A Review and Analysis of Economic Models of Prevention Benefits. A Review and Analysis of Economic Models of Prevention Benefits : Table 5


: Table 5

Abstracted informationSchousboe et al. 2011 Personalizing Mammography by Breast Density and Other Risk Factors for Breast Cancer: Analysis of Health Benefits and Cost-EffectivenessCarles et al. 2011 Cost-effectiveness of early detection of breast cancer in Catalonia (Spain)Wong et al. 2010 Cost-effectiveness analysis of mammography screening in Hong Kong Chinese using state-transition Markov modelingOhnuki et al. 2006 Cost-effectiveness analysis of screening modalities for breast cancer in Japan with special reference to women aged 40-49 years
Framework (BCA, CEA, etc.)CEACEACEACEA
PerspectiveNational health payerPayerSocietalPayer
Target populationAmericansCatalan/SpanishHong Kong ChineseJapanese
Study population (epidemiological)US womenCatalan or SpanishCancer-free Hong Kong Chinese women aged 40 years or olderJapanese women
Study population (economic)1,000,000 womenCohort of 100,000 women1,000 women4 cohorts of 100,000 women for each of 4 strategies, including no screening
Intervention(s)Mammography annually, biennially or every 3 to 4 years with age intervals of 40 to 49, 50 to 59, 60 to 69, 70 to 79 [initial at age 40 years and with breast density of Breast Imaging Reporting and Data System (BI-RADS) categories 1 to 4]20 screening strategies by varying the periodicity of mammography screening exams and age intervals: annual or biennial screening with age intervals that started at 40, 45, 50 years and ended at 69, 70, 74 and 79 yearsMammography biennially (initial at age 40 or 50 years and ending at age 69 or 79 years)Screening strategies: (1) annual clinical breast exam; (2) annual clinical breast exam plus mammogram; (3) biennial clinical breast exam plus mammogram with age intervals of 30-39, 40-49, 50-59, 60-69, 70-79
Comparator(s)No InterventionNo InterventionNo InterventionNo Intervention
Data sourcesUS Surveillance, Epidemiology, and End Results (SEER) database, Tice et al. (2008), Taplin et al. (1995), Yabroff et al. (2008), Breast Cancer Surveillance Consortium (BCSC), medical literatureEarly Detection Program, hospital databases of the IMAS-Hospital del Mar in Barcelona, National Institute of Statistics (INE), Catalan Institute of Statistics (IDESCAT), Catalan Mortality Registry of the Catalan Government's Department of Health, Girona Cancer RegistryUS Surveillance, Epidemiology, and End Results (SEER) database, local sources (government gazette, Hospital Authority), private providers, laboratories and suppliers of consumablesMiyagi Prefectural Cancer Registry; annual report on Vital Statistics of Japan; Grant-in-Aid for Cancer Research survey from the Ministry of Health and Welfare
Valuation of health benefits ($, QALYs, LYS, cases averted)Quality-adjusted life years (QALYs) (EuroQol-5D values for Swedish women), number of women screened over 10 years to prevent 1 death from breast cancerYears of life (YL), quality-adjusted life years (QALYs), lives extended (LE); QALY weights derived from EuroQol EQ-5DLife expectancy, quality-adjusted life expectancyLife-years saved, survival duration
CostsFilm mammography, direct costs of DCIS (Ductal Carcinoma In Situ) and invasive breast cancer, false-positive results generating additional proceduresScreening mammogram, administrative, early recall mammogram, invasive tests, non-invasive complementary tests, in-hospital, ambulatory visits, chemotherapy, other hospital labs and radiological tests, radiotherapy, hormone therapy (adjuvant tamoxifen)Screening mammography, follow-up abnormal screens, treatment of invasive of DCIS (Ductal Carcinoma In Situ) and invasive cancer, terminal care, transportation, timeScreening examinations, diagnostic, initial treatment, terminal care (no further specification)
Time horizonLifetimeLifetime50 yearsLifetime
Discount rate (annual)Costs and benefits 3%Costs and benefits 3%Costs and benefits 3%Costs and benefits 3%
Model design (static/dynamic)Dynamic (Markov microsimulation, probabilistic sensitivity analysis/Monte Carlo simulation)Dynamic (Markov, stochastic/probabilistic sensitivity analysis)Dynamic (Markov, probabilistic sensitivity analysis/Monte Carlo simulation)Dynamic (No further information specified)
Sensitivity analysis (parameters)One-way: DCIS (Ductal Carcinoma In Situ) incidence, breast cancer incidence, mortality, costs and disutilityOne or multi-way not specified. Loss of QALYs due to test results' anxiety considered; increased drug costs; longer follow-up times; changed ratio non-invasive tests due to limited estimation information; screening program participation to 50% (vs. 100%); double costs of invasive tests for screen-detected tumors to account for difficulty of detecting non-palpable lesionsOne or multi-way not specified. Clinical, cost parameters (no further specification)One-way: sensitivity and specificity of screening strategy; costs of screening
Value of informationN/SN/SN/SN/S
Generalizability/scalability of findings    
Distributional or equity analysisN/SN/SN/SN/S
ResultsBiennial 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.Biennial strategies 50-69, 45-69 or annual 45-69, 40-69 and 40-74 were selected as cost-effective for both effect measures (YL or QALYs). The ICER increases considerably when moving from biennial to annual scenarios. Moving from no screening to biennial 50-69 years represented an ICER of 4,469€ per QALY.Compared to no screening, a single cohort undergoing biennial mammography would cost US$33,200 to $55,400 per QALY saved depending on the age group. Compared to no screening, a multiple cohort undergoing biennial mammography would cost US$32,800 to $38,700 per QALY saved depending the age group.In women aged 40–49 years, annual combined modality saved 852.9 lives and the cost/survival duration was 3,394,300 yen/year, whereas for biennial combined modality the corresponding figures were 833.8 and 2,025,100 yen/year, respectively. Annual clinical breast examination did not confer any advantages in terms of effectiveness (815.5 lives saved) or cost-effectiveness (3,669,900 yen/year). While the annual combined modality was the most effective with respect to life years saved among women aged 40–49 years, biennial combined modality was found to provide the highest cost-effectiveness.
LimitationsDoes not apply to women who carry BRCA1 or BRCA 2 mutation; data limitation on screening frequency; modest interrater reproducibility in qualitative BI-RAD classification; used stage distributions by age but not breast density in the absence of mammography results; mortality rates decreased for early detection or improved treatment; film instead of digital mammographyData limitations - where not available, borrowed from other sources; did not obtain confidence intervals of model outputs; did not consider indirect costs; did not take into account overdiagnosis effects on costs and benefits; DCIS (Ductal Carcinoma In Situ) cases were not included (would increase cost and decrease quality of life)Did not have aggregate local stage-specific treatment costs for invasive breast cancer (used individual itemized costs); did not evaluate newer technologies to detect breast lesions (MRI, ultrasound, full-field digital mammography or computer-aided detection techniquesNot an RCT; data limitations for sensitivity and specificity of screening