Linking Quantitative Microbial Risk Assessment and Epidemiological Data: Informing Safe Drinking Water Trials in Developing Countries Kyle S. Enger, † Kara L. Nelson, ‡ Thomas Clasen, § Joan B. Rose, † and Joseph N. S. Eisenberg* ,∥ † Department of Fisheries and Wildlife, 13 Natural Resources Building, Michigan State University, East Lansing, Michigan, 48824, United States ‡ Department of Civil and Environmental Engineering, 760 Davis Hall, University of California, Berkeley, California, 94720, United States § Department of Disease Control, London School of Hygiene & Tropical Medicine, North Courtyard, Third Floor, Keppel Street, University of London, London, WC1E 7HT, United Kingdom ∥ Department of Epidemiology, University of Michigan School of Public Health, M5065 SPH II, 1415 Washington Heights, Ann Arbor, Michigan, 48109, United States * S Supporting Information ABSTRACT: Intervention trials are used extensively to assess household water treatment (HWT) device efficacy against diarrheal disease in developing countries. Using these data for policy, however, requires addressing issues of generalizability (relevance of one trial in other contexts) and systematic bias associated with design and conduct of a study. To illustrate how quantitative microbial risk assessment (QMRA) can address water safety and health issues, we analyzed a published randomized controlled trial (RCT) of the LifeStraw Family Filter in the Congo. The model accounted for bias due to (1) incomplete compliance with filtration, (2) unexpected antimicrobial activity by the placebo device, and (3) incomplete recall of diarrheal disease. Effectiveness was measured using the longitudinal prevalence ratio (LPR) of reported diarrhea. The Congo RCT observed an LPR of 0.84 (95% CI: 0.61, 1.14). Our model predicted LPRs, assuming a perfect placebo, ranging from 0.50 (2.5− 97.5 percentile: 0.33, 0.77) to 0.86 (2.5−97.5 percentile: 0.68, 1.09) for high (but not perfect) and low (but not zero) compliance, respectively. The calibration step provided estimates of the concentrations of three pathogen types (modeled as diarrheagenic E. coli, Giardia, and rotavirus) in drinking water, consistent with the longitudinal prevalence of reported diarrhea measured in the trial, and constrained by epidemiological data from the trial. Use of a QMRA model demonstrated the importance of compliance in HWT efficacy, the need for pathogen data from source waters, the effect of quantifying biases associated with epidemiological data, and the usefulness of generalizing the effectiveness of HWT trials to other contexts. ■ INTRODUCTION Diarrhea is a major cause of infectious disease mortality, accounting for 17% of deaths in children under 5 years of age; only pneumonia accounts for a similarly high share of mortality in this age group. 1 Diarrheal mortality has declined from approximately 5 million in 1980 2 to 2 million in 2000 and 2004. 3,4 However, the incidence of diarrhea has remained at 2− 3 episodes per child-year from 1980 to 2000. 2,5,6 Contaminated drinking water is an important route of transmission for diarrheal pathogens. Recent reviews indicate that household water treatment (HWT) interventions, which can improve microbiological quality at the point of use, can be more protective against diarrhea than interventions at the water source in the developing world. 7−10 HWT addresses not only contamination of the source water, but also recontamination during collection, transport, and storage in the home. 11 The long-term sustainability and scalability of HWT remain important issues of discussion. The randomized controlled trial (RCT) is considered the gold standard study design in epidemiology; it is the study design with the least systematic bias, and therefore the highest internal validity. Two important components of RCT design for internal validity are the randomization of subjects to the intervention and the nonintervention groups, and blinding of the subject and investigator to group assignment. It is difficult to blind HWT interventions because these devices are visually obvious and cannot be concealed from participants or investigators. It is also difficult to develop a placebo HWT filter that does not remove pathogens, but improves the appearance of water like an effective filter. 12 Other biases may also affect the internal validity of an estimate derived from the Received: December 10, 2011 Revised: April 3, 2012 Accepted: April 9, 2012 Published: April 9, 2012 Article pubs.acs.org/est © 2012 American Chemical Society 5160 dx.doi.org/10.1021/es204381e | Environ. Sci. Technol. 2012, 46, 5160−5167