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Quantifying the incidence of severe-febrile-illness hospital admissions in sub-Saharan Africa
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Quantifying the incidence of severe-febrile-illness hospital admissions in sub-Saharan Africa
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Journal Article
Quantifying the incidence of severe-febrile-illness hospital admissions in sub-Saharan Africa
2019
Overview
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.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
/ Adult
/ Africa South of the Sahara - epidemiology
/ Aged
/ Analysis
/ Big Data
/ Child
/ Community-Acquired Infections - complications
/ Community-Acquired Infections - diagnosis
/ Community-Acquired Infections - epidemiology
/ Etiology
/ Female
/ Fever
/ Hospital admission and discharge
/ Hospitalization - statistics & numerical data
/ Humans
/ Infant
/ Malaria
/ Male
/ Medical Overuse - statistics & numerical data
/ Medicine
/ Medicine and Health Sciences
/ Patient Admission - statistics & numerical data
