Gender differences in the association of individual and contextual socioeconomic status with hypertension in 230 Latin American cities from the SALURBAL study: a multilevel analysis.

doi:10.21203/rs.3.rs-2485417/v1

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Research Article

Gender differences in the association of individual and contextual socioeconomic status with hypertension in 230 Latin American cities from the SALURBAL study: a multilevel analysis.

https://doi.org/10.21203/rs.3.rs-2485417/v1

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published 11 Aug, 2023

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Background: Despite global interest in gender disparities and social determinants of hypertension, research in urban areas and regions with a high prevalence of hypertension, such as Latin America, is very limited.

The objective of this study was to examine associations between the individual- and area-level socioeconomic status and hypertension in adults living in 230 cities in eight Latin America countries.

Methods: In this cross-sectional study, we used harmonized data from 109,184 adults (aged 18-97 years) from the SALURBAL (Salud Urbana en America Latina/Urban Health in Latin America) project. Hypertension was assessed by self-report. The individual-, sub-city- and city-level education were used as proxies of socioeconomic status. All models were stratified by gender.

Results: While individual-level education was positively associated with higher odds of hypertension among men (university education or higher versus less than primary: Odds Ratio [OR] = 1.63; 95% confidence interval [CI] = 1.45-1.83) the reverse was true among women (university education or higher versus lower than primary: OR = 0.66, 95%CI = 0.60-0.73), with both associations showing a dose-response pattern. For both genders, living in sub-city areas with higher educational achievement was associated with higher odds of hypertension (OR per standard deviation [SD] = 1.05, 95%CI = 1.01-1.10; OR = 1.09 per SD, 95%CI = 1.03-1.16, for women and men, respectively). The association of city-level education with hypertension varied across countries. In Peru, there was an inverse association (lower proportion of hypertension with a better education at the city level) in women and men, in other countries, no association was observed.

Conclusion: The social patterning of hypertension differs by gender and by the level of analysis highlighting the importance of context- and gender-sensitive approaches and policies to reduce the prevalence of hypertension in Latin America.

Urban Health

Hypertension

Education

Socioeconomic Status

Latin America

Multilevel Analysis

Hypertension, a major contributor to cardiovascular disease [1], is a major global health challenge and disproportionately affects populations in low- and middle-income countries (LMICs) [2]. Recently, studies have gone beyond traditional behavioral risk factors and have shown that the individual's socioeconomic conditions and the contextual characteristics of the places where people live are related to hypertension prevalence [3–5]. Although socioeconomic conditions have been identified as important factors in increasing the burden of hypertension in LMICs [1], evidence is limited and results are not always consistent in the context of Latin America [6, 7].

In high-income countries, studies have generally reported an inverse association of hypertension with socioeconomic status (SES) at the individual level [6] and at the area level [8, 9]. In contrast, evidence on the association of individual-level and area-level SES with hypertension in Latin America remains comparatively scarce and conflicting. At the individual level, some studies reported an inverse association of SES with hypertension [3, 4, 10, 11]. At the area level, there have been mixed findings. For example, one study reported no association of hypertension with neighborhood- and city-level education in Argentina [3]. In Brazil, income inequality at the level of Federation Units was not associated with hypertension [4], but another study found that the prevalence of hypertension was significantly higher among residents living in census tracts with lower levels of education [12]. Brazilian Federation Units with a higher Human Development Index had a higher prevalence of hypertension [4]. In Colombia, residents of departments with high-income inequality had a higher prevalence of hypertension [5].

Both individual-level and area-level SES may influence the development of hypertension through a multiplicity of processes. Individual-level SES may be related to hypertension through its impact on behaviors such as diet and physical activity, as well as through stress-related processes [6, 13]. Area-level SES may be a proxy for environmental features such as food, physical activity, health care, and transportation environments [14], and has been correlated with social stressors, such as violence, which may also affect hypertension prevalence [15]. In addition to direct effects, area-level SES can reduce or amplify the effect of individual SES on hypertension [7]. Moreover, both individual-level and area-level SES may influence access to screening, early detection, and treatment of hypertension [16–18].

