"id":439640645,"identity":"e2701038-a0da-4f42-bc5a-3be8761cdc40","order_by":4,"name":"Maria Fernanda Lima-Costa","email":"","orcid":"","institution":"Fiocruz Minas: Fundacao Oswaldo Cruz Instituto Rene Rachou","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Fernanda","lastName":"Lima-Costa","suffix":""},{"id":439640646,"identity":"c006596c-f077-4bbd-a67e-70faa47f03d7","order_by":5,"name":"Waleska Teixeira Caiaffa","email":"","orcid":"","institution":"UFMG: Universidade Federal de Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Waleska","middleName":"Teixeira","lastName":"Caiaffa","suffix":""}],"badges":[],"createdAt":"2024-09-29 17:59:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5176210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5176210/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11524-025-00998-5","type":"published","date":"2025-09-02T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80275813,"identity":"62e7aab5-8d1d-42ce-b373-9b8f7add7c03","added_by":"auto","created_at":"2025-04-10 04:55:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003csup\u003e1 \u003c/sup\u003eAge adjustment was conducted using the age distribution of the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of arterial hypertension adjusted\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e by age according to: a) individual education and b) Brazilian Deprivation Index (IBP) of the census tract (in tertiles).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5176210/v1/12559ef76295b283abe81718.png"},{"id":90827916,"identity":"124a3262-9961-405c-ac7b-39fedfcb3e43","added_by":"auto","created_at":"2025-09-08 16:03:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1049788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5176210/v1/1e8bac8c-bfe5-4b34-b62f-3175efaf375a.pdf"},{"id":80274765,"identity":"056bf578-f315-482a-9319-02c74948163c","added_by":"auto","created_at":"2025-04-10 04:39:33","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":190751,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALMATERIALApril3rd.docx","url":"https://assets-eu.researchsquare.com/files/rs-5176210/v1/e51812a40fa845273477c103.docx"},{"id":80275231,"identity":"7ecefe7f-b2ad-4163-bffc-a226eeb6eb17","added_by":"auto","created_at":"2025-04-10 04:47:33","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":12114,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatalegendsApril3rd.docx","url":"https://assets-eu.researchsquare.com/files/rs-5176210/v1/b2a62cc6f8363e3277e5d725.docx"}],"financialInterests":"","formattedTitle":"Gender Differences in Social Determinants of Hypertension Among Older Brazilian Adults Residing in Urban Areas: A Multilevel Approach from the ELSI-Urbe","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe prevalence of hypertension, the leading cause of cardiovascular diseases and premature death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], is decreasing in high-income countries but increasing in low- and middle-income countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In these regions, beyond the rapid population aging, urbanization has been recognized as an important determinant of the growing burden of hypertension and its risk factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Urban living exposes individuals to higher pollution levels (air, water, light, and noise), increased stress, and reduced access to green spaces [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, urban environments contribute to nutritional transition, decreased physical activity, and higher consumption of tobacco and alcohol [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Increased exposure to these factors is not evenly distributed across urban populations and tends to be concentrated among the most socioeconomically disadvantaged groups [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong urban residents, individual socioeconomic conditions are strongly associated with hypertension in high-income countries, particularly among women [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In low- and middle-income countries, however, studies are limited and suggest the presence of significant heterogeneities in the social patterning of hypertension [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Socioeconomic characteristics at the neighborhood level \u0026mdash; that is, the local context (e.g., census tracts) \u0026mdash; are also linked to hypertension [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A previous study demonstrated that residing in a neighborhood with a lower socioeconomic level was associated with an increase in blood pressure over time and a higher incidence of hypertension over a 9.7-year follow-up period [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite this evidence, studies investigating neighborhood-level effects in low- and middle-income countries remain scarce and are predominantly based on local samples, often restricted to a single city [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, nationally representative studies have examined the urban environment at broader geographic scales, such as entire cities or larger spatial units like the Brazilian Federative Units [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndividual and neighborhood socioeconomic conditions can contribute to the development of hypertension through multiple processes. At the individual level, socioeconomic conditions can influence health behaviors and stress-related processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Neighborhood socioeconomic conditions, in turn, encompass environmental characteristics such as the availability of physical spaces for purchasing healthy foods, safe places for physical activity, transportation services, and access to healthcare services [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, residing in a neighborhood with greater social vulnerability, characterized by poor sanitation, unhealthy housing, high crime rates, violence, and inadequate investment in human capital, can elevate chronic stress levels, thereby contributing to the development of hypertension [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, research indicates that older individuals [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and women [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] are more susceptible to the characteristics of the environment where they live.