Including Decision-Analytic Benefit-Harm Evaluation in HTA - Different Age Ranges and Screening Intervals in Breast Cancer Screening in Germany
Author(s)
Sroczynski G1, Hallsson LR1, Mühlberger N1, Kuehne F2, Jahn B3, Kölsch H4, Sauerland S4, Angelescu K4, Siebert U5
1UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Hall i.T., 7, Austria, 2UMIT TIROL - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Hall i.T., 7, Austria, 3UMIT - University for Health Sciences, Medical Informatics and Technology / ONCOTYROL- Center for Personalized Cancer Medicine, Hall i. T., Austria, 4Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany, 5Harvard T.H. Chan School of Public Health / UMIT - University for Health Sciences, Medical Informatics and Technology / ONCOTYROL - Center for Personalized Cancer Medicine, Boston (Harvard); Tirol (UMIT), MA, USA
Presentation Documents
OBJECTIVES: To complement an IQWiG benefit assessment, we conducted a decision-analytic modelling study to evaluate the long-term benefits and harms of extending the age ranges for breast cancer (BC) screening with mammography in Germany compared to current biennial BC screening age 50-69 years.
METHODS: We developed a Markov-state-transition model simulating BC progression including ductal carcinoma in situ (DCIS) to evaluate BC screening strategies including different age ranges and screening intervals. International data for mammography accuracy along with German epidemiologic, clinical data and age-specific quality-of-life (QoL) data were used. Outcomes included detected DCIS and invasive BC, BC-related deaths, life years (LY), and quality-adjusted life years (QALY), number of positive, false-positive, and total mammograms, overdiagnosis, and the incremental harm-benefit ratio (IHBR) measuring the potential harm associated with additional mammograms per LY gained (LYG). Comprehensive sensitivity analyses were conducted.
RESULTS: In the base-case analysis, the highest potential gain in life years was achieved with mammography at age 45-79 (annual, age 45-49y; biennial, 50-79y) with 10.0 LYG per 100 participating women compared with current screening. The highest gain in QALYs is expected by biennial mammography at ages 45-74 (3.5 QALYs gained/100 women vs. current screening). Lowering the start age to 45 years (biennial, age 45-69y) has an IHBR of 47 additional mammograms/LYG (vs. current screening). Compared to this screening, additionally extending mammography to 74 years (biennial, age 45-74y) results in 96 additional mammograms/LYG. Annual screening at age 45-49 or extending biennial screening to age 79 results in substantially less favorable IHBRs. Overdiagnoses occurred mainly due to DCIS. Key results were robust in sensitivity analyses.
CONCLUSIONS: Based on our results, extension of the age range for mammography may prevent additional BC deaths and increase remaining life expectancy. Considering QoL, biennial screening from age 45 to 74 years may provide an acceptable benefit-harm balance.
Conference/Value in Health Info
Value in Health, Volume 25, Issue 12S (December 2022)
Code
HTA170
Topic
Clinical Outcomes, Epidemiology & Public Health, Study Approaches
Topic Subcategory
Clinical Outcomes Assessment, Decision Modeling & Simulation, Public Health, Relating Intermediate to Long-term Outcomes
Disease
No Additional Disease & Conditions/Specialized Treatment Areas