Public Heterogeneous Preferences for Low-Dose Computed Tomography Lung Cancer Screening Service Delivery in Western China: A Discrete Choice Experiment

Document Type : Original Article

Authors

1 Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China

2 Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China

3 School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China

4 HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China

5 School of Public Administration, Sichuan University, Chengdu, China

6 Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China

7 Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China

8 Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China

9 State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Chengdu, China

Abstract

Background 
Lung cancer screening (LCS) with low-dose computed tomography (LDCT) is an efficient method that can reduce lung cancer mortality in high-risk individuals. However, few studies have attempted to measure the preferences for LDCT LCS service delivery. This study aimed to generate quantitative information on the Chinese population’s preferences for LDCT LCS service delivery.
 
Methods 
The general population aged 40 to 74 in the Sichuan province of China was invited to complete an online discrete choice experiment (DCE). The DCE required participants to answer 14 discrete choice questions comprising five attributes: facility levels, facility ownership, travel mode, travel time, and out-of-pocket cost. Choice data were analyzed using mixed logit and latent class logit models. 
 
Results 
The study included 2,529 respondents, with 746 (29.5%) identified as being at risk for lung cancer. Mixed logit model analysis revealed that all five attributes significantly influenced respondents’ choices. Facility levels had the highest relative importance (44.4%), followed by facility ownership (28.1%), while out-of-pocket cost had the lowest importance (6.4%). The atrisk group placed relatively more importance on price and facility ownership compared to the nonrisk group. Latent class logit model identified five distinct classes with varying preferences.
 
Conclusion 
This study revealed significant heterogeneity in preferences for lung cancer screening service attributes among the Chinese population, with facility level and facility ownership being the most important factors. The findings underscore the need for tailored strategies targeting different subgroup preferences to increase screening participation rates and improve early detection outcomes.