\u003c/p\u003e \u003cp\u003eBrazil, the most populous country in Latin America and a middle-income nation, has undergone rapid urbanization and population aging in recent years. Currently, over 60% of the population lives in urban areas, and 15.6% are classified as older adults [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This demographic transition has been accompanied by an epidemiological transition characterized by an increasing prevalence of non-communicable chronic diseases and their risk factors, such as hypertension, which are more common in this age group [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This study contributes by using data from a nationally representative survey of Brazilians aged 50 years and over to assess the association between neighborhood characteristics and hypertension. It also addresses a gap in the literature by incorporating intra-urban data, enabling the identification of more vulnerable groups and supporting the planning of more effective public health interventions.\u003c/p\u003e \u003cp\u003eTherefore, the goal of this study was to investigate the association of individual and contextual socioeconomic conditions with hypertension by gender, among older adults residing in urban areas of Brazil. We hypothesized that poor individual and contextual socioeconomic conditions would be associated with a higher prevalence of hypertension. In addition, differences by individual and contextual socioeconomic conditions on hypertension would be higher among women than men.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThis study used data from the project ELSI-Urbe, \"The influence of the physical and social environment on the health of older Brazilian adults: a longitudinal multimethod population-based study,\u0026rdquo; coordinated by the Belo Horizonte Urban Health Observatory of the Universidade Federal de Minas Gerais (OSUBH-UFMG). The objective of ELSI-Urbe is to identify the attributes of the neighborhood\u0026rsquo;s physical and social environment that influence the health and well-being of older adults over time. This project is nested within the Brazilian Longitudinal Study of Aging (ELSI-Brazil).\u003c/p\u003e \u003cp\u003eThe ELSI-Brazil is a population-based longitudinal study, representative of the non-institutionalized Brazilians aged 50 and over residing in 70 municipalities across all five major regions of the country [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The sampling design of ELSI-Brazil utilized a multistage stratified approach, combining stratification of primary sampling units (municipalities), census tracts, and households. Data were collected using a structured questionnaire divided into three main modules: (1) household, (2) individual, and (3) physical measurements (including anthropometry, blood pressure, and physical functioning). The first wave was conducted in 2015\u0026ndash;2016, with participation from 9,412 individuals, and the second wave took place in 2019\u0026ndash;2021, with 9,949 participants. Detailed information regarding study design and recruitment methods was published elsewhere [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The ELSI-Brazil was approved by the Ethics Committee of the Oswaldo Cruz Foundation \u0026ndash; Minas Gerais (CAAE: 34649814.3.0000.5091); all participants or legal guardians have signed an informed consent.\u003c/p\u003e \u003cp\u003eThe ELSI-Urbe developed a multilevel data structure to integrate individual-level data collected in ELSI-Brazil with contextual data obtained from open-access sources and aggregated across different geographic units of residence (regions, municipalities, neighborhoods, and census tracts). At the individual level, only participants residing in urban areas were included. At the contextual level, the smallest geographic unit used for aggregation was the census tract of the participants\u0026rsquo; residence.\u003c/p\u003e \u003cp\u003eEach participant was assigned a common geocode to link individual and contextual data. This geocode was used exclusively to construct the multilevel database and was deleted after processing to ensure confidentiality and compliance with data protection guidelines. Non-public geographic identifiers were stored in a secure environment with access restricted to authorized researchers.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy sample\u003c/h3\u003e\n\u003cp\u003e For this study, the ELSI-Urbe sample was structured using a two-level multilevel design: individuals, obtained from the first wave of ELSI-Brazil, nested within their residential census tracts. Thus, the eligible population consisted of participants residing in urban areas with available census tract information (n\u0026thinsp;=\u0026thinsp;7,751 individuals nested within 609 census tracts).\u003c/p\u003e \u003cp\u003eOur analyses were restricted to eligible individuals who completed the interview independently \u0026mdash; without the assistance of a proxy respondent \u0026mdash; and who had complete data on both the outcome and the exposure variables at the individual and contextual levels. Further details on the inclusion and exclusion criteria are provided in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003eDependent Variable\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHypertension\u003c/h2\u003e \u003cp\u003e Participants were defined as having hypertension if they reported that a physician had told them that they had hypertension. The information was obtained through the following question: \u0026ldquo;Has a doctor ever told you that you have hypertension (high blood pressure)?