Disease mapping and spatio-temporal analysis: importance of expected-case computation criteria

Abstract

The municipal, spatial pattern of male stomach cancer mortality in Spain, spanning the period 1989-2008, was studied, comparing the results of depicting mortality using different expected-case computation methods in a spatial and spatio- temporal modelling context. Expected cases for each municipality were first calculated by two methods: (i) using reference rates for each 5-year period; and (ii) using average reference rates for the overall period. This was visualised by two types of models: (i) independent maps for each period based on the model proposed by Besag, York and Mollié; and (ii) a series of maps over time based on a model with spatio-temporal interaction terms. An additional model, based on mortality rate ratios as an alternative to the traditional use of standardised mortality ratios, was also fitted. Integrated nested Laplace approximations were used as the Bayesian inference tool. The results show that, in general, the geographical pattern was maintained across the study period, and that the maps differed appreciably according to the method used to obtain the expected number of cases. While the use of average reference rates appears to be the most suitable choice where the aim is to study time trends by area, it may nevertheless mask the spatial pattern in situations where the time trend is very marked and the study period is long. When it comes to studying changes in the spatial pattern of stomach cancer mortality, we feel that it is most useful to plot independent maps by period and use the "local" rates for each period as reference in the computation of expected cases.