Modelling the impact of multiple sclerosis on life expectancy,quality-adjusted life years and total lifetime costs: Evidence from Australia

Abstract

Objectives:

To quantify life expectancy (LE), quality-adjusted life years (QALYs) and total lifetime societal costs for a hypothetical cohort of Australians with multiple sclerosis (MS).

Methods:

A 4-state Markov model simulated progression from no/mild to moderate and severe disability and death for a cohort of 35-year-old women over a lifetime horizon. Death risks were calculated from Australian life tables, adjusted by disability severity. State-dependent relapse probabilities and associated disutilities were considered. Probabilities of MS progression and relapse were estimated from AusLong and TasMSL MS epidemiological databases. Annual societal (direct and indirect) costs (2017 Australian dollars) and health-state utilities for each state were derived from the Australian MS Longitudinal Study. Costs were discounted at 5% annually.

Results:

Mean (95% confidence interval (CI)) LE from age 35 years was 42.7 (41.6–43.8) years. This was 7.5 years less than the general Australian population. Undiscounted QALYs were 28.2 (26.3–30.0), a loss of 13.1 QALYs versus the Australian population. Discounted lifetime costs were $942,754 ($347,856–$2,820,219).

Conclusion:

We have developed a health economics model of the progression of MS, calculating the impact of MS on LE, QALYs and lifetime costs in Australia. It will form the basis for future cost-effectiveness analyses of interventions for MS.

Keywords

Multiple sclerosis Markov model life expectancy QALYs lifetime costs Australia

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References

Ahmad

Taylor

van der Mei

, et al. The impact of multiple sclerosis severity on health state utility values: Evidence from Australia. Mult Scler 2017; 23(8): 1157–1166.

Palmer

Colman

O’Leary

, et al. The economic impact of multiple sclerosis in Australia in 2010. Mult Scler 2013; 19(12): 1640–1646.

Slof

Gras

Sativex® in multiple sclerosis spasticity: A cost-effectiveness model. Expert Rev Pharmacoecon Outcomes Res 2012; 12: 439–441.

Carter

Vogan

Afzali

HHA

. Governments need better guidance to maximise value for money: The case of Australia’s Pharmaceutical Benefits Advisory Committee. Appl Health Econ Health Policy 2016; 14(4): 401–407.

Thomas

Kersten

, et al. A pragmatic parallel arm multi-centre randomised controlled trial to assess the effectiveness and cost-effectiveness of a group-based fatigue management programme (FACETS) for people with multiple sclerosis. J Neurol Neurosurg Psychiatry 2013; 84(10): 1092–1099.

Gray

Clarke

Wolstenholme

, et al. Applied methods of cost-effectiveness analysis in healthcare. Oxford: Oxford University Press, 2010.

Caloyeras

Zhang

Wang

, et al. Cost-effectiveness analysis of interferon beta-1b for the treatment of patients with a first clinical event suggestive of multiple sclerosis. Clin Ther 2012; 34(5): 1132–1144.

Nuijten

Hutton

Cost-effectiveness analysis of interferon beta in multiple sclerosis: A Markov process analysis. Value Health 2002; 5(1): 44–54.

Phillips

Humphreys

Assessing cost-effectiveness in the management of multiple sclerosis. Clinicoecon Outcomes Res 2009; 1: 61.

10.

Ahmad

van der Mei

Taylor

, et al. Estimation of annual probabilities of changing disability levels in Australians with relapsing-remitting multiple sclerosis. Mult Scler 2019; 25(13): 1800–1808.

11.

Briggs

Sculpher

An introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998; 13(4): 397–409.

12.

Kobelt

Berg

Atherly

, et al. Costs and quality of life in multiple sclerosis: A cross-sectional study in the United States. Neurology 2006; 66(11): 1696–1702.

13.

Barbosa

Taylor

Godfrey

, et al. Modelling lifetime QALYs and health care costs from different drinking patterns over time: A Markov model. Int J Methods Psychiatr Res 2010; 19(2): 97–109.

14.

Siebert

Alagoz

Bayoumi

, et al. State-transition modeling: A report of the ISPOR-SMDM modeling good research practices task force–3. Value Health 2012; 15(6): 812–820.

15.

Mount Hood 4 Modeling Group. Computer modeling of diabetes and its complications: A report on the Fourth Mount Hood Challenge Meeting. Diabetes Care 2007; 30(6): 1638–1646.

16.

Kobelt

Berg

Lindgren

, et al. Costs and quality of life of patients with multiple sclerosis in Europe. J Neurol Neurosurg Psychiatry 2006; 77(8): 918–926.

17.

Pokorski

RJ.

Long-term survival experience of patients with multiple sclerosis. J Insur Med 1997; 29(2): 101–106.

18.

