Brouwer Andrew F, Kraay Alicia N M, Zahid Mondal H, Eisenberg Marisa C, Freeman Matthew C, Eisenberg Joseph N S
Department of Epidemiology, University of Michigan, Michigan, USA.
Institute for Disease Modeling, a Program Within the Global Health Division of the Bill and Melinda Gates Foundation, Seattle, WA, USA.
Infect Dis Model. 2025 Feb 3;10(2):649-659. doi: 10.1016/j.idm.2025.02.002. eCollection 2025 Jun.
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.
腹泻病是幼儿发病和死亡的主要原因。改善水、环境卫生和个人卫生（WASH）历来对公共卫生的重大改善起到了推动作用，但许多单项干预措施未能持续减轻腹泻病负担。能够估计在特定背景下单项WASH改善措施潜在影响的分析工具，将有助于项目管理人员和政策制定者设定能够带来健康改善的目标。我们开发了一种疾病传播模型，以模拟一项针对单一干预措施的干预试验。我们考虑了背景因素，包括现有的WASH条件和基线疾病患病率，以及干预性WASH因素，包括社区覆盖率、依从性、效果以及可干预的传播比例。我们在两种看似合理的疾病传播情景中，展示了干预效果对背景和干预因素的敏感性，这两种情景具有相同的疾病传播潜力和干预效果，但基线疾病负担以及背景/干预因素不同。能否通过单一因素实现疾病消除，取决于其他因素的值，因此在一种情景中能够实现疾病消除的变化，在另一种情景中可能无效。社区覆盖率与背景因素和干预因素都有很强的相互作用。例如，随着干预依从性的提高，增加干预社区覆盖率的积极影响呈非线性增加。在情景1中基线疾病患病率较低（还有其他差异），我们的模型预测，相对较低的覆盖率就能实现大幅降低。相比之下，在情景2中，基线疾病患病率较高，需要高覆盖率和高依从性才能实现强大的干预效果。在制定干预措施时，考虑背景条件和干预参数都很重要。我们的机制建模方法可为制定因地制宜的政策建议提供指导。
