Importance:
Lung cancer screening has been widely implemented in Europe and the US. However, there is little evidence on participation and diagnostic yields in population-based lung cancer screening in China.
Objective:
To assess the participation rate and detection rate of lung cancer in a population-based screening program and the factors associated with participation.
Design, Setting, And Participants:
This cross-sectional study used data from the Cancer Screening Program in Urban China from October 2013 to October 2019, with follow-up until March 10, 2020. The program is conducted at centers in 8 cities in Henan Province, China. Eligible participants were aged 40 to 74 and were evaluated for a high risk for lung cancer using an established risk score system.
Main Outcomes And Measures:
Overall and group-specific participation rates by common factors, such as age, sex, and educational level, were calculated. Differences in participation rates between those groups were compared. The diagnostic yield of both screening and nonscreening groups was calculated.
Results:
The study recruited 282 377 eligible participants and included 55 428 with high risk for lung cancer; the mean (SD) age was 55.3 (8.1) years, and 34 966 participants (63.1%) were men. A total of 22 260 participants underwent LDCT (participation rate, 40.16%; 95% CI, 39.82%-40.50%). The multivariable logistic regression model showed that female sex (odds ratio [OR], 1.64; 95% CI, 1.52-1.78), former smoking (OR, 1.26; 95% CI, 1.13-1.41), lack of physical activity (OR, 1.19; 95% CI, 1.14-1.24), family history of lung cancer (OR, 1.73; 95% CI, 1.66-1.79), and 7 other factors were associated with increased participation of LDCT screening. Overall, at 6-year follow-up, 78 participants in the screening group (0.35%; 95% CI, 0.29%-0.42%) and 125 in the nonscreening group (0.38%; 95% CI, 0.33%-0.44%) had lung cancer detected, which resulted in an odds ratio of 0.93 (95% CI, 0.70-1.23; P = .61).
Conclusions And Relevance:
The low participations rate in the program studied suggests that an improved strategy is needed. These findings may provide useful information for designing effective population-based lung cancer screening strategies in the future.
Citing Articles
The relationship between depression and cardiovascular disease in older people: results from a large-scale epidemiological cohort study in Japan.
Komuro K, Komuro J, Kaneko H, Suzuki Y, Okada A, Fujiu K
Eur Geriatr Med. 2025; .
PMID: 39891821
DOI: 10.1007/s41999-024-01128-1.
Development of a Prediction Model and Risk Score for Self-Assessment and High-Risk Population Identification in Liver Cancer Screening: Prospective Cohort Study.
Li X, Wang Y, Li H, Wang L, Zhu J, Yang C
JMIR Public Health Surveill. 2025; 10:e65286.
PMID: 39761122
PMC: 11702484.
DOI: 10.2196/65286.
Interpretable machine learning model for digital lung cancer prescreening in Chinese populations with missing data.
Zhang S, Wang Q, Hu X, Zhang B, Sun S, Yuan Y
NPJ Digit Med. 2024; 7(1):327.
PMID: 39562681
PMC: 11576743.
DOI: 10.1038/s41746-024-01309-z.
Cost-effectiveness of lung cancer screening by low-dose CT in China: a micro-simulation study.
Du Y, Li Y, Sidorenkov G, Vliegenthart R, Heuvelmans M, Dorrius M
J Natl Cancer Cent. 2024; 2(1):18-24.
PMID: 39035210
PMC: 11256619.
DOI: 10.1016/j.jncc.2021.11.002.
Knowledge, attitude, and practice of lung cancer screening and associated factors among high-risk population in Lanzhou, China: A cross-sectional study.
Zhang T, Mao Z, Li W, Ma M, Li G, Qiao X
Medicine (Baltimore). 2024; 103(9):e37431.
PMID: 38428855
PMC: 10906634.
DOI: 10.1097/MD.0000000000037431.
Risk-based lung cancer screening in heavy smokers: a benefit-harm and cost-effectiveness modeling study.
