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A SYSTEMATIC APPROACH FOR ASSESSING, IN THE ABSENCE OF FULL EVIDENCE, WHETHER MULTICOMPONENT INTERVENTIONS CAN BE MORE COST-EFFECTIVE THAN SINGLE COMPONENT INTERVENTIONS

Part of: Special Collection - Methods

Published online by Cambridge University Press:  11 September 2017

Janne C. Mewes ,
Lotte M.G. Steuten ,
Maarten J. IJzerman  and
Wim H. van Harten
Janne C. Mewes
Affiliation:
Health Technology and Services Research, Faculty of Behavioral, Management and Social Sciences, University of Twente
Lotte M.G. Steuten
Affiliation:
Hutchinson Institute for Cancer Outcomes Research & University of Washington
Maarten J. IJzerman
Affiliation:
Health Technology and Services Research, Faculty of Behavioral, Management and Social Sciences, and MIRA Institute for Biomedical Technology & Technical Medicine, University of Twente
Wim H. van Harten
Affiliation:
Health Technology and Services Research, Faculty of Behavioral, Management and Social Sciences, University of Twente Department of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Rijnstate General [email protected]
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Abstract

Objectives: Multicomponent interventions (MCIs), consisting of at least two interventions, are common in rehabilitation and other healthcare fields. When the effectiveness of the MCI versus that of its single interventions is comparable or unknown, evidence of their expected incremental cost-effectiveness can be helpful in deciding which intervention to recommend. As such evidence often is unavailable this study proposes an approach to estimate what is more cost-effective; the MCI or the single intervention(s).

Methods: We reviewed the literature for potential methods. Of those identified, headroom analysis was selected as the most suitable basis for developing the approach, based on the criteria of being able to estimate the cost-effectiveness of the single interventions versus that of the MCI (a) within a limited time frame, (b) in the absence of full data, and (c) taking into account carry-over and interaction effects. We illustrated the approach with an MCI for cancer survivors.

Results: The approach starts with analyzing the costs of the MCI. Given a specific willingness-to-pay-value, it is analyzed how much effectiveness the MCI would need to generate to be considered cost-effective, and if this is likely to be attained. Finally, the cost-effectiveness of the single interventions relative to the potential of the MCI for being cost-effective can be compared.

Conclusions: A systematic approach using headroom analysis was developed for estimating whether an MCI is likely to be more cost effective than one (or more) of its single interventions.

Keywords

Cost-effectivenessMulticomponentMultidimensionalMethodology
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 33 , Issue 4: Mini-Theme: Stories of HTA Impact , 2017 , pp. 444 - 453
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

