Martin J Yaffe1,2,3, Nicole Mittmann4,5,6, Oguzhan Alagoz6,7, Amy Trentham-Dietz6, Anna Na Tosteson8, Natasha K Stout9. 1. 1 Physical Sciences Program, Sunnybrook Research Institute, Toronto, Canada. 2. 2 Departments of Medical Biophysics and Medical Imaging, University of Toronto, Toronto, Canada. 3. 3 Ontario Institute for Cancer Research, Toronto, Canada. 4. 4 Health Outcomes and PharmacoEconomic (HOPE) Research Centre, Sunnybrook Research Institute, Toronto, Canada. 5. 5 Applied Research in Cancer Control, Department of Pharmacology, University of Toronto, Toronto, Canada. 6. 7 Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, USA. 7. 8 Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, USA. 8. 9 The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, USA. 9. 10 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA.
Abstract
OBJECTIVES: Incidence-based mortality quantifies the distribution of cancer deaths and life-years lost, according to age at detection. We investigated the temporal distribution of the disease burden, and the effect of starting and stopping ages and interval between screening mammography examinations, on incidence-based mortality. METHODS: Incidence-based mortality was estimated using an established breast cancer simulation model, adapted and validated to simulate breast cancer incidence, screening performance, and delivery of therapies in Canada. Ten strategies were examined, with varying starting age (40 or 50), stopping age (69 or 74), and interval (1, 2, 3 years), and "No Screening." Life-years lost were computed as the difference between model predicted time of breast cancer death and that estimated from life tables. RESULTS: Without screening, 70% of the burden in terms of breast cancer deaths extends between ages 45 and 75. The mean of the distribution of ages of detection of breast cancers that will be fatal in an unscreened population is 61.8 years, while the mean age of detection weighted by the number of life-years lost is 55, a downward shift of 6.8 years. Similarly, the mean age of detection for the distribution of life-years gained through screening is lower than that for breast cancer deaths averted. CONCLUSION: Incidence-based mortality predictions from modeling elucidate the age dependence of the breast cancer burden and can provide guidance for optimizing the timing of screening regimens to achieve maximal impact. Of the regimens studied, the greatest lifesaving effect was achieved with annual screening beginning at age 40.
OBJECTIVES: Incidence-based mortality quantifies the distribution of cancer deaths and life-years lost, according to age at detection. We investigated the temporal distribution of the disease burden, and the effect of starting and stopping ages and interval between screening mammography examinations, on incidence-based mortality. METHODS: Incidence-based mortality was estimated using an established breast cancer simulation model, adapted and validated to simulate breast cancer incidence, screening performance, and delivery of therapies in Canada. Ten strategies were examined, with varying starting age (40 or 50), stopping age (69 or 74), and interval (1, 2, 3 years), and "No Screening." Life-years lost were computed as the difference between model predicted time of breast cancer death and that estimated from life tables. RESULTS: Without screening, 70% of the burden in terms of breast cancer deaths extends between ages 45 and 75. The mean of the distribution of ages of detection of breast cancers that will be fatal in an unscreened population is 61.8 years, while the mean age of detection weighted by the number of life-years lost is 55, a downward shift of 6.8 years. Similarly, the mean age of detection for the distribution of life-years gained through screening is lower than that for breast cancer deaths averted. CONCLUSION: Incidence-based mortality predictions from modeling elucidate the age dependence of the breast cancer burden and can provide guidance for optimizing the timing of screening regimens to achieve maximal impact. Of the regimens studied, the greatest lifesaving effect was achieved with annual screening beginning at age 40.
Entities:
Keywords:
Breast cancer screening; age to begin screening; incidence-based mortality; mammography; mammography screening regimens; quality-adjusted life-years; screening interval
Authors: Maria Alice Franzoi; Gilberto Schwartsmann; Sérgio Jobim de Azevedo; Guilherme Geib; Facundo Zaffaroni; Pedro E R Liedke Journal: J Racial Ethn Health Disparities Date: 2019-05-17
Authors: Matteo Floris; Giovanna Pira; Paolo Castiglia; Maria Laura Idda; Maristella Steri; Maria Rosaria De Miglio; Andrea Piana; Andrea Cossu; Antonio Azara; Caterina Arru; Giovanna Deiana; Carlo Putzu; Valeria Sanna; Ciriaco Carru; Antonello Serra; Marco Bisail; Maria Rosaria Muroni Journal: Oncol Lett Date: 2022-08-08 Impact factor: 3.111