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PRIORITIES FOR HEALTH ECONOMIC METHODOLOGICAL RESEARCH: RESULTS OF AN EXPERT CONSULTATION

Part of: Special Collection - Methods

Published online by Cambridge University Press:  30 October 2017

David Tordrup Open the ORCID record for David Tordrup [Opens in a new window] ,
Christos Chouaid Open the ORCID record for Christos Chouaid [Opens in a new window] ,
Pim Cuijpers Open the ORCID record for Pim Cuijpers [Opens in a new window] ,
William Dab ,
Johanna Maria van Dongen ,
Jaime Espin ,
Bengt Jönsson ,
Christian Léonard ,
David McDaid  and
Martin McKee Open the ORCID record for Martin McKee [Opens in a new window]
...Show all authors
David Tordrup
Affiliation:
World Health Organization, Representation to the EU [email protected]
Christos Chouaid
Affiliation:
Respiratory Medicine Department, Centre Hospitalier Intercommunal Creteil
Pim Cuijpers
Affiliation:
Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam
William Dab
Affiliation:
French National Institute for Science, Technology and Management (Cnam), Chair of Hygiene and Safety
Johanna Maria van Dongen
Affiliation:
Department of Health Sciences, Vrije Universiteit Amsterdam
Jaime Espin
Affiliation:
Andalusian School of Public Health
Bengt Jönsson
Affiliation:
Department of Economics, Stockholm School of Economics
Christian Léonard
Affiliation:
Belgian Health Care Knowledge Centre
David McDaid
Affiliation:
Personal Social Services Research Unit, London School of Economics and Political Science
Martin McKee
Affiliation:
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine
José Pereira Miguel
Affiliation:
Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina de Lisboa
Anita Patel
Affiliation:
Centre for Primary Care & Public Health, Queen Mary University of London
Jean-Yves Reginster
Affiliation:
Department of Public Health Sciences, University of Liège
Walter Ricciardi
Affiliation:
Institute of Hygiene, Preventive Medicine and Public Health, Catholic University of the Sacred Heart Rome
Maureen Rutten-van Molken
Affiliation:
Institute for Medical Technology Assessment/Institute of Health Care Policy and Management, Erasmus University Rotterdam
Valentina Prevolnik Rupel
Affiliation:
Institute for Economic Research
Tracey Sach
Affiliation:
Norwich Medical School, University of East Anglia
Franco Sassi
Affiliation:
Health Division, Organisation for Economic Co-operation and Development (OECD)
Norman Waugh
Affiliation:
Warwick Medical School, University of Warwick
Roberto Bertollini
Affiliation:
World Health Organization, Representation to the EU
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Abstract

Background: The importance of economic evaluation in decision making is growing with increasing budgetary pressures on health systems. Diverse economic evidence is available for a range of interventions across national contexts within Europe, but little attention has been given to identifying evidence gaps that, if filled, could contribute to more efficient allocation of resources. One objective of the Research Agenda for Health Economic Evaluation project is to determine the most important methodological evidence gaps for the ten highest burden conditions in the European Union (EU), and to suggest ways of filling these gaps.

Methods: The highest burden conditions in the EU by Disability Adjusted Life Years were determined using the Global Burden of Disease study. Clinical interventions were identified for each condition based on published guidelines, and economic evaluations indexed in MEDLINE were mapped to each intervention. A panel of public health and health economics experts discussed the evidence during a workshop and identified evidence gaps.

Results: The literature analysis contributed to identifying cross-cutting methodological and technical issues, which were considered by the expert panel to derive methodological research priorities.

Conclusions: The panel suggests a research agenda for health economics which incorporates the use of real-world evidence in the assessment of new and existing interventions; increased understanding of cost-effectiveness according to patient characteristics beyond the “-omics” approach to inform both investment and disinvestment decisions; methods for assessment of complex interventions; improved cross-talk between economic evaluations from health and other sectors; early health technology assessment; and standardized, transferable approaches to economic modeling.

