Performance of cancer cluster Q-statistics for case-control residential histories

Abstract

Few investigations of health event clustering have evaluated residential mobility, though causative exposures for chronic diseases such as cancer often occur long before diagnosis. Recently developed Q-statistics incorporate human mobility into disease cluster investigations by quantifying space- and time-dependent nearest neighbor relationships. Using residential histories from two cancer case-control studies, we created simulated clusters to examine Q-statistic performance. Results suggest the intersection of cases with significant clustering over their life course, Qi, with cases who are constituents of significant local clusters at given times, Qit, yielded the best performance, which improved with increasing cluster size. Upon comparison, a larger proportion of true positives were detected with Kulldorf’s spatial scan method if the time of clustering was provided. We recommend using Q-statistics to identify when and where clustering may have occurred, followed by the scan method to localize the candidate clusters. Future work should investigate the generalizability of these findings.

Spatial epidemiology of cancer: a review of data sources, methods and risk factors

“…The importance of creating maps that accurately describe disease spatial distribution patterns appeared to be a consensual issue (Kulldorff et al, 2006) though the method used to achieve this was not consensual. Some articles intended to define the best method for some type of analysis for some particular datasets by comparing the results of the application of different spatial analysis methods (Bailony et al, 2011;Biggeri et al, 2009;Chen et al, 2008a;Colonna, 2004;Dasgupta et al, 2014;Goovaerts, 2005Goovaerts, , 2006aHegarty et al, 2010;Huang et al, 2008;Kaldor and Clayton, 1989;Kulldorff et al, 2006;Meliker et al, 2009;Sherman et al, 2014;Sloan et al, 2012;Zhou et al, 2008b). Table 5 shows a classification of some of most common spatial issues covered by research papers, as well as methods used to answer them.…”

“…First, in combined analysis of geographically aggregated data, difficulties may arise when they are not grouped according to the same geographical boundaries (Blackley et al, 2012;Goovaerts, 2006a); second, analysis results of aggregated data should be considered true only at their scale of aggregation and should not be extrapolated to other aggregation or disaggregation levels (Fortunato et al, 2011) since inconsistencies in results obtained at different scales may arise Xiao, 2011, 2012); third, the spatial patterns obtained based on aggregated data can result from the level of aggregation chosen and not from the distribution of the phenomenon under review itself (Krewski et al, 2005); and fourth, data are often aggregated into geographical areas defined for political or administrative reasons , which may not always be the most appropriate for undertaking a particular study (Goovaerts, 2006a). If the areas' aggregation criteria does not take into account the area characteristics in terms of health, the modifiable areal unit problem (MAUP) may arise (Luo, 2013;Shi, 2009;Sloan et al, 2012) and the risk of aggregating areas with very different characteristics could emerge (Thompson et al, 2007).…”

“…The ability to incorporate the individual's residential history is also a strong point noted in Q-statistics (Sloan et al, 2012).…”

pyJacqQ: Python Implementation of Jacquez's Q-Statistics for Space-Time Clustering of Disease Exposure in Case-Control Studies

“…This entire procedure, involving redistribution of case-control status and recalculation of all statistics, is repeated for several permutations and a pseudo-p value is calculated for each statistic as (a + 1) ÷ (b + 1) (Jacquez et al 2005). Here a is the number of permutations where the statistic was at least as extreme as observed in the original data and b is the total number of permutations conducted (Jacquez et al 2005 Jacquez's Q is a promising method given its capacity to pinpoint the location and times of space-time clusters and could be of great potential to epidemiologists and public health informaticians, however, there have been mixed findings regarding its utility (Sloan et al 2012;Nordsborg, Meliker, Ersboll, Jacquez, and Raaschou-Nielsen 2013). An analysis of the method using simulated data conducted by Sloan et al (2012) found that investigating significant intersections of Q i and Q it proved the best strategy.…”

“…Here a is the number of permutations where the statistic was at least as extreme as observed in the original data and b is the total number of permutations conducted (Jacquez et al 2005 Jacquez's Q is a promising method given its capacity to pinpoint the location and times of space-time clusters and could be of great potential to epidemiologists and public health informaticians, however, there have been mixed findings regarding its utility (Sloan et al 2012;Nordsborg, Meliker, Ersboll, Jacquez, and Raaschou-Nielsen 2013). An analysis of the method using simulated data conducted by Sloan et al (2012) found that investigating significant intersections of Q i and Q it proved the best strategy. They also found that Q statistics performs best when the population is large and mobile but recommend checking the findings with scan statistics (Sloan et al 2012).…”