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What Breast Cancer Screening Program do Rural Women Prefer? A Discrete Choice Experiment in Jiangsu, China

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Abstract

Background

Chinese rural women aged 35–64 years are encouraged to complete breast cancer screening (BCS) free of charge. However, it is challenging to reach a satisfying BCS uptake rate. In this study, rural women’s preferences and preferences heterogeneity were measured for the development of strategies to enhance participation in BCS.

Methods

A cross-sectional survey with a discrete choice experiment (DCE) was conducted via convenience sampling via face-to-face interviews in Jiangsu, China. Six DCE attributes were identified through a systematic literature review; our previous study of Chinese rural women’s BCS intentions; a qualitative work involving in-depth interviews with rural women (n = 13), medical staff (n = 4), and health care managers (n = 2); and knowledge of realistic and actionable policy. The D-efficient design was generated using Ngene 1.3.0. A mixed logit model (MXL) in Stata 18.0 was used to estimate the main effect of attribute levels on rural women’s preferences. The relative importance and willingness to utilize BCS services (WTU) were also estimated. The heterogeneous preferences were analyzed by a latent class model (LCM). Sociodemographic status was used to predict the characteristics of class membership. The WTU for different classes was also calculated.

Results

A total of 451 rural women, aged 35–64 years, were recruited. The MXL results revealed that the screening interval (SI) was the most important attribute for rural women with regard to utilizing BCS services, followed by the level of screening, the attitude of medical staff, ways to get knowledge and information, people who recommend screening, and time spent on screening (TSS). Rural women preferred a BCS service with a shorter TSS; access to knowledge and information through multiple approaches; a shorter SI; a recommendation from medical staff or workers from the village or community, and others; the enthusiasm of medical staff; and medical staff with longer tenures in the field. Two classes named “process driven” and “efficiency driven” were identified by the preference heterogeneity analysis of the LCM.

Conclusion

There is a higher uptake of breast cancer screening when services are tailored to women's preferences. The screening interval was the most important attribute for rural women in China with a preference for a yearly screening interval versus longer intervals. 

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References

  1. Latest Global Cancer Data: Cancer Burden Rises to 19.3 Million New Cases and 10.0 Million Cancer Deaths. 2020. https://www.iarc.who.int/wp-content/uploads/2020/12/pr292_E.pdf. Accessed 26 July 2021.

  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, 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):209–49.

    Article  PubMed  Google Scholar 

  3. Zheng RS, Zhang SW, Zeng HM, et al. Cancer incidence and mortality in China, 2016. JNCC. 2022;2:1–9.

    Google Scholar 

  4. Lei S, Zheng R, Zhang S, Chen R, Wang S, Sun K, et al. Breast cancer incidence and mortality in women in China: temporal trends and projections to 2030. Cancer Biol Med. 2021;18(3):900–9.

    Article  PubMed  PubMed Central  Google Scholar 

  5. National Health Family Planning Commission of PRC. China Health and Family Planning Statistics Yearbook 2016. Beijing: Beijing Union Medical University Press; 2017.

    Google Scholar 

  6. Li H. Overview of strategies for cervical and breast cancer screening. Beijing Med J. 2014;36:893–4.

    CAS  Google Scholar 

  7. Tabar L, Yen MF, Vitak B, Chen HH, Smith RA, Duffy SW. Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening. Lancet. 2003;361:1405–10.

    Article  PubMed  Google Scholar 

  8. Government of the People’s Republic of China, Healthy China Initiative (2019–2030). http://www.gov.cn/xinwen/2019-07/15/content_5409694.htm. Accessed 26 Mar 2022.

  9. Sun Y, Ma Y, Cao M, Hu Z, Lin W, Chen M, et al. Breast and cervical cancer screening adherence in Jiangsu, China: an ecological perspective. Front Public Health. 2022;10:967495.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Huang W. Analysis of screening status and influencing factors for breast and cervical cancer. Shenzhen J Integr Tradit Chin West Med. 2017;2717:118–9.

    Google Scholar 

  11. Chen Zh, Zhang Q, Wang Q, Feng X. Coverage and associated factors of cervical and breast cancer screening among childbearing women in Jilin province. Chin J Public Health. 2017;33:1170–3.

    Google Scholar 

  12. Gong P. Screening rate of women’s cervical and breast cancer and their influential factors in Wenling. Chin J Public Health Manag. 2015;31:142–5.