\u0026rdquo; (\u0026ldquo;no\u0026rdquo;; \u0026ldquo;yes\u0026rdquo;; \u0026ldquo;yes, only during pregnancy\u0026rdquo;; \u0026ldquo;didn\u0026rsquo;t know/didn\u0026rsquo;t answer\u0026rdquo;). Participants who answered \u0026ldquo;yes, only during pregnancy\u0026rdquo; or \u0026ldquo;didn\u0026rsquo;t know/didn\u0026rsquo;t answer\u0026rdquo; were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent Variables\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndividual-level education\u003c/h2\u003e \u003cp\u003eEducation was used as a proxy for individual socioeconomic conditions due to its wide availability for both sexes and stability over time [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Beyond its impact on labor market access and income, education is linked to health behaviors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and stands out as the strongest predictor of hypertension compared to income or occupation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This variable was obtained from the question: \u0026ldquo;What was the highest grade in school that you completed?\u0026rdquo;. The question had the following answer options: 1) never studied; 2) 1st grade (Elementary School); 3) 2nd grade (Elementary School); 4) 3rd grade (Elementary School); 5) 4th grade (Elementary School); 6) 5th grade (Elementary School); 7) 6th grade (Middle School); 8) 7th grade (Middle School); 9) 8th grade (Middle School); 10) 9th grade (High School); 11) 10th grade (High School); 12) 11/12th grade (High School); 13) school for adults; 14) some college (incomplete); 15) complete college; 16) specialization/medical residency; 17) Master\u0026rsquo;s degree; 18) Doctoral degree (Ph.D.). In Brazil, most of the older population has a low level of education [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For this study, education was categorized as follows: \u0026le; 4 years; 5 to 8 years; \u0026ge; 9 years. These categories better capture educational inequalities within this cohort, as demonstrated in a study that used the same categorization [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBrazilian Deprivation Index (IBP)\u003c/h3\u003e\n\u003cp\u003eThe Brazilian Deprivation Index (IBP, acronym in Portuguese), an indicator developed using data from the 2010 Demographic Census [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], was used as a continuous variable to characterize the contextual socioeconomic condition at the census tract level. The IBP is based on three components: (1) the percentage of households with a per capita income\u0026thinsp;\u0026le;\u0026thinsp;1/2 minimum wage; (2) the percentage of individuals over 7 years of age who are illiterate; and (3) the average percentage of individuals with inadequate access to basic sanitation services \u0026mdash; including sewage, water supply, garbage collection \u0026mdash; as well as the absence of a bathroom, bathtub, or shower [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A deprivation score was created by summing the standardized z-scores of these three indicators, weighted by the population of each census tract. It was designed to measure social inequalities at the smallest geographic units in Brazil. Higher IBP scores indicate greater deprivation, whereas lower scores represent less deprived areas [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eOther Variables\u003c/h3\u003e\n\u003cp\u003eAge and gender were also included in this study. Age (in years) was used as an adjustment variable due to its direct association with the prevalence of hypertension, particularly in older populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Gender, defined as sex assigned at birth and categorized as men or women, was considered a potential moderator and used as a stratification variable, based on evidence indicating biological, behavioral, and social differences in the prevalence and determinants of hypertension between men and women [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were stratified by gender. Initially, descriptive statistics were performed using frequency distributions (%), means, and standard deviations (SD) according to the hypertension status. Posteriorly, age-adjusted hypertension prevalence estimates, and their respective 95% confidence intervals (95% CI) were calculated according to individual education categories (\u0026le;\u0026thinsp;4, 5\u0026ndash;8, \u0026ge; 9 years of education) and the tertiles of the IBP of the census tract (the 1st tertile corresponds to the stratum with the least deprivation and the 3rd tertile to the stratum with the greatest deprivation). In this analysis, the IBP was categorized into tertiles using the subsample of census tracts included in the study. Adjusted prevalence estimates were calculated using a binary logistic regression model.\u003c/p\u003e \u003cp\u003eBivariate and adjusted associations between the outcome and independent variables were analyzed using two-level multilevel logistic regression (individuals and census tracts), including a random effect for the census tract. First, we adjusted a model without independent variables but with a random effect at the census tract level to assess the contribution of variability between census tracts to hypertension (Model 1). Model 2 included individual education and Model 3 included individual education and the IBP. Models 2 and 3 were adjusted for age (in years). Odds ratios (OR) and their respective 95% CI were estimated. We reported the median odds ratio (MOR) for all models to show the residual area-level heterogeneity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. MOR represents the median value of the OR between areas with higher and lower risks when randomly picking two areas. The proportion of change in variance (PCV) was also reported for each model.