Income inequality and self-reported health in a representative sample of 27 017 residents of state capitals of Brazil

Income inequality and self-reported health in a representative sample of 27 017 residents of... Abstract Background The association between income inequality and health has been analyzed predominantly in developed countries with modest levels of inequality. The study aimed to analyze the association between income inequality and self-reported health (SRH) in the adult population of the 27 Brazilian capitals. Methods Individuals aged 18 years or older from the National Health survey residing in Brazilian capitals in 2013 were analyzed (n = 27 017). Bayesian multilevel models were applied after controlling for individual factors and area-level socioeconomic characteristics. Results We found a significant association between income inequality and SRH, even after controlling for individual and contextual factors. The results indicate greater odds of poor SRH among those living in areas with medium (OR = 1.31, 95% CI: 1.17–1.47) and high income inequality level (OR = 1.39, 95% CI: 1.24–1.56). Income inequality remained significantly associated with SRH, even after controlling for other contextual socioeconomic characteristics, such as local illiteracy rate, violence and per capita income. Conclusions The study highlights the importance of the individual and contextual characteristics associated with SRH. Our findings suggest that city-level income inequality can have a detrimental effect on individual health, over and above other contextual socioeconomic characteristics and individual factors. epidemiology, public health, social determinants Introduction Income inequality has increased for most countries during the last decade.1 Despite a recent decrease, Brazil still ranks among the countries with the highest Gini Coefficient, especially within the Americas.2 Recent economic trends indicate that income inequality will remain a fast-growing challenge for all developed and developing countries during the next decades.3 The association between income inequality and worst health has been analyzed predominantly within developed countries with modest levels of inequality, and most of the findings indicate income inequality associated with increased odds for worse self-reported health (SRH).4–7 For developing countries with high inequality, however, studies have observed inconsistent findings. Two analyses on mental health, one in South Africa8 and another in Mexico,9 did not find a significant association between income inequality and depressive symptoms, the same result observed in China when the risk of having health problems was analyzed.10 On the other hand, an ecological study by Rasella et al.11 found that income inequality of Brazilian states was negatively associated with average life expectancy. SRH is a robust indicator to assess the health status of populations.12 This subjective measure of health conditions is considered to indirectly take into account aspects not accounted by other instruments, such as psychological and social factors.13 SRH is considered an important indicator of morbidity and mortality,14 and has recently been found to be the best predictor for all-cause mortality when compared to 655 different demography, health and lifestyle variables.15 Recent trends on income inequality, and its potential deepening due to further progressions in the automation of the labor force,3 encourage new studies on the social determinants of health and the influence of contextual factors, especially in countries with historically marked social inequalities such as Brazil. However, studies that investigated the association between income inequality and SRH after controlling for area-level socioeconomic characteristics are still scarce in developing countries. Thus, multilevel studies that consider the influence of both individual and contextual factors on health are needed to identify the relationship between area-level income inequality and SRH. The present study addresses these gaps by analyzing the association between income inequality and SRH in the adult population of the 27 Brazilian capitals, after controlling for individual factors and area-level socioeconomic characteristics. Methods Data The study used data from the National Health survey, a representative multi-stage probabilistic sample of Brazilian adults aged 18 and older. The survey was carried out in 2013 and the sampling process aimed to be representative for the country, Brazilian states and capitals. Data were obtained from a household questionnaire that collected information on socioeconomic characteristics, use of health services, SRH, presence of chronic diseases and lifestyle.16 The National Health survey was coordinated by the Brazilian Institute of Geography and Statistics (IBGE) and was approved by the National Research Ethics Committee (Case No. 328.159/2013) of the National Health Council. Variables The dependent variable was SRH. The participants were asked to self-rate their health in a classification scale consisting of very good, good, regular, bad and very bad. In a recent study,15 this measure was found to be the strongest predictor of 5-year all-cause mortality when compared to more than 600 other variables, indicating that a self-reported information can be an important health status indicator. For the purpose of this study, as used in other surveys that assessed health through this measure, SRH was subsequently categorized into two response categories: good health (regular, good and very good classifications) and poor health (poor and very poor classifications).17 At the individual level, the variables included as controls were gender, age (categorized into 18–24, 25–39, 40–59 and 60 or more years), race (white, mixed, black and others), education attainment (categorized according of the total number of years of formal education into incomplete elementary school, complete elementary school, complete high school and some college or more), marital status (with or without partner), smoking and the presence of hypertension, diabetes, hypercholesterolemia and depression. At the contextual level, socioeconomic indicators were calculated using data from the 2010 Census for each of the 27 Brazilian capitals (including the Federal District), each with an average population of 1 237 902, totaling 33 423 348 adult residents in Brazilian capitals. The main exposure of interest was income inequality, measured by the Gini Index. The Gini Index is based on the Lorenz cumulative frequency curve, and it ranges from 0 (perfect equality) to 1.0 (perfect inequality). Details of its calculation method have been described elsewhere.18 Additional contextual covariates analyzed were illiteracy rate, violence and per capita