The Latin American context, with its high level of urbanization and inequalities [19] and a high prevalence of hypertension in the adult population (as high as 40% based on some estimates) [2, 20], presents a unique opportunity to investigate the associations of individual-level and area-level SES with hypertension. Understanding how social conditions at various levels relate to non-communicable diseases risk factors like hypertension is critical to achieving sustainable development goals [21].

Using unique data compiled and harmonized by the SALURBAL (Salud Urbana en America Latina/Urban Health in Latin America) project [22], we investigated how individual-level and area-level SES are associated with hypertension in adults from 230 cities in eight Latin America countries. We hypothesized that lower individual and area SES would be independently associated with a higher prevalence of hypertension.

Study design

The SALURBAL project compiled and harmonized health, social, and environmental data for all cities above 100,000 inhabitants in eleven Latin American countries [23]. Briefly, SALURBAL defines cities as a single administrative unit (e.g., municipio) or a combination of adjacent administrative units (e.g., several municipios) that are part of the built-up area of the urban agglomeration as determined from satellite imagery. Each “sub-city” is an administrative unit fully nested within a “city”. The “sub-city” units were identified in each country as the smallest geographic administrative units for which health data was easily available. Approximately half of the cities included only one sub-city unit. In this study, we used survey data from 230 cities (encompassing 673 sub-city units) in 8 countries: Argentina, Brazil, Chile, Colombia, El Salvador, Guatemala, Mexico, and Peru.

Of 124,743 survey respondents aged 18 years and who resided in SALURBAL cities, we restricted our analyses to those with exposure information both at the individual and area-level, and those with outcome data. The final sample was 109,184 persons from 230 cities (673 sub-cities). Details on exclusions are shown in Figure S1. The median number of individuals per sub-city and city were 65 and 343.5, respectively.

SALURBAL survey data and outcome

SALURBAL compiled, harmonized, and geocoded individual-level data from nationally representative cross-sectional surveys. For this analysis, we included surveys with hypertension data: Argentina (2013), Brazil (2013), Chile (2010), Colombia (2007), El Salvador (2014), Guatemala (2002), Mexico (2012), and Peru (2016). Surveys were generally conducted by government agencies in different countries for purposes of risk factor surveillance often using similar questions.

The outcome was hypertension. Participants were defined as having hypertension if they reported that a physician told them that they had hypertension and if they reported using medications to lower blood pressure prescribed by a healthcare provider (i.e., both conditions had to be fulfilled). We have included the use of drugs in the definition to increase specificity. Gestational hypertension was excluded except in Argentina and Guatemala where the survey questions used did not exclude physician-diagnosed hypertension during pregnancy. This definition was used to incorporate data from as many countries as possible while maximizing comparability across countries.

Exposures

The three key exposures investigated were individual-level education, a proxy of individual-level SES, and summary scores of sub-city and city education, used as indicators of area-level SES. The sub-city level was included to capture heterogeneity within cities.

Individual-level education was harmonized across countries/surveys and classified into: (1) less than primary (2) primary: individuals who completed primary education, but with incomplete secondary education; (3) secondary: individuals with complete secondary education, complete non-university postsecondary education (e.g., technical school), or with incomplete university education; (4) university or higher: individuals who completed a university degree or with complete/incomplete graduate studies.

The population educational attainment score refers to educational achievement in the overall population. This measure was created using aggregated census data from the individual level of education: (1) the percentage of the population aged 25 years or older that has completed high school level or above, and (2) the percentage of the population aged 25 years or older that has completed university level or above [24]. A score was created by summing the standardized Z-scores of the two variables. Z scores were created for cities, based on the distributions of all cities, and sub-cities, based on the distributions of all sub-cities. Higher score values signify better educational achievement in the population [24].

Other variables included individual-level age (in years) and gender.

Statistical Analysis

All analyses were stratified by gender because of previous evidence of gender differences in the associations between economic conditions and hypertension [3, 5, 6]. For analytical purposes, El Salvador and Guatemala were grouped into one analytical unit named Central America. Descriptive statistics, using frequency distributions, means, and standard deviations (SD), are presented by countries and hypertension status.

