Department of Biostatistics, School of Medicine, Virginia Commonwealth University, One Capitol Square, 830 East Main Street, Richmond, VA 23298, USA.
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA.
Int J Environ Res Public Health. 2023 Feb 17;20(4):3582. doi: 10.3390/ijerph20043582.
Leukemia is the most common childhood cancer in industrialized countries, and the increasing incidence trends in the US suggest that environmental exposures play a role in its etiology. Neighborhood socioeconomic status (SES) has been found to be associated with many health outcomes, including childhood leukemia. In this paper, we used a Bayesian index model approach to estimate a neighborhood deprivation index (NDI) in the analysis of childhood leukemia in a population-based case-control study (diagnosed 1999 to 2006) in northern and central California, with direct indoor measurements of many chemicals for 277 cases and 306 controls <8 years of age. We considered spatial random effects in the Bayesian index model approach to identify any areas of significantly elevated risk not explained by neighborhood deprivation or individual covariates, and assessed if groups of indoor chemicals would explain any elevated spatial risk areas. Due to not all eligible cases and controls participating in the study, we conducted a simulation study to add non-participants to evaluate the impact of potential selection bias when estimating NDI effects and spatial risk. The results in the crude model showed an odds ratio (OR) of 1.06 and 95% credible interval (CI) of (0.98, 1.15) for a one unit increase in the NDI, but the association became slightly inverse when adjusting for individual level covariates in the observed data (OR = 0.97 and 95% CI: 0.87, 1.07), as well as when using simulated data (average OR = 0.98 and 95% CI: 0.91, 1.05). We found a significant spatial risk of childhood leukemia after adjusting for NDI and individual-level covariates in two counties, but the area of elevated risk was partly explained by selection bias in simulation studies that included more participating controls in areas of lower SES. The area of elevated risk was explained when including chemicals measured inside the home, and insecticides and herbicides had greater effects for the risk area than the overall study. In summary, the consideration of exposures and variables at different levels from multiple sources, as well as potential selection bias, are important for explaining the observed spatial areas of elevated risk and effect estimates.
白血病是工业化国家中最常见的儿童癌症，而美国不断上升的发病率趋势表明，环境暴露在其病因学中起作用。邻里社会经济地位 (SES) 已被发现与许多健康结果相关，包括儿童白血病。在本文中，我们使用贝叶斯指数模型方法来估计美国北加州和中加州一项基于人群的病例对照研究（1999 年至 2006 年诊断）中儿童白血病的邻里剥夺指数（NDI），该研究对 277 例病例和 306 例<8 岁的对照进行了许多化学物质的直接室内测量。我们在贝叶斯指数模型方法中考虑了空间随机效应，以识别任何无法用邻里剥夺或个体协变量解释的显著升高风险区域，并评估室内化学物质组是否可以解释任何升高的空间风险区域。由于并非所有符合条件的病例和对照都参与了研究，我们进行了一项模拟研究，以添加非参与者来评估在估计 NDI 效应和空间风险时潜在选择偏差的影响。在未调整模型中，NDI 每增加一个单位，比值比（OR）为 1.06，95%可信区间（CI）为（0.98，1.15），但在观察数据中调整个体水平协变量时，该关联略有反转（OR=0.97，95%CI：0.87，1.07），以及在使用模拟数据时（平均 OR=0.98，95%CI：0.91，1.05）。我们发现，在调整了 NDI 和个体水平协变量后，在两个县存在儿童白血病的显著空间风险，但在包括更多参与的 SES 较低地区的对照的模拟研究中，风险升高区域部分可以用选择偏差来解释。当包括在家中测量的化学物质时，风险升高区域得到了解释，并且杀虫剂和除草剂对风险区域的影响大于整个研究。总之，从多个来源考虑不同水平的暴露和变量，以及潜在的选择偏差，对于解释观察到的空间风险升高区域和效应估计非常重要。