    Google Scholar 

  13. Sun Y, Yuan J, Liu W, Qin B, Hu Z, Li J, et al. Predicting rural women’s breast cancer screening intention in China: a PLS-SEM approach based on the theory of planned behavior. Front Public Health. 2022;10:858788.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kohler RE, Gopal S, Lee CN, Weiner BJ, Reeve BB, Wheeler SB. Breast cancer knowledge, behaviors, and preferences in malawi: implications for early detection interventions from a discrete choice experiment. J Glob Oncol. 2017;3(5):480–9.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wong XY, Groothuis-Oudshoorn CG, Tan CS, van Til JA, Hartman M, Chong KJ, et al. Women’s preferences, willingness-to-pay, and predicted uptake for single-nucleotide polymorphism gene testing to guide personalized breast cancer screening strategies: a discrete choice experiment. Patient Prefer Adherence. 2018;12:1837–52.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Vass CM, Rigby D, Payne K. Investigating the heterogeneity in women’s preferences for breast screening: does the communication of risk matter? Value Health. 2018;21(2):219–28.

    Article  PubMed  Google Scholar 

  17. Sicsic J, Pelletier-Fleury N, Moumjid N. Women’s benefits and harms trade-offs in breast cancer screening: results from a discrete-choice experiment. Value Health. 2018;21(1):78–88.

    Article  PubMed  Google Scholar 

  18. Gerard K, Shanahan M, Louviere J. Using stated preference discrete-choice modelling to inform health care decision-making: a pilot study of breast screening participation. Appl Econ. 2003;35:1073–85.

    Article  Google Scholar 

  19. Peacock S, Apicella C, Andrews L, Tucker K, Bankier A, Daly MB, et al. A discrete choice experiment of preferences for genetic counselling among Jewish women seeking cancer genetics services. Br J Cancer. 2006;95(10):1448–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tsunematsu M, Kawasaki H, Masuoka Y, Kakehashi M. Factors affecting breast cancer screening behavior in Japan—assessment using the health belief model and conjoint analysis. Asian Pac J Cancer Prev. 2013;14(10):6041–8.

    Article  PubMed  Google Scholar 

  21. Taguchi R, Yamazaki Y, Nakayama K. Eliciting preferences for mammography: using a discrete choice experiment approach. Nihon Koshu Eisei Zasshi. 2010;57(2):83–94.

    PubMed  Google Scholar 

  22. Gerard K, Shanahan M, Louviere J. Using discrete choice modelling to investigate breast screening participation. In: Ryan M, Gerard K, Amaya-Amaya M, editors. Using discrete choice experiments to value health and health care. Berlin: Springer; 2008. p. 117–37.

    Chapter  Google Scholar 

  23. Gyrd-Hansen D, Søgaard J. Analysing public preferences for cancer screening programmes. Health Econ. 2001;10:617–34.

    Article  CAS  PubMed  Google Scholar 

  24. Bilger M, Özdemir S, Finkelstein EA. Demand for cancer screening services: results from randomized controlled discrete choice experiments. Value Health. 2020;23(9):1246–55.

    Article  PubMed  Google Scholar 

  25. McFadden D. Conditional logit analysis of qualitative choice behaviour. In: Zarembka P, editor. Frontiers in econometrics. New York: Academic Press; 1974. p. 105–42.

    Google Scholar 

  26. Hanemann M. Discrete-continuous models of consumer demand. Econometrica. 1984;52(3):541–61.

    Article  Google Scholar 

  27. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21:145–72.

    Article  PubMed  Google Scholar 

  28. Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32:883–902.

    Article  PubMed  Google Scholar 

  29. Howard K, Salkeld G. Does attribute framing in discrete choice experiments influence willingness to pay? Results from a discrete choice experiment in screening for colorectal cancer. Value Health. 2009;12(2):354–63.

    Article  PubMed  Google Scholar 

  30. Kistler CE, Hess TM, Howard K, Pignone MP, Crutchfield TM, Hawley ST, et al. Older adults’ preferences for colorectal cancer-screening test attributes and test choice. Patient Prefer Adherence. 2015;9:1005–16.

    PubMed  PubMed Central  Google Scholar 

  31. Brenner A, Howard K, Lewis C, et al. Comparing 3 values clarification methods for colorectal cancer screening decision-making: a randomized trial in the US and Australia. J Gen Intern Med. 2014;29(3):507–13.

