Local Bladder Cancer Clusters in Southeastern Michigan Accounting for Risk Factors, Covariates and Residential Mobility

<div><p>Background</p><p>In case control studies disease risk not explained by the significant risk factors is the unexplained risk. Considering unexplained risk for specific populations, places and times can reveal the signature of unidentified risk factors and risk factors not fully accounted for in the case-control study. This potentially can lead to new hypotheses regarding disease causation.</p><p>Methods</p><p>Global, local and focused Q-statistics are applied to data from a population-based case-control study of 11 southeast Michigan counties. Analyses were conducted using both year- and age-based measures of time. The analyses were adjusted for arsenic exposure, education, smoking, family history of bladder cancer, occupational exposure to bladder cancer carcinogens, age, gender, and race.</p><p>Results</p><p>Significant global clustering of cases was not found. Such a finding would indicate large-scale clustering of cases relative to controls through time. However, highly significant local clusters were found in Ingham County near Lansing, in Oakland County, and in the City of Jackson, Michigan. The Jackson City cluster was observed in working-ages and is thus consistent with occupational causes. The Ingham County cluster persists over time, suggesting a broad-based geographically defined exposure. Focused clusters were found for 20 industrial sites engaged in manufacturing activities associated with known or suspected bladder cancer carcinogens. Set-based tests that adjusted for multiple testing were not significant, although local clusters persisted through time and temporal trends in probability of local tests were observed.</p><p>Conclusion</p><p>Q analyses provide a powerful tool for unpacking unexplained disease risk from case-control studies. This is particularly useful when the effect of risk factors varies spatially, through time, or through both space and time. For bladder cancer in Michigan, the next step is to investigate causal hypotheses that may explain the excess bladder cancer risk localized to areas of Oakland and Ingham counties, and to the City of Jackson.</p></div

Summary of analysis results of global tests with 999 randomizations.

<p>^a Q statistics over the entire study period 1940–2003 using year-based measure of time.</p><p>^b Q statistics using age-based measure of time.</p><p>^c Q_F statistics about industries in operation from 1943 to 1999 using year-based measure of time.</p><p>^d The count of the number of clusters found significant at <i>α</i> ≤ 0.05.</p><p>^e The significance of the number of clusters found significant at <i>α</i> ≤ 0.05.</p><p>Summary of analysis results of global tests with 999 randomizations.</p

Time plot of p(Q_F) using year-based measure of time.

<p>Values plotted are the probability of focused clustering when all of the industrial sites are considered simultaneously (e.g. probability of global focused clustering). The period of significant global focused clustering observed in 1974–1975 is attributable to the focused cluster that arose in the City of Jackson.</p

Time plot of p(Q_t) using year-based measure of time.

<p>Minima on the time plot indicate time periods when global spatial clustering of cases were statistically significant.</p

The 20 industries whose business address histories were found to be persistent centers of bladder cancer case clusters.

<p>The 20 industries whose business address histories were found to be persistent centers of bladder cancer case clusters.</p

The risk factors and covariates included in the logistic model and their corresponding coefficients.

<p>Details on odds ratios, confidence intervals, risk factors and covariates, and arsenic exposure estimation are given by Meliker et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref012" target="_blank">12</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref013" target="_blank">13</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref014" target="_blank">14</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref015" target="_blank">15</a>].</p><p>^a There was frequency matching on age, race, and gender which explains the small size of their coefficients.</p><p>The risk factors and covariates included in the logistic model and their corresponding coefficients.</p

Map of space-only clusters of non-Hodgkin lymphoma in Denmark.

<p>One area showing statistically significant clustering of non-Hodgkin lymphoma cases (area no. 8), and two areas showing borderline clustering (area no. 9 and 10), based on spatial scan statistics of SaTScan and a maximum cluster size of 10% of the total population, using year when clusters were suggested by Q-statistics. None of these cluster regions were consistently found with both control groups.</p

Map of space-time clusters of non-Hodgkin lymphoma in Denmark adjusted for potential confounding factors.

<p>Three areas showing statistically significant clustering of non-Hodgkin lymphoma cases in Denmark identified with Q-statistics, based on 15 nearest neighbours and adjusted analyses. The circles indicate the extent of the clusters, not the number of cases comprising each cluster. None of these cluster regions were consistently found with both control groups.</p

Adjusted Analysis.

<p>Results of the cluster analysis of testicular cancer in Denmark, adjusted for family history of testicular cancer, following the same format as used in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120285#pone.0120285.t002" target="_blank">Table 2</a>.</p><p>Adjusted Analysis.</p

Results of adjusted^a space-time cluster analyses performed in SpaceStat, based on 15 nearest neighbours, 999 permutations, and by three different time scales.

a<p>Adjusted for previous history of autoimmune disease, HIV/AIDS or organ transplantation.</p>b<p>The total number of statistically significant cases with a <i>Q_ik</i> p-value of 0.001, which indicates the number of cases that are centers of clusters over their life-course. ^c Number of statistically significant <i>Q_ik</i> (p  =  0.001) cases that also have significant <i>Q_ikt</i> (p ≤ 0.05) and are members of a cluster of at least 4 cases. ^d Indicate where and when the cases tend to cluster.</p>e<p>Refers to the map in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060800#pone-0060800-g002" target="_blank">figure 2</a>, which shows the suggested clusters of NHL in Denmark based on the adjusted analyses. ^f Control group.</p