income. Age-adjusted homicide rate was used as a proxy for local violence, with data provided from the Mortality Information System from the Ministry of Health of Brazil. Because of non-linearity, all the contextual variables were divided by tertiles and categorized into low, medium and high. Statistical analysis Multilevel logistic regressions were used to analyze the association between income inequality and SRH in order to control for the clustering within the capitals of residence and to test for individual and area-level characteristics. The intraclass correlation coefficient (ICC) was estimated to quantify the proportion of variance in SRH that could be explained at the contextual and individuals levels. The first level of the models referred to individual characteristics and the second level to the socioeconomic characteristics of the capital of residence. The model parameters were estimated with Bayesian multilevel models, a recommended approach to decrease bias in multilevel analysis with dichotomous outcomes, and which allows the comparison of model fit by comparing the values of Bayesian Information Criterion (BIC) of the individual models.19 We also tested the cross-level interaction between gender and income inequality using an interaction term in the multilevel analyses to determine if the association between income inequality and SRH differed between men and women. The analyses were performed with Stata V13.1 (Stata Corporation, College Station, TX, USA, 2013). The descriptive statistics of the socioeconomic characteristics, lifestyles and presence of chronic diseases were performed using the survey mode procedure, considering the sampling weight and individual clustering (secondary sampling units) within census tracts (primary sampling units), due to the complex sample design. Multilevel analyses used the gllamm command, which allows the inclusion of weights for complex sample designs. Results Sample characteristics The sample consisted of 27 017 individuals of both sexes, aged 18 years or older, residing in one of the 27 Brazilian capitals (including the Federal District) in 2013. Descriptive analysis of socioeconomic characteristics indicate that most of the individuals were female (55%) and lived without a partner (56%). A little less than half of individuals (48%) were younger than 40 years, 34% were aged 40–59 years and 18% aged 60 years or older. Regarding race, 47% reported being white and around half being mixed or black (41% mixed and 10% black). As for education attainment, less than a quarter of individuals reported not having completed elementary school (24%), and more than 60% had completed at least high school (Table 1). Table 1 Distribution of the characteristics of the individuals' residents in Brazilian capitals, 2013, Brazil Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  aAbsolute numbers on the unweighted sample. bWeighted sample proportion. cχ2 test. Source: PNS, 2013. View Large Table 1 Distribution of the characteristics of the individuals' residents in Brazilian capitals, 2013, Brazil Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  aAbsolute numbers on the unweighted sample. bWeighted sample proportion. cχ2 test. Source: PNS, 2013. View Large Regarding lifestyle and the presence of chronic diseases, most individuals never smoked (71%), and of those with a smoking history, 16.5% were former smokers and 13% current smokers. Of the total sample, 4.5% reported poor health. The prevalence of hypertension and diabetes were 20.5 and 7%, respectively, and a previous diagnosis of depression was reported by 7% of the population (Table 1). Descriptive analyses of the contextual characteristics indicated high levels of inequality among the 27 Brazilian capitals. The income inequality range, as measured by Gini index, varied from a low of 0.54 to a high of 0.69. Bivariate associations with SRH We found a statistically significant association of poor SRH with socioeconomic characteristics, except for marital status. More specifically, a significantly higher presence of poor SRH was observed in women in comparison to man, among higher age groups and among individuals with low education attainment (Table 1). Smoking and presence of chronic diseases were also associated with poor SRH. Those with a smoking history had higher poor self-rated health in comparison with never smokers. We also observed a higher presence of poor SRH between those with chronic diseases (Table 1). Multilevel regression models Multilevel modeling was applied to analyze the association between income inequality and SRH, while adjusting for individual and contextual factors. Table 2 presents the results from the multilevel analyses for the association between SRH and income inequality, controlling for individual characteristics. Women were more likely to present poor SRH in comparison to men (OR = 1.52, 95% CI: 1.26–1.83). Those living with a partner were significantly less likely to report poor SRH in comparison with those living without a partner (OR = 0.88, 95% CI: 0.81–0.96). Regarding income inequality, those living in the medium (OR = 1.31, 95% CI: 1.17–1.47) and high income inequality level (OR = 1.39, 95% CI: 1.24–1.56) presented higher odds for poor SRH, even after controlling for individual characteristics. When a sex × income inequality interaction was included, no significant differences were observed between men and women regarding SRH (results not shown). Table 2 Multilevel logistic models of poor SRH according to demographic, socioeconomic, lifestyle, presence of chronic diseases and contextual socioeconomic, 2013, Brazil   Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)    Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)  *P < 0.05. **P ≤ 0.001. View Large Table 2 Multilevel logistic models of poor SRH according to demographic, socioeconomic, lifestyle, presence of chronic diseases and contextual socioeconomic, 2013, Brazil   Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)    Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)  *P < 0.05. **P ≤ 0.001. View Large Table 3 presents the results for the association between income inequality and self-rated health after controlling for other contextual socioeconomic characteristics. Higher income inequality (medium and high) remained significantly associate with poor SRH. A significant higher odd of poor SRH was also observed among those living in areas with medium and high illiteracy levels (OR = 1.09, 95% CI: 1.01–1.18 and OR = 1.53, 95% CI: 1.40–1.67, respectively). Similar results were obtained for area-level violence, both for medium (OR = 1.54, 95% CI: 1.42–1.66) and high violence levels (OR = 1.66, 95% CI: 1.51–1.81). On the other hand, income was negatively associated with higher odds of poor self-rated health, for