\u003c/p\u003e \u003cp\u003eA sensitivity analysis was conducted using measured blood pressure to assess the robustness of the findings. Hypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg and/or the use of antihypertensive drugs. Additionally, a sensitivity analysis was performed considering only census tracts with at least three eligible older adults, as some census tracts had a small number of older adults in the sample. All estimates accounted for sampling weights and the complex sample design. The survey command (svy) was used for descriptive analysis. Analyses were performed using the statistical software STATA version 17, with a significance level of 5%.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFrom the total of 7,751 eligible participants for the present analysis, 6,767 completed the interview independently, without the assistance of a proxy respondent, and provided complete information for all variables of interest. Therefore, the final sample comprised 6,767 participants distributed across 605 census tracts. Details on exclusions are presented in Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. The median number of participants per census tract was 11.2 (minimum\u0026thinsp;=\u0026thinsp;1; maximum\u0026thinsp;=\u0026thinsp;24), with 2.9% of census tracts having up to three participants (n\u0026thinsp;=\u0026thinsp;18) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of participants by Brazilian census tracts. \u0026sup1; Unweighted.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal \u0026sup1;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWomen \u0026sup1;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMen \u0026sup1;\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\u003eNumber of participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of census tracts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants per census tract\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\u003eMedian (Minimum \u0026ndash; Maximum)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2 (1\u0026ndash;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0 (1\u0026ndash;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9 (1\u0026ndash;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;3 participants per census tract, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e \u0026sup1; Unweighted.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e displays the descriptive characteristics and bivariate analysis according to the hypertension status for the total sample and stratified by gender. The mean age of participants was 61.6 years (SD\u0026thinsp;=\u0026thinsp;9.1), with 54.4% being women, and 44.3% having between 0 and 4 years of education. The mean age was 62.0 years (SD\u0026thinsp;=\u0026thinsp;9.5) for women and 61.1 years (SD\u0026thinsp;=\u0026thinsp;8.6) for men. Regarding individual education, 45.6% of women and 42.7% of men had up to 4 years of education. The overall prevalence of hypertension was 51.4% (95% CI\u0026thinsp;=\u0026thinsp;49.3\u0026ndash;53.6%) (data not shown in table), with a significantly higher proportion among women (57.8%; 95% CI\u0026thinsp;=\u0026thinsp;54.4\u0026ndash;61.1%) compared to men (42.2%; 95% CI\u0026thinsp;=\u0026thinsp;38.9\u0026ndash;45.6%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of study participants and bivariate analysis1 stratified by gender.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal Sample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTOTAL SAMPLE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.6 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.3 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.7 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04 (1.03\u0026ndash;1.05)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.28 (1.15\u0026ndash;1.43)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual 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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le; 4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74 (0.65\u0026ndash;0.85)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge; 9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.58 (0.51\u0026ndash;0.66)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eContextual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazilian Deprivation Index, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.21 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.16 (1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.25 (1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.98\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (unweighted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,186\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\u003e\u003cstrong\u003eWOMEN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.0 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.9 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.8 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.05 (1.04\u0026ndash;1.06)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual 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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le; 4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.71 (0.59\u0026ndash;0.85)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge; 9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.48 (0.41\u0026ndash;0.56)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eContextual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazilian Deprivation Index, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.20 (1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.10 (1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.31 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.06 (1.02\u0026ndash;1.10)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (unweighted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,686\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\u003e\u003cstrong\u003eMEN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndividual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.1 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.6 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.7 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.03 (1.02\u0026ndash;1.04)***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual 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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le; 4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 (0.67\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge; 9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.76 (0.62\u0026ndash;0.92)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eContextual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazilian Deprivation Index, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.21 (1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.23 (1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;1.19 (1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.92\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en (unweighted)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,500\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\" colspan=\"5\"\u003e\n \u003cp\u003eSD: standard deviation. OR: odds ratio. CI: confidence interval. Significance level: **\u0026lt;0.01, ***\u0026lt;0.001.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThe bivariate model between individual and contextual characteristics and arterial hypertension was carried out using multilevel analysis with a random effect at two levels: individual nested in the census tract.\u003c/p\u003e\n \u003cp\u003eAll estimates considered the sampling weights and the complex sample design.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe bivariate analysis indicated that women are 28% more likely to report hypertension compared to men. For both genders, age showed a positive association with hypertension. Among women, higher education levels were associated with lower odds of hypertension, while residing in census tracts with higher deprivation was associated with higher odds of this disease. Among men, those with 9 years or more of education exhibited lower odds of hypertension (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the age-adjusted prevalence of hypertension for the total sample and stratified by gender, according to individual education categories and tertiles of the IBP of the census tract. The prevalence of hypertension showed a statistically significant dose-response gradient concerning individual education only among women, with a higher prevalence observed among those with lower education levels (\u0026le;\u0026thinsp;4 years) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Similarly, a significant dose-response gradient in the prevalence of hypertension was observed across the tertiles of the IBP, also only among women, with a higher prevalence among those residing in census tracts with higher deprivation (3rd IBP tertile) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the fixed and random parameters between individual education, IBP of the census tract, and hypertension, stratified by gender. Concerning random parameters, the empty model shows neighborhood-level variance in hypertension (Model 1). The MOR at the neighborhood-level was 1.16 in women and 1.55 in men, showing that there are significant differences between neighborhoods. If individuals are selected from two randomly chosen neighborhoods, the odds of reporting hypertension in neighborhoods with more hypertensive individuals are 16% and 55% higher in women and men, respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdjusted associations between individual education, Brazilian Deprivation Index (IBP) of the census tract, and arterial hypertension, stratified by gender, for older Brazilian adults residing in urban areas.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWOMEN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual 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\u003e\u0026le; 4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85 (0.71\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.73\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge; 9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59 (0.50\u0026ndash;0.70)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.62 (0.52\u0026ndash;0.74)**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazilian Deprivation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04 (1.01\u0026ndash;1.09)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRandom parameters\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance (standard error)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,024 (0,040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,024 (0,042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,018 (0,041)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\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\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMEN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual 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\u003e\u0026le; 4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 to 8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.77\u0026ndash;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.75\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge; 9 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.73\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84 (0.68\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrazilian Deprivation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.90-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRandom parameters\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance (standard error)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,215 (0,074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,146 (0,070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,141 (0,068)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMOR\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\u003e1,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,42\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\"\u003eOR: odds ratio. CI: confidence interval. Significance level: *\u0026lt;0.05, **\u0026lt;0.01. PCV: Proportion change in variance. MOR: Median odds-ratio.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eThe Brazilian Deprivation Index of the census tract was standardized to a mean of 0 and a standard deviation (SD) of 1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel 1: Null model (random effect at the census tract level).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel 2: Education, adjusted for age (random effect at the census tract level).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel 3: Education and Brazilian Deprivation Index, adjusted for age (random effect at the census tract level).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAll estimates considered the sampling weights and the complex sample design.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding the fixed parameters, model 3 results show that the odds of hypertension were 38% lower among women with 9 years or more of education compared to those with 4 years or fewer of education. Additionally, IBP was positively associated with hypertension among women, indicating that higher deprivation of the census tract was associated with higher odds of women reporting hypertension. For men, neither individual education nor IBP was significantly associated with hypertension.\u003c/p\u003e\n\u003cp\u003eThe sensitivity analyses using measured blood pressure to define hypertension yielded results similar to those of the main analysis for both women and men, maintaining consistency in directionality and association patterns for both exposures in the full model. The results for the random parameters were also consistent with the main analysis (Supplementary Table\u0026nbsp;1). Similarly, multilevel models that included only census tracts with at least three eligible older adults showed results comparable to those observed in the main analysis (data not shown).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study examined the association between socioeconomic conditions at different levels (individual and neighborhood) and hypertension in a nationally representative sample of older Brazilian adults living in urban areas. Our findings revealed a high prevalence of hypertension, particularly among women, aligning with previous literature [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, we observed an inverse association between individual education and hypertension among women. Furthermore, residing in census tracts with higher levels of deprivation was associated with increased odds of hypertension in women. In contrast, no significant associations were found among men.\u003c/p\u003e \u003cp\u003eResearch conducted in high-income countries shows a higher prevalence of hypertension among individuals with lower levels of education compared to those with higher education, with this association being more consistent and stronger among women [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Data from low- and middle-income countries, including those from Latin America, show variability in educational gradients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For instance, a recent study analyzing data from eight Latin American countries (Argentina, Brazil, Chile, Colombia, El Salvador, Guatemala, Mexico, and Peru) found that higher education was associated with lower odds of hypertension in women and higher odds in men [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Studies examining the association between education and hypertension among urban residents (aged 18 years and over) in Argentina [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and Brazil [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] also observed an inverse association in women but no association in men. Thus, our findings regarding the association between education and hypertension align with those reported among women in high-income countries and Latin America, and they support previous Latin American studies that reported no association among men.\u003c/p\u003e \u003cp\u003eWhile the reasons for observed gender differences remain unclear, several explanations linking higher education to a decreased risk of hypertension in women warrant consideration. Education may provide greater benefits for women and compensate for potential disparities in access to other resources such as income, power, and authority [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, the distribution of risk factors and access to healthcare based on socioeconomic position may differ between men and women [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The chronic stress stemming from work and home environments, such as informal employment, single motherhood, role overload, and experiences of violence, may also affect men and women differently [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study also found that census tract deprivation, as measured by the IBP, is associated with hypertension, but the patterns vary by gender. Among women, residing in deprived census tracts increases the odds of hypertension. However, similar results were observed in a recent study investigating the association between census tract-level education and hypertension in Argentina among participants aged 18 years and older [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There are several plausible pathways through which census tract and/or neighborhood socioeconomic deprivation may contribute to hypertension. These include aspects of the neighborhood\u0026rsquo;s-built environment, such as the availability of processed food outlets, physical activity facilities, healthcare access, transportation options, and exposure to environmental pollutants [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferential psychosocial stress may also explain the gender disparities found in our study regarding the relationship between census tract deprivation and hypertension. Individuals living in areas of higher socioeconomic deprivation report greater daily difficulties and higher exposure to chronic social stressors in terms of both frequency and severity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These experiences