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Informing disinvestment through cost-effectiveness modelling

Is lack of data a surmountable barrier?

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Abstract

The mandatory nature of recommendations made by the National Institute for Health and Clinical Excellence (NICE) in the UK has highlighted inherent difficulties in the process of disinvestment in existing technologies to fund NICE-approved technologies. A lack of evidence on candidate technologies means that the process of disinvestment is subject to greater uncertainty than the investment process, and inefficiencies may occur as a result of the inverse evidence law.

This article describes a potential disinvestment scenario and the options for the decision maker, including the conduct of value of information analyses. To illustrate the scenario, an economic evaluation of a disinvestment candidate (screening for amblyopia and strabismus) is presented. Only very limited data were available. The reference case analysis found that screening is not cost effective at currently accepted values of a QALY. However, a small utility decrement due to unilateral vision loss reduced the incremental cost per QALY gained, with screening expected to be extremely cost effective.

The discussion highlights the specific options to be considered by decision makers in light of the model-based evaluation. It is shown that the evaluation provides useful information to guide the disinvestment decision, providing a range of focused options with respect to the decision and the decision-making process.

A combination of explicit model-based evaluation, and pragmatic and generalizable approaches to interpreting uncertainty in the decision-making process is proposed, which should enable informed decisions around the disinvestment of technologies with weak evidence bases.

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References

  1. House of Commons Health Committee, National Institute for Health and Clinical Excellence. First report of session 2007-08. Vol. I. Report, together with formal minutes, 10 January 2008, London: The Stationery Office [online]. Available from URL: http://www.parliament.the-stationery-office.co.uk/pa/cm200708/cmselect/cmhealth/27/27.pdf [Accessed 2008 Aug 28]

    Google Scholar 

  2. National Institute for Health and Clinical Excellence. Response to the Health Select Committee’s report on the National Institute for Health and Clinical Excellence [online]. Available from URL: http://www.nice.org.uk/media/F51/D0/HSC2007OfficialResponseFINAL.pdf [Accessed 2008 Aug 28]

  3. Elshaug AG, Moss JR, Littlejohns P, et al. Identifying existing health care services that do not provide value for money. Med J Aust 2009; 190(5): 269–73

    PubMed  Google Scholar 

  4. Chilcott J, Brennan A, Booth A, et al. The role of modelling in planning and prioritising clinical trials. Health Technol Assess 2003; 7(23): iii, 1-125

    Google Scholar 

  5. Eckermann S, Willan AR. Expected value of information and decision making in HTA. Health Econ 2007; 16(2): 195–209

    Article  PubMed  Google Scholar 

  6. Snowdon SK, Stewart-Brown SL. Preschool vision screening. Health Technol Assess 1997; 1(8): i–iv, 1-83

    PubMed  CAS  Google Scholar 

  7. Carlton J, Karnon J, Czoski-Murray C, et al. The clinical effectiveness and cost-effectiveness of screening programmes for amblyopia and strabismus in children up to the age of 4–5 years: a systematic review and economic evaluation. Health Technol Assess 2008; 12(25): iii, xi–194

    Google Scholar 

  8. Pediatric Eye Disease Investigator Group. A randomized trial of atropine versus patching for treatment of moderate amblyopia in children. Arch Ophthalmol 2002; 120: 268–78

    Article  Google Scholar 

  9. Repka MX, Cotter SA, Beck RW, et al. A randomized trial of atropine regimens for treatment of moderate amblyopia in children. Ophthalmology 2004; 111: 2076–85

    Article  PubMed  Google Scholar 

  10. Karnon J, McIntosh A, Dean J, et al. A prospective hazard and improvement analytic approach to predicting the effectiveness of medication error interventions. Saf Sci 2007; 45: 523–39

    Article  Google Scholar 

  11. Karnon J, Czoski Murray C, Smith KJ, et al. A hybrid cohort individual sampling natural history model of age-related macular degeneration: assessing the cost-effectiveness of screening using probabilistic calibration. Med Decis Making. Epub 2009 Jan 6

    Google Scholar 

  12. Dobson V, Fulton AB, Sebris SL. Cycloplegic refractions of infants and young children: the axis of astigmatism. Invest Ophthalmol Vis Sci 1984; 25(1): 83–7

    PubMed  CAS  Google Scholar 

  13. Abrahamsson M, Fabian G, Andersson AK, et al. A longitudinal study of a population based sample of astigmatic children: I. Refraction and amblyopia. Acta Ophthalmol 1990; 68(4): 428–34

    CAS  Google Scholar 

  14. Abrahamsson M, Fabian G, Sjostrand J. A longitudinal study of a population based sample of astigmatic children: II. The changeability of anisometropia. Acta Ophthalmol 1990; 68(4): 435–40

    CAS  Google Scholar 

  15. Dobson V, Sebris SL. Longitudinal study of acuity and stereopsis in infants with or at-risk for esotropia. Invest Ophthalmol Vis Sci 1989; 30(6): 1146–58

    PubMed  CAS  Google Scholar 

  16. Townshend AM, Holmes JM, Evans LS. Depth of anisometropic amblyopia and difference in refraction. Am J Ophthalmol 1993; 116(4): 431–6

    PubMed  CAS  Google Scholar 

  17. Donahue SP. The relationship between anisometropia, patient age, and the development of amblyopia. Trans Am Ophthalmol Soc 2005; 103: 313–36

