title: Which strategies reduce breast cancer mortality most? Collaborative modeling of optimal screening, treatment, and obesity prevention.
abstract: BACKGROUND
  US breast cancer mortality is declining, but thousands of women still die each year.
  
  
  METHODS
  Two established simulation models examine 6 strategies that include increased screening and/or treatment or elimination of obesity versus continuation of current patterns. The models use common national data on incidence and obesity prevalence, competing causes of death, mammography characteristics, treatment effects, and survival/cure. Parameters are modified based on obesity (defined as BMI  ≥  30 kg/m(2) ). Outcomes are presented for the year 2025 among women aged 25+ and include numbers of cases, deaths, mammograms and false-positives; age-adjusted incidence and mortality; breast cancer mortality reduction and deaths averted; and probability of dying of breast cancer.
  
  
  RESULTS
  If current patterns continue, the models project that there would be about 50,100-57,400 (range across models) annual breast cancer deaths in 2025. If 90% of women were screened annually from ages 40 to 54 and biennially from ages 55 to 99 (or death), then 5100-6100 fewer deaths would occur versus current patterns, but incidence, mammograms, and false-positives would increase. If all women received the indicated systemic treatment (with no screening change), then 11,400-14,500 more deaths would be averted versus current patterns, but increased toxicity could occur. If 100% received screening plus indicated therapy, there would be 18,100-20,400 fewer deaths. Eliminating obesity yields 3300-5700 fewer breast cancer deaths versus continuation of current obesity levels.
  
  
  CONCLUSIONS
  Maximal reductions in breast cancer deaths could be achieved through optimizing treatment use, followed by increasing screening use and obesity prevention.
authors.affiliation: Department of Oncology, Georgetown University, Lombardi Comprehensive Cancer Center, Washington, DC, USA. mandelbj@georgetown.edu
authors.forename: Jeanne
authors.initials: J
authors.lastname: Mandelblatt
authors.forename: Nicolien
authors.initials: N
authors.lastname: van Ravesteyn
authors.forename: Clyde
authors.initials: C
authors.lastname: Schechter
authors.forename: Yaojen
authors.initials: Y
authors.lastname: Chang
authors.forename: An-Tsun
authors.initials: AT
authors.lastname: Huang
authors.forename: Aimee M
authors.initials: AM
authors.lastname: Near
authors.forename: Harry
authors.initials: H
authors.lastname: de Koning
authors.forename: Ahmedin
authors.initials: A
authors.lastname: Jemal
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000209:etiology / Q000401:mortality* / Q000517:prevention & control / Q000628:therapy*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D015994:Incidence; D008327:Mammography; D008403:Mass Screening / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D009765:Obesity / Q000150:complications* / Q000453:epidemiology* / Q000517:prevention & control; D016019:Survival Analysis; D014481:United States / Q000453:epidemiology
keywords: breast cancer; mammography; modeling; obesity; simulation; treatment