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标题：Review of Epidemiological Studies of Drinking-Water Turbidity in Relation to Acute Gastrointestinal Illness
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作者：Anneclaire J. De Roos ; Patrick L. Gurian ; Lucy F. Robinson 等
期刊名称：Environmental Health Perspectives
印刷版ISSN：0091-6765
电子版ISSN：1552-9924
出版年度：2017
卷号：125
期号：8
页码：086003
DOI：10.1289/EHP1090
语种：English
出版社：OCR Subscription Services Inc
摘要：Background: Turbidity has been used as an indicator of microbiological contamination of drinking water in time-series studies attempting to discern the presence of waterborne gastrointestinal illness; however, the utility of turbidity as a proxy exposure measure has been questioned. Objectives: We conducted a review of epidemiological studies of the association between turbidity of drinking-water supplies and incidence of acute gastrointestinal illness (AGI), including a synthesis of the overall weight of evidence. Our goal was to evaluate the potential for causal inference from the studies. Methods: We identified 14 studies on the topic (distinct by region, time period and/or population). We evaluated each study with regard to modeling approaches, potential biases, and the strength of evidence. We also considered consistencies and differences in the collective results. Discussion: Positive associations between drinking-water turbidity and AGI incidence were found in different cities and time periods, and with both unfiltered and filtered supplies. There was some evidence for a stronger association at higher turbidity levels. The studies appeared to adequately adjust for confounding. There was fair consistency in the notable lags between turbidity measurement and AGI identification, which fell between 6 and 10 d in many studies. Conclusions: The observed associations suggest a detectable incidence of waterborne AGI from drinking water in the systems and time periods studied. However, some discrepant results indicate that the association may be context specific. Combining turbidity with seasonal and climatic factors, additional water quality measures, and treatment data may enhance predictive modeling in future studies. https://doi.org/10.1289/EHP1090