Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.
Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration.
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DOI：
10.1073/pnas.2315463120
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发表时间：
2024-01-09
影响因子：
11.1
通讯作者：
Lo, Nathan C.
中科院分区：
文献类型：
--
作者：
Singer, Benjamin J.;Coulibaly, Jean T.;Park, Hailey J.;Andrews, Jason R.;Bogoch, Isaac I.;Lo, Nathan C.
血吸虫病是一种寄生虫病，全球感染人数超过1.5亿，高传播热点对消除工作构成关键挑战。本研究利用流行病学、环境、人口统计学和健康数据，开发统计模型，在大规模治疗行动之前预测血吸虫属传播热点的三种常见定义。这些模型使用基于治疗开始4年后的患病率或感染强度的热点定义来预测热点，具有中等准确性，但基于患病率随时间的相对变化的热点定义进行预测时准确性较低。这些模型在进一步验证后，可能在未来用于确定高风险社区的优先级，以便更频繁地进行监测、治疗和螺类控制。
血吸虫病是一种被忽视的热带疾病，影响超过1.5亿人。血吸虫传播热点——即大规模药物治疗后感染率没有充分下降的社区——是消除血吸虫病的关键挑战。目前识别热点的方法需要在基线调查和随后的大规模药物治疗2 - 5年后进行评估。在此，我们利用流行病学、调查和遥感数据，通过比较三种常见的热点定义，开发统计模型在治疗前的基线水平预测热点。在对五个流行国家的589个社区的随机试验的重新分析中，一个回归模型预测曼氏血吸虫感染率在第5年是否会超过世界卫生组织10%的阈值（“患病率热点”），其敏感度为86%，特异度为74%，阴性预测值（NPV）为93%（假设热点患病率为30%），一个埃及血吸虫的回归模型达到90%的敏感度、90%的特异度和96%的NPV。一个随机森林模型预测曼氏血吸虫中重度感染率在第5年是否会超过1%的公共卫生目标（“强度热点”），其敏感度为92%，特异度为79%，NPV为96%，一个埃及血吸虫的提升树模型达到77%的敏感度、95%的特异度和91%的NPV。基线患病率是所有模型中的首要预测因子。在训练数据中未涉及的国家以及基于患病率随时间相对降低的第三种热点定义（“持续热点”）的预测准确性较低。这些模型可能是一种工具，用于确定高风险社区的优先级，以便更频繁地对血吸虫病进行监测或干预，但热点的预测仍然是一个挑战。
Schistosomiasis is a parasitic disease infecting over 150 million people worldwide, with hotspots of high transmission posing a key challenge to elimination efforts. This study develops statistical models to predict three common definitions of hotspots of Schistosoma spp. transmission prior to mass treatment campaigns, using epidemiologic, environmental, demographic, and health data. The models predict hotspots with moderate accuracy using a hotspot definition based on prevalence or infection intensity 4 y after treatment initiation but perform less accurately on a hotspot definition based on relative change in prevalence over time. These models, after further validation, may have a future role to prioritize high-risk communities for more frequent surveillance, treatment, and snail control. Schistosomiasis is a neglected tropical disease affecting over 150 million people. Hotspots of Schistosoma transmission—communities where infection prevalence does not decline adequately with mass drug administration—present a key challenge in eliminating schistosomiasis. Current approaches to identify hotspots require evaluation 2–5 y after a baseline survey and subsequent mass drug administration. Here, we develop statistical models to predict hotspots at baseline prior to treatment comparing three common hotspot definitions, using epidemiologic, survey-based, and remote sensing data. In a reanalysis of randomized trials in 589 communities in five endemic countries, a regression model predicts whether Schistosoma mansoni infection prevalence will exceed the WHO threshold of 10% in year 5 (“prevalence hotspot”) with 86% sensitivity, 74% specificity, and 93% negative predictive value (NPV; assuming 30% hotspot prevalence), and a regression model for Schistosoma haematobium achieves 90% sensitivity, 90% specificity, and 96% NPV. A random forest model predicts whether S. mansoni moderate and heavy infection prevalence will exceed a public health goal of 1% in year 5 (“intensity hotspot”) with 92% sensitivity, 79% specificity, and 96% NPV, and a boosted trees model for S. haematobium achieves 77% sensitivity, 95% specificity, and 91% NPV. Baseline prevalence is a top predictor in all models. Prediction is less accurate in countries not represented in training data and for a third hotspot definition based on relative prevalence reduction over time (“persistent hotspot”). These models may be a tool to prioritize high-risk communities for more frequent surveillance or intervention against schistosomiasis, but prediction of hotspots remains a challenge.
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影响因子：
3.8
作者：
Assaré RK;Tian-Bi YN;Yao PK;N'Guessan NA;Ouattara M;Yapi A;Coulibaly JT;Meïté A;Hürlimann E;Knopp S;Utzinger J;N'Goran EK
通讯作者：
N'Goran EK
影响因子：
7.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
3.8
作者：
King CH;Sutherland LJ;Bertsch D
通讯作者：
Bertsch D
DOI：
10.1002/joc.5086
发表时间：
2017-10-01
期刊：
INTERNATIONAL JOURNAL OF CLIMATOLOGY
影响因子：
--
作者：
Fick, Stephen E.;Hijmans, Robert J.
通讯作者：
Hijmans, Robert J.
影响因子：
56.3
作者：
Gray, Darren J.;McManus, Donald P.;Ross, Allen G.
通讯作者：
Ross, Allen G.