The associations of individual-level and area-level SES with hypertension were examined using three-level multilevel logistic regression, including a random intercept for city and sub-city. All models were controlled for age. Model 1 included individual- and city-level education and country as fixed effects. Model 2 included individual- and sub-city-level education and country as fixed effects. Model 3 included all the exposures jointly and country as fixed effects. In Model 4, we included multiplicative interactions between city-level education and country by including the corresponding interaction terms in Model 3. To explore whether there was heterogeneity in the associations by country, we conducted stratified analyses by country and gender (Supplementary Material Figure S2). Since Peru was the only country that presented a statistically significant and inverse association between city-level education and hypertension, we present the difference by grouping countries into two groups (group 1: Argentina, Central America, Brazil, Chile, Colombia, and Mexico; group 2: Peru) and including the appropriate interaction term in the model.

To determine if there were interaction between individual- and area-level SES, we estimated a fifth model (Models 5A and 5B) by including in Model 3 an interaction term between individual- and city-level education (Model 5A) and individual- and sub-city-level education (Model 5B). We plotted the predicted probabilities resulting from the models by individual education category and city- and sub-city-level education.

Sensitivity analysis was performed in model 4 using the objective measure to define the outcome: hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg or the use of antihypertensive drugs. These analyses excluded Argentina because blood pressure measurements were not available and were restricted to smaller subsamples in other countries because measurements were not available for all respondents.

The analyses were performed using Stata version 12.0 (StataCorp LP, College Station, USA) and R software. The level of significance was set at 5%. The SALURBAL study protocol was approved by the Drexel University Institutional Review Board with ID #1612005035.

The individual-level and area-level characteristics of the 109,184 individuals distributed in 673 sub-cities nested in 230 cities, according to country, are presented in Table 1. The median of participants per sub-city was 65 (interquartile ranges [IQR] = 123) and per city was 343.5 (IQR = 506). The highest percentage of survey participants lived in Brazil and Mexico (24.2% and 23.8%, respectively), while Chile and Central America had the lowest (2.4% and 2.6%, respectively). The overall proportion of hypertension was 13.0% (95%CI = 12.9–13.1). Argentina and Brazil had the highest proportions of hypertension (17.5% and 16.5%, respectively), while Peru (6.5%) and Colombia (7.7%) had the lowest. The mean age of the population studied was 42.7 years (SD = 16.4) and 57.8% were women. The distribution of participants by age, gender, and education was found to differ significantly across countries.