Australian Government Department of Health. Technical guidelines for preparing assessment reports for the Medical Services Advisory Committee – Medical Service Type: Therapeutic (version 2.0). Canberra: Australian Government Department of Health, March 2016, http://www.msac.gov.au/internet/msac/publishing.nsf/Content/9C7DCF1C2DD56CBECA25801000123C32/$File/TherapeuticTechnicalGuidelines-Final-March2016-Version2.0-accessible.pdf

19.

Drummond

Barbieri

Cook

, et al. Transferability of economic evaluations across jurisdictions: ISPOR Good Research Practices Task Force report. Value Health 2009; 12(4): 409–418.

20.

Jackson

Multi-state modelling with R: The msm package (version 1.6.6). Cambridge: School of Clinical Medicine, MRC Biostatistics Unit, 2018.

21.

Australian Bureau of Statistics. 3302.0.55.001 - Life tables, states, territories and Australia, 2012–2014. Canberra, ACT, Australia: Australian Bureau of Statistics.

22.

Melendez-Torres

Auguste

Armoiry

, et al. Clinical effectiveness and cost-effectiveness of beta-interferon and glatiramer acetate for treating multiple sclerosis: Systematic review and economic evaluation. Health Technol Assess 2017; 21(52): 1–352.

23.

Yousefi

Taheri

Sahraian

MA.

Cost-effectiveness of alemtuzumab and natalizumab for relapsing-remitting multiple sclerosis treatment in Iran: Decision analysis based on an indirect comparison. J Med Econ 2019; 22: 71–84.

24.

Hernandez

Guo

Toro-Diaz

, et al. Peginterferon beta-1a versus other self-injectable disease-modifying therapies in the treatment of relapsing-remitting multiple sclerosis in Scotland: A cost-effectiveness analysis. J Med Econ 2017; 20(3): 228–238.

25.

Bozkaya

Livingston

Migliaccio-Walle

, et al. The cost-effectiveness of disease-modifying therapies for the treatment of relapsing-remitting multiple sclerosis. J Med Econ 2017; 20(3): 297–302.

26.

Taylor

Palmer

Simpson

Jr , et al. Assessing possible selection bias in a national voluntary MS longitudinal study in Australia. Mult Scler 2013; 19(12): 1627–1631.

27.

Australian Institute of Health and Welfare. Health expenditure Australia 2014–15. Health and welfare expenditure series no. 57. Cat. no. HWE 67. Canberra: Australian Bureau of Statistics, 2016, http://www.aihw.gov.au/WorkArea/DownloadAsset.aspx?id=60129557188 (accessed January 2017).

28.

Australian Bureau of Statistics. Total hourly rates of pay excluding bonuses: All sectors by state, original (financial year index numbers for year ended June quarter), Cat No. 6345.0 Wage Price Index. Canberra, ACT, Australia: Australian Bureau of Statistics, June 2016.

29.

Murphy

Herrman

Hawthorne

, et al. Australian WHOQoL instruments: User’s manual and interpretation guide. Melbourne, VIC, Australia: Australian WHOQOL Field Study Centre, 2000.

30.

Viney

Norman

King

, et al. Time trade-off derived EQ-5D weights for Australia. Value Health 2011; 14(6): 928–936.

31.

O’Connell

Kelly

Fogarty

, et al. Economic costs associated with an MS relapse. Mult Scler Relat Disord 2014; 3(6): 678–683.

32.

Briggs

Weinstein

Fenwick

, et al. Model parameter estimation and uncertainty analysis: A report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group–6. Med Decis Making 2012; 32(5): 722–732.

33.

Menzies Institute for Medical Research. Benchmark for quality-adjusted life years calculated using EQ-5D: Australia, 2017, http://menzies.utas.edu.au/__data/assets/pdf_file/0012/986394/QALY_Benchmarks_06_16_2017.pdf (accessed November 2017).

34.

Milo

Miller

Revised diagnostic criteria of multiple sclerosis. Autoimmun Rev 2014; 13(4–5): 518–524.

35.

Scalfari

Knappertz

Cutter

, et al. Mortality in patients with multiple sclerosis. Neurology 2013; 81(2): 184–192.

36.

Campbell

Ghushchyan

McQueen

, et al. Burden of multiple sclerosis on direct, indirect costs and quality of life: National US estimates. Mult Scler Relat Disord 2014; 3(2): 227–236.

37.

Lee

Baxter

Limone

, et al. Cost-effectiveness of fingolimod versus interferon beta-1a for relapsing remitting multiple sclerosis in the United States. J Med Econ 2012; 15(6): 1088–1096.

38.

Palmer

Tucker

DM.

Cost and clinical implications of diabetes prevention in an Australian setting: A long-term modeling analysis. Prim Care Diabetes 2012; 6(2): 109–121.

39.

Grima

Torrance

Francis

, et al. Cost and health related quality of life consequences of multiple sclerosis. Mult Scler 2000; 6(2): 91–98.

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