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Title: Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey
Authors: Yoel Lubell
Sarah G. Staedke
Brian M. Greenwood
Moses R. Kamya
Malcolm Molyneux
Paul N. Newton
Hugh Reyburn
Robert W. Snow
Umberto D'Alessandro
Mike English
Nick Day
Peter Kremsner
Arjen Dondorp
Wilfred Mbacham
Grant Dorsey
Seth Owusu-Agyei
Kathryn Maitland
Sanjeev Krishna
Charles Newton
Geoffrey Pasvol
Terrie Taylor
Lorenz von Seidlein
Nicholas J. White
Fred Binka
Anne Mills
Christopher J.M. Whitty
London School of Hygiene & Tropical Medicine
Mahidol University
Makerere University
University of Malawi College of Medicine
Mahosot Hospital
Nuffield Department of Clinical Medicine
Kilimanjaro Christian Medical Centre
Kenya Medical Research Institute
Prins Leopold Instituut voor Tropische Geneeskunde
Albert Schweitzer Hospital
Universitat Tubingen
Universite de Yaounde I
University of California, San Francisco
Kintampo Health Research Centre
Wellcome Trust Research Laboratories Nairobi
St George's University of London
Imperial College London
Michigan State University College of Osteopathic Medicine
Teule Hospital
Malaria Clinical Trials Alliance
Keywords: Agricultural and Biological Sciences;Biochemistry, Genetics and Molecular Biology
Issue Date: 7-Mar-2011
Citation: PLoS ONE. Vol.6, No.2 (2011)
Abstract: Background: Modelling is widely used to inform decisions about management of malaria and acute febrile illnesses. Most models depend on estimates of the probability that untreated patients with malaria or bacterial illnesses will progress to severe disease or death. However, data on these key parameters are lacking and assumptions are frequently made based on expert opinion. Widely diverse opinions can lead to conflicting outcomes in models they inform. Methods and Findings: A Delphi survey was conducted with malaria experts aiming to reach consensus on key parameters for public health and economic models, relating to the outcome of untreated febrile illnesses. Survey questions were stratified by malaria transmission intensity, patient age, and HIV prevalence. The impact of the variability in opinion on decision models is illustrated with a model previously used to assess the cost-effectiveness of malaria rapid diagnostic tests. Some consensus was reached around the probability that patients from higher transmission settings with untreated malaria would progress to severe disease (median 3%, inter-quartile range (IQR) 1-5%), and the probability that a non-malaria illness required antibiotics in areas of low HIV prevalence (median 20%). Children living in low transmission areas were considered to be at higher risk of progressing to severe malaria (median 30%, IQR 10-58%) than those from higher transmission areas (median 13%, IQR 7-30%). Estimates of the probability of dying from severe malaria were high in all settings (medians 60-73%). However, opinions varied widely for most parameters, and did not converge on resurveying. Conclusions: This study highlights the uncertainty around potential consequences of untreated malaria and bacterial illnesses. The lack of consensus on most parameters, the wide range of estimates, and the impact of variability in estimates on model outputs, demonstrate the importance of sensitivity analysis for decision models employing expert opinion. Results of such models should be interpreted cautiously. The diversity of expert opinion should be recognised when policy options are debated. © 2011 Lubell et al.
ISSN: 19326203
Appears in Collections:Scopus 2011-2015

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