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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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          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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          Most cited references17

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          Overdiagnosis of malaria in patients with severe febrile illness in Tanzania: a prospective study.

          To study the diagnosis and outcomes in people admitted to hospital with a diagnosis of severe malaria in areas with differing intensities of malaria transmission. Prospective observational study of children and adults over the course a year. 10 hospitals in north east Tanzania. 17,313 patients were admitted to hospital; of these 4474 (2851 children aged under 5 years) fulfilled criteria for severe disease. Details of the treatment given and outcome. Altitudes of residence (a proxy for transmission intensity) measured with a global positioning system. Blood film microscopy showed that 2062 (46.1%) of people treated for malaria had Plasmodium falciparum (slide positive). The proportion of slide positive cases fell with increasing age and increasing altitude of residence. Among 1086 patients aged > or = 5 years who lived above 600 metres, only 338 (31.1%) were slide positive, while in children < 5 years living in areas of intense transmission (< 600 metres) most (958/1392, 68.8%) were slide positive. Among 2375 people who were slide negative, 1571 (66.1%) were not treated with antibiotics and of those, 120 (7.6%) died. The case fatality in slide negative patients was higher (292/2412, 12.1%) than for slide positive patients (142/2062, 6.9%) (P < 0.001). Respiratory distress and altered consciousness were the strongest predictors of mortality in slide positive and slide negative patients and in adults as well as children. In Tanzania, malaria is commonly overdiagnosed in people presenting with severe febrile illness, especially in those living in areas with low to moderate transmission and in adults. This is associated with a failure to treat alternative causes of severe infection. Diagnosis needs to be improved and syndromic treatment considered. Routine hospital data may overestimate mortality from malaria by over twofold.
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            Consulting the oracle: ten lessons from using the Delphi technique in nursing research.

            The aim of this paper was to provide insight into the Delphi technique by outlining our personal experiences during its use over a 10-year period in a variety of applications. As a means of achieving consensus on an issue, the Delphi research method has become widely used in healthcare research generally and nursing research in particular. The literature on this technique is expanding, mainly addressing what it is and how it should be used. However, there is still much confusion and uncertainty surrounding it, particularly about issues such as modifications, consensus, anonymity, definition of experts, how 'experts' are selected and how non-respondents are pursued. This issues that arise when planning and carrying out a Delphi study include the definition of consensus; the issue of anonymity vs. quasi-anonymity for participants; how to estimate the time needed to collect the data, analyse each 'round', feed back results to participants, and gain their responses to this feedback; how to define and select the 'experts' who will be asked to participate; how to enhance response rates; and how many 'rounds' to conduct. Many challenges and questions are raised when using the Delphi technique, but there is no doubt that it is an important method for achieving consensus on issues where none previously existed. Researchers need to adapt the method to suit their particular study.
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              Cost-effectiveness of malaria diagnostic methods in sub-Saharan Africa in an era of combination therapy.

