Abstract
BackgroundThis paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile.MethodsLocal, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only.ResultsStatistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study.ConclusionData with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account.
Highlights
This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories
Background on cluster tests Cluster tests work within a hypothesis testing framework that proceeds by calculating a statistic to quantify a relevant aspect of spatial pattern in a health outcome
At the time of this writing, geocoding and data collection are ongoing; the results reported in this manuscript are entirely preliminary and should not be used to draw any conclusions regarding the spatial patterns of bladder cancer in Michigan
Summary
This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Cluster tests work within a hypothesis testing framework that proceeds by calculating a statistic (e.g. clustering metric) to quantify a relevant aspect of spatial pattern in a health outcome (e.g. case/control location, disease incidence, or mortality rate). The alternative hypothesis describes the spatial pattern that the test is designed to detect This may be a specific alternative, such as a circular cluster for the scan statistic, or it may be the omnibus "not the null hypothesis". The null spatial model is a mechanism for generating the reference distribution This may be based on distribution theory, or it may use randomization (e.g. Monte Carlo) techniques.
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