Sociodemographic Determinants of Acute Myocardial Infarction Hospitalization Risks in Florida

Abstract

Background Identifying social determinants of myocardial infarction ( MI ) hospitalizations is crucial for reducing/eliminating health disparities. Therefore, our objectives were to identify sociodemographic determinants of MI hospitalization risks and to assess if the impacts of these determinants vary by geographic location in Florida. Methods and Results This is a retrospective ecologic study at the county level. We obtained data for principal and secondary MI hospitalizations for Florida residents for the 2005–2014 period and calculated age‐ and sex‐adjusted MI hospitalization risks. We used a multivariable negative binomial model to identify sociodemographic determinants of MI hospitalization risks and a geographically weighted negative binomial model to assess if the strength of associations vary by location. There were 645 935 MI hospitalizations (median age, 72 years; 58.1%, men; 73.9%, white). Age‐ and sex‐adjusted risks ranged from 18.49 to 69.48 cases/10 000 persons, and they were significantly higher in counties with low education levels (risk ratio [ RR ]=1.033, P <0.0001) and high divorce rate ( RR , 0.995; P =0.018). However, they were significantly lower in counties with high proportions of rural ( RR , 0.996; P <0.0001), black (RR, 1.026; P =0.032), and uninsured populations ( RR , 0.983; P =0.040). Associations of MI hospitalization risks with education level and uninsured rate varied geographically ( P for non‐stationarity test=0.001 and 0.043, respectively), with strongest associations in southern Florida ( RR for <high school education, 1.036–1.041; RR for uninsured rate, 0.971–0.976). Conclusions Black race, divorce, rural residence, low education level, and lack of health insurance were significant determinants of MI hospitalization risks, but associations with the latter 2 were stronger in southern Florida. Thus, interventions for addressing MI hospitalization risks need to prioritize these populations and allocate resources based on empirical evidence from global and local models for maximum efficiency and effectiveness.