Code used in data analysis.

Microbial contamination of drinking water contributes to disease burdens that disproportionately impact infants and children and are largely preventable through suitable design, operation, monitoring, and management of improved water systems. The World Health Organization (WHO) has published guidance on water safety planning, water quality monitoring, and management approaches, including recommendations on sanitary inspection (SI) of water systems to detect and manage microbial hazards associated with fecal contamination. SI is a low-cost risk assessment tool for water systems based on observable risk factors (RFs) associated with potential water safety hazards. While SI has been previously studied, much of the literature has not quantitatively explored rainfall interactions with SI risk as drivers of fecal contamination. We merged remote-sensing rainfall estimates with SI and water quality data collected from 966 rural boreholes in Ethiopia, Ghana, and Burkina Faso. Logistic regressions (binary and ordinal) were used to characterize associations of total SI score, as well as individual risk factors (RFs), and classes of RFs (i.e., “Source,” “Transport,” and “Barrier” risks) with fecal indicator bacteria (FIB) occurrence, controlling for rainfall (over the past 1–15 days before sampling). We found associations (P E. coli risk categories controlling for fifteen-day total rainfall. Furthermore, interactions between rainfall and risk factors in the “barrier” category, and the “transport” category were associated with E. coli occurrence. Several individual RFs were also significantly associated with microbial contamination. Incorporating precipitation into models improved model fit characteristics (improved Pseudo R squared and AIC value); specifically, accounting for cumulative rainfall during the fifteen days before sampling improved model fit (increased pseudo-R2 from 0.035 to 0.05) for E. coli contamination. These findings can inform design, construction, maintenance, and monitoring of boreholes and prompt timely remediation of defects in such systems, potentially enhancing water safety.

饮用水微生物污染可引发疾病负担，该负担对婴幼儿群体的影响尤为突出，而通过对改良供水系统开展合理设计、规范运维、常态化监测与精细化管理，此类污染在很大程度上是可以预防的。世界卫生组织（World Health Organization, WHO）发布了水安全规划、水质监测与管理方法相关指南，其中包含针对供水系统卫生检查（sanitary inspection, SI）的建议，用于识别并管控与粪便污染相关的微生物危害。卫生检查是一种低成本的供水系统风险评估工具，其基于与潜在水安全危害相关的可观测风险因素（risk factors, RFs）。尽管此前已有针对卫生检查的相关研究，但现有文献大多未定量探究降雨与卫生检查风险的交互作用作为粪便污染驱动因素的影响。我们将遥感降雨估算数据，与从埃塞俄比亚、加纳、布基纳法索的966口农村钻孔水井（boreholes）中收集的卫生检查及水质数据进行整合。采用逻辑回归（logistic regressions）模型，包括二元逻辑回归与有序逻辑回归，以采样前1至15天的降雨作为控制变量，分析总卫生检查得分、单项风险因素以及风险因素类别（即“来源（Source）”、“运输（Transport）”与“屏障（Barrier）”风险）与粪便指示菌（fecal indicator bacteria, FIB）检出情况的关联。我们发现，在控制15天总降雨量的条件下，总卫生检查得分与大肠杆菌（E. coli）风险类别之间存在显著关联（P < 0.05）。此外，“屏障”类别与“运输”类别中的风险因素与降雨的交互作用，与大肠杆菌的检出情况存在关联。多项单项风险因素也与微生物污染存在显著关联。将降雨数据纳入模型后，模型拟合性能得到改善，具体表现为伪决定系数（Pseudo R squared）与赤池信息准则（Akaike Information Criterion, AIC）均得到优化；其中，纳入采样前15天的累积降雨量，可使大肠杆菌污染模型的拟合效果显著提升，伪决定系数从0.035升至0.05。上述研究结果可为钻孔水井的设计、建造、运维与监测提供决策参考，并推动此类系统缺陷的及时修复，有望进一步提升供水安全性。