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Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

ABSTRACT: The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.

SUBMITTER: Thomas LJ

PROVIDER: S-EPMC8944260 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

Thomas Loring J LJ Huang Peng P Yin Fan F Xu Junlan J Almquist Zack W ZW Hipp John R JR Butts Carter T CT

Proceedings of the National Academy of Sciences of the United States of America 20220314 12

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion ...[more]

PMID: 35286198

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Project description:Similar to obstetric outcomes, rates of SARS-CoV-2 (COVID-19) infection are not homogeneously distributed among populations; risk factors accumulate in discrete locations. This study aimed to investigate the geographical correlation between pre-COVID-19 regional preterm birth (PTB) disparities and subsequent COVID-19 disease burden. We performed a retrospective, ecological cohort study of an upstate New York birth certificate database from 2004 to 2018, merged with publicly available community resource data. COVID-19 rates from 2020 were used to allocate ZIP codes to "low-," "moderate-," and "high-prevalence" groups, defined by median COVID-19 diagnosis rates. COVID-19 cohorts were associated with poverty and educational attainment data from the US Census Bureau. The dataset was analyzed for the primary outcome of PTB using ANOVA. GIS mapping visualized PTB rates and COVID-19 disease rates by ZIP code. Within 38 ZIP codes, 123,909 births were included. The median COVID-19 infection rate was 616.5 (per 100 K). PTB (all) and COVID-19 were positively correlated, with high- prevalence COVID-19 ZIP codes also being the areas with the highest prevalence of PTB (F = 11.06, P = .0002); significance was also reached for PTB < 28 weeks (F = 15.87, P < .0001) and periviable birth (F = 16.28, P < .0001). Odds of PTB < 28 weeks were significantly higher in the "high-prevalence" COVID-19 cohort compared to the "low-prevalence" COVID 19 cohort (OR 3.27 (95% CI 2.42-4.42)). COVID-19 prevalence was directly associated with number of individuals below poverty level and indirectly associated with median household income and educational attainment. GIS mapping demonstrated ZIP code clustering in the urban center with the highest rates of PTB < 28 weeks overlapping with high COVID-19 disease burden. Historical disparities in social determinants of health, exemplified by PTB outcomes, map community distribution of COVID-19 disease burden. These data should inspire socioeconomic policies supporting economic vibrancy to promote optimal health outcomes across all communities.

Dataset Information

Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

Publications

Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

Similar Datasets

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