Dataset Information

RELATIVE EFFECTIVENESS IN BREAST CANCER TREATMENT: A HEALTH PRODUCTION APPROACH.

ABSTRACT:

Background

Pharmaceuticals' relative effectiveness has come to the fore in the policy arena, reflecting the need to understand how relative efficacy (what can work) translates into added benefit in routine clinical use (what does work). European payers and licensing authorities assess value for money and post-launch benefit-risk profiles, and efforts to standardize assessments of relative effectiveness across the European Union (EU) are under way. However, the ways that relative effectiveness differs across EU healthcare settings are poorly understood.

Methods

To understand which factors influence differences in relative effectiveness, we developed an analytical framework that treats the healthcare system as a health production function. Using evidence on breast cancer from England, Spain, and Sweden as a case study, we investigated the reasons why the relative effectiveness of a new drug might vary across healthcare systems. Evidence was identified from a literature review and national clinical guidance.

Results

The review included thirteen international studies and thirty country-specific studies. Cross-country differences in population age structure, deprivation, and educational attainment were consistently associated with variation in outcomes. Screening intensity appeared to drive differences in survival, although the impact on mortality was unclear.

Conclusions

The way efficacy translates into relative effectiveness across health systems is likely to be influenced by a range of complex and interrelated factors. These factors could inform government and payer policy decisions on ways to optimize relative effectiveness, and help increase understanding of the potential transferability of data on relative effectiveness from one health system to another.

SUBMITTER: Puig-Peiro R

PROVIDER: S-EPMC4824961 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCurrent guidelines recommend mammography every 1 or 2 years starting at age 40 or 50 years, regardless of individual risk for breast cancer.ObjectiveTo estimate the cost-effectiveness of mammography by age, breast density, history of breast biopsy, family history of breast cancer, and screening interval.DesignMarkov microsimulation model.Data sourcesSurveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and the medical literature.Target populationU.S. women aged 40 to 49, 50 to 59, 60 to 69, and 70 to 79 years with initial mammography at age 40 years and breast density of Breast Imaging Reporting and Data System (BI-RADS) categories 1 to 4.Time horizonLifetime.PerspectiveNational health payer.InterventionMammography annually, biennially, or every 3 to 4 years or no mammography.Outcome measuresCosts per quality-adjusted life-year (QALY) gained and number of women screened over 10 years to prevent 1 death from breast cancer.Results of base-case analysisBiennial mammography cost less than $100,000 per QALY gained for women aged 40 to 79 years with BI-RADS category 3 or 4 breast density or aged 50 to 69 years with category 2 density; women aged 60 to 79 years with category 1 density and either a family history of breast cancer or a previous breast biopsy; and all women aged 40 to 79 years with both a family history of breast cancer and a previous breast biopsy, regardless of breast density. Biennial mammography cost less than $50,000 per QALY gained for women aged 40 to 49 years with category 3 or 4 breast density and either a previous breast biopsy or a family history of breast cancer. Annual mammography was not cost-effective for any group, regardless of age or breast density.Results of sensitivity analysisMammography is expensive if the disutility of false-positive mammography results and the costs of detecting nonprogressive and nonlethal invasive cancer are considered.LimitationResults are not applicable to carriers of BRCA1 or BRCA2 mutations.ConclusionMammography screening should be personalized on the basis of a woman's age, breast density, history of breast biopsy, family history of breast cancer, and beliefs about the potential benefit and harms of screening.Primary funding sourceEli Lilly, Da Costa Family Foundation for Research in Breast Cancer Prevention of the California Pacific Medical Center, and Breast Cancer Surveillance Consortium.