Supplementary file 1_Integrating forest data and health facility surveys to optimise risk-based malaria surveillance in the Philippines.docx

IntroductionMalaria transmission is highly spatially heterogeneous. Within Southeast Asia, forested landscapes are associated both with increased malaria transmission and reduced healthcare access. Identifying environments with malaria foci is a priority for control and elimination programmes.
MethodsHere, we integrate health facility and environmental data to identify optimal surveillance approaches across a forested district in the Philippines. We conducted convenience surveys of health facility attendees utilising tablet-based applications to geolocate participant residences. Malaria infection was assessed using both routine (microscopy and rapid diagnostic test) and molecular methods. Integrating remote-sensing derived data, we assessed how fine-scale environmental factors influence the spatial distributions of malaria infections, diagnostic sensitivity and health-seeking behavior. We evaluated costs and probability of detecting malaria foci for multiple surveillance approaches using different diagnostic methods and target populations defined by landscape data.
ResultsWe demonstrate that health facility-based surveys increase the probability of detecting malaria infections by increasing numbers of individuals screened and spatial coverage of surveillance systems. We additionally show sensitivity of routine malaria diagnostics varies spatially, with the decreased sensitivity in forests. By targeting diagnostic methods to high-risk environments, we developed a model approach for how to use landscape data within disease surveillance systems. Risk-based surveillance incorporating forest data is highly cost-effective and increases the probability of detecting malaria foci over three-fold compared to routine surveillance.
DiscussionTogether, this illustrates the essential role of environmental data in designing risk-based surveillance to provide an operationally feasible and cost-effective method to characterise malaria transmission.

引言
疟疾传播具有高度的空间异质性。在东南亚区域内，森林覆盖景观既与疟疾传播风险升高相关，也会导致医疗服务可及性下降。识别存在疟疾疫点的环境区域，是疟疾防控与消除计划的首要任务。

研究方法
本研究整合医疗机构（health facility）数据与环境数据，旨在确定菲律宾某森林覆盖县区的最优疟疾监测方案。本研究针对医疗机构就诊者开展便利抽样调查，借助基于平板电脑的应用程序对受访者的居住地址进行地理定位。研究采用常规检测手段：显微镜检查（microscopy）与快速诊断试验（rapid diagnostic test），以及分子生物学方法对疟疾感染情况进行评估。本研究整合遥感衍生数据（remote-sensing derived data），探究精细尺度下的环境因素如何影响疟疾感染的空间分布、诊断敏感性以及就医行为。本研究针对多种监测方案，结合不同诊断方法与基于景观数据划定的目标人群，评估了各类方案的实施成本以及发现疟疾疫点的概率。

研究结果
本研究证实，基于医疗机构的调查可通过增加筛查人数与监测系统的空间覆盖范围，提升疟疾感染的检出概率。此外，本研究发现常规疟疾诊断方法的敏感性存在空间差异，在森林区域内敏感性显著降低。通过将诊断方法针对性应用于高风险环境，本研究构建了一套在疾病监测系统中应用景观数据的模型框架。整合森林景观数据的风险导向型监测（risk-based surveillance）方案具有极高的成本效益，相较于常规监测方案，其发现疟疾疫点的概率提升了三倍以上。

讨论
综上，本研究结果阐明了环境数据在设计风险导向型监测方案中的核心作用，可为疟疾传播特征的刻画提供一种兼具操作可行性与成本效益的技术路径。