BACKGROUND: Support for the adverse effect of high income inequality on population health has come from studies that focus on larger areas, such as the US states, while studies at smaller geographical areas (eg, neighbourhoods) have found mixed results. METHODS: We used propensity score matching to examine the relationship between income inequality and mortality rates across 96 neighbourhoods (distritos) of the municipality of São Paulo, Brazil. RESULTS: Prior to matching, higher income inequality distritos (Gini ≥ 0.25) had slightly lower overall mortality rates (2.23 per 10,000, 95% CI -23.92 to 19.46) compared to lower income inequality areas (Gini <0.25). After propensity score matching, higher inequality was associated with a statistically significant higher mortality rate (41.58 per 10,000, 95% CI 8.85 to 73.3). CONCLUSION: In São Paulo, the more egalitarian communities are among some of the poorest, with the worst health profiles. Propensity score matching was used to avoid inappropriate comparisons between the health status of unequal (but wealthy) neighbourhoods versus equal (but poor) neighbourhoods. Our methods suggest that, with proper accounting of heterogeneity between areas, income inequality is associated with worse population health in São Paulo.
BACKGROUND: Support for the adverse effect of high income inequality on population health has come from studies that focus on larger areas, such as the US states, while studies at smaller geographical areas (eg, neighbourhoods) have found mixed results. METHODS: We used propensity score matching to examine the relationship between income inequality and mortality rates across 96 neighbourhoods (distritos) of the municipality of São Paulo, Brazil. RESULTS: Prior to matching, higher income inequality distritos (Gini ≥ 0.25) had slightly lower overall mortality rates (2.23 per 10,000, 95% CI -23.92 to 19.46) compared to lower income inequality areas (Gini <0.25). After propensity score matching, higher inequality was associated with a statistically significant higher mortality rate (41.58 per 10,000, 95% CI 8.85 to 73.3). CONCLUSION: In São Paulo, the more egalitarian communities are among some of the poorest, with the worst health profiles. Propensity score matching was used to avoid inappropriate comparisons between the health status of unequal (but wealthy) neighbourhoods versus equal (but poor) neighbourhoods. Our methods suggest that, with proper accounting of heterogeneity between areas, income inequality is associated with worse population health in São Paulo.
Authors: Joana Raquel Raposo Dos Santos; Hellen Geremias Dos Santos; Carlos Manuel Matias Dias; Alexandre Dias Porto Chiavegatto Filho Journal: Int J Public Health Date: 2020-04-21 Impact factor: 3.380