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

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  • Paul Roddy
  • Ursula Dalrymple
  • Tomas O Jensen
  • Sabine Dittrich
  • V Bhargavi Rao
  • Daniel A Pfeffer
  • Katherine A Twohig
  • Teri Roberts
  • Oscar Bernal
  • Ethan Guillen

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.

Suggested Citation

  • Paul Roddy & Ursula Dalrymple & Tomas O Jensen & Sabine Dittrich & V Bhargavi Rao & Daniel A Pfeffer & Katherine A Twohig & Teri Roberts & Oscar Bernal & Ethan Guillen, 2019. "Quantifying the incidence of severe-febrile-illness hospital admissions in sub-Saharan Africa," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-19, July.
    • Handle: RePEc:plo:pone00:0220371
    DOI: 10.1371/journal.pone.0220371
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    References listed on IDEAS

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    1. Kristensen, Kasper & Nielsen, Anders & Berg, Casper W. & Skaug, Hans & Bell, Bradley M., 2016. "TMB: Automatic Differentiation and Laplace Approximation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i05).
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    1. Judith U. Oguzie & Brittany A. Petros & Paul E. Oluniyi & Samar B. Mehta & Philomena E. Eromon & Parvathy Nair & Opeoluwa Adewale-Fasoro & Peace Damilola Ifoga & Ikponmwosa Odia & Andrzej Pastusiak & , 2023. "Metagenomic surveillance uncovers diverse and novel viral taxa in febrile patients from Nigeria," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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