What are the benefits and harms of risk stratified screening as part of the NHS breast screening Programme? Study protocol for a multi-site non-randomised comparison of BC-predict versus usual screening (NCT04359420)

David P French, Susan Astley, Adam R Brentnall, Jack Cuzick, Richard Dobrashian, Stephen W Duffy, Louise S Gorman, Elaine F Harkness, Fiona Harrison, Michelle Harvie, Anthony Howell, Andrew Jerrison, Matthew Machin, Anthony J Maxwell, Lorna McWilliams, Katherine Payne, Nadeem Qureshi, Helen Ruane, Sarah Sampson, Paula Stavrinos, Emma Thorpe, Fiona Ulph, Tjeerd van Staa, Victoria Woof, D Gareth Evans, David P French, Susan Astley, Adam R Brentnall, Jack Cuzick, Richard Dobrashian, Stephen W Duffy, Louise S Gorman, Elaine F Harkness, Fiona Harrison, Michelle Harvie, Anthony Howell, Andrew Jerrison, Matthew Machin, Anthony J Maxwell, Lorna McWilliams, Katherine Payne, Nadeem Qureshi, Helen Ruane, Sarah Sampson, Paula Stavrinos, Emma Thorpe, Fiona Ulph, Tjeerd van Staa, Victoria Woof, D Gareth Evans

Abstract

Background: In principle, risk-stratification as a routine part of the NHS Breast Screening Programme (NHSBSP) should produce a better balance of benefits and harms. The main benefit is the offer of NICE-approved more frequent screening and/ or chemoprevention for women who are at increased risk, but are unaware of this. We have developed BC-Predict, to be offered to women when invited to NHSBSP which collects information on risk factors (self-reported information on family history and hormone-related factors via questionnaire; mammographic density; and in a sub-sample, Single Nucleotide Polymorphisms). BC-Predict produces risk feedback letters, inviting women at high risk (≥8% 10-year) or moderate risk (≥5 to < 8% 10-year) to have discussion of prevention and early detection options at Family History, Risk and Prevention Clinics. Despite the promise of systems such as BC-Predict, there are still too many uncertainties for a fully-powered definitive trial to be appropriate or ethical. The present research aims to identify these key uncertainties regarding the feasibility of integrating BC-Predict into the NHSBSP. Key objectives of the present research are to quantify important potential benefits and harms, and identify key drivers of the relative cost-effectiveness of embedding BC-Predict into NHSBSP.

Methods: A non-randomised fully counterbalanced study design will be used, to include approximately equal numbers of women offered NHSBSP (n = 18,700) and BC-Predict (n = 18,700) from selected screening sites (n = 7). In the initial 8-month time period, women eligible for NHSBSP will be offered BC-Predict in four screening sites. Three screening sites will offer women usual NHSBSP. In the following 8-months the study sites offering usual NHSBSP switch to BC-Predict and vice versa. Key potential benefits including uptake of risk consultations, chemoprevention and additional screening will be obtained for both groups. Key potential harms such as increased anxiety will be obtained via self-report questionnaires, with embedded qualitative process analysis. A decision-analytic model-based cost-effectiveness analysis will identify the key uncertainties underpinning the relative cost-effectiveness of embedding BC-Predict into NHSBSP.

Discussion: We will assess the feasibility of integrating BC-Predict into the NHSBSP, and identify the main uncertainties for a definitive evaluation of the clinical and cost-effectiveness of BC-Predict.

Trial registration: Retrospectively registered with clinicaltrials.gov (NCT04359420).

Keywords: Anxiety; Breast cancer; Chemoprevention; Early detection; High risk; Mammographic density; Psychological impact; Risk stratification; Screening; Tyrer-Cuzick.

Conflict of interest statement

Prof Cuzick and Dr. Brentnall report receiving royalty payments through Cancer Research UK for commercial use of the Tyrer-Cuzick algorithm. All other authors report no competing interests.