    Article  PubMed  Google Scholar 

  32. Cheng J, Pullenayegum E, Marshall DA, Marshall JK, Thabane L, et al. An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening. BMC Med Res Methodol. 2012;12(1):1–17.

    Article  Google Scholar 

  33. Groothuis-Oudshoorn CG, Fermont JM, Vantil JA, Ijzerman MJ, et al. Public stated preferences and predicted up-take for genome-based colorectal cancer screening. BMC Med Inform Decis Mak. 2014;14:18.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Organization WH. How to conduct a discrete choice experiment for health workforce recruitment and retention in remote and rural areas: a user guide with case studies. World Health Organization, 2012.

  35. Li S, Liu S, Ratcliffe J, Gray A, Chen G. Preferences for cervical cancer screening service attributes in rural China: a discrete choice experiment. Patient Prefer Adherence. 2019;13:881–9.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wordsworth S, Ryan M, Skåtun D, Waugh N. Women’s preferences for cervical cancer screening: a study using a discrete choice experiment. Int J Technol Assess Health Care. 2006;22(3):344–50.

    Article  PubMed  Google Scholar 

  37. Chamot E, Mulambia C, Kapambwe S, Shrestha S, Parham GP, Macwan’gi M, et al. Preference for human papillomavirus-based cervical cancer screening: results of a choice-based conjoint study in Zambia. J Low Genit Tract Dis. 2015;19(2):119–23.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Fiebig DG, Haas M, Hossain I, Street DJ, Viney R. Decisions about Pap tests: what influences women and providers? Soc Sci Med. 2009;68(10):1766–74.

    Article  PubMed  Google Scholar 

  39. De Bekker-Grob EW, Rose JM, Donkers B, Ssink-Bot ML, Bangma CH, Steyerberg EW. Men’s preferences for prostate cancer screening: a discrete choice experiment. Br J Cancer. 2013;108(3):533–41.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Pingone MP, Crutchfield TM, Brown PM, Hawley ST, Laping JL, Lewis CL, et al. Using a discrete choice experiment to inform the design of programs to promote colon cancer screening for vulnerable populations in North Carolina. BMC Health Serv Res. 2014;14:611.

    Article  Google Scholar 

  41. National Health Commission of the People’s Republic of China. China Health Statistics Yearbook 2022. Beijing: Peking Union Medical College Press; 2022.

  42. Wang L, Chen W, Jiang Z, Lin N, Shi H, Jiang Y, et al. Effect Evaluation on launching survey for technicist of cervical cancer/breast cancer screening in Jiangsu Province. Med Innov China. 2014;11(22):95–8.

    Google Scholar 

  43. Choice Metrics. Ngene1.3 User Manual & Reference Guide, 2021.

  44. Livingstone KM, Abbott G, Lamb KE, Dullaghan K, Worsley T, McNaughton SA. Understanding meal choices in young adults and interactions with demographics, diet quality, and health behaviors: a discrete choice experiment. J Nutr. 2021;151(8):2361–71.

    Article  PubMed  Google Scholar 

  45. Orme B. Sample size issues for conjoint analysis studies sequim: sawtooth software technical paper. Sequim: Sawtooth Software; 1998. p. 6–9.

    Google Scholar 

  46. Johnson R, Orme B. Getting the most from CBC. Sequim: Sawtooth Software Research Paper Series, Sawtooth Software; 2003.

  47. 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):373–84.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Lancsar E, Louviere J, Flynn T. Several methods to investigate relative attribute impact in stated preference experiments. Soc Sci Med. 2007;64(8):1738–53.

    Article  PubMed  Google Scholar 

  49. Bridges JF. Stated preference methods in health care evaluation: an emerging methodological paradigm in health economics. Appl Health Econ Health Policy. 2003;2(4):213–24.

    PubMed  Google Scholar 

  50. Jiangsu Provincial Bureau of Statistics, Office of the leading Group of Jiangsu Province for the Seventh National Population Census. Jiangsu Population Census Yearbook 2020 (Book 1). Beijing: Chinese Statistics Press; 2022.

  51. Chen CH, Kong L. Guidelines for prevention and control of overweight and obesity in Chinese adults. Beijing: People’s Medical Publishing House; 2006.

