ArticlemedRxiv : the preprint server for health sciences2025
Socio-Spatial Patterns of Suicide Mortality in the United States.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
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Abstract
Suicide mortality in the United States exhibits substantial geographical and sociodemographic heterogeneity. Yet the role of large-scale social networks in shaping this variation remains underexplored. We integrate data on county-level suicide mortality (2010-2022) and Facebook's Social Connectedness Index (SCI) to assess how both the risk of suicide mortality and the effect of firearm restriction policies propagate through inter-county social ties. First, using two-way fixed effects regression models with sociodemographic, economic, and spatial controls, we find that a one-standard-deviation increase in (SCI-weighted) suicide mortality in socially connected counties is associated with an increase of 2.78 suicide deaths per 100,000 people in the focal county (95% CI: 1.06- 4.50). Second, we examine Extreme Risk Protection Orders (ERPOs) - state-level firearm policies that allow temporary restriction of firearm access for individuals at risk of self-harm - and show that counties with stronger (Facebook) social ties to ERPO-adopting states experience reductions in suicide mortality, even without local policy implementation. Our findings suggest that a one-standard-deviation increase in ERPO social exposure is associated with a decrease of 0.301 suicide deaths per 100,000 people in the focal county (95% CI: 0.480-0.121). This protective association persists after adjusting for geographical proximity and including state-by-year fixed effects that capture time-varying state-level factors. In sum, our findings suggest that social networks can facilitate the diffusion of both harmful exposures and protective interventions. This socio-spatial structuring of suicide mortality underscores the need for network-driven prevention strategies that incorporate social network topology (e.g., SCI-derived influence metrics), alongside more traditional approaches based on geographical targeting.
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