Abstract
Background: The current community-based colorectal cancer (CRC) screening program in Shanghai, launched in 2013, invited individuals aged 50–74 years to triennial screening with a qualitative faecal immunochemical test (FIT) and questionnaire-based risk assessment (RA). We aimed to evaluate the effectiveness and cost-effectiveness of the existing Shanghai screening program and compare it to using a validated two-sample quantitative FIT. Methods: We simulated four strategies (no screening, Shanghai FIT, Shanghai FIT + RA and validated FIT) for the Shanghai screening program and evaluated CRC incidence, CRC mortality, the number of life years gained (LYG), the number of FITs, and colonoscopies required for each. An incremental cost-effectiveness analysis was performed to assess the cost- effectiveness of each strategy. Results: All screening modalities reduced CRC incidence and CRC mortality, gained extra number of LYG compared to no screening. Screening using the Shanghai FIT and validated FIT reduced CRC incidence from 45 cases to 43 per 1,000 simulated individuals (4.4%). Incidence was reduced to 42 cases (6.7%) using the Shanghai FIT + RA. All screening strategies reduced CRC mortality by 10.0% (from 10 to 9 deaths) and resulted in 6 to 7 LYG. The validated FIT was the most cost-effective among the evaluated strategies (ICER ¥26,461 per LYG). Conclusions: Our findings show that the current Shanghai screening program is (cost-) effective compared to no screening, but changing to a validated FIT would make the program more efficient.
Original language | English |
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Article number | 101891 |
Journal | Preventive Medicine Reports |
Volume | 29 |
DOIs | |
Publication status | Published - Oct 2022 |
Bibliographical note
Funding Information:This study is funded by Research Grant for Health Science and Technology of Pudong Health and Family Planning Commission of Shanghai, China (Grant No.PW2017A-7). This research benefitted from our participation in the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) (grant number: U01‐CA199335).
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© 2022 The Authors