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OBJECTIVES: To identify existing guidelines and develop a synthesised guideline plus accompanying checklist. In addition to provide guidance on key theoretical, methodological and practical issues and consider the implications of this research for what might be expected of future decision-analytic models.
DATA SOURCES: Electronic databases.
REVIEW METHODS: A systematic review of existing good practice guidelines was undertaken to identify and summarise guidelines currently available for assessing the quality of decision-analytic models that have been undertaken for health technology assessment. A synthesised good practice guidance and accompanying checklist was developed. Two specific methods areas in decision modelling were considered. The first method's topic is the identification of parameter estimates from published literature. Parameter searches were developed and piloted using a case-study model. The second topic relates to bias in parameter estimates; that is, how to adjust estimates of treatment effect from observational studies where there are risks of selection bias. A systematic literature review was conducted to identify those studies looking at quantification of bias in parameter estimates and the implication of this bias.
RESULTS: Fifteen studies met the inclusion criteria and were reviewed and consolidated into a single set of brief statements of good practice. From this, a checklist was developed and applied to three independent decision-analytic models. Although the checklist provided excellent guidance on some key issues for model evaluation, it was too general to pick up on the specific nuances of each model. The searches that were developed helped to identify important data for inclusion in the model. However, the quality of life searches proved to be problematic: the published search filters did not focus on those measures specific to cost-effectiveness analysis and although the strategies developed as part of this project were more successful few data were found. Of the 11 studies meeting the criteria on the effect of selection bias, five concluded that a non-randomised trial design is associated with bias and six studies found 'similar' estimates of treatment effects from observational studies or non-randomised clinical trials and randomised controlled trials (RCTs). One purpose of developing the synthesised guideline and checklist was to provide a framework for critical appraisal by the various parties involved in the health technology assessment process. First, the guideline and checklist can be used by groups that are reviewing other analysts' models and, secondly, the guideline and checklist could be used by the various analysts as they develop their models (to use it as a check on how they are developing and reporting their analyses). The Expert Advisory Group (EAG) that was convened to discuss the potential role of the guidance and checklist felt that, in general, the guidance and checklist would be a useful tool, although the checklist is not meant to be used exclusively to determine a model's quality, and so should not be used as a substitute for critical appraisal.
CONCLUSIONS: The review of current guidelines showed that although authors may provide a consistent message regarding some aspects of modelling, in other areas conflicting attributes are presented in different guidelines. In general, the checklist appears to perform well, in terms of identifying those aspects of the model that should be of particular concern to the reader. The checklist cannot, however, provide answers to the appropriateness of the model structure and structural assumptions, as these may be seen as a general problem with generic checklists and do not reflect any shortcoming with the synthesised guidance and checklist developed here. The assessment of the checklist, as well as feedback from the EAG, indicated the importance of its use in conjunction with a more general checklist or guidelines on economic evaluation. Further methods research into the following areas would be valuable: the quantification of selection bias in non-controlled studies and in controlled observational studies; the level of bias in the different non-RCT study designs; a comparison of results from RCTs with those from other non-randomised studies; assessment of the strengths and weaknesses of alternative ways to adjust for bias in a decision model; and how to prioritise searching for parameter estimates.
OBJECTIVES: Health-care technology reviews now increasingly include outcome costs as well as clinical effects. This study reports the findings and implications of a survey to explore the usefulness of the National Health Service Economic Evaluation Database (NHS EED) within this process.
METHODS: Postal survey of lead authors of Technology Assessment Reviews (TARs) commissioned by the United Kingdom's National Institute for Clinical Excellence (NICE). The questionnaire investigated the usefulness of NHS EED in terms of (a) search strategy, (b) data extraction, (c) quality assessment, and (d) determining requirements for new modeling studies. Qualitative data were requested, including opinions regarding NHS EED.
RESULTS: NHS EED was used in 90 percent of all identified reviews (n = 46). The questionnaire response rate was 63 percent. The percentage of scores 3 or above (most useful), 2 or below (least useful), or N/A were, respectively, (a) search strategy= 62 percent, 23 percent, 15 percent; (b) data extraction = 23 percent, 27 percent, 50 percent, (c) quality assessment= 38 percent, 19 percent, 42 percent; (d) determining requirements for new modeling studies = 27 percent, 23 percent, 50 percent. The results were expanded further in the qualitative data from the respondents.
