Improving the effectiveness of water, sanitation, and handwashing interventions: a simulation approach to generalizing the outcomes of intervention trials

Background Recent large-scale trials have revealed that interventions improving water, sanitation, and hygiene (WASH) in low-income settings may not confer the expected health gains for young children; evidence-based guidance is needed to inform programs and future studies. Objectives We aimed to generalize the results of the WASH Benefits Bangladesh randomized controlled trial to other contexts or scenarios using a mechanistic disease transmission model and counterfactual simulations. Methods A disease transmission model was developed to account for transmission across multiple environmental pathways, multiple interventions (water (W), sanitation (S), hygiene (H), nutrition (N)) applied individually and in combination, adherence to interventions, and the impact of individuals not enrolled in the study. Leveraging a set of mechanistic parameter combinations fit to the WASH Benefits Bangladesh trial (n=17,187) using a Bayesian sampling approach, we simulated trial outcomes under counterfactual scenarios to estimate how changes in intervention completeness, coverage, fidelity and adherence, and efficacy, as well as baseline WASH conditions and disease burden, impacted intervention effectiveness. Results Increasing community coverage was associated with the greatest impact on intervention effectiveness (e.g., 26·9% and 52·7% median increases in effectiveness in the WSH and WSHN intervention arms when increasing coverage to 20%). The effect of community coverage on effectiveness depended on intervention completeness, i.e., the fraction of transmission that was along pathways modified by the interventions. Intervention effectiveness was reduced in counterfactual simulations with lower levels of preexisting WASH conditions or increased baseline disease burden. Individual interventions had complementary but not synergistic effects when combined. Discussion Next-generation WASH programs must address coverage and completeness and account for the fact that effect of individual-level WASH improvements will be blunted the further the community is from achieving herd protection. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Protocols ### Funding Statement This work was funded by the Bill & Melinda Gates Foundation (grant INV-005081) and the National Science Foundation (grant DMS-1853032). The original WASH-B Bangladesh trial was also funded by the Bill & Melinda Gates Foundation (grant OPPGD759). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study only used openly available human data that were originally located at: https://osf.io/tprw2/ I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The WASH Benefits Bangladesh data is publicly available at https://osf.io/tprw2/. The data and code underlying the results of this paper are available on a GitHub repository at https://github.com/afbrouwer/WASH\_RCT\_transmission_modeling. [https://github.com/afbrouwer/WASH\_RCT\_transmission_modeling][1] [1]: https://github.com/afbrouwer/WASH_RCT_transmission_modeling