Differentiating anomalous disease intensity with confounding variables in space
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nternational Journal of Health Geographics Open Access
METHODOLOGY
Differentiating anomalous disease intensity with confounding variables in space Chih‑Chieh Wu1* and Sanjay Shete2
Abstract Background: The investigation of perceived geographical disease clusters serves as a preliminary step that expedites subsequent etiological studies and analysis of epidemicity. With the identification of disease clusters of statistical significance, to determine whether or not the detected disease clusters can be explained by known or suspected risk factors is a logical next step. The models allowing for confounding variables permit the investigators to determine if some risk factors can explain the occurrence of geographical clustering of disease incidence and to investigate other hidden spatially related risk factors if there still exist geographical disease clusters, after adjusting for risk factors. Methods: We propose to develop statistical methods for differentiating incidence intensity of geographical dis‑ ease clusters of peak incidence and low incidence in a hierarchical manner, adjusted for confounding variables. The methods prioritize the areas with the highest or lowest incidence anomalies and are designed to recognize hierarchi‑ cal (in intensity) disease clusters of respectively high-risk areas and low-risk areas within close geographic proximity on a map, with the adjustment for known or suspected risk factors. The data on spatial occurrence of sudden infant death syndrome with a confounding variable of race in North Carolina counties were analyzed, using the proposed methods. Results: The proposed Poisson model appears better than the one based on SMR, particularly at facilitating discrimi‑ nation between the 13 counties with no cases. Our study showed that the difference in racial distribution of live births explained, to a large extent, the 3 previously identified hierarchical high-intensity clusters, and a small region of 4 mutually adjacent counties with the higher race-adjusted rates, which was hidden previously, emerged in the south‑ west, indicating that unobserved spatially related risk factors may cause the elevated risk. We also showed that a large geographical cluster with the low race-adjusted rates, which was hidden previously, emerged in the mid-east. Conclusion: With the information on hierarchy in adjusted intensity levels, epidemiologists and public health officials can better prioritize the regions with the highest rates for thorough etiologic studies, seeking hidden spatially related risk factors and precisely moving resources to areas with genuine highest abnormalities. Keywords: Geographical disease cluster, Hierarchical, Incidence clustering, Sudden infant death Syndrome Introduction An important issue in spatial and temporal statistics is whether a set of discrete points are distributed randomly or they show a variety of signs of clustering. One *Correspondence: [email protected] 1 Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung Uni
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