Figures

Fig. 1
Fig. 1
Timeline of Psychological-Impact study integrated with BC-Predict and NHSBSP
Fig. 2
Fig. 2
Study Participant Data Flow Diagram

References

    1. Cancer Research UK. Breast cancer diagnosis and treatment statistics. Available from: (Retrieved 6 March 2018)..
    1. King MC, Marks JH, Mandell JB. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2. Science. 2003;302:643–646.
    1. Tryggvadottir L, Sigvaldason H, Olafsdottir GH, et al. Population-based study of changing breast cancer risk in Icelandic BRCA2 mutation carriers, 1920-2000. J Natl Cancer Inst. 2006;98(2):116–122.
    1. Evans DG, Shenton A, Woodward E, et al. Penetrance estimates for BRCA1 and BRCA2 based on genetic testing in the service setting: higher risk in recent birth cohorts. BMC Cancer. 2008;8:155.
    1. NHS Digital. Breast Screening Programme, England, 2016–17. Available from: (Retrieved 6 March 2018).
    1. National Institute of Health and Clinical Excellence. Familial breast cancer: classification and care of people at risk of familial breast cancer and management of breast cancer and related risks in people with a family history of breast cancer. NICE clinical guideline 164, 2013.
    1. Evans DG, Brentnall AR, Harvie M, et al. Breast cancer risk in young women in the National Breast Screening Programme: implications for applying NICE guidelines for additional screening and chemoprevention. Cancer Prev Res. 2014;7(10):993–1001.
    1. Evans DG, Donnelly LS, Harkness EF, et al. Breast cancer risk feedback to women in the UK NHS breast screening population. Br J Cancer. 2016;114(9):1045–1052.
    1. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23(7):1111–1130.
    1. Evans DG, Astley SM, Stavrinos P, et al. Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study. Southampton (UK): NIHR Journals Library; Aug (Programme Grants for Applied Research; 2016. p. 11.
    1. Gray E, Donten A, Karssemeijer N, et al. Evaluation of a National Stratified Breast Screening Programme in the United Kingdom: a model-based cost-effectiveness analysis. Value Health. 2017;20(8):1100–1109.
    1. Evans DG, Harkness EF, Brentnall AR, et al. Breast cancer pathology and stage are better predicted by risk stratification models that include mammographic density and common genetic variants. Breast Cancer Res Tr. 2019;176:141–148.
    1. Long H, Brooks J, Harvie M, Maxwell A, French DP. How do women experience a false positive test result from breast screening? A systematic review and thematic synthesis. Br J Cancer. 2019;121:351–358.
    1. Brentnall AR, Harkness E, Astley S, et al. Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Breast Cancer Res. 2015;17(1):147.
    1. Evans DG, Howell A. Can the breast screening appointment be used to provide risk assessment and prevention advice? Breast Cancer Res. 2015;17(1):84.
    1. French DP, Southworth J, Howell A, et al. Psychological impact of providing women with personalized ten-year breast cancer risk estimates. Br J Cancer. 2018;118:1648–1657.
    1. Marteau TM, Dormandy E, Michie S. A measure of informed choice. Health Expect. 2001;4(2):99–108.
    1. French DP, Howell A, Evans DG. Psychosocial issues of a population approach to high genetic risk identification: behavioural, emotional and informed choice issues. Breast. 2018;37:148–153.
    1. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ. 1999;18(3):341–364.
    1. Pashayan N, Antoniou A, Ivanus U, et al. Personalised early detection and prevention of breast cancer: ENVISION network consensus statement. Nat Rev Clin Oncol. 2020; Forthcoming.
    1. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. BMJ. 2008;337:a1655.
    1. Thomas VN, Saleem T, Abraham R. Barriers to effective uptake of cancer screening among black and minority ethnic groups. Int J Palliat. 2005;11(11):562.
