Cost-effectiveness of mammography from a publicly funded health care system perspective

Abstract

Our economic analysis showed that both benefits of mortality reduction and costs rose together linearly with the number of lifetime screens per women. The decision on how to screen is related mainly to willingness to pay and additional considerations such as the number of women recalled after a positive screening result.

The cost-effectiveness of mammography-based female breast cancer screening in Canadian populations: a systematic review

“…A notable difference between the articles was the presence of confidence intervals. The 2015 and 2018 papers by Mittman et al did not contain confidence intervals or any other metric expressing uncertainty about their point estimates [10,11]. However, given that the papers incorporated a variety of cost, sensitivity, and incidence data to generate their models, this may explain the omission of confidence intervals within these studies.…”

“…Mittman et al studies for biennial and triennial screening of average-risk women between the ages of 50-69 years fall below the threshold of $100,000/QALY [10,11]. The 2014 study by Pataky et al on the cost-effectiveness of annual vs. biennial mammography breast cancer screening for women with dense breasts found an ICER of $565,912/QALY [8].…”

“…Mittmann et al [11] 2018 Annual screening per 1000 women costs the healthcare system $7.4-10.7 million compared to a no-screening scenario, which would cost the system $3.0 million per 1000 women. All screening programs increased life years gained and QALYs.…”

“…The included studies varied in the populations studied, which were subdivided into two categories: those presenting a cost-effectiveness analysis for screening women at an average-risk for breast cancer, and those assessing cost-effectiveness for high-risk women. [10,11].…”

Valuing the health states associated with breast cancer screening programmes: A systematic review of economic measures

“…For screening disutility, almost half of the economic evaluations (29,48,51,54,56,58,60,63,65,66,68,73,74,78,84) used expert VAS utilities derived from a second study in the Netherlands (32), but only three economic evaluations (51,54,73) considered the generalisability of the expert sample to the general population in the model to which this was applied. Other economic evaluations made their own adjustments to local population EQ-5D or SF-6D data (47,49,50,59,69,79,81) applied to reflect this uncertainty in more than half of the economic evaluations, which may bias results (QALYs) toward more frequent screening (29,49,52,54,59).…”