密歇根州东南部的局部膀胱癌聚集情况，涉及风险因素、协变量和居住流动性。
Local bladder cancer clusters in southeastern Michigan accounting for risk factors, covariates and residential mobility.
作者信息
Jacquez Geoffrey M, Shi Chen, Meliker Jaymie R
机构信息
Department of Geography, University at Buffalo, The State University of New York, Buffalo, New York, United States of America; BioMedware Inc., Ann Arbor, Michigan, United States of America.
Department of Geography, University at Buffalo, The State University of New York, Buffalo, New York, United States of America.
出版信息
PLoS One. 2015 Apr 9;10(4):e0124516. doi: 10.1371/journal.pone.0124516. eCollection 2015.
BACKGROUND
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.
METHODS
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.
RESULTS
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.
CONCLUSION
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.
背景
在病例对照研究中，未被显著风险因素解释的疾病风险即为无法解释的风险。考虑特定人群、地点和时间的无法解释的风险，可能会揭示未被识别的风险因素以及在病例对照研究中未得到充分考虑的风险因素的特征。这有可能引出关于疾病病因的新假设。
方法
将全局、局部和聚焦Q统计量应用于对密歇根州东南部11个县开展的一项基于人群的病例对照研究的数据。分析使用了基于年份和年龄的时间度量。分析针对砷暴露、教育程度、吸烟、膀胱癌家族史、职业性膀胱癌致癌物暴露、年龄、性别和种族进行了调整。
结果
未发现病例的显著全局聚集。这样的发现将表明病例随时间相对于对照存在大规模聚集。然而，在兰辛附近的英厄姆县、奥克兰县以及密歇根州的杰克逊市发现了高度显著的局部聚集。杰克逊市的聚集在工作年龄段被观察到，因此与职业病因相符。英厄姆县的聚集随时间持续存在，表明存在基于地理区域的广泛暴露。针对20个从事与已知或疑似膀胱癌致癌物相关制造活动的工业场所发现了聚焦聚集。尽管局部聚集随时间持续存在且观察到局部检验概率的时间趋势，但针对多重检验进行调整的基于集合的检验并不显著。
结论
Q分析为从病例对照研究中剖析无法解释的疾病风险提供了一个强大工具。当风险因素的影响在空间、时间或时空两者上发生变化时，这一工具尤为有用。对于密歇根州的膀胱癌，下一步是调查可能解释奥克兰县和英厄姆县以及杰克逊市局部地区膀胱癌风险过高的因果假设。
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