Literature DB >> 32484237

Risk stratification in breast cancer screening: Cost-effectiveness and harm-benefit ratios for low-risk and high-risk women.

Valérie D V Sankatsing1, Nicolien T van Ravesteyn1, Eveline A M Heijnsdijk1, Mireille J M Broeders2,3, Harry J de Koning1.   

Abstract

In mammography screening programmes, women are screened according to a one-size-fits-all principle. Tailored screening, based on risk levels, may lead to a better balance of benefits and harms. With microsimulation modelling, we determined optimal mammography screening strategies for women at lower (relative risk [RR] 0.75) and higher (RR 1.8) than average risk of breast cancer, eligible for screening, using the incremental cost-effectiveness ratio (ICER) of current uniform screening in the Netherlands (biennial [B] 50-74) as a threshold ICER. Strategies varied by interval (annual [A], biennial, triennial [T]) and age range. The number of life-years gained (LYG), breast cancer deaths averted, overdiagnosed cases, false-positive mammograms, ICERs and harm-benefit ratios were calculated. Optimal risk-based screening scenarios, below the threshold ICER of €8883/LYG, were T50-71 (€7840/LYG) for low-risk and B40-74 (€6062/LYG) for high-risk women. T50-71 screening in low-risk women resulted in a 33% reduction in false-positive findings, a similar reduction in costs and improved harm-benefit ratios compared to the current screening schedule. B40-74 in high-risk women led to an increase in screening benefit, compared to current B50-74 screening, but a relatively higher increase in false-positive findings. In conclusion, optimal screening consisted of a longer interval and lower stopping age than current uniform screening for low-risk women, and a lower starting age for high-risk women. Extending the interval for women at lower risk from biennial to triennial screening reduced harms and costs while maintaining most of the screening benefit.
© 2020 UICC.

Entities:  

Keywords:  breast cancer; cost-effectiveness; harm-benefit ratio; mammography screening; risk stratification

Mesh:

Year:  2020        PMID: 32484237     DOI: 10.1002/ijc.33126

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  7 in total

1.  Effects of cancer screening restart strategies after COVID-19 disruption.

Authors:  Lindy M Kregting; Sylvia Kaljouw; Lucie de Jonge; Erik E L Jansen; Elisabeth F P Peterse; Eveline A M Heijnsdijk; Nicolien T van Ravesteyn; Iris Lansdorp-Vogelaar; Inge M C M de Kok
Journal:  Br J Cancer       Date:  2021-03-15       Impact factor: 9.075

2.  Women's health behaviour change after receiving breast cancer risk estimates with tailored screening and prevention recommendations.

Authors:  Linda Rainey; Daniëlle van der Waal; Louise S Donnelly; Jake Southworth; David P French; D Gareth Evans; Mireille J M Broeders
Journal:  BMC Cancer       Date:  2022-01-16       Impact factor: 4.430

3.  Women's perceptions of PERSPECTIVE: a breast cancer risk stratification e-platform.

Authors:  Saima Ahmed; Emmanuelle Lévesque; Rosalind Garland; Bartha Knoppers; Michel Dorval; Jacques Simard; Carmen G Loiselle
Journal:  Hered Cancer Clin Pract       Date:  2022-02-24       Impact factor: 2.857

Review 4.  The current status of risk-stratified breast screening.

Authors:  Ash Kieran Clift; David Dodwell; Simon Lord; Stavros Petrou; Sir Michael Brady; Gary S Collins; Julia Hippisley-Cox
Journal:  Br J Cancer       Date:  2021-10-26       Impact factor: 9.075

Review 5.  Cancer Screening Recommendations During the COVID-19 Pandemic: Scoping Review.

Authors:  Sumit K Shah; Pearl A McElfish
Journal:  JMIR Cancer       Date:  2022-02-24

6.  Development and validation of clinical prediction models for breast cancer incidence and mortality: a protocol for a dual cohort study.

Authors:  Ashley Kieran Clift; Julia Hippisley-Cox; David Dodwell; Simon Lord; Mike Brady; Stavros Petrou; Gary S Collins
Journal:  BMJ Open       Date:  2022-03-28       Impact factor: 2.692

7.  Finding the optimal mammography screening strategy: A cost-effectiveness analysis of 920 modelled strategies.

Authors:  Lindy M Kregting; Valérie D V Sankatsing; Eveline A M Heijnsdijk; Harry J de Koning; Nicolien T van Ravesteyn
Journal:  Int J Cancer       Date:  2022-03-21       Impact factor: 7.316

  7 in total

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