Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-25T04:30:27.840Z Has data issue: false hasContentIssue false

Incorporating Household Spillovers in Cost Utility Analysis: A Case Study Using Behavior Change in COPD

Published online by Cambridge University Press:  08 May 2019

Arjun Bhadhuri*
Affiliation:
European Center of Pharmaceutical Medicine, University of Basel, Basel, Switzerland
Hareth Al-Janabi
Affiliation:
Health Economics Unit, University of Birmingham, Birmingham, United Kingdom
Sue Jowett
Affiliation:
Health Economics Unit, University of Birmingham, Birmingham, United Kingdom
Kate Jolly
Affiliation:
Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom
*
Author for correspondence: Arjun Bhadhuri, E-mail: [email protected]

Abstract

Objectives

It is important to capture all health effects of interventions in cost-utility analyses conducted under a societal or healthcare perspective. However, this is rarely done. Household spillovers (health effects on patients’ other household members) may be particularly likely in the context of technologies and interventions to change behaviors that are interdependent in the household. Our objective was to prospectively collect outcome data from household members and illustrate how these can be included in a cost-utility analysis of a behavior change intervention in chronic obstructive pulmonary disease (COPD).

Methods

Data were collected from patients’ household members (n = 153) alongside a randomized controlled trial of a COPD self-management intervention. The impact of the intervention on household members’ EQ-5D-5L scores (primary outcome), was evaluated. Analyses were then carried out to estimate household members’ quality-adjusted life-years (QALYs) and assess the impact of including these QALYs on cost-effectiveness.

Results

The intervention had a negligible spillover on household members’ EQ-5D-5L scores (−0.007; p = .75). There were also no statistically significant spillovers at the 5 percent level in household member secondary outcomes. In the base-case model, the inclusion of household member QALYs in the incremental cost-effectiveness ratio (ICER) denominator marginally increased the ICER from GBP 10,271 (EUR 13,146) to GBP 10,991 (EUR 14,068) per QALY gained.

Conclusions

This study demonstrates it is feasible to prospectively collect and include household members’ outcome data in cost utility analysis, although the study highlighted several methodological issues. In this case, the intervention did not impact significantly on household members’ health or health behaviors, but inclusion of household spillovers may make a difference in other contexts.

Type
Method
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Informed consent was obtained from all participants of this study. The models and methodology used in the research are not proprietary. The data used in this research are under the management of Kate Jolly.