areas with medium (OR = 0.68, 95% CI: 0.62–0.76) and high (OR = 0.43, 95% CI: 0.37–0.48) per capita income. Table 3 Multilevel logistic models of poor SRH adjusted by individual factorsa according to contextual socioeconomic characteristics, illiteracy rate, violence, average income and income inequality, 2013, Brazil   Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)    Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)  *P < 0.05. **P ≤ 0.001. aIndividuals factors: gender, age, race, education attainment and marital status. Table 3 Multilevel logistic models of poor SRH adjusted by individual factorsa according to contextual socioeconomic characteristics, illiteracy rate, violence, average income and income inequality, 2013, Brazil   Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)    Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)  *P < 0.05. **P ≤ 0.001. aIndividuals factors: gender, age, race, education attainment and marital status. Discussion Main finding of this study The present study highlights the association between area-level income inequality and SRH among adult residents of Brazilian capitals. At the individual level, female gender, advanced age, low education attainment, living without a partner and having hypertension, diabetes or depression were statistically associated with higher odds of poor SRH. At the contextual level, in comparison with those who live in the lowest income inequality areas, living in areas with higher income inequality was significantly associated with poor SRH, even after controlling for individual variables and other contextual socioeconomic characteristics such as illiteracy rate, violence and per capita income. The association found between self-rated health and income inequality brings new insights to the international literature on social determinants of health by analyzing a large and representative sample of adults living in 27 capitals of a developing country with very high levels of inequality. What is already known on this topic An important discussion in the literature concerns the influence of income inequality across different levels of area aggregation. Studies where smaller aggregation scales were analyzed, such as neighborhoods, have provided consistent evidence on the relationship between contextual factors and health outcomes.20 However, the results of cross-country studies are still not robust, possibly due to the distance of the place of residence to the extremes of income distribution, and the difficulty in comparing data with restricted variability in income distribution from developed countries with data of greater income variability from developing countries.21 The explanation for how income inequality may influence population health is still an open area of debate. One of the most influential theories, originally put forward by Wilkinson,22 argues that income inequality affects health directly, and is robust to controls for individual and contextual factors. Our results point to this direction, suggesting that the negative effect of income inequality on health are not necessarily mediated by individual characteristics, such as race and socioeconomic factors,23 nor other contextual characteristics, when measured in a society with large and persistent social inequalities. What this study adds To the best of our knowledge, this study is the first to analyze the association between self-rated health and other contextual characteristics of Brazilian capitals besides income inequality, such as illiteracy and violence rates, after considering the influence of the individual characteristics. We found statistically significant associations between self-rated health and area-level illiteracy rates. In comparison with those living in the areas with lower illiteracy rates, living in areas with medium and high levels of illiteracy was associated with poor SRH on our adjusted model. Although individual education attainment has been frequently shown to influence individual health,24,25 not much is known about the association between area-level education and health outcomes in Brazil. New studies should analyze this further, possibly by considering other factors that may mediate the triangular relationship between illiteracy, income inequality and health, such as the unemployment rate,26 especially in countries with marked social inequalities. Our findings also show that individuals are more likely to have poor health in areas with higher violence rates, even after adjusting for individual factors and area-level income inequality. In addition to higher income inequality being detrimental to social cohesion27 and social isolation,28 fear of area violence may exacerbate these feelings, which may then lead to an even worse health condition.29–31 Although factors such as social cohesion and isolation were not directly tested in this study, the results are consistent with the theory that the effects of income inequality on health may extend to other problems of the social gradient.7 Limitations of this study The interpretation of these findings should consider its limitations. First, the sample is representative of the adult population residing in the 27 Brazilian capitals, so one should not draw conclusions for the other areas of the country. Second, the use of cross-sectional data results does not allow the establishment of causal inferences, and need to be interpreted as associations. Third, despite having a relatively acceptable response rate (86.0%), the possibility of response bias cannot be ignored. Conclusions Our study highlights the importance of contextual characteristics associated with SRH. Our findings suggest that income inequality may be a detrimental factor on self-rated health, even when the effects of individual and other area-level factors are considered. New knowledge of the association between self-rated health with individual and contextual factors can help to improve social programs and the planning of health care strategies on the largest country in Latin America. Funding This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo—FAPESP [2014/12716-3]. References 1 ISSC IDS. UNESCO. World Social Science Report 2016, Challenging Inequalities: Pathways to a Just World . Paris: UNESCO Publishing, 2016. 2 World Bank. World Development Indicators. http://databank.worldbank.org/ (9 January 2016, date last accessed). 3 Dabla-Norris ME, Kochhar MK, Suphaphiphat MN et al.  . Causes and consequences of income inequality: a global perspective. International Monetary Fund 2015. 