1. Comprehensive Cancer Centre the Netherlands (IKNL). Cancer rehabilitation - Nation-Wide Guideline. Utrecht: IKNL; 2011.Google Scholar
2. Balinsky, W, Muennig, P. The costs and outcomes of multifaceted interventions designed to improve the care of congestive heart failure in the inpatient setting: A review of the literature. Med Care Res Rev. 2003;60:275–293.Google Scholar
3. Chou, R, Loeser, JD, Owens, DK, et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain. Spine. 2009;34:1066–1077.CrossRefGoogle ScholarPubMed
4. Prvu Bettger, JA, Stineman, MG. Effectiveness of multidisciplinary rehabilitation services in postacute care: State-of-the-science. A review. Arch Phys Med Rehabil. 2007;88:1526–1534.Google Scholar
5. Smeets, RJ, Severens, JL, Beelen, S, et al. More is not always better: Cost-effectiveness analysis of combined, single behavioral and single physical rehabilitation programs for chronic low back pain. Eur J Pain. 2009;13:71–81.Google Scholar
6. IOM (Institute of Medicine). Cognitive rehabilitation therapy for traumatic brain injury: Evaluating the evidence. Washington, DC: The National Academies Press; 2011.Google Scholar
7. Ollendorf, DA, Silverstein, MD, Andry, A, et al. Management options for patients with low back disorders: Final appraisal document. Boston: Institute for Clinical and Economic Review; 2011.Google Scholar
8. Saevarsson, S, Halsband, U, Kristjánsson, Á. Designing rehabilitation programs for neglect: Could 2 be more than 1+1? Appl Neuropsychol. 2011;18:95–106.Google Scholar
9. Ollenschlager, G. Improving the quality of health care: Using international collaboration to inform guideline programmes by founding the Guidelines International Network (G-I-N). Qual Saf Health Care. 2004;13:455–460.Google Scholar
10. Qaseem, A, Forland, F, Macbeth, F, et al. Guidelines International Network: Toward international standards for clinical practice guidelines. Ann Intern Med. 2012;156:525–531.Google Scholar
11. Mason, J, Eccles, M, Freemantle, N, et al. A framework for incorporating cost-effectiveness in evidence-based clinical practice guidelines. Health Policy. 1999;47:37–52.Google Scholar
12. Rickles, D, Hawe, P, Shiell, A. A simple guide to chaos and complexity. J Epidemiol Commun Health. 2007;61:933–937.Google Scholar
13. Lovemen, E, Frampton, GK, Shepherd, J, et al. The clinical effectiveness and cost-effectiveness of long-term weight management schemes for adults: A systematic review. Health Technol Assess. 2011;15:1–182.Google Scholar
14. Ramsey, SD, Willke, RJ, Glick, H, et al. Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report. Value Health. 2015;18:161–172.CrossRefGoogle ScholarPubMed
15. Hay, J, Smeeding, J, Carroll, N, et al. Good research practices for measuring drug costs in cost effectiveness analyses: Issues and recommendations: The ISPOR Drug Cost Task Force Report–Part 1. Value Health. 2010;13:3–7.CrossRefGoogle Scholar
16. Guyatt, G, Oxman, A, Vist, G, et al. GRADE: What is “quality of evidence” and why is it important to clinicans? BMJ. 2008;336:995–998.Google Scholar
17. Danish Centre for Evaluation and Health Technology Assessment (DACEHTA). Introduction to mini-HTA - A management and decision support tool for the hospital service. Copenhagen: DACEHTA; 2005.Google Scholar
18. Senn, S. Cross-over trials in clinical research. Chichester: John Wiley & Sons; 1993.Google Scholar
19. synergy, Geary N. Understanding. Am J Physiol Endocrinol Metab. 2013;304:E237–E253.Google Scholar
20. Hollingsworth, B, Peacock, S. Efficiency measurement in health and health care. New York: Routledge; 2008.Google Scholar
21. Duijts, SFA, Van Beurden M, Oldenburg HSA, et al. Efficacy of cognitive behavioral therapy and physical exercise in alleviating treatment-induced menopausal symptoms in patients with breast cancer: Results of a randomized, controlled, multicenter trial. J Clin Oncol. 2012;30:4124–4133.Google Scholar
22. Mewes, J, Steuten, LD, Oldenburg, H SFA, et al. Cost-effectiveness of cognitive behavioral therapy and physical exercise for alleviating treatment-induced menopausal symptoms in breast cancer patients. J Cancer Surviv. 2014;9:126–135.Google Scholar
