Social Network Structure Rivals Smoking and Income as a Predictor of U.S. County Mortality

Chris Soria and Dennis Feehan

No kvmx6_v3, SocArXiv from Center for Open Science

Abstract: Personal networks influence health and mortality at the individual level, but less is known about how population-scale social network structure relates to mortality. This study examines how US county-level social network structure relates to mortality disparities. Using measures from 21 billion Facebook friendships, we investigate how two structural features of population social networks – cohesiveness and diversity – are associated with age-standardized and age-specific mortality rates. Bivariate results show that measures of social network structure rival smoking rates, median income, and educational attainment in their association with mortality rates. Social network structure remains predictive of mortality even after controlling for traditional measures like socioeconomic status and rural/urban classification. Network diversity is associated with lower mortality in both bivariate and multivariate analyses. Network clustering is associated with higher mortality bivariately, but this association reverses after controlling for county-level demographic and socioeconomic factors, revealing a protective effect masked by confounding. Age-stratified analyses further complicate this picture, showing that clustering predicts lower mortality among adults aged 15-64 but higher mortality among those 70 and older. These findings highlight social network structure as an important dimension of place-based health disparities, one not fully captured by conventional measures of socioeconomic composition or spatial segregation.

Date: 2026-01-21
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:kvmx6_v3

DOI: 10.31219/osf.io/kvmx6_v3

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