title: Estimates of over-diagnosis of breast cancer due to population-based mammography screening in South Australia after adjustment for lead time effects.
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.
  
  
  METHODS
  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.affiliation: Research Fellow, School of Population Health, University of South Australia, Adelaide, Australia kerri.beckmann@unisa.edu.au.
authors.forename: Kerri
authors.initials: K
authors.lastname: Beckmann
authors.affiliation: Professor of Cancer Screening, Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom.
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Duffy
authors.affiliation: Professor of Epidemiology and Public Health, School of Population Health, University of Adelaide, Adelaide, Australia.
authors.forename: John
authors.initials: J
authors.lastname: Lynch
authors.affiliation: Dean, School of Health Sciences, Swinburne University of Technology, Melbourne, Australia.
authors.forename: Janet
authors.initials: J
authors.lastname: Hiller
authors.affiliation: Clinical Director, BreastScreen SA, Adelaide, Australia.
authors.forename: Gelareh
authors.initials: G
authors.lastname: Farshid
authors.affiliation: Chair of Cancer Epidemiology and Population Health, School of Population Health, University of South Australia, Adelaide, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Roder
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000453:epidemiology; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000453:epidemiology; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008327:Mammography / Q000379:methods*; D008403:Mass Screening / Q000379:methods*; D000069342:Medical Overuse; D008875:Middle Aged; D015995:Prevalence; D012307:Risk Factors; D013021:South Australia / Q000453:epidemiology; D013997:Time Factors
keywords: Breast cancer; Mammography screening; Over-diagnosis