Table 1

Individual-level and area-level characteristics of the analytic sample by country. SALURBAL study (N = 109,184).
Sample characteristics
	TOTAL	Argentina	Brazil	Chile	Colombia	Mexico	Peru	Central America ^a
Number of participants	109,184	21,286	26,398	2,669	18,142	25,995	11,88	2,814
Number of cities	230	33	27	19	33	91	23	4
Participants per city
Median (1stQ-3rdQ)	343.5 (135;641)	509 (415;679)	820 (735;1,100)	84 (33;174)	402 (195;630)	188 (89;387)	311 (162;622)	667.5 (250;1,157)
Min – Max	17 − 3,901	23 − 3,901	513–2,543	17–793	61 − 3,376	17 − 2,370	90 − 3,278	160–1,319
Number of sub-cities	673	108	27	70	57	245	149	17
Participants per sub-city
Median (1stQ-3rdQ)	65 (30;153)	85 (49;338.5)	820 (735;1,100)	20.5 (14;47)	195 (66;402)	62 (33;121)	49 (20;107)	77 (38;160)
Min – Max	5 − 3,092	7–885	513–2,543	5–239	14 − 3,092	14–835	5–521	18 − 1,319
Area-level characteristics
City-level education, median (1stQ-3rdQ)	0.13 (-0.66;1.02)	-0.54 (-1.12;-0.33)	0.76 (0.25;1.16)	-1.06 (-1.42;0.69)	-1.17 (-0.86;0.32)	0.25 (-0.95;0.52)	2.93 (1.33;3.49)	-1.71 (-1.71;-0.84)
Sub-city-level education, median (1stQ-3rdQ)	0.25 (-0.56;1.19)	-0.34 (-0.74;-0.07)	1.19 (0.60;1.72)	− 0.65 (-1.08;-0.22)	0.07 (-0.48-0.64)	-0.14 (-0.98;0.57)	1.20 (0.58;2.14)	-1.65 (-1.65;-1.39)
Individual-level characteristics
Age, mean (SD)	42.7 (16.4)	44.8 (17.9)	43.4 (16.6)	46.8 (17.6)	39.1 (14.0)^b	43.6 (16.1)	39.7 (15.6)	42.9 (16.6)
Women, n (%)	63,126 (57.8)	11,991 (56.3)	15,692 (59.4)	1,601 (60.0)	10,366 (57.1)	14,760 (56.8)	6,835 (57.5)	1,881 (66.8)
Educational level, n (%)
Less than primary	18,856 (17.3)	1,982 (9.3)	5,626 (21.3)	284 (10.6)	3,104 (17.1)	5,361 (20.6)	1,516 (12.8)	983 (34.9)
Primary	36,674 (33.6)	7,841 (36.8)	5,724 (21.7)	922 (34.5)	6,269 (34.6)	12,674 (48.8)	2,065 (17.4)	1,179 (41.9)
Secondary	39,310 (36.0)	7,865 (36.9)	10,214 (38.7)	1,239 (46.3)	7,150 (39.4)	5,439 (20.9)	6,845 (57.6)	561 (19.9)
University or higher	14,344 (13.1)	3,598 (16.9)	4,834 (18.3)	227 (8.5)	1,619 (8.9)	2,521 (9.7)	1,454 (12.2)	91 (3.2)
Hypertension, n (%)	14,208 (13.0)	3,725 (17.5)	4,364 (16.5)	358 (13.4)	1,399 (7.7)	3,158 (12.1)	773 (6.5)	431 (15.3)
Women	9,355 (14.8)	2,317 (19.3)	2,891 (18.4)	259 (16.2)	961 (9.3)	2,111 (14.3)	480 (7.0)	336 (17.9)
Men	4,853 (10.5)	1,408 (15.5)	1,473 (13.8)	99 (9.3)	438 (5.6)	1,047 (9.3)	293 (5.8)	95 (10.1)
^a Central America: El Salvador and Guatemala grouped together.
^b Residents of Colombia had a lower average age, as the survey in this country did not include individuals over 70 years of age.

The proportion of self-reported hypertension was 10.5% (95%CI = 10.2–10.8) for men and 14.8% (95%CI = 14.5–15.1) for women (Table 2). Participants with hypertension were, on average, older and concentrated in the lower education categories as compared with those without hypertension, both for men and women. Also, individuals with hypertension had slightly higher sub-city-level SES and lower city-level SES than those without hypertension for both sexes.

Table 3 shows associations of individual-, sub-city- and city-level SES with hypertension. The random intercept remained significant for all models in both sexes, indicating residual variability in hypertension between cities and sub-cities. In women, higher individual-level SES was associated with lower odds of hypertension after the full model adjustment. In contrast, higher sub-city-level SES was associated with higher odds of hypertension in all models. City-level SES was not associated with hypertension even after joint adjustment of the three exposure variables, age, and country-fixed effects. Exploration of interactions between city SES and country showed that lower city-level SES was associated with lower odds of hypertension in Peru but no association was observed for Argentina, Brazil, Central America, Chile, Colombia, and Mexico.

