Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey.
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 lac...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yoel Lubell [verfasserIn] Sarah G Staedke [verfasserIn] Brian M Greenwood [verfasserIn] Moses R Kamya [verfasserIn] Malcolm Molyneux [verfasserIn] Paul N Newton [verfasserIn] Hugh Reyburn [verfasserIn] Robert W Snow [verfasserIn] Umberto D'Alessandro [verfasserIn] Mike English [verfasserIn] Nick Day [verfasserIn] Peter Kremsner [verfasserIn] Arjen Dondorp [verfasserIn] Wilfred Mbacham [verfasserIn] Grant Dorsey [verfasserIn] Seth Owusu-Agyei [verfasserIn] Kathryn Maitland [verfasserIn] Sanjeev Krishna [verfasserIn] Charles Newton [verfasserIn] Geoffrey Pasvol [verfasserIn] Terrie Taylor [verfasserIn] Lorenz von Seidlein [verfasserIn] Nicholas J White [verfasserIn] Fred Binka [verfasserIn] Anne Mills [verfasserIn] Christopher J M Whitty [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Übergeordnetes Werk: |
In: PLoS ONE - Public Library of Science (PLoS), 2007, 6(2011), 2, p e17439 |
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| Übergeordnetes Werk: |
volume:6 ; year:2011 ; number:2, p e17439 |
| Links: |
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| DOI / URN: |
10.1371/journal.pone.0017439 |
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| Katalog-ID: |
DOAJ028833694 |
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| 245 | 1 | 0 | |a Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. |
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| 520 | |a 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.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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| 700 | 0 | |a Malcolm Molyneux |e verfasserin |4 aut | |
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| 700 | 0 | |a Lorenz von Seidlein |e verfasserin |4 aut | |
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10.1371/journal.pone.0017439 doi (DE-627)DOAJ028833694 (DE-599)DOAJ864ecd20983047018e6a2e44bab3bbf8 DE-627 ger DE-627 rakwb eng Yoel Lubell verfasserin aut Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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.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. Medicine R Science Q Sarah G Staedke verfasserin aut Brian M Greenwood verfasserin aut Moses R Kamya verfasserin aut Malcolm Molyneux verfasserin aut Paul N Newton verfasserin aut Hugh Reyburn verfasserin aut Robert W Snow verfasserin aut Umberto D'Alessandro verfasserin aut Mike English verfasserin aut Nick Day verfasserin aut Peter Kremsner verfasserin aut Arjen Dondorp verfasserin aut Wilfred Mbacham verfasserin aut Grant Dorsey verfasserin aut Seth Owusu-Agyei verfasserin aut Kathryn Maitland verfasserin aut Sanjeev Krishna verfasserin aut Charles Newton verfasserin aut Geoffrey Pasvol verfasserin aut Terrie Taylor verfasserin aut Lorenz von Seidlein verfasserin aut Nicholas J White verfasserin aut Fred Binka verfasserin aut Anne Mills verfasserin aut Christopher J M Whitty verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 6(2011), 2, p e17439 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:6 year:2011 number:2, p e17439 https://doi.org/10.1371/journal.pone.0017439 kostenfrei https://doaj.org/article/864ecd20983047018e6a2e44bab3bbf8 kostenfrei http://europepmc.org/articles/PMC3044764?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 2, p e17439 |
| spelling |
10.1371/journal.pone.0017439 doi (DE-627)DOAJ028833694 (DE-599)DOAJ864ecd20983047018e6a2e44bab3bbf8 DE-627 ger DE-627 rakwb eng Yoel Lubell verfasserin aut Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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.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. Medicine R Science Q Sarah G Staedke verfasserin aut Brian M Greenwood verfasserin aut Moses R Kamya verfasserin aut Malcolm Molyneux verfasserin aut Paul N Newton verfasserin aut Hugh Reyburn verfasserin aut Robert W Snow verfasserin aut Umberto D'Alessandro verfasserin aut Mike English verfasserin aut Nick Day verfasserin aut Peter Kremsner verfasserin aut Arjen Dondorp verfasserin aut Wilfred Mbacham verfasserin aut Grant Dorsey verfasserin aut Seth Owusu-Agyei verfasserin aut Kathryn Maitland verfasserin aut Sanjeev Krishna verfasserin aut Charles Newton verfasserin aut Geoffrey Pasvol verfasserin aut Terrie Taylor verfasserin aut Lorenz von Seidlein verfasserin aut Nicholas J White verfasserin aut Fred Binka verfasserin aut Anne Mills verfasserin aut Christopher J M Whitty verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 6(2011), 2, p e17439 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:6 year:2011 number:2, p e17439 https://doi.org/10.1371/journal.pone.0017439 kostenfrei https://doaj.org/article/864ecd20983047018e6a2e44bab3bbf8 kostenfrei http://europepmc.org/articles/PMC3044764?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 2, p e17439 |
