Spatial Analysis of Notified Cryptosporidiosis Infections in Brisbane, Australia

Purpose

This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia.

Methods

We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis.

Results

Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%.

Conclusions

There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.

Key Words: CART, Cryptosporidiosis, Socioeconomic Factors, Spatial Analysis

Selected Abbreviations and Acronyms: GIS, geographic information systems, CART, classification and regression models, SEIFA, social economic index for areas, SLA, statistical local areas, SEBCIR, spatial empirical Bayes cryptosporidiosis incidence rates, GLMM, generalized linear mixed model