              To evaluate the relative cost-effectiveness in different sub-Saharan African settings of presumptive treatment, field-standard microscopy and rapid diagnostic tests (RDTs) to diagnose malaria. We used a decision tree model and probabilistic sensitivity analysis applied to outpatients presenting at rural health facilities with suspected malaria. Costs and effects encompassed those for both patients positive on RDT (assuming artemisinin-based combination therapy) and febrile patients negative on RDT (assuming antibiotic treatment). Interventions were defined as cost-effective if they were less costly and more effective or had an incremental cost per disability-adjusted life year averted of less than US$ 150. Data were drawn from published and unpublished sources, supplemented with expert opinion. RDTs were cost-effective compared with presumptive treatment up to high prevalences of Plasmodium falciparum parasitaemia. Decision-makers can be at least 50% confident of this result below 81% malaria prevalence, and 95% confident below 62% prevalence, a level seldom exceeded in practice. RDTs were more than 50% likely to be cost-saving below 58% prevalence. Relative to microscopy, RDTs were more than 85% likely to be cost-effective across all prevalence levels, reflecting their expected better accuracy under real-life conditions. Results were robust to extensive sensitivity analysis. The cost-effectiveness of RDTs mainly reflected improved treatment and health outcomes for non-malarial febrile illness, plus savings in antimalarial drug costs. Results were dependent on the assumption that prescribers used test results to guide treatment decisions. RDTs have the potential to be cost-effective in most parts of sub-Saharan Africa. Appropriate management of malaria and non-malarial febrile illnesses is required to reap the full benefits of these tests.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                24 February 2011
                : 6
                : 2
                [1 ]London School of Hygiene & Tropical Medicine, London, United Kingdom
                [2 ]Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
                [3 ]Makerere University and the University of California San Francisco Research Collaboration, Mulago Hospital, Kampala, Uganda
                [4 ]Department of Medicine, Makerere University School of Medicine, Kampala, Uganda
                [5 ]Department of Medicine, College of Medicine, University of Malawi, Blantyre, Malawi
                [6 ]Wellcome Trust-Mahosot Hospital-Oxford Tropical Medicine Research Collaboration, Mahosot Hospital, Vientiane, Lao PDR
                [7 ]Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
                [8 ]Joint Malaria Programme, Kilimanjaro Christian Medical Centre, Moshi, Tanzania
                [9 ]Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, Kenya Medical Research Institute, Nairobi, Kenya
                [10 ]Department of Parasitology, Institute of Tropical Medicine, Antwerp, Belgium
                [11 ]Medical Research Unit, Albert Schweitzer Hospital, Lambarene, Gabon
                [12 ]Institut für Tropenmedizin, Universität Tübingen, Tübingen, Germany
                [13 ]Biotechnology Center, University of Yaoundé, Yaoundé, Cameroon
                [14 ]Department of Medicine, San Francisco General Hospital, University of California San Francisco, San Francisco, California, United States of America
                [15 ]Kintampo Health Research Centre, Ghana Health Service, Kintampo, Ghana
                [16 ]Kenya Medical Research Institute -Wellcome Trust Research Programme, Centre of Geographic Medicine Research Coast, Kilifi, Kenya
                [17 ]Faculty of Medicine and Biomedical Sciences, St. George's Hospital Medical School, London, United Kingdom
                [18 ]Department of Medicine, Imperial College, London, United Kingdom
                [19 ]Blantyre Malaria Project, College of Medicine, Blantyre, Malawi
                [20 ]Department of Internal Medicine, Michigan State University College of Osteopathic Medicine, East Lansing, Michigan, United States of America
                [21 ]Joint Malaria Programme, Teule Hospital, Muheza, Tanzania
                [22 ]Malaria Clinical Trials Alliance, International Network for the Demographic Evaluation of Populations and Their Health in Developing Countries Network, Accra, Ghana
                Université Pierre et Marie Curie, France
                Author notes

                Conceived and designed the experiments: YL SGS AJM CJMW. Performed the experiments: YL SGS BMG MRS MM PN HR RWS UDA ME ND PK AD WM GD SOA KM SK CN GP TT LvS NJW FB AJM CJMW. Analyzed the data: YL SGS CJMW AJM. Wrote the paper: YL SGS CJMW AJM. Provided comments throughout the process and commented on the manuscript: YL SS BMG MRS MM PN HR RWS UDA ME ND PK AD WM GD SOA KM SK CN GP TT LVS NJW FB AJM CJMW. Drafted the manuscript: YL SGS AM CJMW. Participated in the survey: BMG MRK MM PNN HR RWS UDA ME ND PK AD WM GD SOA KM SK CN GP TT LvS NJW FB SGS CJMW. Revised the manuscript: BMG MRK MM PNN HR RWS UDA ME ND PK AD WM GD SOA KM SK CN GP TT LvS NJW FB SGS CJMW.

                Lubell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 9
                Research Article
                Clinical Research Design
                Qualitative Studies
                Clinical Epidemiology
                Infectious Disease Epidemiology
                Pediatric Epidemiology
                Global Health
                Infectious Diseases
                Parasitic Diseases
                Plasmodium Falciparum
                Tropical Diseases (Non-Neglected)
                Infectious Disease Control
                Infectious Disease Modeling
                Non-Clinical Medicine
                Health Economics
                Public Health



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