23. Duijts, SFA, Oldenburg, HSA, van Beurden, M, et al. Cognitive behavioral therapy and physical exercise for climacteric symptoms in breast cancer patients experiencing treatment-induced menopause: Design of a multicenter trial. BMC Womens Health. 2009;9:15.Google Scholar
24. Ivarsson, T, Spetz, AC, Hammar, M. Physical exercise and vasomotor symptoms in postmenopausal women. Maturitas. 1998;29:139–146.CrossRefGoogle ScholarPubMed
25. Li, C, Samsioe, G, Borgfeldt, C, et al. Menopause-related symptoms: What are the background factors? A prospective population-based cohort study of Swedish women (The Women's Health in Lund Area study). Am J Obstet Gynecol. 2003;189:1646–1653.Google Scholar
26. Hunter, M. Cognitive behavioural interventions for premenstrual and menopausal symptoms. J Reprod Infant Psychol. 2003;21:183–193.Google Scholar
27. McKinlay, SM, Brambilla, DJ, Posner, JG. Reprint of the normal menopausal transition. Maturitas. 2008;61:4–16.Google Scholar
28. Duijts, SFA, Faber, MM, Oldenburg, HSA, et al. Effectiveness of behavioral techniques and physical exercise on psychosocial functioning and health-related quality of life in breast cancer patients and survivors-a meta-analysis. Psycho-Oncology. 2011;20:115–126.Google Scholar
29. Eccles, M, Mason, J. How to develop cost-conscious guidelines. Health Technol Assess. 2001;5:1–78.Google Scholar
30. Gandjour, A, Lauterbach, KW. A method for assessing the cost-effectiveness and the break-even point of clinical practice guidelines. Int J Technol Assess Health Care. 2001;17:503–516.CrossRefGoogle ScholarPubMed
31. OECD. Cost-benefit analysis and the environment: Recent developments. Paris: OECD; 2006.Google Scholar
32. Dixon, J. Economic cost-benefit analysis (CBA) of project environmental issues and mitigation measures: Implementation guideline. Washington, DC: Inter-American Development Bank; 2012.Google Scholar
33. Heinzerling, L, Ackerman, F. Pricing the priceless: Cost-benefit analysis of environmental protection. Washington, DC: Georgetown University; 2002.Google Scholar
34. Liu, W, Kuramoto, SJ, Stuart, EA. An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research. Prev Sci. 2013;14:570–580.Google Scholar
35. Basu, A, Dale, W, Elstein, A, et al. A linear index for predicting joint health-states utilities from single health-states utilities. Health Econ. 2009;18:403–419.CrossRefGoogle ScholarPubMed
36. Barasa, EW, Ayieko, P, Cleary, S, et al. A multifaceted intervention to improve the quality of care of children in district hospitals in Kenya: A cost-effectiveness analysis. PLoS Med. 2012;9:e1001238.Google Scholar
37. Lee, CC, Czaja, SJ, Schulz, R. The moderating influence of demographic characteristics, social support, and religious coping on the effectiveness of a multicomponent psychosocial caregiver intervention in three racial ethnic groups. J Gerontol B Psychol Sci Soc Sci. 2010;65B:185–194.Google Scholar
38. Helgeson, VS, Lepore, SJ, Eton, DT. Moderators of the benefits of psychoeducational interventions for men with prostate cancer. Health Psychol. 2006;25:348–354.Google Scholar
39. Schootman, M, Deshpande, AD, Pruitt, S, et al. Estimated effects of potential interventions to prevent decreases in self-rated health among breast cancer survivors. Ann Epidemiol. 2012;22:79–86.Google Scholar
34. Barasa, EW, English, M. Viewpoint: Economic evaluation of package of care interventions employing clinical guidelines. Trop Med Int Health. 2011;16:97–104.Google Scholar
41. Ahern, J, Hubbard, A, Galea, S. Estimating the effects of potential public health interventions on population disease burden: A step-by-step illustration of causal inference methods. Am J Epidemiol. 2009;169:1140–1147.Google Scholar
42. Hardeman, W. A causal modelling approach to the development of theory-based behaviour change programmes for trial evaluation. Health Educ Res. 2005;20:676-87.CrossRefGoogle Scholar
43. Gandjour, A. A model to predict the cost-effectiveness of disease management programs. Health Econ. 2009;19:697–715.Google Scholar
44. Brooks, JM, Fang, G. Interpreting treatment-effect estimates with heterogeneity and choice: Simulation model results. Clin Ther. 2009;31:902–919.CrossRefGoogle ScholarPubMed
