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大规模治疗后疟疾感染的再现:一项模拟研究。

Resurgence of malaria infection after mass treatment: a simulation study.

机构信息

Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051, Basel, Switzerland.

University of Basel, Petersplatz 1, Basel, Switzerland.

出版信息

Malar J. 2019 Dec 5;18(1):409. doi: 10.1186/s12936-019-3019-0.

DOI:10.1186/s12936-019-3019-0
PMID:31805947
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6896478/
Abstract

BACKGROUND

Field studies are evaluating if mass drug administration (MDA) might shorten the time to elimination of Plasmodium falciparum malaria, when vector control measures and reactive surveillance strategies are scaled-up. A concern with this strategy is that there may be resurgence of transmission following MDA.

METHODS

A conceptual model was developed to classify possible outcomes of an initial period of MDA, followed by continuously implementing other interventions. The classification considered whether elimination or a new endemic stable state is achieved, and whether changes are rapid, transient, or gradual. These categories were informed by stability analyses of simple models of vector control, case management, and test-and-treat interventions. Individual-based stochastic models of malaria transmission (OpenMalaria) were then used to estimate the probability and likely rates of resurgence in realistic settings. Effects of concurrent interventions, including routine case management and test-and-treat strategies were investigated.

RESULTS

Analysis of the conceptual models suggest resurgence will occur after MDA unless transmission potential is very low, or the post-MDA prevalence falls below a threshold, which depends on both transmission potential and on the induction of bistability. Importation rates are important only when this threshold is very low. In most OpenMalaria simulations the approximately stable state achieved at the end of the simulations was independent of inclusion of MDA and the final state was unaffected by importation of infections at plausible rates. Elimination occurred only with high effective coverage of case management, low initial prevalence, and high intensity test-and-treat. High coverage of case management but not by test-and-treat induced bistability. Where resurgence occurred, its rate depended mainly on transmission potential (not treatment rates).

CONCLUSIONS

A short burst of high impact MDA is likely to be followed by resurgence. To avert resurgence, concomitant interventions need either to substantially reduce average transmission potential or to be differentially effective in averting or clearing infections at low prevalence. Case management at high effective coverage has this differential effect, and should suffice to avert resurgence caused by imported cases at plausible rates of importation. Once resurgence occurs, its rate depends mainly on transmission potential, not on treatment strategies.

摘要

背景

当扩大病媒控制措施和反应性监测策略时,现场研究正在评估大规模药物治疗(MDA)是否可以缩短消除恶性疟原虫疟疾的时间。该策略存在的一个担忧是,在 MDA 之后可能会出现传播的反弹。

方法

开发了一个概念模型来对 MDA 初始阶段之后连续实施其他干预措施的可能结果进行分类。该分类考虑了是否达到消除或新的地方性稳定状态,以及变化是快速、短暂还是渐进的。这些类别是基于对病媒控制、病例管理和检测治疗干预措施的简单模型的稳定性分析得出的。然后,使用疟疾传播的个体基随机模型(OpenMalaria)来估计在现实环境中反弹的概率和可能速度。同时还研究了包括常规病例管理和检测治疗策略在内的并发干预措施的效果。

结果

概念模型分析表明,除非传播潜力非常低,或者 MDA 后流行率低于取决于传播潜力和双稳态诱导的阈值,否则 MDA 后将发生反弹。只有当这个阈值非常低时,输入率才重要。在大多数 OpenMalaria 模拟中,模拟结束时达到的近似稳定状态与 MDA 的纳入无关,最终状态不受以合理速度输入感染的影响。只有在高覆盖率的病例管理、低初始流行率和高强度检测治疗下才会出现消除。高覆盖率的病例管理而不是检测治疗会引起双稳态。在发生反弹的情况下,其速度主要取决于传播潜力(而不是治疗率)。

结论

高强度 MDA 的短暂爆发很可能会导致反弹。为了避免反弹,伴随的干预措施要么需要大幅降低平均传播潜力,要么需要在低流行率下更有效地预防或清除感染。高覆盖率的病例管理具有这种差异效应,足以避免在合理输入率下由输入病例引起的反弹。一旦发生反弹,其速度主要取决于传播潜力,而不是治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/fc0fc0ab3cbd/12936_2019_3019_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/ccb8790db9fa/12936_2019_3019_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/1f0a38b8e30c/12936_2019_3019_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/9e662abda44d/12936_2019_3019_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/4a63bec651d4/12936_2019_3019_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/fc0fc0ab3cbd/12936_2019_3019_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/ccb8790db9fa/12936_2019_3019_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/0481b7ddc31c/12936_2019_3019_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/c5699a2d9bf1/12936_2019_3019_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/ac8244554f3f/12936_2019_3019_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/1f0a38b8e30c/12936_2019_3019_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/9e662abda44d/12936_2019_3019_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/4a63bec651d4/12936_2019_3019_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2b5/6896478/fc0fc0ab3cbd/12936_2019_3019_Fig8_HTML.jpg

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