Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Department of Civil and Environmental Engineering, School of Engineering, Tufts University, Medford, MA 02155, USA.
Int J Environ Res Public Health. 2021 Feb 28;18(5):2353. doi: 10.3390/ijerph18052353.
Fecal indicator bacteria (FIB) values are widely used to assess microbial contamination in drinking water and to advance the modeling of infectious disease risks. The membrane filtration (MF) testing technique for FIB is widely adapted for use in low- and middle-income countries (LMICs). We conducted a systematic literature review on the use of MF-based FIB data in LMICs and summarized statistical methods from 172 articles. We then applied the commonly used statistical methods from the review on publicly available datasets to illustrate how data analysis methods affect FIB results and interpretation. Our findings indicate that standard methods for processing samples are not widely reported, the selection of statistical tests is rarely justified, and, depending on the application, statistical methods can change risk perception and present misleading results. These results raise concerns about the validity of FIB data collection, analysis, and presentation in LMICs. To improve evidence quality, we propose a FIB data reporting checklist to use as a reminder for researchers and practitioners.
粪便指示菌(FIB)值被广泛用于评估饮用水中的微生物污染,并推进传染病风险模型的建立。用于 FIB 的膜过滤(MF)检测技术已广泛适用于低收入和中等收入国家(LMICs)。我们对基于 MF 的 FIB 数据在 LMICs 中的使用进行了系统的文献综述,并从 172 篇文章中总结了统计方法。然后,我们将综述中常用的统计方法应用于公开可用的数据集,以说明数据分析方法如何影响 FIB 结果和解释。我们的研究结果表明,处理样本的标准方法并未广泛报告,统计检验的选择很少有充分的理由,而且根据应用的不同,统计方法可能会改变风险感知并呈现误导性结果。这些结果引起了人们对 LMICs 中 FIB 数据收集、分析和呈现的有效性的关注。为了提高证据质量,我们提出了一个 FIB 数据报告清单,供研究人员和从业者使用,以作为提醒。