    1. Jack RH, Møller H, Robson T, Davies EA. Breast cancer screening uptake among women from different ethnic groups in London: a population-based cohort study. BMJ Open. 2014;4(10):e005586.
    1. Renshaw C, Jack RH, Dixon S, Møller H, Davies EA. Estimating attendance for breast cancer screening in ethnic groups in London. BMC Public Health. 2010;10(1):157.
    1. Woof VG, Ruane H, Ulph F, et al. Engagement barriers and service inequities in the NHS Breast Screening Programme: Views from British-Pakistani women. J Med Screen 2020; Forthcoming.
    1. Karbani G, Lim JNW, Hewison J, et al. Culture, attitude and knowledge about breast Cancer and preventive measures: a qualitative study of south Asian breast Cancer patients in the UK. Asian Pac J Cancer Prev. 2011;12(6):1619.
    1. Woof VG, Ruane H, French DP, et al. The introduction of risk stratified screening into the NHS breast screening Programme: views from British-Pakistani women. BMC Cancer. 2020;20:452.
    1. Department for Communities and Local Government. The English indices of deprivation. Available from: . Accessed 23 Jan 2018 (2015).
    1. Marteau TM, Becker H. The development of a six-item short-form of the state scale of the Spielberger state-trait anxiety inventory (STAI) Br J Clin Psychol. 1992;31:301–306.
    1. Lerman C, Trock B, Rimer BK, Jepson C, Brody D, Boyce A. Psychological side effects of breast cancer screening. Health Psychol. 1991;10:259–267.
    1. Weinstein ND. What does it mean to understand a risk? Evaluating risk comprehension. J Natl Cancer I Monogr. 1999;25:15–20.
    1. Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall; 1980.
    1. Hersch J, Barratt A, Jansen J, Irwig L, McGeechan K, Jacklyn G, et al. Use of a decision aid including information on overdetection to support informed choice about breast cancer screening: a randomised controlled trial. Lancet. 2015;385(9978):1642–1652.
    1. Dolan P. Modelling valuations for EuroQol health states. Med Care. 1997;35(11):1095–1108.
    1. Al-Janabi H, Flynn T, Coast J. Development of a self-report measure of capability wellbeing for adults: the ICECAP-A. Qual Life Res. 2012;21:167–176.
    1. French DP, Maissi E, Marteau TM. The psychological costs of inadequate cervical smear test results. Br J Cancer. 2004;91:1887–1892.
    1. Jones B, Jarvis P, Lewis JA, Ebbutt AF. Trials to assess equivalence. The importance of rigorous methods. BMJ. 1996;313:36–39.
    1. Evans DG, Warwick J, Astley SM, et al. Assessing individual breast cancer risk within the UK National Health Service Breast Screening Programme: a new paradigm for cancer prevention. Cancer Prev Res. 2012;5(7):943–951.
    1. Donnelly LS, Evans DG, Wiseman J, et al. Uptake of tamoxifen in consecutive premenopausal women under surveillance in a high-risk breast cancer clinic. Br J Cancer. 2014;110(7):1681–1687.
    1. van Hout B, Janssen MF, Feng YS, et al. Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Health. 2012;15(5):708–715.
    1. Flynn TN, Huynh E, Peters TJ, et al. Scoring the ICECAP-A capability instrument. Estimation of a UK general population tariff. Health Econ. 2015;24:258–269.
    1. Bond M, Pavey T, Welch K, et al. Systematic review of the psychological consequences of false positive screening mammograms. Health Technol Assess. 2013;17:1–170.
    1. Spielberger CD, Gorsuch RL, Luschen RE. State trait anxiety inventory manual. Palo Alto, CA: Consulting Psychologists Press; 1970.
    1. Sculpher M, Drummond M, Buxton M. The iterative use of economic evaluation as part of the process of health technology assessment. J Health Serv Res Policy. 1997;2:26–30.
    1. Frick KD. Micro-costing quantity data collection methods. Med Care. 2009;47(7 Suppl 1):S76–S81.
    1. Iglesias CP, Thompson A, Rogowski WH, Payne K. Reporting guidelines for the use of expert judgement in model-based economic evaluations. Pharmacoeconomics. 2016;34(11):1161–1172.