    PubMed  Google Scholar 

  18. Ingram RM, Gill LE, Goldacre MJ. Emmetropisation and accommodation in hypermetropic children before they show signs of squint: a preliminary analysis. Bull Soc Belg Ophtalmol 1994; 253: 41–56

    CAS  Google Scholar 

  19. Ingram RM, Walker C, Wilson JM, et al. Prediction of amblyopia and squint by means of refraction at age 1 year. Br J Ophthalmol 1986; 70: 12–5

    Article  PubMed  CAS  Google Scholar 

  20. Williams C, Harrad RA, Harvey I, et al. Screening for amblyopia in preschool children: results of a population-based, randomised controlled trial. ALSPAC Study Team. Ophthal Epidemiol 2001; 8(5): 279–95

    CAS  Google Scholar 

  21. Stayte M, Reeves B, Wortham C, et al. Ocular and vision defects in preschool children. Br J Ophthalmol 1993; 77: 228–32

    Article  PubMed  CAS  Google Scholar 

  22. Schmidt P, Maguire M, Dobson V, et al. Comparison of preschool vision screening tests as administered by licensed eye care professionals in the Vision In Preschoolers Study. Ophthalmology 2004; 111(4): 637–50

    Article  PubMed  Google Scholar 

  23. Stewart CE, Moseley MJ, Stephens DA, et al., on behalf of the MOTAS cooperative. The treatment dose response in amblyopia therapy: results from the monitored occlusion treatment of amblyopia study (MOTAS). Invest Ophthalmol Vis Sci 2005; 45: 3048–54

    Article  Google Scholar 

  24. Rahi J, Logan S, Timms C, et al. Risk, causes, and outcomes of visual impairment after loss of vision in the non-amblyopic eye: a population-based study. Lancet 2002; 360(9333): 597–602

    Article  PubMed  Google Scholar 

  25. Meads C, Moore D. The clinical effectiveness and cost utility of photodynamic therapy for wet age-related macular degeneration: a systematic review and economic evaluation. Health Technol Assess 2001; 7(9): 1–98

    Google Scholar 

  26. Brown MM, Brown GC, Sharma S, et al. Quality of life associated with unilateral and bilateral good vision. Ophthalmology 2001; 108: 643–8

    Article  PubMed  CAS  Google Scholar 

  27. Rahi JS, Cumberland PM, Peckham CS. Does amblyopia affect educational, health, and social outcomes? Findings from 1958 British birth cohort. BMJ 2006; 332: 820–5

    Article  PubMed  CAS  Google Scholar 

  28. Rubin GS, Munoz B, Bandeen-Roche K, et al. Monocular versus binocular visual acuity as measures of vision impairment and predictors of visual disability. Invest Ophthalmol Vis Sci 2000; 41(11): 3327–34

    PubMed  CAS  Google Scholar 

  29. Kandel GL, Grattan PE, Bedell HE. Are the dominant eyes of amblyopes normal? Am J Optometry Physiol Optics 1980; 57(1): 1–6

    Article  CAS  Google Scholar 

  30. Bruce A, Hurst M, Abbott H, et al. The incidence of refractive error and anomalies of binocular vision in infants. Br Orthoptic J 1991; 48: 32–5

    Google Scholar 

  31. Graham PA. Epidemiology of strabismus. Br J Ophthalmol 1974; 58(3): 224–31

    Article  PubMed  CAS  Google Scholar 

  32. Hopkisson B, Clarke JR, Oelman BJ. Residual amblyopia in recruits to the British Army. BMJ 1982; 285(6346): 940

    Article  PubMed  CAS  Google Scholar 

  33. Williams C, Horwood J, Northstone K, et al. The timing of patching treatment and a child’s well-being. Br J Ophthalmol 2006; 90: 670–1

    Article  PubMed  CAS  Google Scholar 

  34. Karnon J. Planning the efficient allocation of research funds: an adapted application of a non-parametric Bayesian value of information analysis. Health Pol 2002; 61(3): 329–47

    Article  Google Scholar 

  35. NICE guide to the methods of health technology appraisal. London: NICE, 2004

  36. Nutbeam D. How does evidence influence public health policy? Tackling health inequalities in England. Health Promot J Aust 2003; 14: 154–58

    Google Scholar 

  37. Koopmanschap MA, Rutten FFH, van Ineveld BM, et al. The friction cost method for estimating the indirect costs of disease. J Health Econ 1995; 14: 171–89

    Article  PubMed  CAS  Google Scholar 

  38. Weinstein MC, O’Brien B, Hornberger J, et al. Principles of good practice for decision analytic modeling in healthcare evaluation: report of the ISPOR Task Force on Good Research Practices-Modeling Studies. Value Health 2003; 6(1): 9–17

    Article  PubMed  Google Scholar 

  39. Philips Z, Bojke L, Sculpher M, et al. Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment. Pharmacoeconomics 2006; 24(4): 355–71

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The case study reported in this article was funded by a project grant from the Health Technology Assessment programme (04/32/05). There are no known conflicts of interest.

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Correspondence to Jonathan Karnon.

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Karnon, J., Carlton, J., Czoski-Murray, C. et al. Informing disinvestment through cost-effectiveness modelling. Appl Health Econ Health Policy 7, 1–9 (2009). https://doi.org/10.1007/BF03256137

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