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. 2021 May 11;96(19):897-907.
doi: 10.1212/WNL.0000000000011892. Epub 2021 Mar 25.

Neighborhood Socioeconomic Status and Stroke Incidence: A Systematic Review

Yeonwoo Kim  1 , Erica Twardzik  1 , Suzanne E Judd  1 , Natalie Colabianchi  2
Affiliations

Neighborhood Socioeconomic Status and Stroke Incidence: A Systematic Review

Yeonwoo Kim et al. Neurology. .

Abstract

Objective: To summarize overall patterns of the impact of neighborhood socioeconomic status (nSES) on stroke incidence and uncover potential gaps in the literature, we conducted a systematic review of studies examining the association between nSES and stroke incidence, independent of individual SES.

Methods: Four electronic databases and reference lists of included articles were searched, and corresponding authors were contacted to locate additional studies. A keyword search strategy included the 3 broad domains of neighborhood, SES, and stroke. Eight studies met our inclusion criteria (e.g., nSES as an exposure, individual SES as a covariate, and stroke incidence as an outcome). We coded study methodology and findings across the 8 studies.

Results: The results provide evidence for the overall nSES and stroke incidence association in Sweden and Japan, but not within the United States. Findings were inconclusive when examining the nSES-stroke incidence association stratified by race. We found evidence for the mediating role of biological factors in the nSES-stroke incidence association.

Conclusions: Higher neighborhood disadvantage was found to be associated with higher stroke risk, but it was not significant in all the studies. The relationship between nSES and stroke risk within different racial groups in the United States was inconclusive. Inconsistencies may be driven by differences in covariate adjustment (e.g., individual-level sociodemographic characteristics and neighborhood-level racial composition). Additional research is needed to investigate potential intermediate and modifiable factors of the association between nSES and stroke incidence, which could serve as intervention points.

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Figures

Figure 1
Figure 1. PRISMA Flow Diagram for Study Selection
Figure 2
Figure 2. Summarized Findings of 6 Included Studies
(A) Presents (1) the coefficients of least advantaged neighborhoods (vs. moderately advantaged neighborhoods) from the article by Carlsson et al., (2) the coefficients of most deprived neighborhoods (vs least deprived neighborhoods) from Honjo et al., and (3) the coefficients of least advantaged neighborhoods (vs most advantaged neighborhoods) from Howard et al., Brown et al., and Yan et al. From the studies by Freedman et al. and Forsberg et al., we presented coefficients of a continuous variable of neighborhood disadvantage, and its higher scores indicate more disadvantaged neighborhoods. (B) Presents (1) the coefficients of the most advantaged neighborhoods (vs moderately advantaged neighborhoods) from the article by Carlsson et al. and (2) the coefficients of a continuous variable of neighborhood advantage, and its higher scores indicate more advantaged neighborhoods. From the study by Clark et al., we presented coefficients of a continuous variable of neighborhood advantage, and its higher scores indicate more advantaged neighborhoods.
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