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Value Health. 2017 Sep;20(8):1100-1109. doi: 10.1016/j.jval.2017.04.012. Epub 2017 Jun 1.

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis.

Author information

1
Manchester Centre for Health Economics, University of Manchester, Manchester, UK; Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
2
Manchester Centre for Health Economics, University of Manchester, Manchester, UK.
3
Manchester Centre for Health Economics, University of Manchester, Manchester, UK; Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.
4
Manchester Centre for Health Economics, University of Manchester, Manchester, UK; University Medical Centre Utrecht, Utrecht, Netherlands.
5
Manchester Centre for Health Economics, University of Manchester, Manchester, UK; Genesis Breast Cancer Prevention Centre and Nightingale Breast Screening Centre, University Hospital of South Manchester, Manchester, UK.
6
Manchester Centre for Health Economics, University of Manchester, Manchester, UK; Department of Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK.
7
Manchester Centre for Health Economics, University of Manchester, Manchester, UK. Electronic address: Katherine.Payne@manchester.ac.uk.

Abstract

OBJECTIVES:

To identify the incremental costs and consequences of stratified national breast screening programs (stratified NBSPs) and drivers of relative cost-effectiveness.

METHODS:

A decision-analytic model (discrete event simulation) was conceptualized to represent four stratified NBSPs (risk 1, risk 2, masking [supplemental screening for women with higher breast density], and masking and risk 1) compared with the current UK NBSP and no screening. The model assumed a lifetime horizon, the health service perspective to identify costs (£, 2015), and measured consequences in quality-adjusted life-years (QALYs). Multiple data sources were used: systematic reviews of effectiveness and utility, published studies reporting costs, and cohort studies embedded in existing NBSPs. Model parameter uncertainty was assessed using probabilistic sensitivity analysis and one-way sensitivity analysis.

RESULTS:

The base-case analysis, supported by probabilistic sensitivity analysis, suggested that the risk stratified NBSPs (risk 1 and risk-2) were relatively cost-effective when compared with the current UK NBSP, with incremental cost-effectiveness ratios of £16,689 per QALY and £23,924 per QALY, respectively. Stratified NBSP including masking approaches (supplemental screening for women with higher breast density) was not a cost-effective alternative, with incremental cost-effectiveness ratios of £212,947 per QALY (masking) and £75,254 per QALY (risk 1 and masking). When compared with no screening, all stratified NBSPs could be considered cost-effective. Key drivers of cost-effectiveness were discount rate, natural history model parameters, mammographic sensitivity, and biopsy rates for recalled cases. A key assumption was that the risk model used in the stratification process was perfectly calibrated to the population.

CONCLUSIONS:

This early model-based cost-effectiveness analysis provides indicative evidence for decision makers to understand the key drivers of costs and QALYs for exemplar stratified NBSP.

KEYWORDS:

breast cancer; cost-effectiveness analysis; discrete event simulation; screening

PMID:
28964442
DOI:
10.1016/j.jval.2017.04.012
[Indexed for MEDLINE]
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