References

1.Al-Janabi, H, Van Exel, J, Brouwer, W, et al. (2016) Measuring health spillovers for economic evaluation: A case study in meningitis. Health Econ 25, 15291544.Google Scholar
2.Sanders, GD, Neumann, PJ, Basu, A, et al. (2016) Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second panel on cost-effectiveness in health and medicine. JAMA 316, 10931103.Google Scholar
3.Brouwer, WBF (2018) The inclusion of spillover effects in economic evaluations: Not an optional extra. PharmacoEconomics doi:10.1007/s40273-018-0730-6.Google Scholar
4.National Institute for Health and Care Excellence (2013) Guide to the methods of technology appraisal. http://www.nice.org.uk/article/pmg9/chapter/foreword.Google Scholar
5.Bhadhuri, A, Jowett, S, Jolly, K, Al-Janabi, H (2017) A comparison of the validity and responsiveness of the EQ-5D-5L and SF-6D for measuring health spillovers: A study of the family impact of meningitis. Med Decis Making 37, 882893.Google Scholar
6.Bobinac, A, van Exel, NJ, Rutten, FF, Brouwer, WB (2010) Caring for and caring about: Disentangling the caregiver effect and the family effect. J Health Econ 29, 549556.Google Scholar
7.Falba, TA, Sindelar, JL (2008) Spousal concordance in health behavior change. Health Serv Res 43(Pt 1), 96116.Google Scholar
8.Wittenberg, E, Prosser, L (2013) Disutility of illness for caregivers and families: A systematic review of the literature. PharmacoEconomics 31, 489500.Google Scholar
9.Christakis, NA (2004) Social networks and collateral health effects. BMJ. 329, 184–5.Google Scholar
10.Al-Janabi, H, Nicholls, J, Oyebode, J (2016) The need to “carer proof” healthcare decisions. BMJ 352:i1651.Google Scholar
11.Tarlow, BJ, Wisniewski, S, Belle, S, Rubert, M, Ory, M, Gallagher-Thompson, D (2004) Positive aspects of caregiving: Contributions of the REACH Project to the development of new measures for Alzheimer's caregiving. Res Aging 26, 429453.Google Scholar
12.Kovacs Burns, K, Nicolucci, A, Holt, RI, et al. (2013) Diabetes attitudes, wishes and needs second study (DAWN2): Cross-national benchmarking indicators for family members living with people with diabetes. Diabet Med 30, 778788.Google Scholar
13.Wittenberg, E, Saada, A, Prosser, L (2013) How illness affects family members: A qualitative interview survey. Patient 6, 257268.Google Scholar
14.Rossini, R, Moscatiello, S, Tarrini, G, et al. (2011) Effects of cognitive-behavioral treatment for weight loss in family members. J Am Diet Assoc 111, 17121719.Google Scholar
15.Gorin, AA, Wing, RR, Fava, JL, et al. (2008) Weight loss treatment influences untreated spouses and the home environment: Evidence of a ripple effect. Int J Obes (Lond) 32, 16781684.Google Scholar
16.Matsuo, T, Kim, MK, Murotake, Y, et al. (2005) Indirect lifestyle intervention through wives improves metabolic syndrome components in men. Int J Obes (Lond) 34, 136145.Google Scholar
17.Schierberl, Scherr AE, Brenchley KJ, McClure, Gorin, AA (2013) Examining a ripple effect: Do spouses’ behavior changes predict each other's weight loss? J Obes 2013:297268.Google Scholar
18.Flood, C, Mugford, M, Stewart, S, Harvey, I, Poland, F, Lloyd-Smith, W (2005) Occupational therapy compared with social work assessment for older people. An economic evaluation alongside the CAMELOT randomised controlled trial. Age Ageing 34, 4752.Google Scholar
19.Meeuwsen, E, Melis, R, Van Der Aa, G, et al. (2013) Cost-effectiveness of one year dementia follow-up care by memory clinics or general practitioners: Economic evaluation of a randomised controlled trial. PLoS One 8, e79797.Google Scholar
20.Sturkenboom, IH, Hendriks, JC, Graff, MJ, et al. (2015) Economic evaluation of occupational therapy in Parkinson's disease: A randomized controlled trial. Mov Disord 30, 10591067.Google Scholar
21.Schawo, S, van der Kolk, A, Bouwmans, C, et al. (2015) Probabilistic markov model estimating cost effectiveness of methylphenidate osmotic-release oral system versus immediate-release methylphenidate in children and adolescents: Which information is needed? PharmacoEconomics 33, 489509.Google Scholar
22.Gani, R, Giovannoni, G, Bates, D, Kemball, B, Hughes, S, Kerrigan, J (2008) Cost-effectiveness analyses of natalizumab (Tysabri) compared with other disease-modifying therapies for people with highly active relapsing-remitting multiple sclerosis in the UK. PharmacoEconomics 26, 617627.Google Scholar
23.Goodrich, K, Kaambwa, B, Al-Janabi, H (2012) The inclusion of informal care in applied economic evaluation: A review. Value Health 15, 975981.Google Scholar
24.Christensen, H, Trotter, CL, Hickman, M, Edmunds, WJ (2014) Re-evaluating cost effectiveness of universal meningitis vaccination (Bexsero) in England: Modelling study. BMJ 349:g5725.Google Scholar
25.Krol, M, Papenburg, J, van Exel, J (2015) Does including informal care in economic evaluations matter? A systematic review of inclusion and impact of informal care in cost-effectiveness studies. PharmacoEconomics 33, 123135.Google Scholar
