Short-Term Association between Myocardial Infarction Hospitalization Risks and Apparent Temperature in Florida: A Time Series Study.

Myocardial Infarction (MI) or heart attack rates have been declining in Florida in the past few decades, but MI remains a leading cause of morbidity in the state, accounting for 117 hospitalizations per day. Furthermore, the decline in hospitalization rates has not occurred uniformly across the state. For instance, between 2000 and 2014, MI hospitalization rates decreased by 33% in the state, but they increased by between 0.2 and 88% in some counties during the same period. Since MI hospitalizations are seasonal, weather-related factors, such as apparent temperature (AT) which combines the effect of temperature and wind speed (Wind Chill) or temperature and humidity (Heat Index) to measure the actual temperature perceived by humans, may partly contribute to differences in MI morbidity risks across the state. However, our understanding of the effects of hot or cold weather on MI morbidity in Florida is limited. This is because previous studies produced biased estimates either by ignoring a large burden of the disease or failing to adjust for potential time-varying confounders such as air pollution and the delayed effects of weather and air pollution. Therefore, the aim of this study is to quantify the association between inpatient MI hospitalization rates and apparent temperature in different weather service regions in Florida, adjusting for the effects of air pollution and the lagged effects of AT and air pollution.

Data for daily inpatient hospitalizations for all MIs occurring among Florida residents between 2005 and 2014 were obtained from the Florida Department of Health. Hourly ambient temperature, relative humidity, and wind speed data from which maximum and minimum AT were computed were obtained from: National Weather Service (NWS) first-order weather stations, Florida Agricultural Weather Network (FAWN) automated weather stations, and Florida State University Center for Ocean-Atmospheric Prediction Studies. Air pollution data (PM_2.5 and O₃) were downloaded from the Environmental Protection Agency website.

A dynamic linear regression model with external variables will be used to quantify the association between daily MI morbidity risks and AT, adjusting for the effects of current and delayed effects of P.M_2.5 and O₃. We will also adjust for the effects of other relevant explanatory variables such as interventions and other seasonal patterns (weekday effects etc.) which may contribute to variations in MI hospitalizations risks over time. Additionally, we will be able to allow for possible time structure of the disturbance series (residual autocorrelation) with the use of a dynamic regression model. Thus, the analyses conducted with this model will produce more realistic effect estimates than the more traditional approaches such as the case-crossover design.

Based on previous studies, we expect daily MI hospitalization risks to be associated with extreme (high and low) AT values. We also expect MI hospitalization risks to vary regionally due to heterogeneity in environmental and socio-demographic characteristics across the state. From a public health perspective, a better understanding of the association between weather and MI morbidity will aid in the development of preventive strategies and lead to better outcomes.