Effect of Missing Data on Estimation of the Impact of Heat Waves: Methodological Concerns for Public Health Practice

(1) Background: To demonstrate the potential effects of missing exposure data and model choice on public health conclusions concerning the impact of heat waves on heat-related morbidity. (2) Methods: Using four different methods to impute missing exposure data, four statistical models (case-crossover, time-series, zero-inflated, and truncated models) are compared. The methods are used to relate heat waves, based on heat index, and heat-related morbidities for Florida from 2005–2012. (3) Results: Truncated models using maximum daily heat index, imputed using spatio-temporal methods, provided the best model fit of regional and statewide heat-related morbidity, outperforming the commonly used case-crossover and time-series analysis methods. (4) Conclusions: The extent of missing exposure data, the method used to impute missing exposure data and the statistical model chosen can influence statistical inference. Further, using a statewide truncated negative binomial model, statistically significant associations between heat-related morbidity and regional heat index effects were identified.