Socioeconomic status influences survival after out-of-hospital cardiac arrest outcomes

Background: Socioeconomic status may influence survival after out-of-hospital cardiac arrest (OHCA). This study is the first to assess the association between village-level income quartiles and survival to hospital discharge after OHCA in a mid-sized Asian city where EMS delivery is standardized and geographic variation in access is minimal. Methods: We conducted a retrospective analysis of 209 adult residential OHCA cases recorded in the Chiayi City EMS registry in 2024. Income quartiles (Q1–Q4) were defined by case-level distribution as ≤New Taiwan (NT)$823,000, NT$823,001–868,000, NT$868,001–931,000, and >NT$931,000. Three multivariable logistic regression models, each meeting the events-per-variable threshold of ≥10:1, were constructed: discharge (2 covariates: income, shockable rhythm), Return of spontaneous circulation (ROSC) >24 h (4 covariates), and ROSC >2 h (6 covariates). Crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported, along with model performance metrics (area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit). Results: Survival to discharge increased from 3.8% in Q1 to 13.5% in Q4 (p = 0.10). ROSC >24 h ranged from 11.3%in Q1 to 26.9% in Q4 (p = 0.09). ROSC >2 h peaked in Q2 (40.4%) compared with 18.9% in Q1 (p = 0.02). Shockable rhythm was a strong predictor of survival (adjusted OR = 17.4, 95% CI 4.2–71.7, p < 0.01). EMS response time differed significantly by income quartile (p = 0.03), with Q2 showing the shortest times. Model AUCs ranged from 0.78 to 0.80. Conclusions: In small urban environments, socioeconomic status appears to influence OHCA outcomes in a non-linear manner, with moderate-income areas demonstrating higher early ROSC rates, potentially due to a greater prevalence of shockable rhythms and favorable community factors. These findings contrast with patterns observed in larger cities and suggest that targeted interventions may help reduce survival disparities.

Out-of-hospital cardiac arrest; Socioeconomic factors; Survival to discharge; Return of spontaneous circulation; Emergency medical services

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