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参与式疾病监测在马拉维农村地区霍乱样腹泻疾病暴发早期检测中的应用:前瞻性队列研究。

Participatory Disease Surveillance for the Early Detection of Cholera-Like Diarrheal Disease Outbreaks in Rural Villages in Malawi: Prospective Cohort Study.

机构信息

Global Studies Institute, University of Geneva, Geneva, Switzerland.

Kamuzu University of Health Sciences, Lilongwe, Malawi.

出版信息

JMIR Public Health Surveill. 2024 Jul 16;10:e49539. doi: 10.2196/49539.

DOI:10.2196/49539
PMID:39012690
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11289577/
Abstract

BACKGROUND

Cholera-like diarrheal disease (CLDD) outbreaks are complex and influenced by environmental factors, socioeconomic conditions, and population dynamics, leading to limitations in traditional surveillance methods. In Malawi, cholera is considered an endemic disease. Its epidemiological profile is characterized by seasonal patterns, often coinciding with the rainy season when contamination of water sources is more likely. However, the outbreak that began in March 2022 has extended to the dry season, with deaths reported in all 29 districts. It is considered the worst outbreak in the past 10 years.

OBJECTIVE

This study aims to evaluate the feasibility and outcomes of participatory surveillance (PS) using interactive voice response (IVR) technology for the early detection of CLDD outbreaks in Malawi.

METHODS

This longitudinal cohort study followed 740 households in rural settings in Malawi for 24 weeks. The survey tool was designed to have 10 symptom questions collected every week. The proxies' rationale was related to exanthematic, ictero-hemorragica for endemic diseases or events, diarrhea and respiratory/targeting acute diseases or events, and diarrhea and respiratory/targeting seasonal diseases or events. This work will focus only on the CLDD as a proxy for gastroenteritis and cholera. In this study, CLDD was defined as cases where reports indicated diarrhea combined with either fever or vomiting/nausea.

RESULTS

During the study period, our data comprised 16,280 observations, with an average weekly participation rate of 35%. Maganga TA had the highest average of completed calls, at 144.83 (SD 10.587), while Ndindi TA had an average of 123.66 (SD 13.176) completed calls. Our findings demonstrate that this method might be effective in identifying CLDD with a notable and consistent signal captured over time (R=0.681404). Participation rates were slightly higher at the beginning of the study and decreased over time, thanks to the sensitization activities rolled out at the CBCCs level. In terms of the attack rates for CLDD, we observed similar rates between Maganga TA and Ndindi TA, at 16% and 15%, respectively.

CONCLUSIONS

PS has proven to be valuable for the early detection of epidemics. IVR technology is a promising approach for disease surveillance in rural villages in Africa, where access to health care and traditional disease surveillance methods may be limited. This study highlights the feasibility and potential of IVR technology for the timely and comprehensive reporting of disease incidence, symptoms, and behaviors in resource-limited settings.

摘要

背景

霍乱样腹泻病 (CLDD) 疫情复杂,受环境因素、社会经济条件和人口动态影响,传统监测方法存在局限性。在马拉维,霍乱被认为是一种地方病。其流行病学特征表现为季节性模式,通常与雨季同时发生,此时水源污染的可能性更大。然而,始于 2022 年 3 月的疫情已扩展至旱季,29 个地区均有死亡报告。这被认为是过去 10 年中最严重的一次疫情爆发。

目的

本研究旨在评估使用交互式语音应答 (IVR) 技术进行参与式监测 (PS) 以早期发现马拉维 CLDD 疫情的可行性和结果。

方法

这是一项纵向队列研究,在马拉维农村地区对 740 户家庭进行了 24 周的随访。该调查工具设计为每周收集 10 个症状问题。代理的基本原理与地方性疾病或事件的出疹、黄疸出血有关,或腹泻和呼吸道/针对急性疾病或事件,以及腹泻和呼吸道/针对季节性疾病或事件。本工作将仅关注 CLDD 作为胃肠炎和霍乱的替代指标。在这项研究中,CLDD 被定义为报告表明腹泻伴有发热或呕吐/恶心的病例。

结果

在研究期间,我们的数据包括 16280 次观察,平均每周参与率为 35%。Maganga TA 的完成通话次数最多,平均为 144.83(SD 10.587),而 Ndindi TA 的平均完成通话次数为 123.66(SD 13.176)。我们的研究结果表明,这种方法可能在识别 CLDD 方面非常有效,能够随着时间的推移捕捉到明显且一致的信号(R=0.681404)。由于在 CBCC 层面开展了宣传活动,研究开始时的参与率略高,随后逐渐下降。就 CLDD 的攻击率而言,我们观察到 Maganga TA 和 Ndindi TA 之间的相似率,分别为 16%和 15%。

结论

PS 已被证明对早期发现疫情具有重要价值。IVR 技术是在非洲农村地区进行疾病监测的一种有前途的方法,在这些地区,获得医疗保健和传统疾病监测方法可能受到限制。本研究强调了 IVR 技术在资源有限环境中及时、全面报告疾病发病率、症状和行为的可行性和潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e109/11289577/c9d763e6a707/publichealth_v10i1e49539_fig7.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e109/11289577/c9d763e6a707/publichealth_v10i1e49539_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e109/11289577/49288ebf3752/publichealth_v10i1e49539_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e109/11289577/3af3c3411635/publichealth_v10i1e49539_fig2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e109/11289577/c9d763e6a707/publichealth_v10i1e49539_fig7.jpg

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