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评估曼德拉区及其周边地区的模型疟疾消除策略:2008 年至 2020 年的时间序列分析。

Evaluation of the model malaria elimination strategy in Mandla district along with its neighbouring districts: a time series analysis from 2008 to 2020.

机构信息

Malaria Elimination Demonstration Project, Mandla, Madhya Pradesh, India.

Asia Pacific Leaders Malaria Alliance (APLMA), Singapore, Singapore.

出版信息

Malar J. 2023 Feb 6;22(1):45. doi: 10.1186/s12936-023-04477-7.

DOI:10.1186/s12936-023-04477-7
PMID:36747302
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9901400/
Abstract

BACKGROUND

Compared to 2017, India achieved a significant reduction in malaria cases in 2020. Madhya Pradesh (MP) is a tribal dominated state of India with history of high malaria burden in some districts. District Mandla of MP state showed a considerable decline in malaria cases between 2000 and 2013, except in 2007. Subsequently, a resurgence of malaria cases was observed during 2014 and 2015. The Malaria Elimination Demonstration Project (MEDP) was launched in 2017 in Mandla with the goal to achieve zero indigenous malaria cases. This project used: (1) active surveillance and case management using T4 (Track fever, Test fever, Treat patient, and Track patient); (2) vector control using indoor residual sprays and long-lasting insecticidal nets; (3) information education communication and behaviour change communication; and (4) regular monitoring and evaluation with an emphasis on operational and management accountability. This study has investigated malaria prevalence trends from 2008 to 2020, and has predicted trends for the next 5 years for Mandla and its bordering districts.

METHODS

The malaria prevalence data of the district Mandla for the period of January 2008 to August 2017 was obtained from District Malaria Office (DMO) Mandla and data for the period of September 2017 to December 2020 was taken from MEDP data repository. Further, the malaria prevalence data for the period of January 2008 to December 2020 was collected from DMOs of the neighbouring districts of Mandla. A univariate time series and forecast analysis was performed using seasonal autoregressive integrated moving average model.

FINDINGS

Malaria prevalence in Mandla showed a sharp decline [- 87% (95% CI - 90%, - 84%)] from 2017 to 2020. The malaria forecast for Mandla predicts zero cases in the next 5 years (2021-2025), provided current interventions are sustained. By contrast, the model has forecasted a risk of resurgence of malaria in other districts in MP (Balaghat, Dindori, Jabalpur, Seoni, and Kawardha) that were not the part of MEDP.

CONCLUSION

The interventions deployed as part of MEDP have resulted in a sustainable zero indigenous malaria cases in Mandla. Use of similar strategies in neighbouring and other malaria-endemic districts in India could achieve similar results. However, without adding extra cost to the existing intervention, sincere efforts are needed to sustain these interventions and their impact using accountability framework, data transparency, and programme ownership from state to district level.

摘要

背景

与 2017 年相比,印度 2020 年的疟疾病例显著减少。中央邦(MP)是印度一个以部落为主的邦,历史上部分地区疟疾负担沉重。MP 邦的曼德拉区在 2000 年至 2013 年间除 2007 年外,疟疾病例大幅减少。随后,2014 年和 2015 年疟疾病例再次出现。2017 年,曼德拉启动了消除疟疾示范项目(MEDP),目标是实现零本土疟疾病例。该项目采用了以下措施:(1)使用 T4(跟踪发热、检测发热、治疗患者和跟踪患者)进行主动监测和病例管理;(2)利用室内滞留喷雾和长效杀虫蚊帐进行病媒控制;(3)信息、教育和宣传以及行为改变交流;(4)定期监测和评估,重点是业务和管理问责制。本研究调查了 2008 年至 2020 年期间的疟疾流行趋势,并预测了曼德拉及其周边地区未来 5 年的趋势。

方法

从曼德拉区疟疾办公室(DMO)获得 2008 年 1 月至 2017 年 8 月期间曼德拉区的疟疾流行数据,从 MEDP 数据存储库获得 2017 年 9 月至 2020 年 12 月期间的数据。此外,还从曼德拉周边地区的 DMO 收集了 2008 年 1 月至 2020 年 12 月期间的疟疾流行数据。使用季节性自回归综合移动平均模型进行了单变量时间序列和预测分析。

结果

曼德拉的疟疾流行率从 2017 年到 2020 年急剧下降[-87%(95%CI-90%,-84%)]。曼德拉的疟疾预测显示,在未来 5 年内(2021-2025 年)将不会出现病例,前提是当前的干预措施得以维持。相比之下,该模型预测 MP 的其他地区(巴罗达、丁多里、贾巴尔普尔、塞奥尼和考瓦尔达)的疟疾有卷土重来的风险,这些地区都不是 MEDP 的一部分。

结论

作为 MEDP 一部分部署的干预措施,使曼德拉实现了可持续的零本土疟疾病例。在印度的邻近地区和其他疟疾流行地区使用类似的策略,可以取得类似的结果。然而,在不增加现有干预措施额外成本的情况下,需要从州到区一级,通过问责制框架、数据透明度和方案所有权,真诚地努力维持这些干预措施及其影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/794075c29782/12936_2023_4477_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/363bec0cd07d/12936_2023_4477_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/0a9aca8c4000/12936_2023_4477_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/6d4b8309b688/12936_2023_4477_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/794075c29782/12936_2023_4477_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/363bec0cd07d/12936_2023_4477_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/0a9aca8c4000/12936_2023_4477_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/57eba618e7a4/12936_2023_4477_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/6d4b8309b688/12936_2023_4477_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/443b/9903477/794075c29782/12936_2023_4477_Fig5_HTML.jpg

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