4 Torre R, Myrskylä M. Income inequality and population health: an analysis of panel data for 21 developed countries, 1975–2006. Popul Stud (Camb)  2014; 68( 1): 1– 13. Google Scholar CrossRef Search ADS PubMed  5 Pabayo R, Kawachi I, Gilman SE. US State-level income inequality and risks of heart attack and coronary risk behaviors: longitudinal findings. Int J Public Health  2015; 60( 5): 573– 88. Google Scholar CrossRef Search ADS PubMed  6 Zheng H. Do people die from income inequality of a decade ago? Soc Sci Med  2012; 75( 1): 36– 45. Google Scholar CrossRef Search ADS PubMed  7 Pickett KE, Wilkinson RG. Income inequality and health: a causal review. Soc Sci Med  2015; 128: 316– 26. Google Scholar CrossRef Search ADS PubMed  8 Adjaye-Gbewonyo K, Avendano M, Subramanian SV et al.  . Income inequality and depressive symptoms in South Africa: a longitudinal analysis of the National Income Dynamics Study. Health Place  2016; 42: 37– 46. Google Scholar CrossRef Search ADS PubMed  9 Fernández-Niño JA, Manrique-Espinoza BS, Bojorquez-Chapela I et al.  . Income inequality, socioeconomic deprivation and depressive symptoms among older adults in Mexico. PLoS One  2014; 9( 9): e108127. Google Scholar CrossRef Search ADS PubMed  10 Bakkeli NZ. Income inequality and health in China: a panel data analysis. Soc Sci Med  2016; 157: 39– 47. Google Scholar CrossRef Search ADS PubMed  11 Rasella D, Aquino R, Barreto ML. Impact of income inequality on life expectancy in a highly unequal developing country: the case of Brazil. J Epidemiol Community Health  2013; 67( 8): 661– 6. Google Scholar CrossRef Search ADS PubMed  12 Peres MA, Masiero AV, Longo GZ et al.  . Self-rated health among adults in Southern Brazil. Rev Saude Publica  2010; 44: 901– 11. Google Scholar CrossRef Search ADS PubMed  13 Wu S, Wang R, Zhao Y et al.  . The relationship between self-rated health and objective health status: a population-based study. BMC Public Health  2013; 13( 1): 320. Google Scholar CrossRef Search ADS PubMed  14 Theme Filha MM, Souza Junior PRBD, Damacena GN et al.  . Prevalence of chronic non-communicable diseases and association with self-rated health: National Health Survey, 2013. Rev Bras de Epidemiol  2015; 18: 83– 96. Google Scholar CrossRef Search ADS   15 Ganna A, Ingelsson E. 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study. Lancet  2015; 386: 533– 40. Google Scholar CrossRef Search ADS PubMed  16 Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional de Saúde 2013. Disponível em: ftp://ftp.ibge.gov.br/PNS/2013/notastecnicas.pdf (14 November 2016, date last accessed). 17 Stenholm S, Pentti J, Kawachi I et al.  . Self-rated health in the last 12 years of life compared to matched surviving controls: the Health and Retirement Study. PLoS One  2014; 9( 9): e107879. Google Scholar CrossRef Search ADS PubMed  18 Castillo-Salgado C, Schneider C, Loyola E et al.  . Measuring health inequalities: Gini coefficient and concentration index. Epidemiol Bull  2001; 22( 1): 3– 4. Google Scholar PubMed  19 Rodriguez G, Goldman N. An assessment of estimation procedures for multilevel models with binary responses. J R Stat Soc A Stat Soc  1995; 158: 73– 89. Google Scholar CrossRef Search ADS   20 Chiavegatto Filho ADP, Kawachi I, Gotlieb SLD. Propensity score matching approach to test the association of income inequality and mortality in Sao Paulo, Brazil. J Epidemiol Community Health  2012; 66: 14– 7. Google Scholar CrossRef Search ADS PubMed  21 Kawachi I, Subramanian SV. Income inequality. In: Berkman LF, Kawachi I, Glymour MM (eds). Social Epidemiology . USA: Oxford University Press, 2014: 126– 51. 22 Wilkinson RG. Unhealthy Societies: The Afflictions of Inequality . London: Routledge, 2002. 23 Deaton A, Lubotsky D. Mortality, inequality and race in American cities and states. Soc Sci Med  2003; 56( 6): 1139– 53. Google Scholar CrossRef Search ADS PubMed  24 Pavão ALB, Werneck GL, Campos MR. Self-rated health and the association with social and demographic factors, health behavior, and morbidity: a national health survey. Cad Saude Publica  2013; 29: 723– 34. Google Scholar CrossRef Search ADS PubMed  25 Barros MBDA, Zanchetta LM, Moura ECD et al.  . Auto-avaliação da saúde e fatores associados, Brasil, 2006. Rev de Saude Publica  2009; 43: 27– 37. Google Scholar CrossRef Search ADS   26 Ribeiro MG. Income inequality: the education in question. Educ Soc  2016. http://dx.doi.org/10.1590/es0101-73302016154254. 27 Stiglitz JE. The Price of Inequality: How Today’s Divided Society Endangers our Future . New York: WW Norton & Company, 2012. 28 Kawachi I, Berkman L. Social cohesion, social capital, and health. In: Berkman LF, Kawachi I, Glymour MM (eds). Social Epidemiology . USA: Oxford University Press, 2014: 290– 319. 29 Wilkinson RG, Pickett KE. Income inequality and social dysfunction. Ann Rev Sociol  2009; 35: 493e511. Google Scholar CrossRef Search ADS   30 Pabayo R, Molnar BE, Kawachi I. The role of neighborhood income inequality in adolescent aggression and violence. J Adolesc Health  2014; 55( 4): 571– 79. Google Scholar CrossRef Search ADS PubMed  31 Subramanian SV, Kawachi I. Income inequality and health: what have we learned so far? Epidemiol Rev  2004; 26( 1): 78e91. http://dx.doi.org/10.1093/epirev/mxh003. Google Scholar CrossRef Search ADS   © The Author(s) 2018. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Public Health Oxford University Press

Income inequality and self-reported health in a representative sample of 27 017 residents of state capitals of Brazil

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Abstract

Abstract Background The association between income inequality and health has been analyzed predominantly in developed countries with modest levels of inequality. The study aimed to analyze the association between income inequality and self-reported health (SRH) in the adult population of the 27 Brazilian capitals. Methods Individuals aged 18 years or older from the National Health survey residing in Brazilian capitals in 2013 were analyzed (n = 27 017). Bayesian multilevel models were applied after controlling for individual factors and area-level socioeconomic characteristics. Results We found a significant association between income inequality and SRH, even after controlling for individual and contextual factors. The results indicate greater odds of poor SRH among those living in areas with medium (OR = 1.31, 95% CI: 1.17–1.47) and high income inequality level (OR = 1.39, 95% CI: 1.24–1.56). Income inequality remained significantly associated with SRH, even after controlling for other contextual socioeconomic characteristics, such as local illiteracy rate, violence and per capita income. Conclusions The study highlights the importance of the individual and contextual characteristics associated with SRH. Our findings suggest that city-level income inequality can have a detrimental effect on individual health, over and above other contextual socioeconomic characteristics and individual factors. epidemiology, public health, social