CONCLUSIONS: NHS EED is a useful tool for a variety of tasks in the NICE/TAR process but not, unsurprisingly, as a replacement for understanding primary studies. There is, however, a need to reduce the impact of time lags between the publication of economic evaluations and the appearance of abstracts relating to them on NHS EED. The results will inform future developments of the NHS EED database, which should increase its usefulness to researchers
This paper provides a first outline of the European Network of Health Economic Evaluation Databases (EURO NHEED) project. The project is funded by the European Commission and will implement, in 7 European centres based in France, Germany, Italy, The Netherlands, Spain, Sweden and the United Kingdom, databases on the economic evaluation of healthcare interventions. The network will be based on two existing and well-established resources, namely the UK's NHS Economic Evaluation Database (NHS EED), and France's Connaissances et Décision en EConomie de la Santé (CODECS) database. EURO NHEED will initially cover 17 European countries and will provide its users with bibliographic records, detailing the main characteristics of all included studies. In addition, structured abstracts will be provided for articles identified as full economic evaluations (cost-benefit, cost-effectiveness or cost-utility), which will offer a detailed critique of the findings and the methodology used. These databases will be accessible free of charge on the Internet. The EURO NHEED project is the first attempt to develop such a resource on a multi-national basis. The project will bring together Health Economists and Information Scientists from the European Union and beyond and is anticipated to facilitate a number of benefits and advances in the field of Health Economics. These include harmonisation and increased understanding of the theory and methodology of economic evaluation in healthcare, the interpretation of the generalisability of studies to target settings, and the influence of healthcare system variations among the European countries. The project will therefore advance the state of the art in collecting, summarising, critiquing and disseminating economic evaluations of healthcare conducted within Europe.
No abstract available
PROBLEM: Diabetic nephropathy (DN) is a common microvascular complication of diabetes and can result in end-stage renal disease (ESRD) necessitating long-term dialysis or kidney transplantation. The costs of these complications are relatively high. The aim of this study was to quantify and compare the rates and annual costs of DN in the USA and the UK.
METHODS: A cost of illness model was used to estimate the numbers of people with DN (microalbuminuria, overt nephropathy, and ESRD) or a previous kidney transplant at a given point in time and the numbers of new kidney transplants during a year. All costs were estimated in 2001 currencies. A sensitivity analysis assessed the robustness of the national annual cost estimates.
RESULTS: In the USA, the total annual medical costs incurred by all payers in managing DN were US$1.9 billion for Type 1 diabetes (range: US$1.0-2.8 billion), US$15.0 billion for Type 2 diabetes (range: US$7.6-22.4 billion), and US$16.8 billion for all diabetes (range: US$8.5-25.2 billion). In the UK, the total annual costs to the National Health Service (NHS) of managing DN were US$231 million (£152 million) for Type 1 diabetes (range: US$190-350 million [£125-230 million]), US$933 million (£614 million) for Type 2 diabetes (range: US$809 million-US$1.4 billion [£532-927 million]), and US$1.2 billion (£765 million) for all diabetes (range: US$999 million-US$1.8 billion [£657 million-£1.2 billion]).
CONCLUSIONS: The total annual cost of DN is 13 times greater in the USA than in the UK. Controlling for the substantially higher number of people at risk, the total cost per person with DN and/or a kidney transplant is 40% higher: US$3735 in the USA and US$2672 (£1758) in the UK.
AIMS: To assess the awareness and use of NHSnet within general practice. To investigate the presence of skills necessary to maximize the benefits of NHSnet connections.
METHODS: Postal survey of general practice staff in the Northern and Yorkshire Region.
RESULTS: At least one completed questionnaire was obtained from 65% of the general practices surveyed, and the individual response rate to the general practice survey was 44%. Ninety per cent of all respondents reported that their practice was connected to the NHSnet, with 59% of respondents reporting that they use NHSnet at least once a week. Although NHSnet was used to search for research information or guidance, all respondents in this survey still reported greater access to and use of paper-based information resources. Respondents indicated that they still needed further training on how to use NHSnet (42%), how to search the Internet (31%) and how to search electronic databases such as medline (49%).
CONCLUSIONS: Since our 1999 survey, reported NHSnet connectivity has increased greatly, with a majority of respondents reporting that they use NHSnet at least once a week. Although encouraging, this level of usage suggests that using the Internet/NHSnet to find research has yet to become a core activity in general practice.
STUDY OBJECTIVE: To estimate the number of accident and emergency (A&E) attendances, admissions to hospital, and the associated costs as a result of unintentional falls in older people.
DESIGN: Analysis of national databases for cost of illness.
SETTING: United Kingdom, 1999, cost to the National Health Service (NHS) and Personal Social Services (PSS).
PARTICIPANTS: Four age groups of people 60 years and over (60-64, 65-69, 70-74, and >/=75) attending an A&E department or admitted to hospital after an unintentional fall. Databases analysed were the Home Accident Surveillance System (HASS) and Leisure Accident Surveillance System (LASS), and Hospital Episode Statistics (HES).
MAIN RESULTS: There were 647,721 A&E attendances and 204,424 admissions to hospital for fall related injuries in people aged 60 years and over. For the four age groups A&E attendance rates per 10,000 population were 273.5, 287.3, 367.9, and 945.3, and hospital admission rates per 10,000 population were 34.5, 52.0, 91.9, and 368.6. The cost per 10,000 population was pound 300,000 in the 60-64 age group, increasing to pound 1,500,000 in the >/=75 age group. These falls cost the UK government pound 981 million, of which the NHS incurred 59.2%. Most of the costs (66%) were attributable to falls in those aged >/=75 years. The major cost driver was inpatient admissions, accounting for 49.4% of total cost of falls. Long term care costs were the second highest, accounting for 41%, primarily in those aged >/=75 years.
CONCLUSIONS: Unintentional falls impose a substantial burden on health and social services.