Keywords

Economic evaluationCost-effectiveness analysisMethodsExpert opinionResearch agenda
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 33 , Issue 6 , 2017 , pp. 609 - 619
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

1. Thomson, S, Foubister, T, Mossialos, E. Financing health care in the European Union: Challenges and policy responses. Geneva: World Health Organization; 2009.Google Scholar
2. Busse, R, Blumel, M, Scheller-Kreinsen, D, Zentner, A. Tackling chronic disease in Europe - Strategies, interventions and challenges. Obs Stud Ser No 20. 2010. http://www.euro.who.int/__data/assets/pdf_file/0008/96632/E93736.pdf (accessed December 1, 2013).Google Scholar
3. European Commission and the Economic Policy Committee (AWG). Joint report on health systems. Occasional Papers 74. 2010. http://ec.europa.eu/economy_finance/publications/occasional_paper/2010/pdf/ocp74_en.pdf (accessed December 3, 2013).Google Scholar
4. OECD. OECD StatExtracts. http://stats.oecd.org. Published 2015 (accessed December 3, 2013).Google Scholar
5. Banta, D. The development of health technology assessment. Health Policy (New York). 2003;63:121–132. doi:10.1016/S0168-8510(02)00059-3.Google Scholar
6. Elshaug, AG, Hiller, JE, Moss, JR. Exploring policy-makers’ perspectives on disinvestment from ineffective healthcare practices. Int J Technol Assess Health Care. 2008;24:1–9.CrossRefGoogle ScholarPubMed
7. Elshaug, AG, Hiller, JE, Tunis, SR, Moss, JR. Challenges in Australian policy processes for disinvestment from existing, ineffective health care practices. Aust New Zealand Health Policy. 2007;4:23. doi:10.1186/1743-8462-4-23.Google Scholar
8. Aguiar-Ibáñez, R, Nixon, J, Glanville, J, et al. Economic evaluation databases as an aid to healthcare decision makers and researchers. Expert Rev Pharmacoecon Outcomes Res. 2005;5:721–732. doi:10.1586/14737167.5.6.721.Google Scholar
9. Buxton, MJ. Economic evaluation and decision making in the UK. Pharmacoeconomics. 2006;24:1133–1142. doi:10.2165/00019053-200624110-00009.Google Scholar
10. Murray, CJL, Vos, T, Lozano, R, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2197–2223. doi:10.1016/S0140-6736(12)61689-4.Google Scholar
11. Koster J. PubReMiner. http://hgserver2.amc.nl/cgi-bin/miner/miner2.cgi. Published 2014 (accessed December 15, 2013).Google Scholar
12. Karyotaki, E, Tordrup, D, Buntrock, C, et al. Economic evidence for the clinical management of major depressive disorder: A systematic review and quality appraisal of economic evaluations alongside randomised controlled trials. Epidemiol Psychiatr Sci. 2016. [Epub ahead of print]. doi:10.1017/S2045796016000421.Google Scholar
13. Ali, S, Stone, MA, Peters, JL, Davies, MJ, Khunti, K. The prevalence of co-morbid depression in adults with Type 2 diabetes: A systematic review and meta-analysis. Diabet Med. 2006;23:1165–1173. doi:10.1111/j.1464-5491.2006.01943.x.Google Scholar
14. Janssen-Heijnen, ML, Schipper, RM, Razenberg, PP, Crommelin, MA, Coebergh, J-WW. Prevalence of co-morbidity in lung cancer patients and its relationship with treatment: A population-based study. Lung Cancer. 1998;21:105–113. doi:10.1016/S0169-5002(98)00039-7.Google Scholar
15. Currie, SR, Wang, J. Chronic back pain and major depression in the general Canadian population. Pain. 2004;107:54–60. doi:10.1016/j.pain.2003.09.015.CrossRefGoogle ScholarPubMed
16. Forster, A, Young, J. Incidence and consequences offalls due to stroke: A systematic inquiry. BMJ. 1995;311:83–86. doi:10.1136/bmj.311.6997.83.Google Scholar
17. Wolf, PA, D'Agostino, RB, Kannel, WB, Bonita, R, Belanger, AJ. Cigarette smoking as a risk factor for stroke: The Framingham Study. JAMA. 1988;259:1025–1029.Google Scholar
18. Hecht, SS. Tobacco smoke carcinogens and lung cancer. J Natl Cancer Inst. 1999;91:1194–1210. doi:10.1093/jnci/91.14.1194.Google Scholar
19. Mannino, DM, Buist, AS. Global burden of COPD: Risk factors, prevalence, and future trends. Lancet. 2007;370:765–773.CrossRefGoogle ScholarPubMed