determinants Introduction Income inequality has increased for most countries during the last decade.1 Despite a recent decrease, Brazil still ranks among the countries with the highest Gini Coefficient, especially within the Americas.2 Recent economic trends indicate that income inequality will remain a fast-growing challenge for all developed and developing countries during the next decades.3 The association between income inequality and worst health has been analyzed predominantly within developed countries with modest levels of inequality, and most of the findings indicate income inequality associated with increased odds for worse self-reported health (SRH).4–7 For developing countries with high inequality, however, studies have observed inconsistent findings. Two analyses on mental health, one in South Africa8 and another in Mexico,9 did not find a significant association between income inequality and depressive symptoms, the same result observed in China when the risk of having health problems was analyzed.10 On the other hand, an ecological study by Rasella et al.11 found that income inequality of Brazilian states was negatively associated with average life expectancy. SRH is a robust indicator to assess the health status of populations.12 This subjective measure of health conditions is considered to indirectly take into account aspects not accounted by other instruments, such as psychological and social factors.13 SRH is considered an important indicator of morbidity and mortality,14 and has recently been found to be the best predictor for all-cause mortality when compared to 655 different demography, health and lifestyle variables.15 Recent trends on income inequality, and its potential deepening due to further progressions in the automation of the labor force,3 encourage new studies on the social determinants of health and the influence of contextual factors, especially in countries with historically marked social inequalities such as Brazil. However, studies that investigated the association between income inequality and SRH after controlling for area-level socioeconomic characteristics are still scarce in developing countries. Thus, multilevel studies that consider the influence of both individual and contextual factors on health are needed to identify the relationship between area-level income inequality and SRH. The present study addresses these gaps by analyzing the association between income inequality and SRH in the adult population of the 27 Brazilian capitals, after controlling for individual factors and area-level socioeconomic characteristics. Methods Data The study used data from the National Health survey, a representative multi-stage probabilistic sample of Brazilian adults aged 18 and older. The survey was carried out in 2013 and the sampling process aimed to be representative for the country, Brazilian states and capitals. Data were obtained from a household questionnaire that collected information on socioeconomic characteristics, use of health services, SRH, presence of chronic diseases and lifestyle.16 The National Health survey was coordinated by the Brazilian Institute of Geography and Statistics (IBGE) and was approved by the National Research Ethics Committee (Case No. 328.159/2013) of the National Health Council. Variables The dependent variable was SRH. The participants were asked to self-rate their health in a classification scale consisting of very good, good, regular, bad and very bad. In a recent study,15 this measure was found to be the strongest predictor of 5-year all-cause mortality when compared to more than 600 other variables, indicating that a self-reported information can be an important health status indicator. For the purpose of this study, as used in other surveys that assessed health through this measure, SRH was subsequently categorized into two response categories: good health (regular, good and very good classifications) and poor health (poor and very poor classifications).17 At the individual level, the variables included as controls were gender, age (categorized into 18–24, 25–39, 40–59 and 60 or more years), race (white, mixed, black and others), education attainment (categorized according of the total number of years of formal education into incomplete elementary school, complete elementary school, complete high school and some college or more), marital status (with or without partner), smoking and the presence of hypertension, diabetes, hypercholesterolemia and depression. At the contextual level, socioeconomic indicators were calculated using data from the 2010 Census for each of the 27 Brazilian capitals (including the Federal District), each with an average population of 1 237 902, totaling 33 423 348 adult residents in Brazilian capitals. The main exposure of interest was income inequality, measured by the Gini Index. The Gini Index is based on the Lorenz cumulative frequency curve, and it ranges from 0 (perfect equality) to 1.0 (perfect inequality). Details of its calculation method have been described elsewhere.18 Additional contextual covariates analyzed were illiteracy rate, violence and per capita income. Age-adjusted homicide rate was used as a proxy for local violence, with data provided from the Mortality Information System from the Ministry of Health of Brazil. Because of non-linearity, all the contextual variables were divided by tertiles and categorized into low, medium and high. Statistical analysis Multilevel logistic regressions were used to analyze the association between income inequality and SRH in order to control for the clustering within the capitals of residence and to test for individual and area-level characteristics. The intraclass correlation coefficient (ICC) was estimated to quantify the proportion of variance in SRH that could be explained at the contextual and individuals levels. The first level of the models referred to individual characteristics and the second level to the socioeconomic characteristics of the capital of residence. The model parameters were estimated with Bayesian multilevel models, a recommended approach to decrease bias in multilevel analysis with dichotomous outcomes, and which allows the comparison of model fit by comparing the values of Bayesian Information Criterion (BIC) of the individual models.19 We also tested the cross-level interaction between gender and income inequality using an interaction term in the multilevel analyses to determine if the association between income inequality and SRH differed between men and women. The analyses were performed with Stata V13.1 (Stata Corporation, College Station, TX, USA, 2013). The descriptive statistics of the socioeconomic characteristics, lifestyles and presence of chronic diseases were performed using the survey mode procedure, considering the sampling