Title: Neighborhood-level measures of socioeconomic status are more correlated with individual-level measures in urban areas compared with less urban areas.
Authors: Xie, Sherrie; Hubbard, Rebecca A; Himes, Blanca E
Published In Ann Epidemiol, (2020 03)
Abstract: PURPOSE: We tested the hypothesis that individual- and neighborhood-level measures of socioeconomic status (SES) are more concordant in urban than rural areas, and we used the previously established association between obesity and self-rated health to illustrate the effect of residual confounding by individual-level SES when only neighborhood-level SES is considered. METHODS: Using data from two population-based surveys, we calculated Spearman's rank correlations between household income and neighborhood socioeconomic advantage across eight Pennsylvania counties. We applied multivariable Poisson regression models with robust variance estimates to estimate the degree to which individual SES confounds the association between obesity and self-rated health when the analysis accounts for neighborhood SES only, and we examined how this confounding varied by county urbanicity. RESULTS: Concordance between household income and neighborhood advantage increased with county urbanicity (ρ = 0.16-0.26 vs. 0.31-0.45 vs. 0.47 in medium metro/micropolitan, suburban, and large metro counties, respectively), while confounding by individual SES on the obesity and self-rated health association decreased with urbanicity (15%-22% vs. 6%-13% vs. 3% in medium metro/micropolitan, suburban, and large metro counties, respectively). CONCLUSIONS: Individual- and neighborhood-level SES measures are poorly correlated outside of urban areas, suggesting that neighborhood-level measures inadequately account for individual SES in rural settings.
PubMed ID:
32151518
MeSH Terms: Adult; Community-Based Participatory Research; Female; Health Surveys; Humans; Male; Middle Aged; Pennsylvania; Residence Characteristics/statistics & numerical data*; Rural Population/statistics & numerical data*; Social Class*; Socioeconomic Factors; Surveys and Questionnaires; Urban Population/statistics & numerical data*