Mammograms and Mortality: How Has the Evidence Evolved?

Abstract

Decades of evidence reveal a complicated relationship between mammograms and mortality. Mammograms may detect deadly cancers early, but they may also lead to the diagnosis and potentially fatal treatment of cancers that would never progress to cause symptoms. I provide a brief history of the evidence on mammograms and mortality, focusing on evidence from clinical trials, and I discuss how this evidence informs mammography guidelines. I then explore the evolution of all-cause mortality relative to breast cancer mortality within an influential clinical trial. I conclude with some responses to the evolving evidence.

Figure 1:

Trends in Mammography and Breast Cancer Incidence in the United States Source. Panel (a): National Center for Health Statistics (2011, 2019); Panel (b): Author’s calculations from Surveillance, Epidemiology, and End Results Program (SEER) (2020). Note. Incidence is age-adjusted to the 2000 US population. Following Bleyer and Welch (2012), cancer stage is given by the SEER historic stage A variable. Early-stage cancers are in situ or localized. Late-stage cancers are regional or distant.

Figure 2:

Trend in Excess Breast Cancer Mortality Rate in Intervention in the Canadian National Breast Screening Study Note. Years after enrollment are constructed for each subject based on the exact calendar date of enrollment. 95 percent confidence intervals in each year after enrollment are obtained from the same 200 bootstrap samples. The standard error on the slope of the line of best fit, in parentheses, is calculated from a panel regression, block bootstrapped by year after enrollment, which takes into account that the point estimates and the line of best fit are estimated. Subjects aged 40–59 at enrollment are included.

Figure 3:

Trend in Excess All-Cause Mortality Rate in Intervention in the Canadian National Breast Screening Study Note. Years after enrollment are constructed for each subject based on the exact calendar date of enrollment. 95 percent confidence intervals in each year after enrollment are obtained from the same 200 bootstrap samples. The standard error on the slope of the line of best fit, in parentheses, is calculated from a panel regression, block bootstrapped by year after enrollment, which takes into account that the point estimates and the line of best fit are estimated. Subjects aged 40–59 at enrollment are included.