Koo Jung-Wan, Parham Frederick, Kohn Michael C, Masten Scott A, Brock John W, Needham Larry L, Portier Christopher J
Environmental Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA.
Environ Health Perspect. 2002 Apr;110(4):405-10. doi: 10.1289/ehp.02110405.
Population-based estimates of environmental exposures using biomarkers can be difficult to obtain for a variety of reasons, including problems with limits of detection, undersampling of key strata, time between exposure and sampling, variation across individuals, variation within individuals, and the ability to find and interpret a given biomarker. In this article, we apply statistical likelihoods, weighted sampling, and regression methods for censored data to the analysis of biomarker data. Urinary metabolites for seven phthalates, reported by Blount et al., are analyzed using these methods. In the case of the phthalates data, we assumed the underlying model to be a log-normal distribution with the mean of the distribution defined as a function of a number of demographic variables that might affect phthalate levels in individuals. Included as demographic variables were age, sex, ethnicity, residency, family income, and education level. We conducted two analyses: an unweighted analysis where phthalate distributions were estimated with changes in the means of these distributions as a function of demographic variables, and a weighted prediction for the general population in which weights were assigned for a subset of the population depending on the frequency of their demographic variables in the general U.S. population. We used statistical tests to determine whether any of the demographic variables affected mean phthalate levels. Individuals with only a high school education had higher levels of di-n-butyl phthalate than individuals with education beyond high school. Subjects who had family income less than $1,500 in the month before sampling and/or only high school education had higher levels of n-butyl benzyl phthalate levels than other groupings. Di(2-ethylhexyl) phthalate was higher in males and/or in urban populations and/or in people who had family income less than $1,500 per month. Our findings suggest that there may be significant demographic variations in exposure and/or metabolism of phthalates and that health-risk assessments for phthalate exposure in humans should consider different potential risk groups.
基于人群的环境暴露生物标志物估计值因多种原因难以获得,包括检测限问题、关键阶层抽样不足、暴露与采样之间的时间间隔、个体间差异、个体内差异以及寻找和解释特定生物标志物的能力。在本文中,我们将统计似然性、加权抽样和删失数据回归方法应用于生物标志物数据分析。我们使用这些方法分析了布朗特等人报告的七种邻苯二甲酸盐的尿代谢物。对于邻苯二甲酸盐数据,我们假设基础模型为对数正态分布,分布均值定义为可能影响个体邻苯二甲酸盐水平的一些人口统计学变量的函数。作为人口统计学变量的有年龄、性别、种族、居住情况、家庭收入和教育水平。我们进行了两项分析:一项未加权分析,其中邻苯二甲酸盐分布是根据这些分布均值随人口统计学变量的变化来估计的;另一项是对一般人群的加权预测,其中根据人口统计学变量在美国一般人群中的频率为一部分人群分配权重。我们使用统计检验来确定是否有任何人口统计学变量影响邻苯二甲酸盐平均水平。只有高中学历的个体比受过高中以上教育的个体有更高水平的邻苯二甲酸二正丁酯。在采样前一个月家庭收入低于1500美元和/或只有高中学历的受试者比其他分组有更高水平的邻苯二甲酸正丁酯苄酯。邻苯二甲酸二(2-乙基己基)酯在男性和/或城市人群和/或每月家庭收入低于1500美元的人群中含量较高。我们的研究结果表明,邻苯二甲酸盐的暴露和/或代谢可能存在显著的人口统计学差异,并且对人类邻苯二甲酸盐暴露的健康风险评估应考虑不同的潜在风险群体。