A mechanistic modeling approach to assessing the sensitivity of outcomes of water, sanitation, and hygiene interventions to local contexts and intervention factors

Background: Diarrheal disease is a leading cause of morbidity and mortality in young children. Water, sanitation, and hygiene (WASH) improvements have historically been responsible for major public health gains by reducing exposure to enteropathogens, but many individual interventions have failed to consistently reduce diarrheal disease burden. Analytical tools that can estimate the potential impacts of individual WASH improvements in specific contexts would support program managers and policymakers to set targets that would yield health gains. Methods: To understand the impact of WASH improvements on diarrhea, we developed a disease transmission model to simulate an intervention trial with a single intervention. We accounted for contextual factors, including preexisting WASH conditions and baseline disease prevalence, as well as intervention WASH factors, including community coverage, compliance, efficacy, and the intervenable fraction of transmission. We illustrated the sensitivity of intervention effectiveness to the contextual and intervention factors in each of two scenarios in which a 50% reduction in disease was achieved through a different combination of factors (higher preexisting WASH conditions, compliance, and intervenable fraction vs higher intervention efficacy and community coverage). Results: Achieving disease elimination depended on more than one factor, and factors that could be used to achieve disease elimination in one scenario could be ineffective in the other scenario. Community coverage interacted strongly with both the contextual and intervention factors. For example, the positive impact of increasing intervention community coverage increased non-linearly with increasing intervention compliance. Additionally, counterfactually improving the contextual preexisting WASH conditions could have a positive or negative effect on the intervention effectiveness, depending on the values of other factors. Conclusions: When developing interventions, it is important to account for both contextual conditions and the intervention parameters. Our mechanistic modeling approach can provide guidance for developing locally specific policy recommendations. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by the Bill & Melinda Gates Foundation (grant INV-005081) and the National Science Foundation (grant DMS-1853032). Study sponsors had no role in the study design, the analysis or interpretation of the results, the writing of the report, or in the decision to submit the paper for publication. ANMK's contributions were directly funded by the Bill and Melinda Gates Foundation and not as part of the foundation grant to the authors. ANMK is an employee of the Bill and Melinda Gates Foundation. However, this study does not necessarily represent the views of the Bill and Melinda Gates Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes 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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes No data are associated with this article. The code is included as supplemental material. The SISE-RCT web app with the single-intervention model is available at https://umich-biostatistics.shinyapps.io/sise_rct/.