Population preferences for breast cancer screening policies: Discrete choice experiment in Belarus


Autoři: Olena Mandrik aff001;  Alesya Yaumenenka aff004;  Rolando Herrero aff001;  Marcel F. Jonker aff005
Působiště autorů: Section of Early Detection and Prevention, International Agency for Research on Cancer, Lyon, France aff001;  Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands aff002;  The University of Sheffield, School of Health and Related Research (ScHARR), Health Economic and Decision Science (HEDS), Sheffield, the United Kingdom aff003;  N.N. Alexandrov National Cancer Center of Belarus, Cancer control department, N.N. Alexandrov National Cancer Centre of Belarus, Liasny, Belarus aff004;  Duke Clinical Research Institute, Duke University, Durham, United States of America aff005;  Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, The Netherlands aff006
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224667

Souhrn

Background

Reaching an acceptable participation rate in screening programs is challenging. With the objective of supporting the Belarus government to implement mammography screening as a single intervention, we analyse the main determinants of breast cancer screening participation.

Methods

We developed a discrete choice experiment using a mixed research approach, comprising a literature review, in-depth interviews with key informants (n = 23), “think aloud” pilots (n = 10) and quantitative measurement of stated preferences for a representative sample of Belarus women (n = 428, 89% response rate). The choice data were analysed using a latent class logit model with four classes selected based on statistical (consistent Akaike information criterion) and interpretational considerations.

Results

Women in the sample were representative of all six geographic regions, mainly urban (81%), and high-education (31%) characteristics. Preferences of women in all four classes were primarily influenced by the perceived reliability of the test (sensitivity and screening method) and costs. Travel and waiting time were important components in the decision for 34% of women. Most women in Belarus preferred mammography screening to the existing clinical breast examination (90%). However, if the national screening program is restricted in capacity, this proportion of women will drop to 55%. Women in all four classes preferred combined screening (mammography with clinical breast examination) to single mammography. While this preference was stronger if lower test sensitivity was assumed, 28% of women consistently gave more importance to combined screening than to test sensitivity.

Conclusion

Women in Belarus were favourable to mammography screening. Population should be informed that there are no benefits of combined screening compared to single mammography. The results of this study are directly relevant to policy makers and help them targeting the screening population.

Klíčová slova:

Belarus – Breast cancer – Cancer screening – Experimental design – Health screening – Mammography – Psychological attitudes – Screening guidelines


Zdroje

1. Giordano L, von Karsa L, Tomatis M, Majek O, de Wolf C, Lancucki L, et al. Mammographic screening programmes in Europe: organization, coverage and participation. Journal of medical screening. 2012;19 Suppl 1:72–82. Epub 2012/11/08. doi: 10.1258/jms.2012.012085 22972813.

2. Mandrik O, Zielonke N, Meheus F, Severens JL, Guha N, Herrero R, et al. Systematic reviews as a “lens of evidence”: determinants of benefits and harms of breast cancer screening. Int J Cancer. 2019 Aug 15;145(4):994–1006. doi: 10.1002/ijc.32211 30762235

3. 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 in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2018;21(1):78–88. Epub 2018/01/07. doi: 10.1016/j.jval.2017.07.003 29304944.

4. Facione NC, Katapodi M. Culture as an influence on breast cancer screening and early detection. Semin Oncol Nurs. 2000;16(3):238–47. Epub 2000/09/01. 10967796.

5. Greenwald ZR, El-Zein M, Bouten S, Ensha H, Vazquez FL, Franco EL. Mobile Screening Units for the Early Detection of Cancer: A Systematic Review. Cancer Epidemiol Biomarkers Prev. 2017;26(12):1679–94. Epub 2017/10/06. doi: 10.1158/1055-9965.EPI-17-0454 28978564.

6. Mansfield C, Tangka FK, Ekwueme DU, Smith JL, Guy GP Jr., Li C, et al. Stated Preference for Cancer Screening: A Systematic Review of the Literature, 1990–2013. Preventing chronic disease. 2016;13:E27. Epub 2016/02/27. doi: 10.5888/pcd13.150433 26916898; PubMed Central PMCID: PMC4768876.

7. GDP (current US$). The Worl Bank. Available from: https://data.worldbank.org/indicator/ny.gdp.mktp.cd.

8. The Order of the Ministry of Health of the Republic of Belarus # 431 from 19.04.2012 About approval of the instruction on the order of conduction the screening of breast cancer in health care facilities of Minsk city 2012–2015, 431 (2012).

9. The Order of the Ministry of Health of Belarus about organization of screening and early diagnostic of cancer #830 from 28.07.2017, (2017).

10. Ben-Akiva M, Lerman SR. Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge: The MIT Press; 1985.

11. Hanemann M. Discrete-Continuous Models of Consumer Demand. Econometrica. 1984;52(3):541–61.

12. McFadden D. Conditional logit analysis of qualitative choice behavior. In: (ed) PZ editor. Frontiers in Econometrics. New York: Academic Press; 1973.

