Kerri Beckmann1, Stephen W Duffy2, John Lynch3, Janet Hiller4, Gelareh Farshid5, David Roder6. 1. Research Fellow, School of Population Health, University of South Australia, Adelaide, Australia kerri.beckmann@unisa.edu.au. 2. Professor of Cancer Screening, Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom. 3. Professor of Epidemiology and Public Health, School of Population Health, University of Adelaide, Adelaide, Australia. 4. Dean, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia. 5. Clinical Director, BreastScreen SA, Adelaide, Australia. 6. Chair of Cancer Epidemiology and Population Health, School of Population Health, University of South Australia, Adelaide, Australia.
Abstract
OBJECTIVE: To estimate over-diagnosis due to population-based mammography screening using a lead time adjustment approach, with lead time measures based on symptomatic cancers only. SUBJECTS: Women aged 40-84 in 1989-2009 in South Australia eligible for mammography screening. METHODS: Numbers of observed and expected breast cancer cases were compared, after adjustment for lead time. Lead time effects were modelled using age-specific estimates of lead time (derived from interval cancer rates and predicted background incidence, using maximum likelihood methods) and screening sensitivity, projected background breast cancer incidence rates (in the absence of screening), and proportions screened, by age and calendar year. RESULTS: Lead time estimates were 12, 26, 43 and 53 months, for women aged 40-49, 50-59, 60-69 and 70-79 respectively. Background incidence rates were estimated to have increased by 0.9% and 1.2% per year for invasive and all breast cancer. Over-diagnosis among women aged 40-84 was estimated at 7.9% (0.1-12.0%) for invasive cases and 12.0% (5.7-15.4%) when including ductal carcinoma in-situ (DCIS). CONCLUSIONS: We estimated 8% over-diagnosis for invasive breast cancer and 12% inclusive of DCIS cancers due to mammography screening among women aged 40-84. These estimates may overstate the extent of over-diagnosis if the increasing prevalence of breast cancer risk factors has led to higher background incidence than projected.
OBJECTIVE: To estimate over-diagnosis due to population-based mammography screening using a lead time adjustment approach, with lead time measures based on symptomatic cancers only. SUBJECTS:Women aged 40-84 in 1989-2009 in South Australia eligible for mammography screening. METHODS: Numbers of observed and expected breast cancer cases were compared, after adjustment for lead time. Lead time effects were modelled using age-specific estimates of lead time (derived from interval cancer rates and predicted background incidence, using maximum likelihood methods) and screening sensitivity, projected background breast cancer incidence rates (in the absence of screening), and proportions screened, by age and calendar year. RESULTS: Lead time estimates were 12, 26, 43 and 53 months, for women aged 40-49, 50-59, 60-69 and 70-79 respectively. Background incidence rates were estimated to have increased by 0.9% and 1.2% per year for invasive and all breast cancer. Over-diagnosis among women aged 40-84 was estimated at 7.9% (0.1-12.0%) for invasive cases and 12.0% (5.7-15.4%) when including ductal carcinoma in-situ (DCIS). CONCLUSIONS: We estimated 8% over-diagnosis for invasive breast cancer and 12% inclusive of DCIS cancers due to mammography screening among women aged 40-84. These estimates may overstate the extent of over-diagnosis if the increasing prevalence of breast cancer risk factors has led to higher background incidence than projected.
Authors: Kevin Jenniskens; Joris A H de Groot; Johannes B Reitsma; Karel G M Moons; Lotty Hooft; Christiana A Naaktgeboren Journal: BMJ Open Date: 2017-12-27 Impact factor: 2.692
Authors: Laszlo Tabar; Tony Hsiu-Hsi Chen; Chen-Yang Hsu; Wendy Yi-Ying Wu; Amy Ming-Fang Yen; Sam Li-Sheng Chen; Sherry Yueh-Hsia Chiu; Jean Ching-Yuan Fann; Kerri Beckmann; Robert A Smith; Stephen W Duffy Journal: J Med Screen Date: 2016-06-23 Impact factor: 2.136