26.Itzler, RF, Chen, PY, Lac, C, El Khoury, AC, Cook, JR (2011) Cost-effectiveness of a pentavalent human-bovine reassortant rotavirus vaccine for children ≤5 years of age in Taiwan. J Med Econ 14, 748758.Google Scholar
27.McCabe, C (2018) Expanding the scope of costs and benefits for economic evaluations in health: Some words of caution. PharmacoEconomics doi:10.1007/s40273-018-0729-z.Google Scholar
28.Sidhu, MS, Daley, A, Jordan, R, et al. (2015) Patient self-management in primary care patients with mild COPD – Protocol of a randomised controlled trial of telephone health coaching. BMC Pulm Med 15, 1516.Google Scholar
29.Hanania, NA, Sharafkhaneh, A (2010) COPD: A guide to diagnosis and clinical management. New York, NY: Humana Press.Google Scholar
30.World Health Organization (2014) The 10 leading causes of death in the world, 2000 and 2012. http://www.who.int/mediacentre/factsheets/fs310/en/.Google Scholar
32.Figueiredo, D, Gabriel, R, Jacome, C, Cruz, J, Marques, A (2014) Caring for relatives with chronic obstructive pulmonary disease: How does the disease severity impact on family carers? Aging Ment Health 18, 385393.Google Scholar
33.Ross, E, Graydon, JE (1997) The impact on the wife of caring for a physically ill spouse. J Women Aging 9, 2335.Google Scholar
34.Gabriel, R, Figueiredo, D, Jacome, C, Cruz, J, Marques, A (2014) Day-to-day living with severe chronic obstructive pulmonary disease: Towards a family-based approach to the illness impacts. Psychol Health 29, 967983.Google Scholar
35.Khan, A, Dickens, AP, Adab, P, Jordan, RE (2017) Self-management behaviour and support among primary care COPD patients: Cross-sectional analysis of data from the Birmingham Chronic Obstructive Pulmonary Disease Cohort. NPJ Prim Care Respir Med 27, 46.Google Scholar
36.Jolly, K, Sidhu, M, Hewitt, C, et al. (2018) Patient self-management in primary care patients with mild COPD - A randomised controlled trial of telephone health coaching. BMJ 361, k2241.Google Scholar
37.Devlin, NJ, Brooks, R (2017) EQ-5D and the EuroQol Group: Past, present and future. Appl Health Econ Health Policy 15, 127137.Google Scholar
38.Devlin, N, Shah, K, Feng, Y, Mulhern, B, van Hout, B (2018) Valuing health-related quality of life: An EQ-5D-5L value set for England. Health Econ 27, 722.Google Scholar
39.Curtis, L, Burns, A (2015) Unit costs of health and social care. Canterbury: PSSRU.Google Scholar
40.European Central Bank (2019) ECB/Eurosystem policy and exchange rates. https://www.ecb.europa.eu/home/html/index.en.html.Google Scholar
41.Hollis, S, Campbell, F (1999) What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ 319, 670674.Google Scholar
42.Torgerson, DJ, Torgerson, CJ (2008) Designing randomised trials in health, education and the social sciences: An introduction. Basingstoke, UK: Palgrave Macmillan.Google Scholar
43.Stewart, S, Harvey, I, Poland, F, Lloyd-Smith, W, Mugford, M, Flood, C (2005) Are occupational therapists more effective than social workers when assessing frail older people? Results of CAMELOT, a randomised controlled trial. Age Ageing 34, 4146.Google Scholar
44.Al-Janabi, H, Van Exel, J, Brouwer, W, Coast, J (2016) A framework for including family health spillovers in economic evaluation. Med Decis Making 36, 176186.Google Scholar
45.Gautun, H, Werner, A, Luras, H (2012) Care challenges for informal caregivers of chronically ill lung patients: Results from a questionnaire survey. Scand J Public Health 40, 1824.Google Scholar
46.Jurj, AL, Wen, W, Li, HL, et al. (2006) Spousal correlations for lifestyle factors and selected diseases in Chinese couples. Ann Epidemiol 16, 285291.Google Scholar
47.Morris, TP, White, IR, Royston, P (2014) Tuning multiple imputation by predictive mean matching and local residual draws. BMC Med Res Methodol 14, 75.Google Scholar
48.Manca, A, Hawkins, N, Sculpher, MJ (2005) Estimating mean QALYs in trial-based cost-effectiveness analysis: The importance of controlling for baseline utility. Health Econ 14, 487496.Google Scholar
49.Tilford, JM, Grosse, SD, Robbins, JM, Pyne, JM, Cleves, MA, Hobbs, CA (2005) Health state preference scores of children with spina bifida and their caregivers. Qual Life Res 14, 10871098.Google Scholar
50.Bobinac, A, van Exel, NJ, Rutten, FF, Brouwer, WB (2011) Health effects in significant others: Separating family and care-giving effects. Med Decis Making 31, 292298.Google Scholar
51.Wittenberg, E, Ritter, GA, Prosser, LA (2013) Evidence of spillover of illness among household members: EQ-5D scores from a US sample. Med Decis Making 33, 235243.Google Scholar
52.Hoefman, RJ, Van Exel, J, Brouwer, W (2013) How to include informal care in economic evaluations. PharmacoEconomics 31, 11051119.Google Scholar
Supplementary material: File

Bhadhuri et al. supplementary material

Bhadhuri et al. supplementary material 1

Download Bhadhuri et al. supplementary material(File)
File 17.3 KB
Supplementary material: Image

Bhadhuri et al. supplementary material

Bhadhuri et al. supplementary material 2

Download Bhadhuri et al. supplementary material(Image)
Image 1.6 MB