    Google Scholar 

  52. Zhuo L, Xu L, Ye J, Sun S, Zhang Y, Burstrom K, et al. Time trade-off value set for EQ-5D-3L based on a nationally representative Chinese population survey. Value Health. 2018;21:1330–7.

    Article  PubMed  Google Scholar 

  53. Yang G, Ye M, Luo J. Research and thinking on two cancer screening capacity and system construction. Jiangsu Health Syst Manag. 2022;33(08):1121–4.

    Google Scholar 

  54. Fan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD, et al. Breast cancer in China. Lancet Oncol. 2014;15:e279–89.

    Article  PubMed  Google Scholar 

  55. Hao J, Chen W, Li N, Shen H, Li J, Wang Y, et al. China guideline for the screening and early detection of female breast cancer (2021, Beijing). China Cancer. 2021;30(03):161–91.

    Google Scholar 

  56. Jing W, Hui L, Xiao X, Wen R. Analysis of influencing factors of patient satisfaction in Henan TCM hospitals based on structural equation modeling. Pract J Med Pharm. 2021;38:569–76.

    Google Scholar 

  57. Hai S, Xu Z, Cheng C, Xin S, Kai M. Analysis on key drivers of patients’ medical experience from the perspective of satisfaction. China Med Herald. 2019;16:42–6.

    Google Scholar 

  58. Lu M, Moritz S, Lorenzetti D, Sykes L, Straus S, Quan H, et al. Systematic review of interventions to increase breast and cervical cancer screening uptake among Asian women. BMC Public Health. 2012;12:413.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Parsa P, Kandiah M, Abdul Rahman H, Zulkefli NM. Barriers for breast cancer screening among Asian women: a mini literature review. Asian Pac J Cancer Prev. 2006;7:509.

    PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to express their appreciation for all the research assistants involved in data collection. Special thanks to the reviewers and the editors for their useful comments.

Author information

Authors and Affiliations

  1. Institute of Medical Humanities, Nanjing Medical University, Nanjing, China

    Yanjun Sun, Huiying Zhang, Zhiqing Hu, Yuhao Ma & Yuan He

  2. School of Marxism, Nanjing Medical University, Nanjing, China

    Yanjun Sun, Huiying Zhang, Zhiqing Hu, Yuhao Ma & Yuan He

  3. School of Nursing, Nanjing Medical University, Nanjing, China

    Yiping Wang & Yuan He

  4. Research Center for Social Risk Management of Major Public Health Events (Key Research Base of Philosophy and Social Sciences of Universities in Jiangsu), Nanjing Medical University, Nanjing, China

    Yuan He

  5. School of Public Health, Nanjing Medical University, Nanjing, China

    Yuan He

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  1. Yanjun Sun
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Corresponding author

Correspondence to Yuan He.

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Funding

This study was funded by the National Natural Science Foundation of China (Grant no. 71804074 and no. 72174092), the “Qing Lan Project” of Jiangsu Province, the research on the psychological mechanism and humanistic care of health behavior of patients with chronic diseases—a specific program from the double-class innovative program for technological research in the School of Public Health, Nanjing Medical University, the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant no. KYCX22_1802), and the research of health instruction to chronic patients and its effects under major public health events—a specific program from the Research Center of Major Public Health Events, Nanjing Medical University. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

Conflict of interest

All authors declare that they have no conflict of interest.

Ethics approval

Approval was obtained from the Ethics Committee of Sir Run Run Hospital, Nanjing Medical University. The grant number is 2019-SR-017. All participants agreed with verbal, informed consent.

Availability of data and material

Data are available upon reasonable request to the authors.

Authors’ contributions

YH acquired funding and made substantial contributions to the conception of the work, supervision, and project administration. YS and YH led interview and survey programming. All authors were included in the selection of attributes and levels. YS, YW, and HZ collected the data. YS conducted the analysis and interpretation of data and drafted the first manuscript. YS, ZH, and YM provided the visualization and language editing. YH revised it critically for important intellectual content. All authors reviewed and approved the final manuscript.

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Sun, Y., Wang, Y., Zhang, H. et al. What Breast Cancer Screening Program do Rural Women Prefer? A Discrete Choice Experiment in Jiangsu, China. Patient (2024). https://doi.org/10.1007/s40271-024-00684-9

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