Table 3

Associations of individual-, city- and sub-city-level education, with hypertension in 230 cities in Latin America.
Individual, city, and sub-city characteristics*	Model 1	Model 2	Model 3	Model 4
Individual, city, and sub-city characteristics*	OR (95%CI)	OR (95%CI)	OR (95%CI)	OR (95%CI)
Women (N = 63,126)
Individual-level education
Less than primary	Ref.	Ref.	Ref.	Ref.
Primary	0.99 (0.93–1.06)	0.99 (0.92–1.06)	0.99 (0.92–1.06)	0.98 (0.92–1.05)
Secondary	0.76 (0.70–0.82)	0.75 (0.70–0.81)	0.75 (0.70–0.81)	0.76 (0.70–0.82)
University or higher	0.67 (0.61–0.74)	0.66 (0.60–0.73)	0.66 (0.60–0.73)	0.67 (0.61–0.74)
City-level education	0.98 (0.93–1.04)		0.95 (0.89–1.01)
Sub-city-level education		1.04 (1.01–1.08)	1.05 (1.01–1.10)	1.07 (1.02–1.12)
Argentina, Central America, Brazil, Chile, Colombia, and Mexico
City-level education	-	-	-	1.01 (0.94–1.08)
Peru
City-level education				0.79 (0.64–0.97)
Intercept variance (SE) (city)	0.0362 (0.1904)	0.0373 (0.1931)	0.0354 (0.1884)	0.0641 (0.2533)
Intercept variance (SE) (sub-city)	0.0262 (0.1622)	0.0241 (0.1555)	0.0258 (0.1608)	0.0272 (0.1652)
Men (N = 46,058)
Individual-level education
Less than primary	Ref.	Ref.	Ref.	Ref.
Primary	1.51 (1.37–1.66)	1.49 (1.36–1.64)	1.50 (1.36–1.65)	1.48 (1.35–1.63)
Secondary	1.59 (1.43–1.76)	1.56 (1.40–1.73)	1.56 (1.40–1.73)	1.58 (1.42–1.75)
University or higher	1.68 (1.49–1.88)	1.63 (1.45–1.83)	1.63 (1.45–1.83)	1.65 (1.47–1.86)
City-level education	0.98 (0.92–1.04)		0.92 (0.85–0.99)
Sub-city-level education		1.06 (1.01–1.12)	1.09 (1.03–1.16)	1.11 (1.05–1.18)
Argentina, Central America, Brazil, Chile, Colombia, and Mexico
City-level education	-	-	-	1.00 (0.92–1.10)
Peru
City-level education	-	-	-	0.77 (0.59–0.99)
Intercept variance (SE) (city)	0.0221 (0.1487)	0.0270 (0.1644)	0.0213 (0.1460)	0.1001 (0.3164)
Intercept variance (SE) (sub-city)	0.0233 (0.1528)	0.0154 (0.1244)	0.0191 (0.1384)	0.0250 (0.1582)
Ref.: reference group. OR: Odds Ratio. 95%CI: 95% confidence intervals. SE: standard error. Bold values have a p-value < 0.05.
* All models were adjusted for individual age. City- and sub-city-level education (educational attainment score) was standardized to a mean of 0 and a standard deviation (SD) of 1. The OR is estimated for a 1 SD (0.30847) and (0.24469) difference in sub-city- and city-educational attainment scores, respectively. Model 1: Individual- and city-level education, adjusted for country-fixed effect. Model 2: Individual- and sub-city-level education, adjusted for country-fixed effect. Model 3: Individual-, sub-city- and city-level education, adjusted for country-fixed effect. Model 4: Model 3 and an interaction term between city-level education and different country groups (p-value for interaction in women = 0.004 and men = 0.012). Combinations of the main effect of city-level education and the interaction coefficient were used to derive estimates for different countries.

In men, higher individual- and sub-city-level SES were associated with higher odds of hypertension in all models (Table 3). In contrast, higher city-level SES was associated with lower odds of hypertension in the model with joint adjustment of the three exposure variables, age, and country-fixed effects. Exploration of interactions showed that this association was stronger in Peru while no association was observed for Argentina, Brazil, Central America, Chile, Colombia, and Mexico.

Figure 1 shows adjusted predicted probabilities of hypertension for individual education by levels of the city- and sub-city SES. Although interaction terms were not always statistically significant, interesting patterns were observed. In women, the inverse association of individual-level SES with hypertension became stronger at higher levels of city and sub-city SES. In men, inverse associations with SES appeared to emerge at high levels of sub-city SES. Estimates from models with interactions are shown in a supplementary table (Supplementary Material Table S1).

In sensitivity analyses, restricted to persons with measured blood pressure or using antihypertensive drugs, we observed similar results in women in terms of directionality and patterns of associations for all three exposures in the full model (Supplementary Material Table S2). However, in men in contrast to what was observed with self-reported hypertension, higher individual-level SES was associated with lower odds of hypertension. Findings for the city- and sub-city-level SES were similar in directionality to those reported in the main analysis.