Liu Y, Xu H, Lv L, Wang X, Kang R, Guo X
BMC Med. 2024; 22(1):73.
PMID: 38369461
PMC: 10875747.
DOI: 10.1186/s12916-024-03292-4.
Distress and its influencing factors among Chinese patients with incidental pulmonary nodules: a cross-sectional study.
Yuan J, Xu F, Ren H, Chen M, Feng S
Sci Rep. 2024; 14(1):1189.
PMID: 38216579
PMC: 10786871.
DOI: 10.1038/s41598-023-45708-w.
Opportunistic Screening With Low-Dose Computed Tomography and Lung Cancer Mortality in China.
Wang L, Qi Y, Liu A, Guo X, Sun S, Zhang L
JAMA Netw Open. 2023; 6(12):e2347176.
PMID: 38085543
PMC: 10716726.
DOI: 10.1001/jamanetworkopen.2023.47176.
Integrated database-based Screening Cohort for Asian Nomadic descendants in China (Scan-China): Insights on prospective ethnicity-focused cancer screening.
Yu Y, Qiao L, Han J, Wang W, Kang W, Zhang Y
Epidemiol Health. 2023; 45:e2023048.
PMID: 37080725
PMC: 10593583.
DOI: 10.4178/epih.e2023048.
Evaluation of a lung cancer screening programme attracting a cohort to actively participate in screening: Honghe Lung Cancer Medical Center Programme.
Li C, Zheng W, Peng S, Feng Z, Li W, Zhu Z
Transl Cancer Res. 2023; 11(12):4349-4358.
PMID: 36644184
PMC: 9834595.
DOI: 10.21037/tcr-22-2523.
Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study.
Guo L, Meng Q, Zheng L, Chen Q, Liu Y, Xu H
JMIR Public Health Surveill. 2023; 9:e41640.
PMID: 36607729
PMC: 9862335.
DOI: 10.2196/41640.
Incidence and mortality of lung cancer in 2018 and the temporal trends from 2010 to 2018 in Henan province, China: a population-based registry study.
Liu Y, Chen Q, Guo L, Xu H, Wang X, Kang R
Ann Transl Med. 2022; 10(18):1005.
PMID: 36267711
PMC: 9577782.
DOI: 10.21037/atm-22-4029.
The prevalence of comorbidity in the lung cancer screening population: A systematic review and meta-analysis.
Almatrafi A, Thomas O, Callister M, Gabe R, Beeken R, Neal R
J Med Screen. 2022; 30(1):3-13.
PMID: 35942779
PMC: 9925896.
DOI: 10.1177/09691413221117685.
Uptake of lung cancer screening with low-dose computed tomography in China: A multi-centre population-based study.
Cao W, Tan F, Liu K, Wu Z, Wang F, Yu Y
EClinicalMedicine. 2022; 52:101594.
PMID: 35923428
PMC: 9340538.
DOI: 10.1016/j.eclinm.2022.101594.
Special issue "The advance of solid tumor research in China": Participants with a family history of cancer have a higher participation rate in low-dose computed tomography for lung cancer screening.
Guo L, Meng Q, Zheng L, Chen Q, Liu Y, Xu H
Int J Cancer. 2022; 152(1):7-14.
PMID: 35362560
PMC: 9790604.
DOI: 10.1002/ijc.34010.
Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay.
Goncalves S, Fong P, Blokhina M
Am J Cancer Res. 2022; 12(1):1-16.
PMID: 35141002
PMC: 8822269.
Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China.
Guo L, Lyu Z, Meng Q, Zheng L, Chen Q, Liu Y
Front Oncol. 2022; 11:766939.
PMID: 35059311
PMC: 8764453.
DOI: 10.3389/fonc.2021.766939.
Preferred Lung Cancer Screening Modalities in China: A Discrete Choice Experiment.
Zhao Z, Du L, Wang L, Wang Y, Yang Y, Dong H
Cancers (Basel). 2021; 13(23).
PMID: 34885217
PMC: 8656503.
DOI: 10.3390/cancers13236110.