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Abstract Background Space-time interaction arises when nearby cases occur at about the same time, and may be attributable to an infectious etiology or from exposures that cause a geographically localized increase in risk. But available techniques for detecting interaction do not account for residential mobility, nor do they evaluate sensitivity to induction and latency periods. This is an important problem for cancer, where latencies of a decade or more occur. Methods New case-only clustering techniques are developed that account for residential mobility, latency and induction periods, relevant covariates (such as age) and risk factors (such as smoking). The statistical behavior of the methods is evaluated using simulated data to assess type I error (false positives) and statistical power. These methods are applied to 374 cases from an ongoing study of bladder cancer in 11 counties in southeastern Michigan, and the ability of the methods to localize space-time interaction at the individual-level is demonstrated. Results Significant interaction is found for induction periods of ~5 years and latency ~19.5 years. Data are still being collected and the observed clusters may be attributable to differential sampling in the study area. Conclusion Residential histories are increasingly available, raising the possibility of routine surveillance in a manner that accounts for individual mobility and that incorporates models of cancer latency and induction. These new techniques provide a mechanism for identifying those geographic locations and times associated with increases in cancer risk above and beyond that expected given covariates and risk factors in geographically mobile populations.

Abstract Background Our progress towards the goal of eliminating racial health disparities requires methods for assessing the existence, magnitude, and statistical significance of health disparities. In comparing disease rates, we must account for the unreliability of rates computed for small minority populations and within sparsely populated areas. Furthermore, as the number of geographic units under study increases, we also must account for multiple testing to assure we do not misclassify disparities as present when they actually are not (false positive). To date and to our knowledge, none of the methodologies in current use simultaneously address all of these important needs. And few, if any studies have undertaken a systematic comparison of methods to identify those that are statistically robust and reliable. Results We introduced six test statistics for quantifying absolute and relative differences between cancer rates measured in distinct groups (i.e. race or ethnicity). These alternative measures were illustrated using age-adjusted prostate and lung cancer mortality rates for white and black males in 688 counties of the Southeastern US (1970–1994). Statistical performance, including power and proportion of false positives, was investigated in simulation studies that mimic different scenarios for the magnitude and frequency of disparities. Two test statistics, which are based on the difference and ratio of rates, consistently outperformed the other measures. Corrections for multiple testing actually increased misclassification compared with the unadjusted tests and are not recommended. One-tailed tests allowed the researcher to consider a priori hypotheses beyond the basic test that the two rates are different. Conclusion The assessment of significant racial disparities across geographic areas is an important tool in guiding cancer control practices, and public health officials must consider the problems of small population size and multiple comparison, and should conduct disparity analyses using both absolute (difference, RD statistic) and relative (ratio, RR statistic) measures. Simple test statistics to assess the significance of rate difference and rate ratio perform well, and their unadjusted p-values provide a realistic assessment of the proportion of type I errors (i.e. disparities wrongly declared significant).

Abstract Background Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn – we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors. Results Global clustering is partly explained by the covariates but remains statistically significant at 12 of the 14 levels of k considered. After accounting for the covariates 26 Local clusters are found in Lapeer, Ingham, Oakland and Jackson counties, with the clusters in Ingham and Oakland counties appearing in 1950 and persisting to the present. Statistically significant focused clusters are found about the business address histories of 22 industries located in Oakland (19 clusters), Ingham (2) and Jackson (1) counties. Clusters in central and southeastern Oakland County appear in the 1930's and persist to the present day. Conclusion These methods provide a systematic approach for evaluating a series of increasingly realistic alternative hypotheses regarding the sources of excess risk. So long as selection of cases and controls is population-based and not geographically biased, these tools can provide insights into geographic risk factors that were not specifically assessed in the case-control study design.

Abstract Background Recent years have seen an expansion in the use of Geographic Information Systems (GIS) in environmental health research. In this field GIS can be used to detect disease clustering, to analyze access to hospital emergency care, to predict environmental outbreaks, and to estimate exposure to toxic compounds. Despite these advances the inability of GIS to properly handle temporal information is increasingly recognised as a significant constraint. The effective representation and visualization of both spatial and temporal dimensions therefore is expected to significantly enhance our ability to undertake environmental health research using time-referenced geospatial data. Especially for diseases with long latency periods (such as cancer) the ability to represent, quantify and model individual exposure through time is a critical component of risk estimation. In response to this need a STIS – a Space Time Information System has been developed to visualize and analyze objects simultaneously through space and time. Results In this paper we present a "first use" of a STIS in a case-control study of the relationship between arsenic exposure and bladder cancer in south eastern Michigan. Individual arsenic exposure is reconstructed by incorporating spatiotemporal data including residential mobility and drinking water habits. The unique contribution of the STIS is its ability to visualize and analyze residential histories over different temporal scales. Participant information is viewed and statistically analyzed using dynamic views in which values of an attribute change through time. These views include tables, graphs (such as histograms and scatterplots), and maps. In addition, these views can be linked and synchronized for complex data exploration using cartographic brushing, statistical brushing, and animation. Conclusion The STIS provides new and powerful ways to visualize and analyze how individual exposure and associated environmental variables change through time. We expect to see innovative space-time methods being utilized in future environmental health research now that the successful "first use" of a STIS in exposure reconstruction has been accomplished.