背景:泰国的目标是到2024年消除疟疾,因此正在规划未来预防在无疟疾省份重建。了解当地地区对疟疾的接受度,可以适当地确定干预措施的目标。当前评估接受性的方法涉及收集昆虫学数据。众所周知,森林覆盖率与疟疾风险有关,作为有利于病媒繁殖和高风险人类行为的环境。
方法:地理定位,匿名,泰国病媒传播病(DVBD)部门2011年至2021年的个体层面监测数据用于计算村一级的发病率和Rc估计值.森林覆盖率是使用公开可用的Hansen数据集中的树冠覆盖密度和森林损失年份的栅格图计算的。发病率和森林覆盖率进行了图形比较,并使用Spearman的rho。当前的疫源地分类系统已应用于过去5年(2017-2021年)的数据以及2021年的森林覆盖率,这些分类之间的比较。开发了一个简单的风险评分来识别具有高接受度的村庄。
结果:从2011年到2021年,两个省的年度病例非线性下降了96.6%(1061至36)。在森林茂密的分区的村庄中,土著年度寄生虫指数(API)和近似Rc较高,5公里内森林覆盖率较高。每年持续报告疟疾病例的疟疾疫源地的森林覆盖率也高于未报告的疫源地。仅在森林覆盖率>25%的分区的村庄中报告了Rc>1。当使用森林覆盖率和最近的病例史应用简单的风险评分时,这些分类与DVBD目前使用的风险分层系统的分类相当.
结论:村庄周围的森林覆盖率与本地疟疾病例之间存在正相关关系。大多数本地传播是在与老挝和柬埔寨接壤的国际边界上森林茂密的分区中观察到的,这是最容易接受的村庄所在的地方。由于人口流动和森林活动,这些地区输入疟疾的风险更大。将森林覆盖率和最近的病例监测数据与脆弱性度量相结合,可能有助于预测疟疾复发风险。
BACKGROUND: Thailand aimed to eliminate malaria by 2024, and as such is planning for future prevention of re-establishment in malaria free provinces. Understanding the receptivity of local areas to malaria allows the appropriate targeting of interventions. Current approaches to assessing receptivity involve collecting entomological data.
Forest coverage is known to be associated with malaria risk, as an environment conducive to both vector breeding and high-risk human behaviours.
METHODS: Geolocated, anonymized, individual-level surveillance data from 2011 to 2021 from the Thai Division of Vector-Borne Disease (DVBD) was used to calculate incidence and estimated Rc at village level.
Forest cover was calculated using raster maps of tree crown cover density and year of
forest loss from the publicly available Hansen dataset. Incidence and
forest cover were compared graphically and using Spearman\'s rho. The current foci classification system was applied to data from the last 5 years (2017-2021) and forest cover for 2021 compared between the classifications. A simple risk score was developed to identify villages with high receptivity.
RESULTS: There was a non-linear decrease in annual cases by 96.6% (1061 to 36) across the two provinces from 2011 to 2021. Indigenous Annual Parasite Index (API) and approximated Rc were higher in villages in highly forested subdistricts, and with higher forest cover within 5 km.
Forest cover was also higher in malaria foci which consistently reported malaria cases each year than those which did not. An Rc > 1 was only reported in villages in subdistricts with > 25%
forest cover. When applying a simple risk score using forest cover and recent case history, the classifications were comparable to those of the risk stratification system currently used by the DVBD.
CONCLUSIONS: There was a positive association between forest coverage around a village and indigenous malaria cases. Most local transmission was observed in the heavily forested subdistricts on the international borders with Laos and Cambodia, which are where the most receptive villages are located. These areas are at greater risk of importation of malaria due to population mobility and forest-going activities. Combining forest cover and recent case surveillance data with measures of vulnerability may be useful for prediction of malaria recurrence risk.