Beate Jahn1, Jovan Todorovic1, Marvin Bundo1, Gaby Sroczynski1, Annette Conrads-Frank1, Ursula Rochau1, Gottfried Endel2, Ingrid Wilbacher2, Nikoletta Malbaski2, Niki Popper3,4, Jagpreet Chhatwal5, Dan Greenberg6, Josephine Mauskopf7, Uwe Siebert8,9,10,11. 1. Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, 6060, Hall in Tyrol, Austria. 2. Evidence-based Health Care, Main Association of Austrian Social Insurance Institutions, Haidingergasse 1, 1030, Vienna, Austria. 3. DWH Simulation Services, DEXHELPP, Neustiftgasse 57-59, 1070, Vienna, Austria. 4. TU Wien, Information and Software Engineering Group (ifs - E194-01), Favoritenstraße 9-11/194-01, 1040, Vienna, Austria. 5. Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 101 Merrimac St, Boston, MA, 02114, USA. 6. Department of Health Systems Management, School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er-Sheva, Israel. 7. RTI Health Solutions, RTI International, 3040 Cornwallis Rd, Durham, NC, 27709, USA. 8. Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Eduard-Wallnoefer-Zentrum 1, 6060, Hall in Tyrol, Austria. uwe.siebert@umit.at. 9. Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 101 Merrimac St, Boston, MA, 02114, USA. uwe.siebert@umit.at. 10. Division of Health Technology Assessment, ONCOTYROL-Center for Personalized Cancer Medicine, Karl-Kapferer-Straße 5, 6020, Innsbruck, Austria. uwe.siebert@umit.at. 11. Department of Health Policy and Management, Center for Health Decision Science, Harvard T.H. Chan School of Public Health, 718 Huntington Avenue, Boston, MA, 02115, USA. uwe.siebert@umit.at.
Abstract
BACKGROUND: Budget impact analyses (BIAs) describe changes in intervention- and disease-related costs of new technologies. Evidence on the quality of BIAs for cancer screening is lacking. OBJECTIVES: We systematically reviewed the literature and methods to assess how closely BIA guidelines are followed when BIAs are performed for cancer-screening programs. DATA SOURCES: Systematic searches were conducted in MEDLINE, EMBASE, EconLit, CRD (Centre for Reviews and Dissemination, University of York), and CEA registry of the Tufts Medical Center. STUDY ELIGIBILITY CRITERIA: Eligible studies were BIAs evaluating cancer-screening programs published in English, 2010-2018. SYNTHESIS METHODS: Standardized evidence tables were generated to extract and compare study characteristics outlined by the ISPOR BIA Task Force. RESULTS: Nineteen studies were identified evaluating screening for breast (5), colorectal (6), cervical (3), lung (1), prostate (3), and skin (1) cancers. Model designs included decision-analytic models (13) and simple cost calculators (6). From all studies, only 53% reported costs for a minimum of 3 years, 58% compared to a mix of screening options, 42% reported model validation, and 37% reported uncertainty analysis for participation rates. The quality of studies appeared to be independent of cancer site. LIMITATIONS: "Gray" literature was not searched, misinterpretation is possible due to limited information in publications, and focus was on international methodological guidelines rather than regional guidelines. CONCLUSIONS: Our review highlights considerable variability in the extent to which BIAs evaluating cancer-screening programs followed recommended guidelines. The annual budget impact at least over the next 3-5 years should be estimated. Validation and uncertainty analysis should always be conducted. Continued dissemination efforts of existing best-practice guidelines are necessary to ensure high-quality analyses.
BACKGROUND: Budget impact analyses (BIAs) describe changes in intervention- and disease-related costs of new technologies. Evidence on the quality of BIAs for cancer screening is lacking. OBJECTIVES: We systematically reviewed the literature and methods to assess how closely BIA guidelines are followed when BIAs are performed for cancer-screening programs. DATA SOURCES: Systematic searches were conducted in MEDLINE, EMBASE, EconLit, CRD (Centre for Reviews and Dissemination, University of York), and CEA registry of the Tufts Medical Center. STUDY ELIGIBILITY CRITERIA: Eligible studies were BIAs evaluating cancer-screening programs published in English, 2010-2018. SYNTHESIS METHODS: Standardized evidence tables were generated to extract and compare study characteristics outlined by the ISPOR BIA Task Force. RESULTS: Nineteen studies were identified evaluating screening for breast (5), colorectal (6), cervical (3), lung (1), prostate (3), and skin (1) cancers. Model designs included decision-analytic models (13) and simple cost calculators (6). From all studies, only 53% reported costs for a minimum of 3 years, 58% compared to a mix of screening options, 42% reported model validation, and 37% reported uncertainty analysis for participation rates. The quality of studies appeared to be independent of cancer site. LIMITATIONS: "Gray" literature was not searched, misinterpretation is possible due to limited information in publications, and focus was on international methodological guidelines rather than regional guidelines. CONCLUSIONS: Our review highlights considerable variability in the extent to which BIAs evaluating cancer-screening programs followed recommended guidelines. The annual budget impact at least over the next 3-5 years should be estimated. Validation and uncertainty analysis should always be conducted. Continued dissemination efforts of existing best-practice guidelines are necessary to ensure high-quality analyses.
Authors: Andreas A Karlsson; Shuang Hao; Alexandra Jauhiainen; K Miriam Elfström; Lars Egevad; Tobias Nordström; Emelie Heintz; Mark S Clements Journal: PLoS One Date: 2021-02-25 Impact factor: 3.240