We investigated associations of individual-level and area-level SES with hypertension in adults from 230 Latin American cities. We found a clear gradient across individual-level SES, but in opposite direction between genders. In women, higher SES levels were associated with a lower proportion of hypertension. In men, higher individual-level SES was associated with a higher proportion of hypertension. Although interaction terms between individual-level and area-level SES were not always statistically significant, descriptive analyses suggested that an inverse association of individual-level SES with hypertension became stringer or emerged as city or sub-city SES increased.

Our results also demonstrated that higher sub-city-level SES was associated with lower odds of hypertension in both genders. Moreover, the association of city-level SES with hypertension varied across countries. In Peru, there was an inverse association (lower proportion of hypertension with a better education at the city level), while in other countries there was no association in women or men.

A recent meta-analysis of 51 studies found that educational attainment was a stronger predictor of hypertension prevalence than income or occupation [6]. However, to our knowledge, transnational studies investigating associations of individual-level and area-level SES with hypertension using the same indicator at different levels and in different countries have not been conducted.

We found that hypertension was inversely associated with education among women, but positively associated with education among men. Evidence limited from LMICs generally shows a higher prevalence of hypertension in women with lower education levels compared with higher education, while for men, evidence is mixed [3, 11, 25, 26]. Explanations for gender disparities may include more physically demanding jobs for less educated men [27] or differential patterning of other risk factors by SES in women and men [28]. Moreover, being a woman and having low education may be linked to higher exposure to chronic stress conditions, such as informal employment, single parenthood and role overload, violence, and stress at home [29, 30].

Furthermore, women's behaviors are heavily influenced by heteronormative gender rules in patriarchal societies, especially the more educated women. For instance, these women are more likely to face pressure to change their bodies to align with social expectations [31]. Surveillance bias may also explain part of the association observed between education and hypertension in men. Men often search less for health systems and medical advice [18]. Nonetheless, men with the highest SES are less likely to remain undiagnosed and report more chronic diseases [16, 17]. Consistent with this argument, our sensitivity analysis based on objective measures of blood pressure revealed that men with higher SES had lower odds of hypertension.

Our study also showed sub-city and city contextual effects. After accounting for individual SES, we found a positive association between sub-city-level SES and hypertension for both genders. Lower access to health care (and consequent diagnosis of hypertension) in areas of lower SES could explain this finding [16–18]. On the other hand, we observed a similar pattern when objective measures of hypertension were used. In consonance with our findings, a positive association between area-level SES and hypertension was also previously reported in Brazil using objective hypertension measures [4].

The mechanisms underlying this association may be related to contextual factors associated with area-level SES including the nature of work [32], access to and consumption of processed foods, sedentary behaviors [33, 34] promoted by work and urban environments (e.g., car dependence), or even factors such as levels of pollution, heat, and noise, all of which have been linked to hypertension [35] and the risk of cardiovascular disease [36].

Significant associations between city-level SES and hypertension were limited to individuals residing in Peru; better educational performance of the population at the municipal level was associated with a lower proportion of hypertension in women and men. While the highest prevalence of hypertension was observed in some Latin American and Caribbean countries, the lowest global prevalence of hypertension was found in Peru [2]. It is possible that the hypertension epidemic is not yet very deep in this country, reflecting a pattern different from others. Thus, the positive association between city-level SES and hypertension in Peru is a question that deserves additional research.

Our results also suggest possible interactions between contextual- and individual-level SES. In women, the inverse association of individual-level SES with hypertension became stronger as sub-city and city SES increased. In men, the positive association of individual-level SES with hypertension was lost, and an inverse gradient emerged (higher SES, lower hypertension prevalence) as sub-city SES increased. This is consistent with findings from prior works showing that inverse social gradients in cardiovascular risks emerge as contextual SES increases [37, 38]. It may be related to the social patterning of risk factors for hypertension that emerges as socioeconomic development increases.

This study has some limitations. First, we use a cross-sectional design, which does not allow us to draw causal inferences; however, descriptive information is also important to public policy. Second, the ascertainment of hypertension status was through self-report, which may have led to differential information bias, with groups with less access to healthcare under-represented, and consequently, resulting in underestimates of education gradients. Third, it is not possible to rule out residual confounding due to unmeasured or unknown confounding factors. Fourth, survey years are not always aligned with the census years from which area-level SES information was drawn. Finally, despite efforts to harmonize surveys across countries, some heterogeneity may still exist and affect our results. Nonetheless, to attenuate these potential remaining differences we used the country as fixed effects for the main analyses.

