Abstract
BackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionWith 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.
Highlights
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
The purpose of this paper is to develop and illustrate new statistical methods for differentiating 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
Instead of dividing the counties into high- and medium-risk categories on the basis of the incidence rates used in the practice and in the existing literature, we propose to divide the counties into high, medium, and low-risk categories, proceed to further differentiate incidence level of counties close within geographic proximity in the high- and low-risk categories respectively with and without the adjustment for confounding variables in a hierarchical manner
Summary
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. The purpose of this paper is to develop and illustrate new statistical methods for differentiating 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. With information on geographical covariate-adjusted incidence clustering patterns on a map, we can determine whether or not the previously detected geographical disease clusters of peak incidence or paucity of incidence can be explained by the covariates incorporated. 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
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