can lead to biological wear and tear, resulting in early health deterioration, and chronic hyperactivity or hypoactivity of allostatic systems [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], contributing to the development of hypertension [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, being a woman and residing in census tracts with higher socioeconomic deprivation may indicate greater distress and stress, given that women are more likely than men to experience chronic stress situations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, sex hormones that regulate the hypothalamic-pituitary-adrenal axis response to psychological stress act differently between men and women [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQuestionnaires containing self-reported measurements have been widely used in population-based surveys [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and other epidemiological studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], particularly in low- and middle-income countries, due to their cost-effectiveness and ease of implementation. Previous study comparing self-reported and measured data have demonstrated the reliability of self-reported hypertension as a valid method for population-level estimation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the present study, sensitivity analysis using objective blood pressure measurements yielded similar results to those obtained in the main analysis based on self-reported hypertension for both women and men, reinforcing the findings of the previous validation study [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, we acknowledge that the IBP, constructed using data from the 2010 Demographic Census, may not capture changes in deprivation distribution over the past decade. To address this limitation, we used individual-level data from baseline ELSI-Brazil, collected in 2015\u0026ndash;2016, shortly after the census. Second, we recognize that other risk factors for hypertension \u0026mdash; such as physical inactivity, unhealthy dietary habits, and obesity \u0026mdash; were not included in our analyses. However, these factors are likely mediators of the associations investigated [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and we aimed to estimate the overall effect and not the direct association between exposure and outcome. Therefore, residual confounding due to unmeasured or unknown factors cannot be entirely ruled out.\u003c/p\u003e \u003cp\u003eOn the other hand, this is the first study in Brazil to simultaneously examine the association of individual and neighborhood socioeconomic conditions with hypertension from a gender perspective, using census tracts and a nationally representative sample. The multilevel analysis allows for distinguishing the effects of the environment from individual characteristics [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], contributing to more effective public policies aimed at reducing health inequalities. Additionally, using a representative sample enhances the generalizability of the findings, making the conclusions applicable to different urban contexts across the country. Additionally, no previous study conducted in Brazil has investigated the relationship of hypertension with census tract socioeconomic deprivation on an urban scale from a gender perspective.\u003c/p\u003e \u003cp\u003eIn conclusion, our study reinforces evidence that individual and contextual socioeconomic conditions are associated with hypertension and highlights variations by gender. Lower individual education and higher IBP of the census tract (indicative of higher deprivation) were linked to increased odds of hypertension among women, whereas such associations were not observed among men. These findings underscore the importance of addressing social and gender disparities in health planning for controlling hypertension among older Brazilian adults. Therefore, implementing and intensifying public policies aimed at addressing social determinants \u0026ndash; such as poverty alleviation and improving access to quality education among socioeconomically disadvantaged groups \u0026ndash; is crucial. Furthermore, considering intra-urban disparities, there is a need for more targeted actions to support vulnerable groups, including enhancing access to essential services, promoting conditional cash transfers, and implementing urban interventions that foster healthy environments in both living and working contexts, particularly for women.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eELSI-Brazil: Brazilian Longitudinal Study of Aging\u003c/p\u003e\n\u003cp\u003eOSUBH-UFMG: Belo Horizonte Urban Health Observatory of the Universidade Federal de Minas Gerais\u003c/p\u003e\n\u003cp\u003eIBP: Brazilian Deprivation Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCIDACS: Center for Data and Knowledge Integration for Health\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD: Standard Deviation\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n\u003cp\u003eOR: Odds Ratio\u003c/p\u003e\n\u003cp\u003ePCV: Proportion change in variance\u003c/p\u003e\n\u003cp\u003eMOR: Median Odds Ratio\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ELSI-Brazil was approved by the Ethics Committee of the Oswaldo Cruz Foundation \u0026ndash; Minas Gerais (CAAE: 34649814.3.0000.5091). All participants or legal guardians have signed an informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on the ELSI-Brazil homepage after registration (https://elsi.cpqrr.fiocruz.br/en/home-english/en-data-access/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eELSI-Brazil was funded by the Ministry of Health: DECIT/SCTIE \u0026ndash; Department of Science and Technology of the Secretariat of Science, Technology and Strategic Inputs (Processes: 404965/2012-1 and TED 28/2017); COPID/DECIV/SAPS - Coordination of Health of the Elderly in Primary Care, Department of Life Cycles of the Secretariat of Primary Health Care (Processes: 20836, 22566, 23700, 25560, 25552 and 27510). ELSI-Urbe was