    1. Buxton MJ, Drummond MF, Van Hout BA, et al. Modelling in economic evaluation: an unavoidable fact of life. Health Econ. 1997;6(3):217–227.
    1. Moore G, Audrey S, Barker M, et al. Process evaluation of complex interventions. UK medical research council (MRC) guidance. BMJ. 2015;350:h1258.
    1. Rainey L, Jervaeus A, Donnelly LS, et al. Women’s perceptions of personalized risk-based breast cancer screening and prevention: an international focus group study. Psychooncology. 2019;28:1056–1062.
    1. O’Reilly M, Parker N. ‘Unsatisfactory saturation’: a critical exploration of the notion of saturated sample sizes in qualitative research. Qual Res. 2013;13(2):190–197.
    1. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychology. 2006;3(2):77–101.
    1. Boyatzis RE. Transforming qualitative information: thematic analysis and code development. Thousand Oaks, CA: Sage; 1998.
    1. Potter J, Wetherell M. Discourse and social psychology: beyond attitudes and behaviour. London: Sage; 1987.
    1. Donten A, Gray E, Payne K. Using mathematical optimisation in model-based cost-effectiveness analyses: a case study of a stratified breast screening programme. Value Health. 2017;20:A751–A752.
    1. Waller J, Osborne K, Wardle J. Enthusiasm for cancer screening. Brit J Cancer. 2015;112:562–566.
    1. Saidi G, Sutton S, Bickler G. A predictive study of reasons for attendance and non-attendance at a breast screening programme. Psychol Health. 1998;13:23–33.
    1. Waller J, Douglas E, Whitaker KL, Wardle J. Women’s responses to information about overdiagnosis in the UK breast screening programme: a qualitative study. BMJ Open. 2013;3:e002703.
    1. Cooke R, French DP. How well do the theory of reasoned action and theory of planned behaviour predict intentions and attendance at screening programmes? A meta-analysis. Psychol Health. 2008;23:745–765.
    1. Parkin DM, Boyd L, Walker LC. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer. 2011;105(Suppl 2):S77–S81.
    1. Tamimi RM, Spiegelman D, Smith-Warner SA, et al. (2016) population attributable risk of modifiable and nonmodifiable breast Cancer risk factors in postmenopausal breast Cancer. Am J Epidemiol. 2016;184:884–893.
    1. Engmann NJ, Golmakani MK, Miglioretti DL, Sprague BL, Kerlikowske K. Population-attributable risk proportion of clinical risk factors for breast Cancer. JAMA Oncol. 2017;3:1228–1236.
    1. Hollands GJ, French DP, Griffin SJ, et al. The effects of communicating genetic risk on risk-reducing health behaviour: systematic review with meta-analysis. BMJ. 2016;352:i1102.
    1. French DP, Cameron E, Benton JS, Deaton C, Harvie M. Can communicating personalised disease risk promote healthy behaviour change? A systematic review of systematic reviews. Ann Behav Med. 2017;51:718–729.
    1. Maas P, Barrdahl M, Joshi AD, et al. Breast Cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2016;2:1295–1302.
    1. Hooper JL, Dite GS, RJ MI, et al. Age-specific breast cancer risk by body mass index and familial risk: prospective family study cohort (ProF-SC) Breast Cancer Res. 2018;20:132.
    1. Harvie M, Pegington M, French DP, et al. Breast cancer risk status influences uptake, retention and efficacy of a weight loss programme amongst breast cancer screening attendees: two randomised controlled trials. BMC Cancer. 2019;19:1089.
    1. Cooper GC, Harvie MN, French DP. Do negative screening test results cause false reassurance? A systematic review. Brit J Health Psychol. 2017;22:958–977.
    1. Esserman LJ. The WISDOM Study: breaking the deadlock in the breast cancer screening debate. npj Breast Cancer. 2017;3, 34.
    1. UNICANCER. My personalised breast screening (MyPeBS). (2018). Available at: . Accessed 14 Sept 2019.

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться