DOI: 10.5061/dryad.7576g50f
title: Data from: Screening of health care workers for tuberculosis: development and validation of a new health economic model to inform practice

description: BACKGROUND: Methods for determining cost-effectiveness of different
 treatments are well established, unlike appraisal of non-drug
 interventions, including novel diagnostics and biomarkers. OBJECTIVE: We
 develop and validate a new health economic model by comparing
 cost-effectiveness of tuberculin skin test, TST; blood test, IGRA; and TST
 followed by IGRA in conditional sequence, in screening health care workers
 for latent or active TB. DESIGN: We focus on healthy life years gained as
 the benefit metric, rather than quality adjusted life years (QALYs) given
 limited data to estimate quality-adjustments of life years with TB and
 complications of treatment, like hepatitis. Healthy life years gained
 refers to the number of TB or hepatitis cases avoided, and the increase in
 life expectancy. We incorporate disease and test parameters informed by
 systematic meta-analyses and clinical practice. Health and economic
 outcomes of each strategy are modelled as a decision tree in Markov
 chains, representing different health states informed by epidemiology.
 Cost and effectiveness values are generated as the individual is cycled
 through 20 years of the model. Key parameters undergo one-way and Monte
 Carlo probabilistic sensitivity analyses. SETTING: Screening health care
 workers in secondary and tertiary care. RESULTS: IGRA is the most
 effective strategy, with incremental costs per healthy life year gained of
 £10,614 - £20,929, base case, £8,021 - £18,348, market costs TST £45, IGRA
 £90, IGRA specificities of 99% - 97%; mean (5%, 95%), £12,060 (£4,137 -
 £38,418) by Monte Carlo analysis. CONCLUSIONS: Incremental costs per
 healthy life year gained, a conservative estimate of benefit, are
 comparable to the £20,000 - £30,000 NICE band for IGRA alone, across wide
 differences in disease and test parameters. Health gains justify IGRA
 costs, even if IGRA tests cost three times TST. This health economic model
 offers a powerful tool for appraising non-drug interventions in the market
 and under development.
description: SupplTable1CostingsbyNatTariffnAfC22010Detailed itemised breakdown of treatment costs for TB, both Pulomnary and Extra-Pulmonary; Latent TB infection; Contact tracing; and Hepatitis. The items are based on the Cambridge TB service and the costs are derived from the NHS national tariff for financial year 2010 - 2011. The health related group codes used are shown. Costs do not include the uplift which varies from centre to centre. This potential shortfall in costs is allowed for in the range of costs modelled within the sensitivity analyses. We also provide a breakdown of potential costs for administering, reading and reporting the mantoux skin test.SupplementaryInformationTables2to7RevExcel file of tabulated data from the oneway sensitivity analyses of the health economic model examining: Table 2 IGRA specificity and sensitivity; Table 3 Clinical parameters, namely all causes death rate, TB death rate, prevalence LTBI, prevalence TB, Relapse rate, Hepatitis rate; IGRA and TST repeat rates; Table 4 IGRA and TST costs; Table 5 IGRA specificity, TST specificity and sensitivity; Table 6 Treatment costs for LTBI, TB, Hepatitis. Table 7 shows the parameters included in the Monte Carlo probabilistic sensitivity analysis with their minimum, peak, maximum values, and the average values calculated by the model.Monte CarloCEDbn12Rerun10to5AllvariableswithCostsCost Effectiveness Distribution of the Monte Carlo probabilistic sensitivity analysis for 12 key clinical and laboratory parameters after 100,000 iterations. The original values and calculation of incremental costs per healthy life year are shown.total number of healthy yearsIGRASpec98n5cnBaseCaseRevTreeAgePro 2009 file containing the health economic model at base casetotal_number_of_healthy_yearsIGRASpec98n5cnBaseCaseRev12DbnaTreeAgePro file with 12 key clinical and laboratory parameters expressed as triangular distribution to enable Monte Carlo probabilistic sensitivity analysis