funded by FAPEMIG-Foundation for Research Support of the State of Minas Gerais (project APQ-02425-21). The author DMC was also supported by the Coordination of Improvement of Higher Education Personnel (CAPES) through a doctoral scholarship. The authors AALF and WTC are supported by the National Council of Scientific and Technological Development (CNPq) through a research productivity scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants and professionals who collaborated with ELSI-Brazil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDMC, ACSA, and WTC conceived the study. DMC and ACSA performed the statistical analyses. DMC drafted the first version of the manuscript. BSM, LSB and MFLC participated in or supported data collection. All authors participated in the interpretation of results and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD Risk Factor Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet\u003c/em\u003e. 2020;396(10258):1223\u0026ndash;49.\u003c/li\u003e\n\u003cli\u003eNCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. \u003cem\u003eLancet\u003c/em\u003e. 2021;398(10304):957\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eSchutte AE, Venkateshmurthy NS, Mohan S, Prabhakaran D. 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Associations of neighborhood area level deprivation with the metabolic syndrome and inflammation among middle- and older-age adults. \u003cem\u003eBMC Public Health\u003c/em\u003e. 2014;14:1319.\u003c/li\u003e\n\u003cli\u003eLima-Costa MF, Peixoto SV, Firmo JOA. Validity of self-reported hypertension and its determinants (the Bambu\u0026iacute; study). \u003cem\u003eRev Saude Publica\u003c/em\u003e [Internet]. 2004 [cited 2025 Apr 1];38(5):637\u0026ndash;42. Available from: http://www.scielo.br/pdf/rsp/v38n5/21750.pdf\u003c/li\u003e\n\u003cli\u003eNejatinamini S, Campbell DJ, Godley J, Minaker LM, Sajobi TT, McCormack GR. The contribution of modifiable risk factors to socioeconomic inequities in cardiovascular disease morbidity and mortality: a nationally representative population-based cohort study. \u003cem\u003ePrev Med\u003c/em\u003e. 2023;171:107497.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neighborhoods, Socioeconomic factors, Hypertension, Multilevel Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5176210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5176210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite growing interest in the social determinants of hypertension, nationally representative studies analyzing the intra-urban effects of individual and contextual socioeconomic conditions within highly unequal urban areas, such as those in Latin America, remain scarce. This paper describes gender disparities in the association of individual and contextual socioeconomic conditions with hypertension among older adults residing in urban areas of Brazil. Data from 6,767 participants from the baseline (2015\u0026ndash;2016) of the Brazilian Longitudinal Study of Aging (ELSI-Brazil), a cohort with a nationally representative sample of community-dwelling adults aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years, were analyzed. Hypertension was self-reported. The measure of individual socioeconomic condition was individual education, and the contextual measure was the Brazilian Deprivation Index (IBP; acronym in Portuguese) of the census tract of the participant\u0026rsquo;s residence. Multilevel logistic regression models (individuals and census tracts), adjusted for age and stratified by gender, were used. The prevalence of hypertension according to individual education and the IBP differed between men and women. In women, higher individual education (\u0026ge;\u0026thinsp;9 years versus \u0026le;\u0026thinsp;4 years of schooling) was associated with a lower chance of hypertension (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.62; 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;0.52\u0026ndash;0.74), and residing in census tracts with higher deprivation was associated with a higher chance of hypertension (OR per standard deviation [SD]\u0026thinsp;=\u0026thinsp;1.04; 95%CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.09). In men, there was no significant association between individual education or IBP and hypertension. Our results suggest that public policies addressing this disease\u0026rsquo;s burden in middle- and low-income countries, such as Brazil, should adopt gender-sensitive strategies and consider the context in which these individuals reside.\u003c/p\u003e","manuscriptTitle":"Gender Differences in Social Determinants of Hypertension Among Older Brazilian Adults Residing in Urban Areas: A Multilevel Approach from the ELSI-Urbe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 04:39:29","doi":"10.21203/rs.3.rs-5176210/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2025-04-07T15:49:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Urban Health","date":"2025-04-04T07:01:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-urban-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jurh","sideBox":"Learn more about [Journal of Urban Health](https://www.springer.com/journal/11524)","snPcode":"11524","submissionUrl":"https://www.editorialmanager.com/jurh","title":"Journal of Urban Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ccc3ca5d-83fb-4106-a692-06954fb912f4","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T15:59:09+00:00","versionOfRecord":{"articleIdentity":"rs-5176210","link":"https://doi.org/10.1007/s11524-025-00998-5","journal":{"identity":"journal-of-urban-health","isVorOnly":false,"title":"Journal of Urban Health"},"publishedOn":"2025-09-02 15:57:12","publishedOnDateReadable":"September 2nd, 2025"},"versionCreatedAt":"2025-04-10 04:39:29","video":"","vorDoi":"10.1007/s11524-025-00998-5","vorDoiUrl":"https://doi.org/10.1007/s11524-025-00998-5","workflowStages":[]},"version":"v1","identity":"rs-5176210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5176210","identity":"rs-5176210","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}