45. Hersh, AL, Black, WC, Tosteson, ANA. Estimating the population impact of an intervention: A decision-analytic approach. Stat Methods Med Res. 1999;8:311–330.CrossRefGoogle ScholarPubMed
46. Becker, MG, Glass, K, Barnes, B, et al. Using mathematical models to assess response to an outbreak of an emerged viral repiratory disease. The Australian National University: National Centre for Epidemiology and Population Health; 2006.Google Scholar
47. Tappenden, P, Chilcott, J, Brennan, A, et al. Whole disease modeling to inform resource allocation decisions in cancer: A methodological framework. Value Health. 2012;15:1127–1136.Google Scholar
48. Belle, SH, Czaja, SJ, Schulz, R, et al. Using a new taxonomy to combine the uncombinable: Integrating results across diverse interventions. Psychol Aging. 2003;18:396–405.Google Scholar
49. Schulz, R, Czaja, SJ, McKay, JR, et al. Intervention taxanomy (ITAX): Describing essential features of interventions (HMC). Am J Health Behav. 2010;34:811–821.CrossRefGoogle Scholar
50. Gitlin, LN, Belle, SH, Burgio, LD, et al. Effect of multicomponent interventions on caregiver burden and depression: The REACH multisite initiative at 6-month follow-up. Psychol Aging. 2003;18:361–374.Google Scholar
51. Schouten, LMT, Grol, RPTM, Hulscher, MEJL. Factors influencing success in quality-improvement collaboratives: Development and psychometric testing of an instrument. Implement Sci. 2010;5:84.CrossRefGoogle ScholarPubMed
52. Czaja, SJ, Schulz, R, Lee, CC, et al. A methodology for describing and decomposing complex psychosocial and behavioral interventions. Psychol Aging. 2003;18:385–395.Google Scholar
53. Cobiac, LJ, Vos, T, Barendregt, JJ. Cost-effectiveness of interventions to promote physical activity: A modelling study. PLoS Med. 2009;6:e1000110.Google Scholar
54. Gentry, D, Herbers, S, Shelton, S, et al. What is it worth? Economic evaluation of the MFH Tobacco Initiative. St. Louis: Missouri Foundation for Health; 2009.Google Scholar
55. Hoaglin, DC, Hawkins, N, Jansen, JP, et al. Conducting indirect-treatment-comparison and network-meta-analysis studies: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 2. Value Health. 2011;14:429–437.Google Scholar
56. Jansen, JP, Fleurence, R, Devine, B, et al. Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: Report of the ISPOR Task Force on indirect treatment comparisons good research practices: part 1. Value Health. 2011;14:417–428.Google Scholar
57. IJzerman, MJ, Steuten, LMG. Early assessment of medical technologies to inform product development and market access: A review of methods and applications. Appl Health Econ Health Policy. 2011;9:331–347.Google Scholar
58. Cosh, E, Girling, A, Lilford, R, et al. Investing in new medical technologies: A decision framework. J Commer Biotechnol. 2007;13:263–271.Google Scholar
59. McAteer, H, Cosh, E, Freeman, G, et al. Cost-effectiveness analysis at the development phase of a potential health technology: Examples based on tissue engineering of bladder and urethra. J Tissue Eng Regen Med. 2007;1:343–349.Google Scholar
60. Girling, A, Lilford, R, Cole, A, Young, T. Headroom approach to device development: Current and future directions. Int J Technol Assess Health Care. 2015;31:331–338.Google Scholar
61. Moore, GF, Audrey, S, Barker, M et al. Process evaluation of complex interventions: Medical Research Council Guideline. BMJ. 2016;350:h1258.Google Scholar
62. Bonell, C, Fletcher, A, Morton, M, et al. Realist randomised controlled trials: A new approach to evaluating complex public health interventions. Soc Sci Med. 2012;75:2299–2306.Google Scholar
63. Moore, G, Audrey, S, Barker, M, et al. Process evaluation of complex interventions: Medical Research Council guidance. London: MRC Population Health Science Research Network; 2014.Google Scholar
64. Moore, G, Audrey, S, Barker, M, et al. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015;350:h1258.Google Scholar
65. Chandler, CIR, DiLiberto, D, Nayiga, S, et al. The PROCESS study: A protocol to evaluate the implementation, mechanisms of effect and context of an intervention to enhance public health centres in Tororo, Uganda. Implement Sci. 2013;8:113.CrossRefGoogle ScholarPubMed