On the other hand, this study has several strengths. As far as we know, this study is the first to examine the association between individual-level and area-level SES with hypertension using the same indicator at different levels. Second, our study included a large sample of individuals (109,184) and cities (230) representing a significant proportion of the urban population of Latin America and used a large harmonized dataset. Third, our multilevel approach allowed us to analyze individual and macro-level contextual factors. In addition, the associations were adjusted for country-fixed effects removing the effect for unmeasured country factors such as differences in healthcare and education systems across countries.

In conclusion, our results demonstrate gender and social inequalities in hypertension in Latin American cities. First, we identified gender differences in the relationship between individual SES and hypertension, with higher individual-level SES associated with lower odds of hypertension among women and higher odds among men. Second, we identified that higher sub-city-level SES was positively associated with hypertension in both women and men. Third, we identified that higher city-level SES was associated with lower odds of hypertension in both sexes in Peru. Thus, our results suggest that strategies to deal with the burden of hypertension in LIMCs should adopt equity-based and context-sensitive efforts.

OR: Odds Ratio

CI: Confidence interval

SD: Standard deviation

IQR: Interquartile ranges

SALURBAL: Salud Urbana en America Latina/Urban Health in Latin America

LMICs: Low- and middle-income countries

SES: Socioeconomic status

Ethics approval

This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects/patients were approved by the Drexel University Institutional Review Board with ID no. 1612005035 and by appropriate site-specific IRBs. In Brazil, this study was approved by the Federal University of Minas Gerais IRB (CAAE: 70209917.00000.5149). For health survey participants, informed consent was obtained from all subjects.

Consent for publication

Not applicable

Availability of data and materials

Data are available upon reasonable request. The SALURBAL study obtained data from health and/or statistical agencies within each country. Some data sources are available under restricted access due to data use agreements between the SALURBAL Study and statistical agencies within the country. Requests for the harmonized data can be obtained by contacting the SALURBAL Data Methods Core ([email protected]) and after completing data use agreements. To learn more about SALURBAL’s dataset, visit https://drexel.edu/lac/ or contact the project at [email protected].

Competing interests

We declare no competing interests.

Funding

The Salud Urbana en América Latina (SALURBAL)/ Urban Health in Latin America project is funded by the Wellcome Trust [205177/Z/16/Z]. For the purpose of open access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript version of this publication. All authors had financial support from SALURBAL. The authors AALF and WTC are also supported by the of the National Council of Scientific and Technological Development (CNPq) through a research productivity scholarship. The author DMC is also supported by the Coordination of Improvement of Higher Education Personnel (CAPES) through a doctoral scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

The authors acknowledge the contribution of all SALURBAL project team members. For more information on SALURBAL and to see a full list of investigators, see https://drexel.edu/lac/salurbal/team/. SALURBAL acknowledges the contributions of many different agencies in generating, processing, facilitating access to data or assisting with other aspects of the project. Please visit https://drexel.edu/lac/data evidence/ data/ for a complete list of data sources. The findings of this study and their interpretation are the responsibility of the authors and do not represent the views or interpretations of the institutions or groups that compiled, collected, or provided the data. The use of data from these institutions does not claim or imply that they have participated in, approved, endorsed, or otherwise supported the development of this publication. They are not liable for any errors, omissions or other defect or for any actions taken in reliance thereon. The authors would like to thank Bruno de Souza Moreira for her valuable comments in earlier versions of the manuscript.

Author Contributions

DMC, ACSA, and WTC conceived the study. DMC, ACSA, and UMS performed the statistical analyses. DMC drafted the first version of the manuscript. ML, CS, AQ, AVDR and AALF participated in or supported data collection. All authors participated in the interpretation of results and approved the final version of the manuscript.

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No competing interests reported.

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Gender differences in the association of individual and contextual socioeconomic status with hypertension in 230 Latin American cities from the SALURBAL study: a multilevel analysis.

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