Disparities in Temporal and Geographic Patterns of Myocardial Infarction Hospitalization Risks in Florida

Evah W. Odoi, Nicholas Nagle, Chris DuClos and Kristina W. Kintziger
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Evah W. Odoi: Comparative and Experimental Medicine, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN 37996, USA
Nicholas Nagle: Department of Geography, The University of Tennessee, Knoxville, TN 37996, USA
Chris DuClos: Environmental Public Health Tracking, Division of Community Health Promotion, Florida Department of Health, Tallahassee, FL 32399, USA
Kristina W. Kintziger: Department of Public Health, The University of Tennessee, Knoxville, TN 37996, USA

IJERPH, 2019, vol. 16, issue 23, 1-25

Abstract: Knowledge of geographical disparities in myocardial infarction (MI) is critical for guiding health planning and resource allocation. The objectives of this study were to identify geographic disparities in MI hospitalization risks in Florida and assess temporal changes in these disparities between 2005 and 2014. This study used retrospective data on MI hospitalizations that occurred among Florida residents between 2005 and 2014. We identified spatial clusters of hospitalization risks using Kulldorff’s circular and Tango’s flexible spatial scan statistics. Counties with persistently high or low MI hospitalization risks were identified. There was a 20% decline in hospitalization risks during the study period. However, we found persistent clustering of high risks in the Big Bend region, South Central and southeast Florida, and persistent clustering of low risks primarily in the South. Risks decreased by 7%–21% in high-risk clusters and by 9%–28% in low-risk clusters. The risk decreased in the high-risk cluster in the southeast but increased in the Big Bend area during the last four years of the study. Overall, risks in low-risk clusters were ahead those for high-risk clusters by at least 10 years. Despite MI risk declining over the study period, disparities in MI risks persist. Eliminating/reducing those disparities will require prioritizing high-risk clusters for interventions.

Keywords: myocardial Infarction; hospitalization risks; geographic disparities; temporal patterns; Kulldorff and Tango’s flexible spatial scan statistics (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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