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Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey

Lubell, Yoel, Staedke, Sarah G., Greenwood, Brian M., Kamya, Moses R., Molyneux, Malcolm, Newton, Paul N., Reyburn, Hugh, Snow, Robert W., D’Alessandro, Umberto, English, Mike, Day, Nicholas P. J., Kremsner, Peter, Dondorp, Arjen M., Mbacham, Wilfred, Dorsey, Grant, Owusu-Agyei, Seth, Maitland, Kathryn, Krishna, Sanjeev, von Seidlein, Lorenz, White, Nicholas J. and et al. (2011). Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. PLoS One,6(2):e17439.

Document type: Journal Article
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IRMA ID kmckayxPUB38
Title Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey
Author Lubell, Yoel
Staedke, Sarah G.
Greenwood, Brian M.
Kamya, Moses R.
Molyneux, Malcolm
Newton, Paul N.
Reyburn, Hugh
Snow, Robert W.
D’Alessandro, Umberto
English, Mike
Day, Nicholas P. J.
Kremsner, Peter
Dondorp, Arjen M.
Mbacham, Wilfred
Dorsey, Grant
Owusu-Agyei, Seth
Maitland, Kathryn
Krishna, Sanjeev
von Seidlein, Lorenz
White, Nicholas J.
et al.
Journal Name PLoS One
Publication Date 2011
Volume Number 6
Issue Number 2
ISSN 1932-6203   (check CDU catalogue open catalogue search in new window)
Start Page e17439
Total Pages 9
Place of Publication United States
Publisher Public Library of Science
HERDC Category C1 - Journal Article (DEST)
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.

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.
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