Data Sources: Probability-based and weight-adjusted 2018 regional household health survey (7501 respondents in 5 counties with wide socioeconomic variation) and an intensive local-area study completed in 2022 (530 respondents from a low-income racially mixed section of a borough consisting of approximately 2,900 households).

Methods: Logistic regression analysis that incorporates interaction effects between regional and individual socioeconomic levels on health status and three-way cross-tabular analysis incorporating race.

Results: Once county-level measures of SES and health status are broken down into smaller areas, better identification of pockets of health need was possible in this urban/suburban region. The impact of individual SES surpassed the impact of county or county subarea socioeconomics to a large extent, based on block logistic regression results. In addition, race was shown to have a similar interaction effect in both the 2018 and 2022 studies, where middle class socioeconomic levels had less of an impact on improving health status for African Americans.

Conclusion: Local health survey analysis can lead to more precise identification of health needs than surveys attempting to cover broad areas and contribute to “small area analysis” methodology. This greater precision will enhance intervention efforts, and their evaluation, by identifying social determinants and the physical and social-psychological consequences of health disparities in areas where need is greatest.