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

Autor: Roddy P; Médecins Sans Frontières - Febrile Illness Diagnostic Programme, New York, United States of America., Dalrymple U; University of Oxford, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom., Jensen TO; Médecins Sans Frontières - Febrile Illness Diagnostic Programme, New York, United States of America., Dittrich S; Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland.; University of Oxford - Nuffield School of Medicine, Oxford, United Kingdom., Rao VB; Médecins Sans Frontières - Manson Unit (MSF UK), London, United Kingdom., Pfeffer DA; University of Oxford, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom.; Menzies School of Health Research and Charles Darwin University, Darwin, Australia., Twohig KA; University of Oxford, Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Oxford, United Kingdom., Roberts T; Médecins Sans Frontières - Febrile Illness Diagnostic Programme, New York, United States of America.; Médecins Sans Frontières - Access Campaign, Geneva, Switzerland., Bernal O; Médecins Sans Frontières - Febrile Illness Diagnostic Programme, New York, United States of America., Guillen E; Médecins Sans Frontières - Febrile Illness Diagnostic Programme, New York, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2019 Jul 25; Vol. 14 (7), pp. e0220371. Date of Electronic Publication: 2019 Jul 25 (Print Publication: 2019).
DOI: 10.1371/journal.pone.0220371
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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