20. Yusuf, S, Reddy, S, Ounpuu, S, Anand, S. Global burden of cardiovascular diseases: Part I: General Considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation. 2001;104:2746–2753. doi:10.1161/hc4601.099487.Google Scholar
21. Feldman, DE, Rossignol, M, Shrier, I, Abenhaim, L. Smoking: A risk factor for development of low back pain in adolescents. Spine (Phila Pa 1976). 1999;24:2492.Google Scholar
22. MacMahon, S, Peto, R, Collins, R, et al. Blood pressure, stroke, and coronary heart disease: Part 1, prolonged differences in blood pressure: Prospective observational studies corrected for the regression dilution bias. Lancet. 1990;335:765–774.CrossRefGoogle ScholarPubMed
23. Kang, JG, Park, C-Y. Anti-obesity drugs: A review about their effects and safety. Diabetes Metab J. 2012;36:13–25. doi:10.4093/dmj.2012.36.1.13.CrossRefGoogle ScholarPubMed
24. Manson, JE. A prospective study of exercise and incidence of diabetes among US male physicians. JAMA. 1992;268:63. doi:10.1001/jama.1992.03490010065031.Google Scholar
25. Barnett, K, Mercer, SW, Norbury, M, Watt, G, Wyke, S, Guthrie, B. Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. Lancet. 2012;380: 37–43.Google Scholar
26. Tordrup, D, Attwill, A, Crosby, L, Bertollini, R. Research agenda for health economic evaluation (forthcoming). http://www.euro.who.int/en/RAHEEproject(accessed August 1, 2015).Google Scholar
27. Britton, A, McKee, M, Black, N, McPherson, K, Sanderson, C, Bain, C. Threats to applicability of randomised trials: Exclusions and selective participation. J Health Serv Res Policy. 1999;4: 112–121.Google Scholar
28. Hägg, L, Johansson, C, Jansson, J-H, Johansson, L. External validity of the ARISTOTLE trial in real-life atrial fibrillation patients. Cardiovasc Ther. 2014;32:214–218. doi:10.1111/1755-5922.12087.Google Scholar
29. Simon, ST, Higginson, IJ, Harding, R, et al. Enhancing patient-reported outcome measurement in research and practice of palliative and end-of-life care. Support Care Cancer. 2012;20:1573–1578.Google Scholar
30. Cohen, D. Attacks on publicly funded trials: What happens when industry does not want to know the answer. BMJ. 2015;350:h1701.Google Scholar
31. May, P, Normand, C, Morrison, RS. Economic impact of hospital inpatient palliative care consultation: Review of current evidence and directions for future research. J Palliat Med. 2014;17:1054–1063.Google Scholar
32. Longworth, L, Sculpher, MJ, Bojke, L, Tosh, JC. Bridging the gap between methods research and the needs of policy makers: A review of the research priorities of the National Institute for Health and Clinical Excellence. Int J Technol Assess Health Care. 2011;27:180–187. doi:10.1017/S0266462311000043.Google Scholar
33. Drummond, M, Marshall, D. IHE Methodology forum: Prioritizing methodological research in the evaluation of health technologies in Canada. Alberta, Canada: IHE; 2010.Google Scholar
34. Chalkidou, K, Whicher, D, Kary, W, Tunis, S. Comparative effectiveness research priorities: Identifying critical gaps in evidence for clinical and health policy decision making. Int J Technol Assess Health Care. 2009;25:241–248. doi:10.1017/S0266462309990225.Google Scholar
35. European Commission. Public consultation on strengthening EU cooperation on Health Technology Assessment (HTA). http://ec.europa.eu/health/technology_assessment/consultations/cooperation_hta_en. Published 2016 (accessed December 22, 2016).Google Scholar
36. George, B, Harris, AH, Mitchell, AJ. Cost effectiveness analysis and the consistency of decision making: Evidence from pharmaceutical reimbursement in Australia 1991–96. Australia: Centre for Health Program Evaluation Melbourne; 1999.Google Scholar
37. Devlin, N, Parkin, D. Does NICE have a cost-effectiveness threshold and what other factors influence its decisions? A binary choice analysis. Health Econ. 2004;13:437–452. doi:10.1002/hec.864.Google Scholar
