Differentiating anomalous disease intensity with confounding variables in space

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 disease 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 hierarchical (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 discrimination 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 southwest, 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.

Spatially varying effects of measured confounding variables on disease risk

“…The analysis of detecting geographical disease clusters of peak incidence and incidence paucity performed by the generalized map-based pattern recognition procedure was presented in our previous report [ 8 ]. We determined the 3 groups of counties to use in constructing hierarchical (in intensity) disease clusters of mutually neighboring high-risk counties with 3 different levels of intensity.…”

“…We recently generalized the ordinary map-based pattern recognition procedure in several important respects, including taking into account covariates that are known or hypothesized risk factors in the modeling, focusing on geographically neighboring areas of incidence paucity as well as peak incidence, and allowing for the use of distance-based neighborhood system in addition to the existing adjacency-based one in the definition of close geographical proximity [ 8 ]. The generalized pattern recognition procedure differentiates incidence intensity of geographical disease clusters of peak incidence and low incidence, adjusted for covariates that are known or hypothesized risk factors, as well as testing for the presence of clustering.…”

“…Because these 2 models are sensitive to different respects of spatially characteristic incidence clustering patterns and structured to provide different spatial clustering information, the geographical disease clusters detected by them are often different. We articulate the difference in sensitivity, applicability, and characteristics between these two models in our recent report [ 8 ].…”

“…The spatial scan statistic is widely used and has been extended to a variety of models for detecting spatial, temporal, and space–time clusters, retrospectively or prospectively [ 7 ]. The generalized map-based pattern recognition procedure that we recently developed is designed to recognize and construct hierarchical (in intensity) disease clusters of respectively high-risk areas and low-risk areas within close geographic proximity or contiguity on a map [ 8 ]. The spatial scan statistic and the generalized map-based pattern recognition procedure are used to detect geographically neighboring areas of peak incidence as well as incidence paucity in a spatial point process in general and allow for confounding variables.…”

“…Two possible confounding variables for SIDS are race and gender. The associations of SIDS incidence with race or gender have seen noted in the statistical and epidemiologic literature [ 7 , 8 , 11 – 15 ]. Information on spatial distribution of the race-specific and gender-specific live births is available in the literature [ 16 ].…”