weight and individual clustering (secondary sampling units) within census tracts (primary sampling units), due to the complex sample design. Multilevel analyses used the gllamm command, which allows the inclusion of weights for complex sample designs. Results Sample characteristics The sample consisted of 27 017 individuals of both sexes, aged 18 years or older, residing in one of the 27 Brazilian capitals (including the Federal District) in 2013. Descriptive analysis of socioeconomic characteristics indicate that most of the individuals were female (55%) and lived without a partner (56%). A little less than half of individuals (48%) were younger than 40 years, 34% were aged 40–59 years and 18% aged 60 years or older. Regarding race, 47% reported being white and around half being mixed or black (41% mixed and 10% black). As for education attainment, less than a quarter of individuals reported not having completed elementary school (24%), and more than 60% had completed at least high school (Table 1). Table 1 Distribution of the characteristics of the individuals' residents in Brazilian capitals, 2013, Brazil Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  aAbsolute numbers on the unweighted sample. bWeighted sample proportion. cχ2 test. Source: PNS, 2013. View Large Table 1 Distribution of the characteristics of the individuals' residents in Brazilian capitals, 2013, Brazil Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  Characteristics  Total  Poor health status  P-valuec  na  Percentb  na  Percentb  Total  27 017  100  1367  4.54    Gender   Male  11 091  45.05  428  3.61  0.000   Female  15 926  54.95  939  5.30  Age categories, years   18–24  3513  15.39  58  1.79  0.000   25–39  9440  32.61  222  2.26   40–59  9152  33.96  569  5.47   60+  4912  18.04  518  9.25  Race   White  11 202  47.21  467  3.67  0.001   Mixed  12 650  40.80  689  5.09   Black  2704  9.96  179  5.48   Other  459  2.04  32  9.03  Education   Incomplete elementary school  6052  24.10  593  9.54  0.000   Complete elementary school  3504  15.37  161  3.77   Complete high school  8553  38.68  265  3.19   Complete graduation  4733  21.84  91  1.70  Marital status   With partner  12 694  44.07  737  4.89  0.097   Without partner  14 323  55.93  630  4.26  Smoking   Never smoked  19 212  70.82  796  3.94  0.000   Former smoker  4347  16.48  348  6.48   Current smoker  3458  12.70  223  5.38  Hypertension   No  20 607  79.60  686  2.92  0.000   Yes  5402  20.40  652  11.22  Diabetes   No  23 188  92.93  980  3.68  0.000   Yes  1661  7.07  294  17.31  Hypercholesterolemia   No  21 032  85.88  891  3.79  0.000   Yes  3416  14.12  372  9.85    Depression   No  25 133  93.15  1089  3.85  0.000   Yes  1884  6.85  278  13.93  aAbsolute numbers on the unweighted sample. bWeighted sample proportion. cχ2 test. Source: PNS, 2013. View Large Regarding lifestyle and the presence of chronic diseases, most individuals never smoked (71%), and of those with a smoking history, 16.5% were former smokers and 13% current smokers. Of the total sample, 4.5% reported poor health. The prevalence of hypertension and diabetes were 20.5 and 7%, respectively, and a previous diagnosis of depression was reported by 7% of the population (Table 1). Descriptive analyses of the contextual characteristics indicated high levels of inequality among the 27 Brazilian capitals. The income inequality range, as measured by Gini index, varied from a low of 0.54 to a high of 0.69. Bivariate associations with SRH We found a statistically significant association of poor SRH with socioeconomic characteristics, except for marital status. More specifically, a significantly higher presence of poor SRH was observed in women in comparison to man, among higher age groups and among individuals with low education attainment (Table 1). Smoking and presence of chronic diseases were also associated with poor SRH. Those with a smoking history had higher poor self-rated health in comparison with never smokers. We also observed a higher presence of poor SRH between those with chronic diseases (Table 1). Multilevel regression models Multilevel modeling was applied to analyze the association between income inequality and SRH, while adjusting for individual and contextual factors. Table 2 presents the results from the multilevel analyses for the association between SRH and income inequality, controlling for individual characteristics. Women were more likely to present poor SRH in comparison to men (OR = 1.52, 95% CI: 1.26–1.83). Those living with a partner were significantly less likely to report poor SRH in comparison with those living without a partner (OR = 0.88, 95% CI: 0.81–0.96). Regarding income inequality, those living in the medium (OR = 1.31, 95% CI: 1.17–1.47) and high income inequality level (OR = 1.39, 95% CI: 1.24–1.56) presented higher odds for poor SRH, even after controlling for individual characteristics. When a sex × income inequality interaction was included, no significant differences were observed between men and women regarding SRH (results not shown). Table 2 Multilevel logistic models of poor SRH according to demographic, socioeconomic, lifestyle, presence of chronic diseases and contextual socioeconomic, 2013, Brazil   Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)    Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)  *P < 0.05. **P ≤ 0.001. View Large Table 2 Multilevel logistic models of poor SRH according to demographic, socioeconomic, lifestyle, presence of chronic diseases and contextual socioeconomic, 2013, Brazil   Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)    Null Model (n = 27017)  Model 1 (n = 20003)  Model 2 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.06**  0.05–0.06  0.04**  0.02–0.06  0.03**  0.02–0.05   Gender    Female  1.52**  1.26–1.83  1.52**  1.26–1.83   Age categories    25–39  1.34  0.83–2.16  1.34  0.83–2.16    40–59  2.86**  1.94–4.21  2.86**  1.95–4.19    60+  4.01**  2.82–5.69  3.99**  2.83–5.64   Race    Mixed  1.25  0.94–1.65  1.25  0.94–1.66    Black  1.07  0.80–1.44  1.06  0.79–1.42    Other  2.73*  1.08–6.86  2.74*  1.08–6.92   Education    Complete elementary school  0.48**  0.42–0.55  0.48**  0.42–0.55    Complete high school  0.42**  0.36–0.49  0.42**  0.36–0.49    Complete graduation  0.20**  0.16–0.26  0.20**  0.16–0.26   Marital status    With partner  0.89*  0.81–0.97  0.88*  0.81–0.96   Smoking    Former smoker  0.94  0.66–1.35  0.94  0.65–1.35    Current smoker  1.18  0.91–1.54  1.17  0.89–1.54   Chronic diseases            Hypertension  2.16**  1.71–2.73  2.16**  1.70–2.74    Diabetes  2.70**  1.77–4.12  2.69**  1.77–4.11    Hypercholesterolemia  1.16  0.81–1.64  1.16  0.81–1.65    Depression  3.10**  2.60–3.70  3.09**  2.59–3.69  2° Level: Neighborhood   Income inequality    Medium  