Modeling Seasonal and Spatiotemporal Variation: The Example of Respiratory Prescribing

Abstract

Many measures of chronic diseases, including respiratory disease, exhibit seasonal variation together with residual correlation between consecutive time periods and neighboring areas. We demonstrate a strategy for modeling data that exhibit both seasonal trend and spatiotemporal correlation, using an application to respiratory prescribing. We analyzed 55 months (2002–2006) of prescribing data from the northeast of England, in the United Kingdom. We estimated the seasonal pattern of prescribing by fitting a dynamic harmonic regression (DHR) model to salbutamol prescribing in relation to temperature. We compared the output of DHR models to static sinusoidal regression models. We used the DHR-fitted values as an offset in mixed-effects models that aimed to account for the remaining spatiotemporal variation in prescribing rates. As diagnostic checks, we assessed spatial and temporal correlation separately and jointly. Our application of a DHR model resulted in a better fit to the seasonal variation of prescribing than was obtained with a static model. After adjusting for the fitted values from the DHR model, we did not detect any remaining spatiotemporal correlation in the model's residuals. Using a DHR model and temperature data to account for the periodicity of prescribing proved to be an efficient way to capture its seasonal variation. The diagnostic procedures indicated that there was no need to model any remaining correlation explicitly.

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