Table 2 Characteristics of reviewed articles
Author (Year) | Country (country income category) | Population description | Treatment strategy | Intervention VS comparison | Study design | Perspective | Time horizon | Cascade Testing | discount rate | Type of uncertainty analysis |
|---|---|---|---|---|---|---|---|---|---|---|
Genetic testing for breast cancer only | ||||||||||
Lim et al. (2018) [30] | Malaysia (UMIC) | Hypothetical cohort of 1000 patients who aged 40 years old with newly diagnosed as early stage (Stage1/2) unilateral BC. | risk-reducing mastectomy (RRM), risk-reducing bilateral salpingo-oophorectomy (RRBSO), tamoxifen chemoprevention, combination of these or neither | BRCA testing VS No testing, performed Routine clinical surveillance only | Decision tree and Markov Model (1 year length of cycle) | payer perspective | Lifetime | No | 3% for costs and health outcomes | One way deterministic sensitivity analyses & probabilistic sensitivity analysis |
Sun et al. (2022) [32] | China (UMIC) | All BC patients VS Family History/clinical-criteria-based testing | Prophylactic mastectomy and salpingo-oophorectomy | a)BRCA1/BRCA2/PALB2 testing for all BC patients b)BRCA1/BRCA2-testing for BC patients with FH/clinical criteria c) No testing | Microsimulation model at the individual level | Societal and Payer perspectives | Lifetime | Yes | 3% for costs and health outcomes | One way deterministic sensitivity analyses & probabilistic sensitivity analysis |
Wu et al. (2023) [29] | China (UMIC) | Patients with TNBC and hormone-receptor (HR)-positive and HER2-negative BC | Standard treatment with Olaparib and RRO as an adjuvant treatment | a) Universal gBRCAtesting for all TNBC and HR-positive HER2-negative BC patients b) No gBRCA testing c) Selected gBRCA testing | A decision tree analytic model based on transitional Markov Chain (1 year length of cycle) | Payer perspectives | 20 years | No | 3% for costs and health outcomes | One way deterministic sensitivity analyses & probabilistic sensitivity analysis |
Genetic testing for breast cancer and ovarian cancer | ||||||||||
Manchanda et al. (2020) [31] | China (UMIC) & Brazil (UMIC) & India (LMIC) | Population-based screening for all women ≥ 30 years old. | RRSO, MMRI/mammography screening, chemoprevention with SERM, RRM | Population-based BRCA1/BRCA2 testing VS clinical-criteria/FH-based testing | Markov Model | Societal and Payer perspectives | Lifetime (China = 48 cycles; Brazil = 49 cycles; India = 38 cycles) | No | 3% for costs and health outcomes | One way deterministic sensitivity analyses & probabilistic sensitivity analysis |
Simoes Correa-Galendi et al. (2021) [33] | Brazil (UMIC) | Healthy women aged 30 years with personal or family history of BRCA-associated cancer and meeting the clinical criteria for genetic testing according to the National Comprehensive Cancer Network (NCCN). | Intensified surveillance, risk-reducing bilateral mastectomy and bilateral salpingo-oophorectomy | BRCA1/BRCA2 testing and counselling VS no genetic testing and counselling | Markov Model | Payer perspectives | 70 years | No | 5% for costs and utilities | One way deterministic sensitivity analyses & probabilistic sensitivity analysis |
Lourencao et al. (2022) [34] | Brazil (UMIC) | Healthy women aged 30 years with personal or family history of BRCA-associated cancer and meeting the clinical criteria for genetic testing according to the National Comprehensive Cancer Network (NCCN). | Intensified surveillance, risk-reducing bilateral mastectomy, bilateral salpingo-oophorectomy, both bilateral mastectomy and bilateral salpingo-oophorectomy | BRCA1/BRCA2 testing and counselling and with surgical/non-surgical preventive options VS No genetic testing and counselling (with standard care) | Markov Model | Payer perspectives | 70 years | Yes | 5% for costs and utilities | Deterministic sensitivity analyses & probabilistic sensitivity analysis |