1.31**  1.17–1.47    High  1.39**  1.24–1.56  BIC (ICC)  13 200 000 (0.007)  8 768 058 (0.019)  8 769 998 (0.040)  *P < 0.05. **P ≤ 0.001. View Large Table 3 presents the results for the association between income inequality and self-rated health after controlling for other contextual socioeconomic characteristics. Higher income inequality (medium and high) remained significantly associate with poor SRH. A significant higher odd of poor SRH was also observed among those living in areas with medium and high illiteracy levels (OR = 1.09, 95% CI: 1.01–1.18 and OR = 1.53, 95% CI: 1.40–1.67, respectively). Similar results were obtained for area-level violence, both for medium (OR = 1.54, 95% CI: 1.42–1.66) and high violence levels (OR = 1.66, 95% CI: 1.51–1.81). On the other hand, income was negatively associated with higher odds of poor self-rated health, for areas with medium (OR = 0.68, 95% CI: 0.62–0.76) and high (OR = 0.43, 95% CI: 0.37–0.48) per capita income. Table 3 Multilevel logistic models of poor SRH adjusted by individual factorsa according to contextual socioeconomic characteristics, illiteracy rate, violence, average income and income inequality, 2013, Brazil   Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)    Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)  *P < 0.05. **P ≤ 0.001. aIndividuals factors: gender, age, race, education attainment and marital status. Table 3 Multilevel logistic models of poor SRH adjusted by individual factorsa according to contextual socioeconomic characteristics, illiteracy rate, violence, average income and income inequality, 2013, Brazil   Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)    Model 1 (n = 20003)  Model 2 (n = 20003)  Model 3 (n = 20003)  OR  CI 95%  OR  CI 95%  OR  CI 95%  1° Level   Intercept  0.03**  0.02–0.04  0.02**  0.01–0.03  0.03**  0.02–0.05  2°Level: Neighborhood   Illiteracy rate, tercile    Medium  1.09**  1.01–1.18            High  1.53**  1.40–1.67           Violence, tercile    Medium      1.54**  1.42–1.66        High      1.66**  1.51–1.81       Average income per capita, tercile    Medium          0.68**  0.62–0.76    High          0.43**  0.37–0.48   Income inequality, tercile    Medium  1.92**  1.81–2.04  1.26**  1.11–1.43  1.12*  1.04–1.22    High  1.23**  1.17–1.29  1.41**  1.28–1.56  1.23**  1.14–1.33   BIC (ICC)  8 764 212 (0.009)  8 768 293 (0.033)  8 767 451 (0.026)  *P < 0.05. **P ≤ 0.001. aIndividuals factors: gender, age, race, education attainment and marital status. Discussion Main finding of this study The present study highlights the association between area-level income inequality and SRH among adult residents of Brazilian capitals. At the individual level, female gender, advanced age, low education attainment, living without a partner and having hypertension, diabetes or depression were statistically associated with higher odds of poor SRH. At the contextual level, in comparison with those who live in the lowest income inequality areas, living in areas with higher income inequality was significantly associated with poor SRH, even after controlling for individual variables and other contextual socioeconomic characteristics such as illiteracy rate, violence and per capita income. The association found between self-rated health and income inequality brings new insights to the international literature on social determinants of health by analyzing a large and representative sample of adults living in 27 capitals of a developing country with very high levels of inequality. What is already known on this topic An important discussion in the literature concerns the influence of income inequality across different levels of area aggregation. Studies where smaller aggregation scales were analyzed, such as neighborhoods, have provided consistent evidence on the relationship between contextual factors and health outcomes.20 However, the results of cross-country studies are still not robust, possibly due to the distance of the place of residence to the extremes of income distribution, and the difficulty in comparing data with restricted variability in income distribution from developed countries with data of greater income variability from developing countries.21 The explanation for how income inequality may influence population health is still an open area of debate. One of the most influential theories, originally put forward by Wilkinson,22 argues that income inequality affects health directly, and is robust to controls for individual and contextual factors. Our results point to this direction, suggesting that the negative effect of income inequality on health are not necessarily mediated by individual characteristics, such as race and socioeconomic factors,23 nor other contextual characteristics, when measured in a society with large and persistent social inequalities. What this study adds To the best of our knowledge, this study is the first to analyze the association between self-rated health and other contextual characteristics of Brazilian capitals besides income inequality, such as illiteracy and violence rates, after considering the influence of the individual characteristics. We found statistically significant associations between self-rated health and area-level illiteracy rates. In comparison with those living in the areas with lower illiteracy rates, living in areas with medium and high levels of illiteracy was associated with poor SRH on our adjusted model. Although individual education attainment has been frequently shown to influence individual health,24,25 not much is known about the association between area-level education and health outcomes in Brazil. New studies should analyze this further, possibly by considering other factors that may mediate the triangular relationship between illiteracy, income inequality and health, such as the unemployment rate,26 especially in countries with marked social inequalities. Our findings also show that individuals are more likely to have poor health in areas with higher violence rates, even after adjusting for individual factors and area-level income inequality. In addition to higher income inequality being detrimental to social cohesion27 and social isolation,28 fear of area violence may exacerbate these feelings, which may then lead to an even worse health condition.29–31 Although factors such as social cohesion and isolation were not directly tested in this study, the results are consistent with the theory that the effects of income inequality on health may extend to other problems of the social gradient.7 Limitations of this study The interpretation of these findings should consider its limitations. First, the sample is representative of the adult population residing in the 27 Brazilian capitals, so one should not draw conclusions for the other areas of the country. Second, the use of cross-sectional data results does not allow the establishment of causal inferences, and need to be interpreted as associations. Third, despite having a relatively acceptable response rate (86.0%), the possibility of response bias cannot be ignored. Conclusions Our study highlights the importance of contextual characteristics associated with SRH. Our findings suggest that income inequality may be a detrimental factor on self-rated health, even when the effects of individual and other area-level factors are considered. New knowledge of the association between self-rated health with individual and contextual factors can help to improve social programs and the planning of health care strategies on the largest country in Latin America. Funding This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo—FAPESP [2014/12716-3]. References 1 ISSC IDS. UNESCO. World Social Science Report 2016, Challenging Inequalities: Pathways to a Just World . Paris: UNESCO Publishing, 2016. 