Modelling exposure to disinfection by-products in drinking water for an epidemiological study of adverse birth outcomes
Modelling exposure to disinfection by-products in drinking water for an epidemiological study of adverse birth outcomes
复制标题
DOI：
10.1038/sj.jea.7500380
复制
发表时间：
2005-03-01
期刊：
影响因子：
--
通讯作者：
Elliott, P
中科院分区：
文献类型：
--
作者：
Whitaker, H;Best, N;Elliott, P
我们正在英国开展一项流行病学研究，研究饮用水中消毒副产品浓度与不良出生结局之间的关系，使用特定水域的三卤甲烷 (THM) 浓度作为暴露指数。在这里，我们使用稀疏的常规收集的 THM 测量值构建统计模型，以获得每个水域平均 THM 浓度的季度估计值。我们使用贝叶斯分层混合模型对 THM 测量进行建模，同时考虑到不同来源类型的水之间 THM 浓度的异质性、THM 浓度的季度变化以及未检测到和四舍五入测量的真实值的不确定性。获得了每个水域的平均 THM 浓度的季度估计值以及水源类型（地面、低地表面或高地表面）的估计值。 THM 浓度估计值通常在 7 月至 9 月（第三季度）最高，并且不同水源之间差异很大。我们的暴露估计分为“低”、“中”和“高”THM 类别。我们建模的季度暴露估计与一个简单的替代方案进行了比较：每个水域原始数据的年度平均值。总之，15-25% 的暴露估计值被不同地分类。与原始年度平均值相比，模型 THM 估计得出的与死产和低出生体重风险相关性的估计稍强且更精确。我们的结论是，在原始数据太稀疏而无法仅根据经验总结进行暴露评估的情况下，我们的建模方法使我们能够提供对 THM 生态暴露的可靠季度估计。
We are conducting an epidemiological study on the association between disinfection by-product concentrations in drinking water and adverse birth outcomes in the UK, using trihalomethane (THM) concentrations over defined water zones as an exposure index. Here we construct statistical models using sparse routinely collected THMs measurements to obtain quarterly estimates of mean THM concentrations for each water zone. We modelled the THM measurements using a Bayesian hierarchical mixture model, taking into account heterogeneity in THM concentrations between water originating from different source types, quarterly variation in THM concentrations and uncertainty in the true value of undetected and rounded measurements. Quarterly estimates of mean THM concentrations plus estimates of the water source type (ground, lowland surface or upland surface) were obtained for each water zone. THM concentration estimates were typically highest from July to September (third quarter), and varied considerably between water sources. Our exposure estimates were categorized into 'low', 'medium' and 'high' THM classes. Our modelled quarterly exposure estimates were compared to a simple alternative: annual means of the raw data for each water zone. In all, 15-25% of exposure estimates were classified differently. The modelled THM estimates led to slightly stronger and more precise estimates of association with risk of still birth and low birth weight than did the raw annual means. We conclude that our modelling approach enabled us to provide robust quarterly estimates of ecological exposure to THMs in a situation where the raw data were too sparse to base exposure assessment on empirical summaries alone.