School of Forest Resources and Conservation, University of Florida, Gainesville, USA.
Emerging Pathogens Institute, University of Florida, Gainesville, USA.
BMC Med. 2020 Jun 19;18(1):149. doi: 10.1186/s12916-020-01609-7.
Mass drug administration and mass-screen-and-treat interventions have been used to interrupt malaria transmission and reduce burden in sub-Saharan Africa. Determining which strategy will reduce costs is an important challenge for implementers; however, model-based simulations and field studies have yet to develop consensus guidelines. Moreover, there is often no way for decision-makers to directly interact with these data and/or models, incorporate local knowledge and expertise, and re-fit parameters to guide their specific goals.
We propose a general framework for comparing costs associated with mass drug administrations and mass screen and treat based on the possible outcomes of each intervention and the costs associated with each outcome. We then used publicly available data from six countries in western Africa to develop spatial-explicit probabilistic models to estimate intervention costs based on baseline malaria prevalence, diagnostic performance, and sociodemographic factors (age and urbanicity). In addition to comparing specific scenarios, we also develop interactive web applications which allow managers to select data sources and model parameters, and directly input their own cost values.
The regional-level models revealed substantial spatial heterogeneity in malaria prevalence and diagnostic test sensitivity and specificity, indicating that a "one-size-fits-all" approach is unlikely to maximize resource allocation. For instance, urban communities in Burkina Faso typically had lower prevalence rates compared to rural communities (0.151 versus 0.383, respectively) as well as lower diagnostic sensitivity (0.699 versus 0.862, respectively); however, there was still substantial regional variation. Adjusting the cost associated with false negative diagnostic results to included additional costs, such as delayed treated and potential lost wages, undermined the overall costs associated with MSAT.
The observed spatial variability and dependence on specified cost values support not only the need for location-specific intervention approaches but also the need to move beyond standard modeling approaches and towards interactive tools which allow implementers to engage directly with data and models. We believe that the framework demonstrated in this article will help connect modeling efforts and stakeholders in order to promote data-driven decision-making for the effective management of malaria, as well as other diseases.
大规模药物干预和大规模筛查与治疗干预措施已被用于阻断疟疾传播并降低撒哈拉以南非洲地区的疾病负担。确定哪种策略可以降低成本是实施者面临的一项重要挑战；然而，基于模型的模拟和现场研究尚未制定出共识指南。此外，决策者通常无法直接与这些数据和/或模型进行交互，结合当地的知识和专长，并重新拟合参数以指导其具体目标。
我们提出了一个基于每种干预措施可能结果和每种结果相关成本的比较大规模药物干预和大规模筛查与治疗成本的通用框架。然后，我们使用来自西非六个国家的公开数据，开发了空间明确的概率模型，根据基线疟疾流行率、诊断性能和社会人口因素（年龄和城市）来估计干预成本。除了比较特定情景外，我们还开发了互动式网络应用程序，允许管理者选择数据源和模型参数，并直接输入自己的成本值。
区域水平模型揭示了疟疾流行率和诊断测试灵敏度和特异性的巨大空间异质性，表明“一刀切”的方法不太可能最大限度地分配资源。例如，布基纳法索的城市社区通常比农村社区的流行率低（分别为 0.151 和 0.383），诊断灵敏度也较低（分别为 0.699 和 0.862）；然而，仍然存在很大的区域差异。调整与假阴性诊断结果相关的成本，包括额外的成本，如延迟治疗和潜在的工资损失，降低了与 MSAT 相关的总成本。
观察到的空间变异性和对特定成本值的依赖不仅支持需要针对特定地点的干预措施，还需要超越标准建模方法，转向允许实施者直接与数据和模型交互的互动工具。我们认为，本文展示的框架将有助于将建模工作与利益相关者联系起来，以促进基于数据的决策，从而有效管理疟疾以及其他疾病。
