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量化撒哈拉以南非洲严重发热疾病住院的发病率。

Quantifying the incidence of severe-febrile-illness hospital admissions in sub-Saharan Africa.

机构信息

Médecins Sans Frontières - Febrile Illness Diagnostic Programme, New York, United States of America.

University of Oxford, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom.

出版信息

PLoS One. 2019 Jul 25;14(7):e0220371. doi: 10.1371/journal.pone.0220371. eCollection 2019.

DOI:10.1371/journal.pone.0220371
PMID:31344116
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6657909/
Abstract

Severe-febrile-illness (SFI) is a common cause of morbidity and mortality across sub-Saharan Africa (SSA). The burden of SFI in SSA is currently unknown and its estimation is fraught with challenges. This is due to a lack of diagnostic capacity for SFI in SSA, and thus a dearth of baseline data on the underlying etiology of SFI cases and scant SFI-specific causative-agent prevalence data. To highlight the public health significance of SFI in SSA, we developed a Bayesian model to quantify the incidence of SFI hospital admissions in SSA. Our estimates indicate a mean population-weighted SFI-inpatient-admission incidence rate of 18.4 (6.8-31.1, 68% CrI) per 1000 people for the year 2014, across all ages within areas of SSA with stable Plasmodium falciparum transmission. We further estimated a total of 16,200,337 (5,993,249-27,321,779, 68% CrI) SFI hospital admissions. This analysis reveals the significant burden of SFI in hospitals in SSA, but also highlights the paucity of pathogen-specific prevalence and incidence data for SFI in SSA. Future improvements in pathogen-specific diagnostics for causative agents of SFI will increase the abundance of SFI-specific prevalence and incidence data, aid future estimations of SFI burden, and enable clinicians to identify SFI-specific pathogens, administer appropriate treatment and management, and facilitate appropriate antibiotic use.

摘要

严重发热疾病(SFI)是撒哈拉以南非洲(SSA)发病率和死亡率的常见原因。SSA 中 SFI 的负担目前尚不清楚,其估计充满挑战。这是因为 SSA 缺乏 SFI 的诊断能力,因此缺乏关于 SFI 病例潜在病因的基线数据,以及 SFI 特定病原体的患病率数据很少。为了强调 SFI 在 SSA 的公共卫生意义,我们开发了一个贝叶斯模型来量化 SSA 中 SFI 住院的发病率。我们的估计表明,在 SSA 具有稳定恶性疟原虫传播的所有年龄段人群中,2014 年 SFI 住院入院率的平均人群加权发病率为 18.4(6.8-31.1,68%可信区间)每 1000 人。我们进一步估计共有 16200337 人(5993249-27321779,68%可信区间)因 SFI 住院。该分析揭示了 SSA 医院中 SFI 的巨大负担,但也突出了 SSA 中 SFI 的病原体特异性患病率和发病率数据的缺乏。未来对 SFI 病原体的病原体特异性诊断的改进将增加 SFI 特异性患病率和发病率数据的丰富度,有助于未来对 SFI 负担的估计,并使临床医生能够识别 SFI 特异性病原体,进行适当的治疗和管理,并促进适当的抗生素使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/293a76afc5e1/pone.0220371.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/759652874183/pone.0220371.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/32b1aed00de4/pone.0220371.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/962ca98c2616/pone.0220371.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/acc0f49d94ff/pone.0220371.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/293a76afc5e1/pone.0220371.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/759652874183/pone.0220371.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/32b1aed00de4/pone.0220371.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/962ca98c2616/pone.0220371.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/acc0f49d94ff/pone.0220371.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7af7/6657909/293a76afc5e1/pone.0220371.g005.jpg

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