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. 2019 Aug;17(4):493-511.
doi: 10.1007/s40258-019-00475-6.

Budget Impact Analysis of Cancer Screening: A Methodological Review

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Budget Impact Analysis of Cancer Screening: A Methodological Review

Beate Jahn et al. Appl Health Econ Health Policy. 2019 Aug.

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.

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