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 spatiotemp

SUBMITTER: Sofianopoulou E 

PROVIDER: S-EPMC5860516 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature