Table 1.

Characteristics of included studies

Study (year)	Study aim	Population	Strategies	Study design	Model structure and software	Perspective and time horizon	Discount rate	Outcome measure
Sun et al. (2018) [23]	To model the cost-effectiveness of a risk-based breast cancer screening programme in urban China, launched in 2012	Urban Chinese women aged 40–69 years with a risk of developing breast cancer	(1) Annual ultrasound screening for high-risk women aged 40–44 years, with further mammography screening for women with suspected results; annual ultrasound and mammography screening for high-risk women aged 45–69 years (2) No screening for women with low risk	CUA	Natural history Markov model (TreeAge software)	Societal perspective; lifetime	3% for both cost and outcome	Cost per QALY gained
Sun et al. (2019) [24]	To compare for the first time the cost-effectiveness of breast cancer screening using clinical breast examination coupled with ultrasound as a primary screening test compared to no screening in rural China	Rural Chinese women aged 35–64 years from rural areas in 31 provinces with no history of breast cancer in China	(1) Clinical breast examination coupled with ultrasound, followed by undergoing mammography or clinical judgement if required every 3 years (2) No screening	CUA	Natural history Markov model (TreeAge software)	Societal perspective; lifetime	3% for both cost and outcome	Cost per QALY gained
Sun et al. (2022) [25]	To estimate cost-effectiveness and population impact of multigene testing for all Chinese patients with breast cancer	Patients with breast cancer in China	(1) BRCA1/BRCA2/PALB2 testing for all patients with breast cancer (2) BRCA1/BRCA2 testing for patients with breast cancer fulfilling family history/clinical criteria (3) No genetic testing	CUA	Patient level microsimulation model (TreeAge-Pro 2018)	Societal and payer perspectives; lifetime	3% for both cost and outcome	Cost per QALY gained; cost per life year gained
Wang et al. (2021) [26]	To assess the cost-effectiveness of implementing a biennial mammography screening programme for Chinese women	Urban Chinese women aged 45–70 years	(1) Mammography screening every 2 years (2) No screening (3) Alternative Scenarios including varying the screening interval (2 or 3 years), screening start age (from age of 40, 45 or 50 years) and stop age (65 or 70 years)	CEA	The Simulation Model on radiation Risk and breast cancer Screening (SiMRiSc) model (C++)	Societal perspective; lifetime	5% for both cost and outcome	Average cost-effectiveness ratio (ACER); cost per life year gained
Yang et al. (2018) [27]	To predict the feasibility of a community-based breast cancer screening strategy in China	Women aged 35–69 years in China	(1) Annual community- based breast cancer screening (first tested by clinical breast examination, then advised to undergo breast ultrasonography and/or mammography) (2) Biennial community- based breast cancer screening (3) Triennial community- based breast cancer screening (4) No screening	CUA	State-transition Markov model	Societal perspective; 50 years	3% for both cost and outcome	Cost per QALY gained

CUA cost–utility analysis, CEA cost-effectiveness analysis, QALY quality-adjusted life year, LYG life year gained