Abstract
BackgroundInterest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years. Evaluations of statistical power provide important information for the selection of an appropriate statistical method in environmentally-related disease cluster investigations. Published power evaluations have not yet addressed the use of models for focused cluster detection and have not fully investigated the issues of disease cluster scale and shape. As meteorological and other factors can impact the dispersion of environmental toxicants, it follows that environmental exposures and associated diseases can be dispersed in a variety of spatial patterns. This study simulates disease clusters in a variety of shapes and scales around a centrally located single pollution source. We evaluate the power of a range of focused cluster tests and generalized linear models to detect these various cluster shapes and scales for count data.ResultsIn general, the power of hypothesis tests and models to detect focused clusters improved when the test or model included parameters specific to the shape of cluster being examined (i.e. inclusion of a function for direction improved power of models to detect clustering with an angular effect). However, power to detect clusters where the risk peaked and then declined was limited.ConclusionFindings from this investigation show sizeable changes in power according to the scale and shape of the cluster and the test or model applied. These findings demonstrate the importance of selecting a test or model with functions appropriate to detect the spatial pattern of the disease cluster.
Highlights
Interest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years
Focused cluster test results Data simulated with distance decline (DD) in relative risk The Linear Risk Score (LRS) Test for Distance Decline, Tango's Focused Test, Stone's Test and the Radial Score Test demonstrated the best power curves for the range of total simulated events (N = 200, 500, 1000)
Focused cluster tests Based on the results presented, we found that Tango's Test, the LRS Test for Distance Decline, Stone's Test and
Summary
Interest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years. Published power evaluations have not yet addressed the use of models for focused cluster detection and have not fully investigated the issues of disease cluster scale and shape. This study simulates disease clusters in a variety of shapes and scales around a centrally located single pollution source. We evaluate the power of a range of focused cluster tests and generalized linear models to detect these various cluster shapes and scales for count data. Over the past several years, there has been an increased interest in the detection of focused clusters of disease, or disease clusters associated with a known pollution source [1] Along with this interest, the development and use of various cluster detection methods have grown. Information is lacking regarding the power of generalized linear models to detect focused clusters of varying scale and shape.
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