A Markov model to analyze cost-effectiveness of screening for severe combined immunodeficiency (SCID) - PubMed (original) (raw)

A Markov model to analyze cost-effectiveness of screening for severe combined immunodeficiency (SCID)

Kee Chan et al. Mol Genet Metab. 2011 Nov.

Abstract

Objective: To evaluate the cost-effectiveness of universal neonatal screening for T cell lymphocytopenia in enhancing quality of life and life expectancy for children with severe combined immunodeficiency (SCID).

Methods: Decision trees were created and analyzed to estimate the cost, life years, and quality adjusted life years (QALYs) across a population when universal screening for lack of T cells is used to detect SCID, as implemented in five states, compared to detection based on recognizing symptoms and signs of disease. Terminal values of each tree limb were derived through Markov models simulating the natural history of three cohorts: unaffected subjects; those diagnosed with SCID as neonates (early diagnosis); and those diagnosed after becoming symptomatic and arousing clinical suspicion (late diagnosis). Models considered the costs of screening and of care including hematopoietic cell transplantation for affected individuals. Key decision variables were derived from the literature and from a survey of families with children affected by SCID, which was used to describe the clinical history and healthcare utilization for affected subjects. Sensitivity analyses were conducted to explore the influence of these decision variables.

Results: Over a 70-year time horizon, the average cost per infant was 8.89withoutscreeningand8.89 without screening and 8.89withoutscreeningand14.33 with universal screening. The model predicted that universal screening in the U.S. would cost approximately 22.4million/yearwithagainof880lifeyearsand802QALYs.Sensitivityanalysesshowedthatscreeningtestspecificityanddiseaseincidencewerecriticaldrivingforcesaffectingtheincrementalcost−effectivenessratio(ICER).AssumingaSCIDincidenceof1/75,000birthsandtestspecificityandsensitivityeachat0.99,screeningremainedcost−effectiveuptoamaximumcostof22.4 million/year with a gain of 880 life years and 802 QALYs. Sensitivity analyses showed that screening test specificity and disease incidence were critical driving forces affecting the incremental cost-effectiveness ratio (ICER). Assuming a SCID incidence of 1/75,000 births and test specificity and sensitivity each at 0.99, screening remained cost-effective up to a maximum cost of 22.4million/yearwithagainof880lifeyearsand802QALYs.Sensitivityanalysesshowedthatscreeningtestspecificityanddiseaseincidencewerecriticaldrivingforcesaffectingtheincrementalcost−effectivenessratio(ICER).AssumingaSCIDincidenceof1/75,000birthsandtestspecificityandsensitivityeachat0.99,screeningremainedcost−effectiveuptoamaximumcostof15 per infant screened.

Conclusion: At our current estimated screening cost of $4.22/infant, universal screening for SCID would be a cost effective means to improve quality and duration of life for children with SCID.

Copyright © 2011 Elsevier Inc. All rights reserved.

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Figures

Figure 1

Figure 1

Markov model analytic decision tree comparing two strategies: 1) newborn screening, and 2) no screening. The following models at the terminal end of each limb depict the possible stochastic processes of affected SCID identified early by screening (Markov model B), affected SCID identified after manifestation of symptoms (Markov model A), or unaffected non-SCID infants (Markov model C).

Figure 2

Figure 2

Transition state Markov model A and B: Both models have all the same health states,; In Model B, one additional transition is allowed as shown by the dotted arrow to demonstrate moving from presymptomatic SCID directly to HCT following screening. Abbreviations as in Table 1.

Figure 3

Figure 3

Transition state Markov model of two states of an unaffected infant (Markov model C).

Figure 4

Figure 4

One-way sensitivity analysis of incremental cost per quality-adjust life year (QALYs) as function of A) prevalence, B) test sensitivity, C) test specificity, D) cost of screening test and E) cost of confirmation and diagnostic testing; and F) the ratio of cost of BMTlate vs. BMTearly (cost BMTlate/cost BMTearly).

Figure 5

Figure 5

Ranges of uncertainty given at a range of different willingness to pay per QALY is illustrated by a cost-effectiveness acceptability curve. Estimates for the variables were varied from a distribution at random for incidence, cost of screening test, sensitivity and specificity. Dotted line indicates the cross-point of the willingness to pay per QALY at 0.50 cost effective.

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