Article
Population health disparities by neighborhood socioeconomic status and the role of spatial spillovers
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Published: | September 25, 2020 |
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Background and current state of (inter)national research: The contextual determinants of population health disparities across neighborhoods with similar socioeconomic characteristics are not well understood. We aimed to estimate self-assessed subjective and objective population health measures, both within and between neighborhoods with similar socioeconomic status (NSES) scores, and assessed the (in)direct potential of contextual factors such as the spillover effect of NSES of adjacent neighborhoods.
Methods or hypothesis: Based on whole-population neighborhood data from the Netherlands we determined the percentage of inhabitants with good or very good self-assessed health (SAH) as well as the percentage of inhabitants with at least one chronic disease (CD) in 11,504 neighborhoods with on average 1,473 inhabitants. Neighborhoods were classified by the quintile of a composite NSES score. Spatial models were estimated by including the NSES of adjacent neighborhoods by constructing a spatial weights matrix. Other neighborhood covariates were population density and the percentage of inhabitants aged 65 and over.
Results: Substantial population health disparities in SAH and CD both between neighborhoods with different and similar NSES scores were observed, with the largest SAH variance in the lowest NSES group. These differences were only partially explained by neighborhood characteristics. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD, adjusted for other explanatory variables. Two hypothetical policy scenarios revealed substantial gains in population health. The first scenario increased the NSES of the lowest 10% of neighbourhoods to the lowest quintile median, which resulted in direct absolute changes of 5.6% (SAH) and 2.25% (CD). Scenario two lifted the entire lowest quintile to a rank-equivalent level of NSES in the second lowest quintile. This resulted in an 18.7% change in SAH and 8.6% in CD. Both scenarios showed substantial spatial spillovers for all NSES neighbourhoods.
Discussion: Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a spatial socioeconomic spillover effect. The mechanisms behind these socioeconomic spillovers need further study, but may already provide interesting leads to policy design aimed at improving population health outcomes of deprived neighborhoods.