Sociodemographic disparities in self-reported stroke: A cross-sectional, population-based analysis

BACKGROUND: Stroke is one of the leading causes of death and disability worldwide. There is a lack of comprehensive studies examining how multiple social determinants of health (SDOH) interact to influence stroke presentation. METHODS: A cross-sectional, population-based analysis was performed using data from the 2023 U.S. National Health Interview Survey. Adult participants (>18 years) with self-reported stroke were compared to those without. Univariable and multivariable logistic regression analysis was performed to assess the association of each SDOH with self-reported stroke prevalence. Odds ratios (ORs) and 95% confidence intervals (CI) were reported. P-values <0.05 were statistically significant. RESULTS: A total of 29,484 individuals responded to the self-reported stroke question, of which 1133 (3.84%) were stroke cases and 28,351 (96.16%) were non-stroke cases. In multivariable analysis, self-reported stroke prevalence was significantly associated with age (36-49: OR = 6.60, p < 0.001; 50-64: OR = 13.77, p < 0.0001; 65-74: OR = 18.80, p < 0.0001; 75+: OR = 32.39, p < 0.0001), race/ethnicity (American Indian: OR = 1.86, p < 0.05), region (Midwest: OR = 1.63, p < 0.001; South: OR = 1.47, p < 0.01), income (IPR 1-2x: OR = 1.68, p < 0.001; <1x: OR = 2.40, p < 0.0001), insurance (medicaid/public: OR = 1.37, p < 0.005; other coverage: OR = 1.85, p < 0.0001), regular place of care (OR = 0.57, p < 0.05), and general health (fair: OR = 3.30, p < 0.0001; poor: OR = 4.39, p < 0.0001). DISCUSSION: These results demonstrate a disproportionate burden of stroke across specific sociodemographic groups. Future research is needed to elucidate the underlying mechanisms perpetuating the observed disparities in stroke patients.