38. Claxton, K, Martin, S, Soares, M, et al. Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Technology Assessment, No. 19.14.) Appendix 1, Systematic review of the literature on the cost-effectiveness threshold. https://www.ncbi.nlm.nih.gov/books/NBK274312/ (accessed May 5, 2015)Google Scholar
39. Cleemput, I, Neyt, M, Thiry, N, De Laet, C, Leys, M. Threshold values for cost-effectiveness in health care. KCE Rep 100C. 2009. https://kce.fgov.be/publication/report/threshold-values-for-cost-effectiveness-in-health-care#.VUjIEPl_NBc (accessed February 23, 2014).Google Scholar
40. Claxton, K, Sculpher, M, Palmer, S, Culyer, AJ. Causes for concern: Is NICE failing to uphold its responsibilities to all NHS patients? Health Econ. 2015;24:1–7. doi:10.1002/hec.3130.Google Scholar
41. Claxton, K, Martin, S, Soares, M, et al. Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Health Technol Assess. 2015;19:1–504. doi:10.3310/hta19140.Google Scholar
42. Bobinac, A, van Exel, NJA, Rutten, FFH, Brouwer, WBF. Willingness to pay for a quality-adjusted life-year: The individual perspective. Value Health. 2010;13:1046–1055. doi:10.1111/j.1524-4733.2010.00781.x.Google Scholar
43. Simoens, S. Pricing and reimbursement of orphan drugs: The need for more transparency. Orphanet J Rare Dis. 2011;6:1172–1176.Google Scholar
44. Baltussen, R, Niessen, L. Priority setting of health interventions: The need for multi-criteria decision analysis. Cost Eff Resour Alloc. 2006;4:14.Google Scholar
45. Tony, M, Wagner, M, Khoury, H, et al. Bridging health technology assessment (HTA) with multicriteria decision analyses (MCDA): Field testing of the EVIDEM framework for coverage decisions by a public payer in Canada. BMC Health Serv Res. 2011;11:329. doi:10.1186/1472-6963-11-329.Google Scholar
46. Pearson, S, Littlejohns, P. Reallocating resources: How should the National Institute for Health and Clinical Excellence guide disinvestment efforts in the National Health Service? J Health Serv Res Policy. 2007;12:160–165. doi:10.1258/135581907781542987.Google Scholar
47. Hughes, DA, Ferner, RE. New drugs for old: Disinvestment and NICE. BMJ. 2010;340:c572. doi:10.1136/bmj.c572.CrossRefGoogle ScholarPubMed
48. Eddama, O, Coast, J. Use of economic evaluation in local health care decision-making in England: A qualitative investigation. Health Policy. 2009;89:261–270. doi:10.1016/j.healthpol.2008.06.004.Google Scholar
49. Wang, R, Lagakos, SW, Ware, JH, Hunter, DJ, Drazen, JM. Statistics in medicine — Reporting of subgroup analyses in clinical trials. N Engl J Med. 2007;357:2189–2194.CrossRefGoogle ScholarPubMed
50. Godwin, M, Ruhland, L, Casson, I, et al. Pragmatic controlled clinical trials in primary care: The struggle between external and internal validity. BMC Med Res Methodol. 2003;3:28. doi:10.1186/1471-2288-3-28.Google Scholar
51. Kaiser, C, Brunner-La Rocca, HP, Buser, PT, et al. Incremental cost-effectiveness of drug-eluting stents compared with a third-generation bare-metal stent in a real-world setting: Randomised Basel Stent Kosten Effektivitäts Trial (BASKET). Lancet. 2005;366:921–929. doi:10.1016/S0140-6736(05)67221-2.Google Scholar
52. Pietri, G, Masoura, P. Market access and reimbursement: The increasing role of real-world evidence. Value Health. 2014;17:A450–A451. doi:10.1016/j.jval.2014.08.1216.CrossRefGoogle ScholarPubMed
53. Davis, C, Lexchin, J, Jefferson, T, Gøtzsche, P, McKee, M. “Adaptive pathways” to drug authorisation: Adapting to industry? BMJ. 2016;354:i4437 Google Scholar
54. New, JP, Bakerly, ND, Leather, D, Woodcock, A. Obtaining real-world evidence: The Salford Lung Study. Thorax. 2014;69:1152–1154. doi:10.1136/thoraxjnl-2014-205259.Google Scholar
55. Tashkin, DP, Ferguson, GT. Combination bronchodilator therapy in the management of chronic obstructive pulmonary disease. Respir Res. 2013;14:49. doi:10.1186/1465-9921-14-49.CrossRefGoogle ScholarPubMed
