From the School of Medicine, University of Leeds, Leeds, LS2 9NL, UK.
Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9NL, UK.
Epidemiology. 2019 Jan;30(1):75-82. doi: 10.1097/EDE.0000000000000921.
Studies investigating the population-mixing hypothesis in childhood leukemia principally use two analytical approaches: (1) nonrandom selection of areas according to specific characteristics, followed by comparisons of their incidence of childhood leukemia with that expected based on the national average; and (2) regression analyses of region-wide data to identify characteristics associated with the incidence of childhood leukemia. These approaches have generated contradictory results. We compare these approaches using observed and simulated data.
We generated 10,000 simulated regions using the correlation structure and distributions from a United Kingdom dataset. We simulated cases using a Poisson distribution with the incidence rate set to the national average assuming the null hypothesis that only population size drives the number of cases. Selection of areas within each simulated region was based on characteristics considered responsible for elevated infection rates (population density and inward migration) and/or elevated leukemia rates. We calculated effect estimates for 10,000 simulations and compared results to corresponding observed data analyses.
When the selection of areas for analysis is based on apparent clusters of childhood leukemia, biased assessments occur; the estimated 5-year incidence of childhood leukemia ranged between zero and eight per 10,000 children in contrast to the simulated two cases per 10,000 children, similar to the observed data. Performing analyses on region-wide data avoids these biases.
Studies using nonrandom selection to investigate the association between childhood leukemia and population mixing are likely to have generated biased findings. Future studies can avoid such bias using a region-wide analytical strategy. See video abstract at, http://links.lww.com/EDE/B431.
研究儿童白血病人群混合假说主要采用两种分析方法：（1）根据特定特征非随机选择区域，然后比较其儿童白血病发病率与全国平均发病率的预期值；（2）对全地区数据进行回归分析，以确定与儿童白血病发病率相关的特征。这些方法得出了相互矛盾的结果。我们使用观察数据和模拟数据比较这些方法。
我们使用来自英国数据集的相关结构和分布生成了 10000 个模拟区域。我们使用泊松分布模拟病例，发病率设定为全国平均水平，假设仅人口规模驱动病例数的零假设。每个模拟区域内的区域选择基于被认为会导致感染率（人口密度和内部移民）和/或白血病率升高的特征。我们计算了 10000 次模拟的效应估计值，并将结果与相应的观察数据分析进行了比较。
当分析区域的选择基于儿童白血病的明显聚集时，会出现有偏差的评估；估计的 5 年儿童白血病发病率在零到每 10000 名儿童 8 例之间，而模拟的每 10000 名儿童 2 例相似，与观察数据一致。对全地区数据进行分析可以避免这些偏差。
使用非随机选择来研究儿童白血病与人群混合之间的关联的研究可能产生了有偏差的发现。未来的研究可以使用全地区分析策略来避免这种偏差。请观看视频摘要，网址为，http://links.lww.com/EDE/B431。
