{"data":{"id":"10.5061/dryad.7576g50f","type":"dois","attributes":{"doi":"10.5061/dryad.7576g50f","prefix":"10.5061","suffix":"dryad.7576g50f","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Eralp, Merve Nazli","nameType":"Personal","givenName":"Merve Nazli","familyName":"Eralp","affiliation":[],"nameIdentifiers":[]},{"name":"Scholtes, Stefan","nameType":"Personal","givenName":"Stefan","familyName":"Scholtes","affiliation":[],"nameIdentifiers":[]},{"name":"Martell, Geraldine","nameType":"Personal","givenName":"Geraldine","familyName":"Martell","affiliation":[],"nameIdentifiers":[]},{"name":"Winter, Robert","nameType":"Personal","givenName":"Robert","familyName":"Winter","affiliation":[],"nameIdentifiers":[]},{"name":"Exley, Andrew Robert","nameType":"Personal","givenName":"Andrew Robert","familyName":"Exley","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Screening of health care workers for tuberculosis: development and validation of a new health economic model to inform practice"}],"publisher":"Dryad","container":{},"publicationYear":2012,"subjects":[{"subject":"Tuberculosis","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Health economics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Immunology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Clinical medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2012-01-13T14:12:07Z","dateType":"Submitted"},{"date":"2012-01-13T16:49:50Z","dateType":"Issued"},{"date":"2012-01-13T16:49:50Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1136/bmjopen-2011-000630","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1086869 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"BACKGROUND: Methods for determining cost-effectiveness of different\n treatments are well established, unlike appraisal of non-drug\n interventions, including novel diagnostics and biomarkers. OBJECTIVE: We\n develop and validate a new health economic model by comparing\n cost-effectiveness of tuberculin skin test, TST; blood test, IGRA; and TST\n followed by IGRA in conditional sequence, in screening health care workers\n for latent or active TB. DESIGN: We focus on healthy life years gained as\n the benefit metric, rather than quality adjusted life years (QALYs) given\n limited data to estimate quality-adjustments of life years with TB and\n complications of treatment, like hepatitis. Healthy life years gained\n refers to the number of TB or hepatitis cases avoided, and the increase in\n life expectancy. We incorporate disease and test parameters informed by\n systematic meta-analyses and clinical practice. Health and economic\n outcomes of each strategy are modelled as a decision tree in Markov\n chains, representing different health states informed by epidemiology.\n Cost and effectiveness values are generated as the individual is cycled\n through 20 years of the model. Key parameters undergo one-way and Monte\n Carlo probabilistic sensitivity analyses. SETTING: Screening health care\n workers in secondary and tertiary care. RESULTS: IGRA is the most\n effective strategy, with incremental costs per healthy life year gained of\n £10,614 - £20,929, base case, £8,021 - £18,348, market costs TST £45, IGRA\n £90, IGRA specificities of 99% - 97%; mean (5%, 95%), £12,060 (£4,137 -\n £38,418) by Monte Carlo analysis. CONCLUSIONS: Incremental costs per\n healthy life year gained, a conservative estimate of benefit, are\n comparable to the £20,000 - £30,000 NICE band for IGRA alone, across wide\n differences in disease and test parameters. Health gains justify IGRA\n costs, even if IGRA tests cost three times TST. This health economic model\n offers a powerful tool for appraising non-drug interventions in the market\n and under development.","descriptionType":"Abstract"},{"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","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.7576G50F</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Eralp, Merve Nazli</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Scholtes, Stefan</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Martell, Geraldine</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Winter, Robert</creatorName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Exley, Andrew Robert</creatorName>
    </creator>
  </creators>
  <titles>
    <title>
      Data from: Screening of health care workers for tuberculosis: development
      and validation of a new health economic model to inform practice
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2012</publicationYear>
  <subjects>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Tuberculosis</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Health economics</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Immunology</subject>
  </subjects>
  <dates>
    <date dateType="Submitted">2012-01-13T14:12:07Z</date>
    <date dateType="Issued">2012-01-13T16:49:50Z</date>
    <date dateType="Available">2012-01-13T16:49:50Z</date>
  </dates>
  <language>en</language>
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    <rights rightsURI="https://spdx.org/licenses/CC0-1.0.html">Creative Commons Zero v1.0 Universal</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">
      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>
    <description descriptionType="Other">
      &lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;SupplTable1CostingsbyNatTariffnAfC22010&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Detailed 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;SupplementaryInformationTables2to7Rev&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Excel 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;Monte CarloCEDbn12Rerun10to5AllvariableswithCosts&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;Cost 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.&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;total number of healthy yearsIGRASpec98n5cnBaseCaseRev&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;TreeAgePro 2009 file containing the health economic model at base case&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4 class="o-heading__level3-file-title"&gt;total_number_of_healthy_yearsIGRASpec98n5cnBaseCaseRev12Dbna&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;TreeAgePro file with 12 key clinical and laboratory parameters expressed as triangular distribution to enable Monte Carlo probabilistic sensitivity analysis&lt;/div&gt;&lt;div class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;
    </description>
  </descriptions>
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