56. Tosh, J, Stevenson, M, Akehurst, R. Health economic modelling of treatment sequences for rheumatoid arthritis: A systematic review. Curr Rheumatol Rep. 2014;16:447. doi:10.1007/s11926-014-0447-2.Google Scholar
57. Brilleman, SL, Purdy, S, Salisbury, C, Windmeijer, F, Gravelle, H, Hollinghurst, S. Implications of comorbidity for primary care costs in the UK: A retrospective observational study. Br J Gen Pract. 2013;63:e274-82. doi:10.3399/bjgp13X665242.CrossRefGoogle ScholarPubMed
58. Tappenden, P, Chilcott, J, Brennan, A, Squires, H, Stevenson, M. Whole disease modeling to inform resource allocation decisions in cancer: A methodological framework. Value Health. 2012;15:1127–1136.Google Scholar
59. Tappenden, P, Chilcott, J, Brennan, A, Squires, H, Glynne-Jones, R, Tappenden, J. Using whole disease modeling to inform resource allocation decisions: Economic evaluation of a clinical guideline for colorectal cancer using a single model. Value Health. 2013;16: 542–553.Google Scholar
60. Tosh, J, Kearns, B, Brennan, A, et al. Innovation in health economic modelling of service improvements for longer-term depression: Demonstration in a local health community. BMC Health Serv Res. 2013;13:150. doi:10.1186/1472-6963-13-150.Google Scholar
61. Hunger, M, Thorand, B, Schunk, M, et al. Multimorbidity and health-related quality of life in the older population: Results from the German KORA-age study. Health Qual Life Outcomes. 2011;9:53. doi:10.1186/1477-7525-9-53.Google Scholar
62. Logsdon, RG, Gibbons, LE, McCurry, SM, Teri, L. Assessing quality of life in older adults with cognitive impairment. Psychosom Med. 2002;64:510–519.Google Scholar
63. Bausewein, C, Simon, ST, Benalia, H, et al. Implementing patient reported outcome measures (PROMs) in palliative care-users’ cry for help. Heal Qual Life Outcomes. 2011;9:1–11.Google Scholar
64. Personal Social Services Research Unit (PSSRU). Adult Social Care Outcomes Toolkit - ASCOT. http://www.pssru.ac.uk/ascot/. Published 2015 (accessed January 3, 2017).Google Scholar
65. Peña-Longobardo, LM, Oliva-Moreno, J, Hidalgo-Vega, Á, Miravitlles, M. Economic valuation and determinants of informal care to disabled people with Chronic Obstructive Pulmonary Disease (COPD). BMC Health Serv Res. 2015;15:101. doi:10.1186/s12913-015-0759-6.Google Scholar
66. Ramsey, S, Willke, R, Briggs, A, et al. Good research practices for cost-effectiveness analysis alongside clinical trials: The ISPOR RCT-CEA Task Force report. Value Health. 2005;8:521–533. doi:10.1111/j.1524-4733.2005.00045.x.Google Scholar
67. Drummond, MF, Jefferson, TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. BMJ. 1996;313:275–283. doi:10.1136/bmj.313.7052.275.CrossRefGoogle Scholar
68. IMS Core Diabetes Model. http://www.core-diabetes.com/. Published 2013 (accessed August 1, 2015).Google Scholar
69. Kristensen, FB, Lampe, K, Chase, DL, et al. Practical tools and methods for health technology assessment in Europe: Structures, methodologies, and tools developed by the European Network for Health Technology Assessment, EUnetHTA. Int J Technol Assess Health Care. 2009;25 (Suppl 2):1–8. doi:10.1017/S0266462309990626.Google Scholar
70. NICE International. The Gates Reference Case - What it is, why it's important, and how to use it. 2014. https://www.nice.org.uk/Media/Default/About/what-we-do/NICE-International/projects/Gates-Reference-case-what-it-is-how-to-use-it.pdf (accessed July 11, 2017).Google Scholar
71. Husereau, D, Drummond, M, Petrou, S, et al. Consolidated Health Economic Evaluation Reporting Standards (CHEERS)—Explanation and elaboration: A report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting Practices Task Force. www.sciencedirect.com(accessed July 11, 2017).Google Scholar
72. World Health Organization. The economics of the social determinants of health and health inequalities: A resource book. ISBN 978 92 4 154862 5. Geneva: WHO; 2013.Google Scholar
73. Lozano, R, Naghavi, M, Foreman, K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380 (9859):2095-128.Google Scholar