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新冠疫情对赞比亚疟疾病例的影响:一项混合效应多层分析。

The effect of COVID-19 on malaria cases in Zambia: a mixed effect multilevel analysis.

机构信息

School of Public Health, Department of Epidemiology and Biostatistics, The University of Zambia, University of Zambia, Lusaka, Zambia.

Southern African Institute for Collaborative Research and Innovation Organization (SAICRIO), Lusaka, Zambia.

出版信息

Malar J. 2024 Mar 22;23(1):85. doi: 10.1186/s12936-024-04882-6.

Abstract

BACKGROUND

The burden of Malaria in Zambia remains a challenge, with the entire population at risk of contracting this infectious disease. Despite concerted efforts by African countries, including Zambia, to implement malaria policies and strategies aimed at reducing case incidence, the region faces significant hurdles, especially with emerging pandemics such as COVID-19. The efforts to control malaria were impacted by the constraints imposed to curb its transmission during the COVID-19 pandemic. The aim of the study was to assess the effect of the COVID-19 pandemic on malaria cases in Zambia and the factors associated by comparing the COVID-19 period and the pre-COVID-19 era.

METHODS

This was a cross-sectional panel study in which routinely collected programmatic data on malaria was used. The data were extracted from the Health Management Information System (HMIS) for the period January 2018 to January 2022. The period 2018 to 2022 was selected purely due to the availability of data and to avoid the problem of extrapolating too far away from the period of interest of the study. A summary of descriptive statistics was performed in which the number of cases were stratified by province, age group, and malaria cases. The association of these variables with the COVID-19 era was checked using the Wilcoxon rank-sum test and Kruskal‒Wallis test as applicable. In establishing the factors associated with the number of malaria cases, a mixed-effect multilevel model using the Poisson random intercept and random slope of the COVID-19 panel. The model was employed to deal with the possible correlation of the number of cases in the non-COVID-19 panel and the expected correlation of the number of cases in the COVID-19 panel.

RESULTS

A total of 18,216 records were extracted from HMIS from January 2018 to January 2022. Stratifying this by the COVID-19 period/era, it was established that 8,852 malaria cases were recorded in the non-COVID-19 period, whereas 9,364 cases were recorded in the COVID-19 era. Most of the people with malaria were above the age of 15 years. Furthermore, the study found a significant increase in the relative incidence of the COVID-19 panel period compared to the non-COVID-19 panel period of 1.32, 95% CI (1.18, 1.48, p < 0.0001). The observed numbers, as well as the incident rate ratio, align with the hypothesis of this study, indicating an elevated incidence rate ratio of malaria during the COVID-19 period.

CONCLUSION

This study found that there was an increase in confirmed malaria cases during the COVID-19 period compared to the non-COVID-19 period. The study also found Age, Province, and COVID-19 period to be significantly associated with malaria cases.

摘要

背景

赞比亚的疟疾负担仍然是一个挑战,全体民众都面临感染这种传染病的风险。尽管包括赞比亚在内的非洲国家已协同努力,实施旨在降低病例发生率的疟疾政策和战略,但该地区仍面临重大障碍,特别是在 COVID-19 等新出现的大流行面前。在 COVID-19 大流行期间,为控制疟疾而做出的努力受到了限制疟疾传播的限制。本研究的目的是评估 COVID-19 大流行对赞比亚疟疾病例的影响,并通过比较 COVID-19 时期和 COVID-19 前时期,分析相关因素。

方法

这是一项横断面面板研究,使用了疟疾常规收集的项目数据。数据来自 2018 年 1 月至 2022 年 1 月期间的卫生管理信息系统(HMIS)。选择 2018 年至 2022 年期间,纯粹是因为数据的可用性,并避免从研究感兴趣的时期推断太远。对描述性统计数据进行了总结,其中按省份、年龄组和疟疾病例对病例数量进行了分层。使用 Wilcoxon 秩和检验和 Kruskal-Wallis 检验检查了这些变量与 COVID-19 时代的相关性。在确定与疟疾病例数量相关的因素时,使用了 COVID-19 面板的泊松随机截距和随机斜率的混合效应多水平模型。该模型用于处理非 COVID-19 面板中病例数量的可能相关性,以及 COVID-19 面板中病例数量的预期相关性。

结果

从 2018 年 1 月至 2022 年 1 月,从 HMIS 中提取了 18216 条记录。按 COVID-19 时期/时代分层,结果表明,非 COVID-19 时期记录了 8852 例疟疾病例,而 COVID-19 时期记录了 9364 例。大多数患有疟疾的人年龄在 15 岁以上。此外,该研究发现 COVID-19 面板期的相对发病率与非 COVID-19 面板期相比显著增加,为 1.32(95%CI,1.18-1.48,p<0.0001)。观察到的数量以及发病率比值与本研究的假设一致,表明 COVID-19 期间的发病率比值升高。

结论

本研究发现,与非 COVID-19 时期相比,COVID-19 期间确诊的疟疾病例有所增加。该研究还发现年龄、省份和 COVID-19 时期与疟疾病例显著相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1d9/10958942/4e0e4ab349ee/12936_2024_4882_Fig1_HTML.jpg

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