13. Wedel ZSM. Heterogeneous Conjoint Choice Designs. Journal of Marketing Research. 2005;42(2):210–8.

14. Jonker MF, Donkers B, de Bekker-Grob EW, Stolk EA. Effect of Level Overlap and Color Coding on Attribute Non-Attendance in Discrete Choice Experiments. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2018;21(7):767–71. Epub 2018/07/15. doi: 10.1016/j.jval.2017.10.002 30005748.

15. Jonker MF, Donkers B, de Bekker-Grob E, Stolk EA. Attribute level overlap (and color coding) can reduce task complexity, improve choice consistency, and decrease the dropout rate in discrete choice experiments. Health economics. 2018. Epub 2018/12/20. doi: 10.1002/hec.3846 30565338.

16. de Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA. Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide. The patient. 2015;8(5):373–84. Epub 2015/03/03. doi: 10.1007/s40271-015-0118-z 25726010; PubMed Central PMCID: PMC4575371.

17. Pacifico DHI, Yoo. lclogit: A Stata command for fitting latent-class conditional logit models via the expectation-maximization algorithm. The Stata Journal 2013;13(3):625–39.

18. Demographical and social statistics. National Statistical committee of the Republic of Belarus, 2019. Available by the link: http://www.belstat.gov.by/ofitsialnaya-statistika/solialnaya-sfera/

19. Jepson R, Clegg A, Forbes C, Lewis R, Sowden A, Kleijnen J. The determinants of screening uptake and interventions for increasing uptake: a systematic review. Health Technol Assess. 2000;4(14):i-vii, 1–133. Epub 2000/09/14. 10984843.

20. Soler-Michel P, Courtial I, Bremond A. [Reattendance of women for breast cancer screening programs. A review]. Rev Epidemiol Sante Publique. 2005;53(5):549–67. Epub 2006/01/26. 16434928.

21. Van den Bruel A, Jones C, Yang Y, Oke J, Hewitson P. People's willingness to accept overdetection in cancer screening: population survey. Bmj. 2015;350:h980. Epub 2015/03/05. doi: 10.1136/bmj.h980 25736617; PubMed Central PMCID: PMC4356995.

22. Vona-Davis L, Rose DP. The influence of socioeconomic disparities on breast cancer tumor biology and prognosis: a review. Journal of women's health (2002). 2009;18(6):883–93. Epub 2009/06/12. doi: 10.1089/jwh.2008.1127 19514831.

23. Hol L, de Bekker-Grob EW, van Dam L, Donkers B, Kuipers EJ, Habbema JD, et al. Preferences for colorectal cancer screening strategies: a discrete choice experiment. British journal of cancer. 2010;102(6):972–80. Epub 2010/03/04. doi: 10.1038/sj.bjc.6605566 20197766; PubMed Central PMCID: PMC2844026.

24. 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. Journal of global oncology. 2017;3(5):480–9. 22. doi: 10.1200/JGO.2016.005371 29094086

25. Brewer NT, Salz T, Lillie SE. Systematic review: the long-term effects of false-positive mammograms. Ann Intern Med. 2007;146(7):502–10. Epub 2007/04/04. doi: 10.7326/0003-4819-146-7-200704030-00006 17404352.

26. Sirovich BE, Woloshin S, Schwartz LM. Screening for cervical cancer: will women accept less? Am J Med. 2005;118(2):151–8. Epub 2005/02/08. doi: 10.1016/j.amjmed.2004.08.021 15694900.

27. Denberg TD, Wong S, Beattie A. Women's misconceptions about cancer screening: implications for informed decision-making. Patient Educ Couns. 2005;57(3):280–5. Epub 2005/05/17. doi: 10.1016/j.pec.2004.07.015 15893209.

28. Nelson HD, Tyne K, Naik A, Bougatsos C, Chan B, Nygren P, et al. U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews. Screening for Breast Cancer: Systematic Evidence Review Update for the US Preventive Services Task Force. Rockville (MD): Agency for Healthcare Research and Quality (US); 2009.

29. Myers ER, Moorman P, Gierisch JM, Havrilesky LJ, Grimm LJ, Ghate S, et al. Benefits and Harms of Breast Cancer Screening: A Systematic Review. Jama. 2015;314(15):1615–34. Epub 2015/10/27. doi: 10.1001/jama.2015.13183 26501537.

30. Richardson E, Malakhova I, Novik I, Famenka A. Belarus: health system review. Health systems in transition. 2013;15(5):1–118. Epub 2013/12/18. 24334702.

31. Goossens LM, Utens CM, Smeenk FW, Donkers B, van Schayck OC, Rutten-van Molken MP. Should I stay or should I go home? A latent class analysis of a discrete choice experiment on hospital-at-home. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2014;17(5):588–96. Epub 2014/08/17. doi: 10.1016/j.jval.2014.05.004 25128052.

32. De Blasi P, James LF, Lau JW. Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models. Bernoulli. 2010;16(3):679–704. doi: 10.3150/09-BEJ233


Článek vyšel v časopise

PLOS One


2019 Číslo 11