Characterizing fecal contamination exposure from drinking water can introduce exposure measurement errors, i.e., differences between the observed and true exposure. These errors can mask the true relationship between fecal contamination exposure and waterborne diseases. We present a framework to quantify the impact of measurement errors on exposure–outcome health effect estimates introduced by variability in measured drinking water fecal contamination levels and household versus community sampling strategies. We matched fecal indicator bacteria (FIB) data for >37,000 drinking water samples to children aged 0–72 months from 19 studies in low- and middle-income countries and took two complementary analytical approaches. We found that household-level exposure assessments may attenuate effect estimates of FIB concentrations in drinking water on diarrhea, and single water samples may attenuate health effect estimates of FIB concentrations on linear growth. To understand the health effects of fecal contamination exposure, measurement error frameworks can be used to estimate more biologically relevant exposures.

表征饮用水中粪便污染物的暴露量会引入暴露量测量误差，即观察到的暴露量与真实暴露量之间的差异。这些错误可能掩盖了粪便污染暴露与水传播疾病之间的真正关系。我们提出了一个框架，用于量化测量误差对暴露的影响-结果的健康影响估计值，该估计值是由所测量的饮用水粪便污染水平的变化以及家庭抽样与社区抽样策略所引起的。我们对来自低收入和中等收入国家的19项研究的0–72个月儿童的粪便指示菌（FIB）数据进行了匹配，收集了> 37,000个饮用水样本，并采取了两种互补的分析方法。我们发现，家庭水平的暴露评估可能会削弱饮用水中FIB浓度对腹泻的影响估计，而单一水样可能会削弱FIB浓度对线性增长的健康影响估计。为了了解粪便污染暴露对健康的影响，可以使用测量误差框架来估计更多与生物学相关的暴露。