The misuse of count data aggregated over time for disease mapping

Abstract

The ongoing spread of spatial analysis techniques for small areas has facilitated the publication of mortality and morbidity Atlases based on time periods that group information spanning several years. Although this is a widespread practice, this paper proves that the use of count data aggregated over time for disease mapping may give inappropriate area-specific relative risk. As a result, both decision-making and healthcare policies could be affected by inappropriate model specifications using aggregated information over time. The Poisson distribution properties were used in order to quantify the bias in area-specific relative risk estimation due to count data aggregated over time. A hierarchical Bayesian model with spatio-temporal random structure is proposed as an alternative to smoothing relative risk if the period of study need to span several years. Methods discussed in this paper were applied to a small-area survey on male mortality from all causes in Southern Spain for the period 1985-1999. The results suggest that particular caution should be taken when interpreting risk maps based on clustered annual data that use models with no temporal structure to smooth out the rates.