Keywords


  1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi:3322/caac.21660
  2. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024;4(1):47-53. doi:1016/j.jncc.2024.01.006
  3. Zeng H, Ran X, An L, et al. Disparities in stage at diagnosis for five common cancers in China: a multicentre, hospital-based, observational study. Lancet Public Health. 2021;6(12):e877-e887. doi:1016/s2468-2667(21)00157-2
  4. Zeng H, Zheng R, Guo Y, et al. Cancer survival in China, 2003-2005: a population-based study. Int J Cancer. 2015;136(8):1921-1930. doi:1002/ijc.29227
  5. Poon C, Haderi A, Roediger A, Yuan M. Should we screen for lung cancer? A 10-country analysis identifying key decision-making factors. Health Policy. 2022;126(9):879-888. doi:1016/j.healthpol.2022.06.003
  6. Bonney A, Malouf R, Marchal C, et al. Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality. Cochrane Database Syst Rev. 2022;8(8):CD013829. doi:1002/14651858.CD013829.pub2
  7. Jonas DE, Reuland DS, Reddy SM, et al. Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2021;325(10):971-987. doi:1001/jama.2021.0377
  8. Dai M, Shi JF, Li N. Cancer screening program in urban China: the program design and the expectancies. Zhonghua Yu Fang Yi Xue Za Zhi. 2013;47(2):179-82. doi:3760/cma.j.issn.0253-9624.2013.02.018
  9. Chen W, Li N, Cao MM, Ren JS, Shi JF, Chen HD, et al. Preliminary analysis of cancer screening program in urban China from 2013 to 2017. China Cancer. 2020;29(1):1-6. doi:11735/j.issn.1004-0242.2020.01.A001
  10. Wen Y, Yu LZ, Du LB, et al. [Analysis of low-dose computed tomography compliance and related factors among high-risk population of lung cancer in three provinces participating in the cancer screening program in urban China]. Zhonghua Yu Fang Yi Xue Za Zhi. 2021;55(5):633-639. doi:3760/cma.j.cn112150-20201015-01286
  11. Norman R, Moorin R, Maxwell S, Robinson S, Brims F. Public attitudes on lung cancer screening and radiation risk: a best-worst experiment. Value Health. 2020;23(4):495-505. doi:1016/j.jval.2019.11.006
  12. Zhao Z, Du L, Wang L, Wang Y, Yang Y, Dong H. Preferred Lung cancer screening modalities in China: a discrete choice experiment. Cancers (Basel). 2021;13(23):6110. doi:3390/cancers13236110
  13. Jia Q, Chen H, Chen X, Tang Q. Barriers to low-dose CT lung cancer screening among middle-aged Chinese. Int J Environ Res Public Health. 2020;17(19):7107. doi:3390/ijerph17197107
  14. Wang GX, Baggett TP, Pandharipande PV, et al. Barriers to lung cancer screening engagement from the patient and provider perspective. Radiology. 2019;290(2):278-287. doi:1148/radiol.2018180212
  15. Mansfield C, Tangka FK, Ekwueme DU, et al. Stated preference for cancer screening: a systematic review of the literature, 1990-2013. Prev Chronic Dis. 2016;13:E27. doi:5888/pcd13.150433
  16. Vass C, Boeri M, Karim S, et al. Accounting for preference heterogeneity in discrete-choice experiments: an ISPOR special interest group report. Value Health. 2022;25(5):685-694. doi:1016/j.jval.2022.01.012
  17. Goossens LM, Utens CM, Smeenk FW, Donkers B, van Schayck OC, Rutten-van Mölken MP. Should I stay or should I go home? A latent class analysis of a discrete choice experiment on hospital-at-home. Value Health. 2014;17(5):588-596. doi:1016/j.jval.2014.05.004
  18. Sever I, Verbič M, Sever EK. Valuing the delivery of dental care: heterogeneity in patients' preferences and willingness-to-pay for dental care attributes. J Dent. 2018;69:93-101. doi:1016/j.jdent.2017.12.005
  19. Bridges JF, Hauber AB, Marshall D, et al. Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011;14(4):403-413. doi:1016/j.jval.2010.11.013
  20. Hall R, Medina-Lara A, Hamilton W, Spencer AE. Attributes used for cancer screening discrete choice experiments: a systematic review. Patient. 2022;15(3):269-285. doi:1007/s40271-021-00559-3
  21. Mandrik O, Yaumenenka A, Herrero R, Jonker MF. Population preferences for breast cancer screening policies: discrete choice experiment in Belarus. PLoS One. 2019;14(11):e0224667. doi:1371/journal.pone.0224667
  22. Ries PW. Physician Contacts by Sociodemographic and Health Characteristics, United States, 1982-83. US Department of Health and Human Services, Public Health Service, National Center for Health Statistics; 1987.
  23. Szinay D, Cameron R, Naughton F, Whitty JA, Brown J, Jones A. Understanding uptake of digital health products: methodology tutorial for a discrete choice experiment using the Bayesian efficient design. J Med Internet Res. 2021;23(10):e32365. doi:2196/32365
  24. He J, Li N, Chen WQ, et al. [China guideline for the screening and early detection of lung cancer (2021, Beijing)]. Zhonghua Zhong Liu Za Zhi. 2021;43(3):243-268. doi:3760/cma.j.cn112152-20210119-00060
  25. de Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA. Sample size requirements for discrete-choice experiments in healthcare: a practical guide. Patient. 2015;8(5):373-384. doi:1007/s40271-015-0118-z
  26. Chen B, Li W, Jia Y, et al. [A cross-sectional investigation on risk factors of lung cancer for residents over 40 years old in Chengdu, Sichuan province, China]. Zhongguo Fei Ai Za Zhi. 2010;13(11):1021-1026. doi:3779/j.issn.1009-3419.2010.11.05
  27. Pan J, Wang J, Tao W, et al. Is low-dose computed tomography for lung cancer screening conveniently accessible in China? A spatial analysis based on cross-sectional survey. BMC Cancer. 2024;24(1):342. doi:1186/s12885-024-12100-4
  28. Reed SD, Yang JC, Gonzalez JM, Johnson FR. Quantifying value of hope. Value Health. 2021;24(10):1511-1519. doi:1016/j.jval.2021.04.1284
  29. Ammi M, Peyron C. Heterogeneity in general practitioners' preferences for quality improvement programs: a choice experiment and policy simulation in France. Health Econ Rev. 2016;6(1):44. doi:1186/s13561-016-0121-7
  30. Ostermann J, Mühlbacher A, Brown DS, et al. Heterogeneous patient preferences for modern antiretroviral therapy: results of a discrete choice experiment. Value Health. 2020;23(7):851-861. doi:1016/j.jval.2020.03.007
  31. Hauber AB, González JM, Groothuis-Oudshoorn CG, et al. Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR conjoint analysis good research practices task force. Value Health. 2016;19(4):300-315. doi:1016/j.jval.2016.04.004
  32. Pacifico D, Yoo HI. lclogit: a Stata command for fitting latent-class conditional logit models via the expectation-maximization algorithm. Stata J. 2013;13(3):625-639. doi:1177/1536867x1301300312
  33. Zimmerman SM. Factors influencing Hispanic participation in prostate cancer screening. Oncol Nurs Forum. 1997;24(3):499-504.
  34. Li Y, Gong W, Kong X, Mueller O, Lu G. Factors associated with outpatient satisfaction in tertiary hospitals in China: a systematic review. Int J Environ Res Public Health. 2020;17(19):7070. doi:3390/ijerph17197070
  35. Dong E, Liu S, Chen M, et al. Differences in regional distribution and inequality in health-resource allocation at hospital and primary health centre levels: a longitudinal study in Shanghai, China. BMJ Open. 2020;10(7):e035635. doi:1136/bmjopen-2019-035635
  36. Tang C, Xu J, Zhang M. The choice and preference for public-private health care among urban residents in China: evidence from a discrete choice experiment. BMC Health Serv Res. 2016;16(1):580. doi:1186/s12913-016-1829-0
  37. Kim ES, Moored KD, Giasson HL, Smith J. Satisfaction with aging and use of preventive health services. Prev Med. 2014;69:176-180. doi:1016/j.ypmed.2014.09.008
  38. Anderson C, Seff LR, Batra A, Bhatt C, Palmer RC. Recruiting and engaging older men in evidence-based health promotion programs: perspectives on barriers and strategies. J Aging Res. 2016;2016:8981435. doi:1155/2016/8981435
  39. Tao W, Yu X, Shao J, Li R, Li W. Telemedicine-enhanced lung cancer screening using mobile computed tomography unit with remote artificial intelligence assistance in underserved communities: initial results of a population cohort study in Western China. Telemed J E Health. 2024. doi:1089/tmj.2023.0648

Articles in Press, Corrected Proof
Available Online from 10 June 2024
  • Receive Date: 28 August 2023
  • Revise Date: 30 March 2024
  • Accept Date: 08 June 2024
  • First Publish Date: 10 June 2024