Cost-Effectiveness of Population-Based Multigene Testing for Breast and Ovarian Cancer Prevention

Abstract

Importance: The current method of BRCA testing for breast and ovarian cancer prevention, which is based on family history, often fails to identify many carriers of pathogenic variants. Population-based genetic testing offers a transformative approach in cancer prevention by allowing for proactive identification of any high-risk individuals and enabling early interventions.

Objective: To assess the lifetime incremental effectiveness, costs, and cost-effectiveness of population-based multigene testing vs family history-based testing.

Design, setting, and participants: This economic evaluation used a microsimulation model to assess the cost-effectiveness of multigene testing (BRCA1, BRCA2, and PALB2) for all women aged 30 to 35 years compared with the current standard of care that is family history based. Carriers of pathogenic variants were offered interventions, such as magnetic resonance imaging with or without mammography, chemoprevention, or risk-reducing mastectomy and salpingo-oophorectomy, to reduce cancer risk. A total of 2000 simulations were run on 1 000 000 women, using a lifetime time horizon and payer perspective, and costs were adjusted to 2022 US dollars. This study was conducted from September 1, 2020, to December 15, 2023.

Main outcomes and measures: The main outcome measure was the incremental cost-effectiveness ratio (ICER), quantified as cost per quality-adjusted life-year (QALY) gained. Secondary outcomes included incremental cost, additional breast and ovarian cancer cases prevented, and excess deaths due to coronary heart disease (CHD).

Results: The study assessed 1 000 000 simulated women aged 30 to 35 years in the US. In the base case, population-based multigene testing was more cost-effective compared with family history-based testing, with an ICER of $55 548 per QALY (95% CI, $47 288-$65 850 per QALY). Population-based multigene testing would be able to prevent an additional 1338 cases of breast cancer and 663 cases of ovarian cancer, but it would also result in 69 cases of excess CHD and 10 excess CHD deaths per million women. The probabilistic sensitivity analyses show that the probability that population-based multigene testing is cost-effective was 100%. When the cost of the multigene test exceeded $825, population-based testing was no longer cost-effective (ICER, $100 005 per QALY; 95% CI, $87 601-$11 6323).

Conclusions and relevance: In this economic analysis of population-based multigene testing, population-based testing was a more cost-effective strategy for the prevention of breast cancer and ovarian cancer when compared with the current family history-based testing strategy at the $100 000 per QALY willingness-to-pay threshold. These findings support the need for more comprehensive genetic testing strategies to identify pathogenic variant carriers and enable informed decision-making for personalized risk management.

Figure 1.. Schematic Diagram of the Microsimulation Model Structure

We assumed a genetic test uptake rate of 0.7. MRI with or without mammography indicates enhanced screening for breast cancer with MRI with or without mammography. Mammography indicates general screening for breast cancer with mammography. MRI indicates magnetic resonance imaging; PVC, pathogenic variant carrier; RRM, risk-reducing mastectomy; RRSO, risk-reducing salpingo-oophorectomy.

Figure 2.. Cost-Effectiveness Acceptability Curve For Sensitivity Analysis

We varied all model parameters simultaneously across their distributions for probabilistic sensitivity analysis. We generated 2000 estimates of incremental costs and incremental effects by sampling from each parameter’s distribution to perform probabilistic sensitivity analysis (2000 iterations). A cost-effectiveness acceptability curve was created by plotting the outcomes of 2000 simulations. The curves illustrate the percentage of simulations that showed population-based testing or family history–based testing to be cost-effective at varying willingness-to-pay thresholds. QALY indicates quality-adjusted life-year.

Figure 3.. Scatterplot for Incremental Lifetime Costs and Effects of Population-Based Multigene Panel Testing Compared With Family History–Based Testing From Probabilistic Sensitivity Analysis

We generated 2000 estimates of incremental costs and incremental effects by sampling from each parameter’s distribution to perform probabilistic sensitivity analysis (2000 simulations). The result of each simulation or iteration in the probabilistic sensitivity analysis (2000 iterations) is represented as a point on the cost-effectiveness plane, forming a “cloud” of potential outcomes. The circle signifies the 95% credible interval of the outcomes. The line indicates the willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY).