Doctoral Dissertations

Date of Award

5-2020

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Comparative and Experimental Medicine

Major Professor

Kristina W Kintziger

Committee Members

Nicholas Nagle, Russel Zaretzki, Kelsey Ellis

Abstract

Knowledge of spatiotemporal disparities in myocardial infarction (MI) risk and the determinants of those disparities is critical for guiding health planning and resource allocation. Therefore, the aims of this study were to: (i) investigate the spatial distribution and clusters of MI hospitalization (MIHosp) and MI mortality (MIMort) risks in Florida over time to identify communities with consistently high MI burdens, (ii) assess temporal trends in geographic disparities in MIHosp and MIMort risks (iii) identify predictors of MIHosp risks.Retrospective MIhosp and MImort data for Florida for 2005-2014 and 2000-2014 periods, respectively, were used. Kulldorff’s circular and Tango’s flexible spatial scan statistics were used to identify spatial clusters, and counties with persistently high or low MIHosp and MIMort risks were identified. Global and local negative binomial models were used to identify predictors of MIHosp risks.MIHosp and MIMort risks declined by 15%-20% and 48% respectively, but there were substantial disparities in space and over time. Persistent clustering of high MIHosp risks occurred in the Big Bend area, South Central and Southeast Florida. Persistent clustering of low risks occurred in southeast and southwest Florida. Clustering of high or low MIMort risks occurred in the same areas as MIHosp risks, but there was no clustering of high MIMort risks in South Central Florida. The risks declined on the overall in all clusters over the study period. However, they decreased more rapidly in high-risk clusters during the first 4-8 years of study, leading to reduced disparities in the short term. Nevertheless, MI risks for high-risk clusters lagged behind those for low-risk clusters by at least a decade. Significant predictors of MIHosp risks included race, marital status, education level, rural residence and lack of health insurance. The impacts of education level and lack of health insurance varied geographically, with strongest associations in southern Florida. In conclusion, MI interventions need to target high-risk clusters to reduce the MI burden and improve population health in Florida. Moreover, the interventions need to consider social contexts, allocating resources based on empirical evidence from global and local models to maximize their efficiency and effectiveness

Comments

Portions of this document were previously published in BMC Public Health. Other portions have been submitted to Journal of American Heart Association and International Journal of Environment and Public Health.

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