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Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up

BACKGROUND: Overdiagnosis, defined as the detection of a cancer that would not become clinically apparent in a woman’s lifetime without screening, has become a growing concern. Similar underlying risk of breast cancer in the screened and control groups is a prerequisite for unbiased estimates of ove...

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Autores principales: Wu, Wendy Yi-Ying, Törnberg, Sven, Elfström, Klara Miriam, Liu, Xijia, Nyström, Lennarth, Jonsson, Håkan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296133/
https://www.ncbi.nlm.nih.gov/pubmed/30558679
http://dx.doi.org/10.1186/s13058-018-1082-z
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author Wu, Wendy Yi-Ying
Törnberg, Sven
Elfström, Klara Miriam
Liu, Xijia
Nyström, Lennarth
Jonsson, Håkan
author_facet Wu, Wendy Yi-Ying
Törnberg, Sven
Elfström, Klara Miriam
Liu, Xijia
Nyström, Lennarth
Jonsson, Håkan
author_sort Wu, Wendy Yi-Ying
collection PubMed
description BACKGROUND: Overdiagnosis, defined as the detection of a cancer that would not become clinically apparent in a woman’s lifetime without screening, has become a growing concern. Similar underlying risk of breast cancer in the screened and control groups is a prerequisite for unbiased estimates of overdiagnosis, but a contemporary control group is usually not available in organized screening programs. METHODS: We estimated the frequency of overdiagnosis of breast cancer due to screening in women 50–69 years old by using individual screening data from the population-based organized screening program in Stockholm County 1989–2014. A hidden Markov model with four latent states and three observed states was constructed to estimate the natural progression of breast cancer and the test sensitivity. Piecewise transition rates were used to consider the time-varying transition rates. The expected number of detected non-progressive breast cancer cases was calculated. RESULTS: During the study period, 2,333,153 invitations were sent out; on average, the participation rate in the screening program was 72.7% and the average recall rate was 2.48%. In total, 14,648 invasive breast cancer cases were diagnosed; among the 8305 screen-detected cases, the expected number of non-progressive breast cancer cases was 35.9, which is equivalent to 0.43% (95% confidence interval (CI) 0.10%–2.2%) overdiagnosis. The corresponding estimates for the prevalent and subsequent rounds were 15.6 (0.87%, 95% CI 0.20%–4.3%) and 20.3 (0.31%, 95% CI 0.07%–1.6%), respectively. The likelihood ratio test showed that the non-homogeneous model fitted the data better than an age-homogeneous model (P <0.001). CONCLUSIONS: Our findings suggest that overdiagnosis in the organized biennial mammographic screening for women 50–69 in Stockholm County is a minor phenomenon. The frequency of overdiagnosis in the prevalent screening round was higher than that in subsequent rounds. The non-homogeneous model performed better than the simpler, traditional homogeneous model. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13058-018-1082-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-62961332018-12-18 Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up Wu, Wendy Yi-Ying Törnberg, Sven Elfström, Klara Miriam Liu, Xijia Nyström, Lennarth Jonsson, Håkan Breast Cancer Res Research Article BACKGROUND: Overdiagnosis, defined as the detection of a cancer that would not become clinically apparent in a woman’s lifetime without screening, has become a growing concern. Similar underlying risk of breast cancer in the screened and control groups is a prerequisite for unbiased estimates of overdiagnosis, but a contemporary control group is usually not available in organized screening programs. METHODS: We estimated the frequency of overdiagnosis of breast cancer due to screening in women 50–69 years old by using individual screening data from the population-based organized screening program in Stockholm County 1989–2014. A hidden Markov model with four latent states and three observed states was constructed to estimate the natural progression of breast cancer and the test sensitivity. Piecewise transition rates were used to consider the time-varying transition rates. The expected number of detected non-progressive breast cancer cases was calculated. RESULTS: During the study period, 2,333,153 invitations were sent out; on average, the participation rate in the screening program was 72.7% and the average recall rate was 2.48%. In total, 14,648 invasive breast cancer cases were diagnosed; among the 8305 screen-detected cases, the expected number of non-progressive breast cancer cases was 35.9, which is equivalent to 0.43% (95% confidence interval (CI) 0.10%–2.2%) overdiagnosis. The corresponding estimates for the prevalent and subsequent rounds were 15.6 (0.87%, 95% CI 0.20%–4.3%) and 20.3 (0.31%, 95% CI 0.07%–1.6%), respectively. The likelihood ratio test showed that the non-homogeneous model fitted the data better than an age-homogeneous model (P <0.001). CONCLUSIONS: Our findings suggest that overdiagnosis in the organized biennial mammographic screening for women 50–69 in Stockholm County is a minor phenomenon. The frequency of overdiagnosis in the prevalent screening round was higher than that in subsequent rounds. The non-homogeneous model performed better than the simpler, traditional homogeneous model. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13058-018-1082-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-17 2018 /pmc/articles/PMC6296133/ /pubmed/30558679 http://dx.doi.org/10.1186/s13058-018-1082-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Wu, Wendy Yi-Ying
Törnberg, Sven
Elfström, Klara Miriam
Liu, Xijia
Nyström, Lennarth
Jonsson, Håkan
Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
title Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
title_full Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
title_fullStr Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
title_full_unstemmed Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
title_short Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
title_sort overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296133/
https://www.ncbi.nlm.nih.gov/pubmed/30558679
http://dx.doi.org/10.1186/s13058-018-1082-z
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