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10.1371/journal.pone.0017439 doi (DE-627)DOAJ028833694 (DE-599)DOAJ864ecd20983047018e6a2e44bab3bbf8 DE-627 ger DE-627 rakwb eng Yoel Lubell verfasserin aut Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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.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. Medicine R Science Q Sarah G Staedke verfasserin aut Brian M Greenwood verfasserin aut Moses R Kamya verfasserin aut Malcolm Molyneux verfasserin aut Paul N Newton verfasserin aut Hugh Reyburn verfasserin aut Robert W Snow verfasserin aut Umberto D'Alessandro verfasserin aut Mike English verfasserin aut Nick Day verfasserin aut Peter Kremsner verfasserin aut Arjen Dondorp verfasserin aut Wilfred Mbacham verfasserin aut Grant Dorsey verfasserin aut Seth Owusu-Agyei verfasserin aut Kathryn Maitland verfasserin aut Sanjeev Krishna verfasserin aut Charles Newton verfasserin aut Geoffrey Pasvol verfasserin aut Terrie Taylor verfasserin aut Lorenz von Seidlein verfasserin aut Nicholas J White verfasserin aut Fred Binka verfasserin aut Anne Mills verfasserin aut Christopher J M Whitty verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 6(2011), 2, p e17439 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:6 year:2011 number:2, p e17439 https://doi.org/10.1371/journal.pone.0017439 kostenfrei https://doaj.org/article/864ecd20983047018e6a2e44bab3bbf8 kostenfrei http://europepmc.org/articles/PMC3044764?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 2, p e17439 |
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10.1371/journal.pone.0017439 doi (DE-627)DOAJ028833694 (DE-599)DOAJ864ecd20983047018e6a2e44bab3bbf8 DE-627 ger DE-627 rakwb eng Yoel Lubell verfasserin aut Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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.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. Medicine R Science Q Sarah G Staedke verfasserin aut Brian M Greenwood verfasserin aut Moses R Kamya verfasserin aut Malcolm Molyneux verfasserin aut Paul N Newton verfasserin aut Hugh Reyburn verfasserin aut Robert W Snow verfasserin aut Umberto D'Alessandro verfasserin aut Mike English verfasserin aut Nick Day verfasserin aut Peter Kremsner verfasserin aut Arjen Dondorp verfasserin aut Wilfred Mbacham verfasserin aut Grant Dorsey verfasserin aut Seth Owusu-Agyei verfasserin aut Kathryn Maitland verfasserin aut Sanjeev Krishna verfasserin aut Charles Newton verfasserin aut Geoffrey Pasvol verfasserin aut Terrie Taylor verfasserin aut Lorenz von Seidlein verfasserin aut Nicholas J White verfasserin aut Fred Binka verfasserin aut Anne Mills verfasserin aut Christopher J M Whitty verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 6(2011), 2, p e17439 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:6 year:2011 number:2, p e17439 https://doi.org/10.1371/journal.pone.0017439 kostenfrei https://doaj.org/article/864ecd20983047018e6a2e44bab3bbf8 kostenfrei http://europepmc.org/articles/PMC3044764?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 2, p e17439 |
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10.1371/journal.pone.0017439 doi (DE-627)DOAJ028833694 (DE-599)DOAJ864ecd20983047018e6a2e44bab3bbf8 DE-627 ger DE-627 rakwb eng Yoel Lubell verfasserin aut Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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.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. Medicine R Science Q Sarah G Staedke verfasserin aut Brian M Greenwood verfasserin aut Moses R Kamya verfasserin aut Malcolm Molyneux verfasserin aut Paul N Newton verfasserin aut Hugh Reyburn verfasserin aut Robert W Snow verfasserin aut Umberto D'Alessandro verfasserin aut Mike English verfasserin aut Nick Day verfasserin aut Peter Kremsner verfasserin aut Arjen Dondorp verfasserin aut Wilfred Mbacham verfasserin aut Grant Dorsey verfasserin aut Seth Owusu-Agyei verfasserin aut Kathryn Maitland verfasserin aut Sanjeev Krishna verfasserin aut Charles Newton verfasserin aut Geoffrey Pasvol verfasserin aut Terrie Taylor verfasserin aut Lorenz von Seidlein verfasserin aut Nicholas J White verfasserin aut Fred Binka verfasserin aut Anne Mills verfasserin aut Christopher J M Whitty verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 6(2011), 2, p e17439 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:6 year:2011 number:2, p e17439 https://doi.org/10.1371/journal.pone.0017439 kostenfrei https://doaj.org/article/864ecd20983047018e6a2e44bab3bbf8 kostenfrei http://europepmc.org/articles/PMC3044764?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2011 2, p e17439 |
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Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey |
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Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey. |
| abstract |
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.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. |
| abstractGer |
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.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. |
| abstract_unstemmed |
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.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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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.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. 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