Leveraging infectious disease models to interpret randomized controlled trials: controlling enteric pathogen transmission through water, sanitation, and hygiene interventions

Abstract

AbstractRandomized controlled trials (RCTs), which evaluate hypotheses in specific contexts, are often considered the gold standard of evidence for infectious disease interventions, but their results cannot immediately generalize to other contexts. Mechanistic models are one approach to generalizing findings between contexts, but infectious disease transmission models are not immediately suited for analyzing RCTs, since they often rely on time-series surveillance data that is rarely collected by RCTs. We developed a modeling framework to explain the main outcome of an infectious disease RCT—relative risk—and applied it to a water, sanitation, and hygiene (WASH) RCT. This model can generalize the RCT results to other contexts and conditions. We developed this compartmental modeling framework to account for key WASH RCT factors: i) transmission across multiple environmental pathways, ii) multiple interventions applied individually and in combination, iii) adherence to interventions or preexisting conditions, and iv) the impact of individuals not enrolled in the study. We employed a hybrid sampling-importance resampling and estimation framework to obtain posterior estimates of mechanistic parameters and their uncertainties and illustrated our model using WASH Benefits Bangladesh RCT data (n=17,187). Our model reproduced reported diarrheal prevalence in this RCT. The baseline estimate of the basic reproduction number ℛ0 for the control arm (1.15, 95% CI: 1.09, 1.27) corresponded to an endemic prevalence of 13% (95% CI: 9–21%) in the absence of intervention or preexisting WASH conditions. No single pathway was likely able to sustain transmission: pathway-specific ℛ0s for water, fomites, and all other pathways were 0.49 (95% CI: 0.07, 0.99), 0.26 (95% CI: 0.04, 0.57), and 0.40 (95% CI: 0.02, 0.88), respectively. An infectious disease modeling approach to evaluating RCTs can complement RCT analysis by providing a rigorous framework for generating data-driven hypotheses that explain trial findings, particularly unexpected null results, opening up existing data to deeper epidemiological understanding.Author summaryA randomized controlled trial (RCT) testing an intervention to reduce infectious disease transmission can provide high-quality scientific evidence about the impact of that intervention in a specific context, but the results are often difficult to generalize to other policy-relevant contexts and conditions. Infectious disease transmission models can be used to explore what might happen to disease dynamics under different conditions, but the standard use of these models is to fit to longitudinal, surveillance data, which is rarely collected by RCTs. We developed a framework to fit an infectious disease model to steady-state diarrheal prevalence data in water, sanitation, and hygiene RCTs, explicitly accounting for completeness, coverage, and compliance. Although this framework is developed with water, sanitation, and hygiene interventions for enteropathogens in mind, it could be extended to other disease contexts. By leveraging existing large-scale RCT data sets, it will be possible to better understand the underlying disease epidemiology and investigate the likely outcomes of policy-relevant scenarios. Ultimately, this work can be incorporated into decision making for public health policy and programs.