2 World Bank. World Development Indicators. http://databank.worldbank.org/ (9 January 2016, date last accessed). 3 Dabla-Norris ME, Kochhar MK, Suphaphiphat MN et al.  . Causes and consequences of income inequality: a global perspective. International Monetary Fund 2015. 4 Torre R, Myrskylä M. Income inequality and population health: an analysis of panel data for 21 developed countries, 1975–2006. Popul Stud (Camb)  2014; 68( 1): 1– 13. Google Scholar CrossRef Search ADS PubMed  5 Pabayo R, Kawachi I, Gilman SE. US State-level income inequality and risks of heart attack and coronary risk behaviors: longitudinal findings. Int J Public Health  2015; 60( 5): 573– 88. Google Scholar CrossRef Search ADS PubMed  6 Zheng H. Do people die from income inequality of a decade ago? Soc Sci Med  2012; 75( 1): 36– 45. Google Scholar CrossRef Search ADS PubMed  7 Pickett KE, Wilkinson RG. Income inequality and health: a causal review. Soc Sci Med  2015; 128: 316– 26. Google Scholar CrossRef Search ADS PubMed  8 Adjaye-Gbewonyo K, Avendano M, Subramanian SV et al.  . Income inequality and depressive symptoms in South Africa: a longitudinal analysis of the National Income Dynamics Study. Health Place  2016; 42: 37– 46. Google Scholar CrossRef Search ADS PubMed  9 Fernández-Niño JA, Manrique-Espinoza BS, Bojorquez-Chapela I et al.  . Income inequality, socioeconomic deprivation and depressive symptoms among older adults in Mexico. PLoS One  2014; 9( 9): e108127. Google Scholar CrossRef Search ADS PubMed  10 Bakkeli NZ. Income inequality and health in China: a panel data analysis. Soc Sci Med  2016; 157: 39– 47. Google Scholar CrossRef Search ADS PubMed  11 Rasella D, Aquino R, Barreto ML. Impact of income inequality on life expectancy in a highly unequal developing country: the case of Brazil. J Epidemiol Community Health  2013; 67( 8): 661– 6. Google Scholar CrossRef Search ADS PubMed  12 Peres MA, Masiero AV, Longo GZ et al.  . Self-rated health among adults in Southern Brazil. Rev Saude Publica  2010; 44: 901– 11. Google Scholar CrossRef Search ADS PubMed  13 Wu S, Wang R, Zhao Y et al.  . The relationship between self-rated health and objective health status: a population-based study. BMC Public Health  2013; 13( 1): 320. Google Scholar CrossRef Search ADS PubMed  14 Theme Filha MM, Souza Junior PRBD, Damacena GN et al.  . Prevalence of chronic non-communicable diseases and association with self-rated health: National Health Survey, 2013. Rev Bras de Epidemiol  2015; 18: 83– 96. Google Scholar CrossRef Search ADS   15 Ganna A, Ingelsson E. 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study. Lancet  2015; 386: 533– 40. Google Scholar CrossRef Search ADS PubMed  16 Instituto Brasileiro de Geografia e Estatística. Pesquisa Nacional de Saúde 2013. Disponível em: ftp://ftp.ibge.gov.br/PNS/2013/notastecnicas.pdf (14 November 2016, date last accessed). 17 Stenholm S, Pentti J, Kawachi I et al.  . Self-rated health in the last 12 years of life compared to matched surviving controls: the Health and Retirement Study. PLoS One  2014; 9( 9): e107879. Google Scholar CrossRef Search ADS PubMed  18 Castillo-Salgado C, Schneider C, Loyola E et al.  . Measuring health inequalities: Gini coefficient and concentration index. Epidemiol Bull  2001; 22( 1): 3– 4. Google Scholar PubMed  19 Rodriguez G, Goldman N. An assessment of estimation procedures for multilevel models with binary responses. J R Stat Soc A Stat Soc  1995; 158: 73– 89. Google Scholar CrossRef Search ADS   20 Chiavegatto Filho ADP, Kawachi I, Gotlieb SLD. Propensity score matching approach to test the association of income inequality and mortality in Sao Paulo, Brazil. J Epidemiol Community Health  2012; 66: 14– 7. Google Scholar CrossRef Search ADS PubMed  21 Kawachi I, Subramanian SV. Income inequality. In: Berkman LF, Kawachi I, Glymour MM (eds). Social Epidemiology . USA: Oxford University Press, 2014: 126– 51. 22 Wilkinson RG. Unhealthy Societies: The Afflictions of Inequality . London: Routledge, 2002. 23 Deaton A, Lubotsky D. Mortality, inequality and race in American cities and states. Soc Sci Med  2003; 56( 6): 1139– 53. Google Scholar CrossRef Search ADS PubMed  24 Pavão ALB, Werneck GL, Campos MR. Self-rated health and the association with social and demographic factors, health behavior, and morbidity: a national health survey. Cad Saude Publica  2013; 29: 723– 34. Google Scholar CrossRef Search ADS PubMed  25 Barros MBDA, Zanchetta LM, Moura ECD et al.  . Auto-avaliação da saúde e fatores associados, Brasil, 2006. Rev de Saude Publica  2009; 43: 27– 37. Google Scholar CrossRef Search ADS   26 Ribeiro MG. Income inequality: the education in question. Educ Soc  2016. http://dx.doi.org/10.1590/es0101-73302016154254. 27 Stiglitz JE. The Price of Inequality: How Today’s Divided Society Endangers our Future . New York: WW Norton & Company, 2012. 28 Kawachi I, Berkman L. Social cohesion, social capital, and health. In: Berkman LF, Kawachi I, Glymour MM (eds). Social Epidemiology . USA: Oxford University Press, 2014: 290– 319. 29 Wilkinson RG, Pickett KE. Income inequality and social dysfunction. Ann Rev Sociol  2009; 35: 493e511. Google Scholar CrossRef Search ADS   30 Pabayo R, Molnar BE, Kawachi I. The role of neighborhood income inequality in adolescent aggression and violence. J Adolesc Health  2014; 55( 4): 571– 79. Google Scholar CrossRef Search ADS PubMed  31 Subramanian SV, Kawachi I. Income inequality and health: what have we learned so far? Epidemiol Rev  2004; 26( 1): 78e91. http://dx.doi.org/10.1093/epirev/mxh003. Google Scholar CrossRef Search ADS   © The Author(s) 2018. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

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Published: Feb 1, 2018

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