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

TL;DR

This paper uses a disease transmission model to show how the effectiveness of water, sanitation, and hygiene interventions depends on local conditions and intervention parameters.

Contribution

The study introduces a mechanistic modeling approach to assess how WASH interventions' outcomes vary with local contexts and intervention factors.

Findings

Intervention effectiveness is highly sensitive to baseline disease prevalence and contextual factors.

Community coverage interacts strongly with compliance and efficacy to determine health outcomes.

Low coverage can be effective in low-prevalence settings, but high coverage is needed in high-prevalence areas.

Abstract

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, 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. 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 plausible disease transmission scenarios with the same disease transmission potential and intervention effectiveness but differing baseline disease burden and contextual/intervention factors. Whether disease elimination could be achieved through a single factor depended on the values of the other factors, so that changes that could achieve disease elimination in one scenario could be ineffective in the other scenario. Community coverage interacted strongly with both the contextual and the intervention factors. For example, the positive impact of increasing intervention community coverage increased non-linearly with increasing intervention compliance. With lower baseline disease prevalence in Scenario 1 (among other differences), our models predicted substantial reductions could be achieved with relatively low coverage. In contrast, in Scenario 2, where baseline disease prevalence was higher, high coverage and compliance were necessary to achieve strong intervention effectiveness. 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.

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