M Goossens1, G Van Hal2, M Van der Burg2, E Kellen3, K Van Herck4, J De Grève5, P Martens6, E Van Limbergen3. 1. Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Centrum voor kankeropsporing (Center for Cancer Detection), Ruddershove 4, 8000 Brugge, Belgium. Electronic address: Mathieu.Goossens@uzbrussel.be. 2. Centrum voor kankeropsporing (Center for Cancer Detection), Ruddershove 4, 8000 Brugge, Belgium; University of Antwerp, Medical Sociology and Health Policy, Universiteitsplein 1, 2610 Antwerp, Belgium. Electronic address: https://www.bevolkingsonderzoek.be. 3. Centrum voor kankeropsporing (Center for Cancer Detection), Ruddershove 4, 8000 Brugge, Belgium; University Hospital Leuven, Campus St. Rafael, Kapucijnenvoer 33, 3000 Leuven, Belgium. Electronic address: https://www.bevolkingsonderzoek.be. 4. Centrum voor kankeropsporing (Center for Cancer Detection), Ruddershove 4, 8000 Brugge, Belgium; Ghent University, Department of Public Health, De Pintelaan 185, 9000 Ghent, Belgium. Electronic address: https://www.bevolkingsonderzoek.be. 5. Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium. 6. Centrum voor kankeropsporing (Center for Cancer Detection), Ruddershove 4, 8000 Brugge, Belgium.
Abstract
BACKGROUND: Mammographic screening may reduce breast cancer mortality by about 20%, provided participation is high and women screen regularly. We quantified independent risk factors for failing to rescreen and built a model to predict how rescreening rates change if these risk factors would be modified. METHODS: Multivariate analysis was used to analyze data from a prospective study which included a self-administered questionnaire and rescreening status 30months after a t0 mammogram, using a random sample of women 50-67years (Belgium 2010-2013). RESULTS: A false positive result at the most recent past mammogram (Odds Ratio=5.0, 95% Confidence Interval 3.6-6.8), an interval until new invitation greater than 25months (Odds Ratio=4.8 for >29months, 95% Confidence Interval 2.9-8.1), waiting times in the mammography unit >1h (Odds Ratio=2.1, 95% Confidence Interval 1.2-3.7) and difficulties in reaching the unit (Odds Ratio=2.5, 95% Confidence Interval 1.4-4.4) were the strongest independent predictors for failing to rescreen. The area under the curve of the receiver operating characteristic analysis was 0.705 for the model development stage and 0.717 for the validation stage and goodness-of-fit was good. CONCLUSIONS: Maintaining an invitation cycle of maximum 25months, limiting waiting time in the mammography unit and lowering the number of false positives could increase breast cancer screening compliance.
BACKGROUND: Mammographic screening may reduce breast cancer mortality by about 20%, provided participation is high and women screen regularly. We quantified independent risk factors for failing to rescreen and built a model to predict how rescreening rates change if these risk factors would be modified. METHODS: Multivariate analysis was used to analyze data from a prospective study which included a self-administered questionnaire and rescreening status 30months after a t0 mammogram, using a random sample of women 50-67years (Belgium 2010-2013). RESULTS: A false positive result at the most recent past mammogram (Odds Ratio=5.0, 95% Confidence Interval 3.6-6.8), an interval until new invitation greater than 25months (Odds Ratio=4.8 for >29months, 95% Confidence Interval 2.9-8.1), waiting times in the mammography unit >1h (Odds Ratio=2.1, 95% Confidence Interval 1.2-3.7) and difficulties in reaching the unit (Odds Ratio=2.5, 95% Confidence Interval 1.4-4.4) were the strongest independent predictors for failing to rescreen. The area under the curve of the receiver operating characteristic analysis was 0.705 for the model development stage and 0.717 for the validation stage and goodness-of-fit was good. CONCLUSIONS: Maintaining an invitation cycle of maximum 25months, limiting waiting time in the mammography unit and lowering the number of false positives could increase breast cancer screening compliance.
Authors: Jinane Ghattas; Vanessa Gorasso; Robby De Pauw; Sophie Thunus; Niko Speybroeck; Brecht Devleesschauwer Journal: Arch Public Health Date: 2022-10-18
Authors: Mathijs C Goossens; Isabel De Brabander; Jacques De Greve; Evelien Vaes; Chantal Van Ongeval; Koen Van Herck; Eliane Kellen Journal: Eur J Cancer Prev Date: 2017-09 Impact factor: 2.497
Authors: Prue C Allgood; Roberta Maroni; Sue Hudson; Judith Offman; Anne E Turnbull; Lesley Peacock; Jim Steel; Geraldine Kirby; Christine E Ingram; Julie Somers; Clare Fuller; Anthony G Threlfall; Rhian Gabe; Anthony J Maxwell; Julietta Patnick; Stephen W Duffy Journal: Lancet Oncol Date: 2017-05-15 Impact factor: 41.316
Authors: Lilu Ding; Svetlana Jidkova; Marcel J W Greuter; Koen Van Herck; Mathieu Goossens; Harlinde De Schutter; Patrick Martens; Guido Van Hal; Geertruida H de Bock Journal: Front Public Health Date: 2021-04-15
Authors: Joanne M Osborne; Carlene Wilson; Amy Duncan; Stephen R Cole; Ingrid Flight; Deborah Turnbull; Donna L Hughes; Graeme P Young Journal: BMC Public Health Date: 2017-08-01 Impact factor: 3.295
Authors: M Goossens; I De Brabander; J De Grève; C Van Ongeval; P Martens; E Van Limbergen; E Kellen Journal: BMC Cancer Date: 2019-10-28 Impact factor: 4.430