Socio-Spatial Patterns of Suicide Mortality in the United States

Abstract

Suicides cause over 49000 deaths yearly in the United States, 55% involving firearms. Suicide mortality exhibits substantial geographical and sociodemographic heterogeneity; yet the role of social networks remains underexplored. To assess how suicide risk and firearm restriction policies propagate through social ties, we integrate county-level suicide mortality data (2010-2022) with the Facebook Social Connectedness Index (SCI). We also examine Extreme Risk Protection Orders (ERPO), state-level policies restricting firearm access for individuals at risk of self-harm. In two-way fixed effects regressions, a one-standard-deviation increase in the SCI-weighted average suicide mortality rate of connected counties was associated with +2.78 deaths per 100,000 in a focal county, while a one-standard-deviation increase in ERPO social exposure was associated with -0.214 deaths per 100,000. These associations persisted when adjusting for geographic proximity and including state-by-year fixed effects, and confirm the effect of social networks on diffusion of both harmful exposures and protective interventions.

Figures (8)

• Figure 1: Role of social ties in county-level suicide mortality. Estimated regression coefficients ($\hat{\zeta}_1$) for suicide mortality rates in socially-connected counties ($s_{-it}$) in two models. Model 1 (red): without adjustment for deaths in spatial proximity ($d_{-it}$). Model 2 (blue): with adjustment for deaths in spatial proximity ($d_{-it}$). Horizontal lines denote 95% confidence intervals (CI). The vertical dashed line indicates the null hypothesis ($\zeta_1 = 0$). Point estimate in model 1: 3.34 (cluster-robust 95% CI: [1.76, 4.93]); point estimate in model 2: 2.78 (cluster-robust 95% CI: [1.06, 4.50]). Both models include county and year fixed effects and sociodemographic control variables (see Table \ref{['tab:socio_spatial_model']}).
• Figure 2: County--level change in ERPO social exposure, 2010–2022. Colors show the change ($\Delta$) in standardized ERPO Social Exposure between 2010 and 2022, measured in within--sample standard--deviation units. Positive (yellow) values indicate that a county’s social ties have become more concentrated in states that enacted ERPOs, whereas positive (purple) values indicate declining exposure. Although the underlying analysis covers all US counties, the map shows only the 48 contiguous states and the District of Columbia; Alaska, Hawaii, and US territories are not shown.
• Figure 3: Estimated coefficients ($\hat{\delta}_1$, $\hat{\theta}_1$) for ERPO social exposure in two specifications. Red point indicates estimate from the baseline model without spatial exposure ($\hat{\delta}_1 = -0.214$, cluster-robust 95% CI: [$-0.342$, $-0.0866$]); blue point indicates estimate from the specification controlling for $\textit{ERPO Spatial Exposure}_{it}$ ($\hat{\theta}_1 = -0.298$, cluster-robust 95% CI: [$-0.475$, $-0.120$]). Horizontal lines denote 95% confidence intervals; vertical dashed line denotes the null hypothesis ($\delta_1 = 0$). Both models include county and state--year fixed effects ($\phi_i$, $\gamma_{st}$) and sociodemographic controls ($\overline{X}_{it}$). Consistent negative and statistically significant estimates indicate the association between suicide mortality and indirect social exposure to ERPO policies is robust to spatial confounding.
• Figure S1: Role of social ties in county-level suicide mortality, with and without controlling for geographical proximity. Estimated regression coefficients ($\hat{\eta}_1$) for suicide mortality rates in socially connected counties ($\tilde{s}_{-it}$) in two models, using age standardization. Model 1 (red): without adjustment for deaths in spatial proximity ($\tilde{d}_{-it}$). Model 2 (blue): with adjustment for deaths in spatial proximity ($\tilde{d}_{-it}$). Horizontal lines denote 95% confidence intervals (CI). The vertical dashed line indicates the null hypothesis ($\eta_1 = 0$). Point estimate in model 1: 1.29 (95% CI: [0.87, 1.70]); point estimate in model 2: 1.09 (95% CI: [0.66, 1.52]). All models include county and year fixed effects as well as time-varying county-level demographic and socioeconomic control variables (see Table \ref{['tab:sensitivity_socio_spatial']}).
• Figure S2: Indirect effects of ERPO implementation on suicide mortality, through social ties, with and without controlling for geographical proximity. Estimated regression coefficients ($\hat{\tau}_1$, $\hat{\omega}_1$) for ${ERPO\ Social\ Exposure}$ in two models. Baseline model (red): without controlling for ${ERPO\ Spatial\ Exposure}$. Alternative model (blue): controlling for ${ERPO\ Spatial\ Exposure}$. Point estimate in baseline model: ($\hat{\tau}_1 = -0.218$, cluster-robust 95% CI: [$-0.314$, $-0.123$]); point estimate in alternative model: ($\hat{\omega}_1 = -0.253$, cluster-robust 95% CI: [$-0.394$, $-0.111$]). Horizontal lines denote 95% confidence intervals (CI). The vertical dashed line indicates the null hypothesis ($\tau_1 = 0$). Both models included county and state--year fixed effects ($\phi_i$, $\gamma_{st}$) as well as demographic and socioeconomic control variables ($\overline{X}_{it}$).