Impact of the COVID-19 Pandemic on Breast Cancer Mortality in the US

Estimates From Collaborative Simulation Modeling

Oguzhan Alagoz; Kathryn P. Lowry; Allison W. Kurian; Jeanne S. Mandelblatt; Mehmet A. Ergun; Hui Huang; Sandra J. Lee; Clyde B. Schechter; Anna N. A. Tosteson; Diana L. Miglioretti; Amy Trentham-Dietz; Sarah J. Nyante; Karla Kerlikowske; Brian L. Sprague; Natasha K. Stout

Disclosures

J Natl Cancer Inst. 2021;113(11):1484-1494. 

In This Article

Results

The models reproduced observed age-adjusted breast cancer mortality in the United States over time (Supplementary Figure 2, available online). The models predicted that the cumulative number of excess breast cancer deaths due to the COVID-19 pandemic's impact on screening, diagnosis of symptomatic cases, and chemotherapy treatment could reach 2487 (model range = 1713–2575) by 2030 (Table 3; Supplementary Tables 3–7, and Supplementary Figures 3–4, available online). This corresponds to a 0.52% (model range = 0.36%-0.56%) increase in breast cancer deaths between 2020 and 2030 compared with usual care with no COVID-19 impact. By 2030, the models project 950 (model range = 860–1297) cumulative excess breast cancer deaths related to reduced screening; 1314 (model range = 266–1325) associated with delayed diagnosis of symptomatic cases, and 151 (model range = 146–207) associated with reduced chemotherapy use in women with hormone-positive, early-stage cancer. The effect of excess mortality associated with changes in screening, diagnosis, and treatment accelerated during 2020–2025 and leveled off thereafter (Table 3; Figure 1).

Figure 1.

Cumulative excess breast cancer mortality according to exemplar model (University of Wisconsin-Madison and Harvard Medical School model) over time. A) The number of cumulative excessive deaths when each disruption is modeled separately. B) The number of excessive deaths when disruptions are combined.

Among the modeled scenarios, reductions in screening use and delays in diagnosis of symptomatic cases contributed the largest numbers of excess deaths. For example, disruptions for these 2 components (scenario 5c) resulted in 2277 (model range = 1576–2365) additional deaths, representing over 90% of the cumulative excess deaths associated with the modeled disruptions in screening, diagnosis, and chemotherapy treatment combined (scenario 6c) during this period (Table 3). The models suggest that the contribution of the modeled delay in diagnosis of symptomatic cases and reduced screening to the additional breast cancer deaths is similar. Disruptions in screening alone (scenario 2c) would lead to 950 (model range = 860–1297) additional deaths, representing 42% of the total excess deaths due to disruptions in screening and diagnosis (Table 3). However, cumulative breast cancer deaths by 2030 were fourfold higher if women skipped their mammogram rather than delayed screening by 6 months (1631 vs 364).

Varying assumptions about the proportion of women experiencing delays did not change the overall patterns of impact of the pandemic on 2030 breast cancer mortality (Table 4; Supplementary Tables 8–14, available online). Under all modeled scenarios, the increase in breast cancer deaths due to pandemic-related disruptions is not predicted to exceed 1% by 2030. In addition, if cancer diagnosis is delayed by 6 months for only 15% of women during the pandemic, the number of excess deaths exceeds the number observed if 50% of asymptomatic women delay screening for 6 months (758 vs 364) (Supplementary Table 11, available online; Table 3). If the modeled pandemic effects on screening, diagnosis, and treatment lasted for 12 months instead of 6 months, the number of additional deaths approximately doubles (Figure 2; Table 4; Supplementary Figures 5–6, available online). Sensitivity analysis on pandemic-related other-cause mortality input did not lead to any major changes in the results (Supplementary Table 15, available online).

Figure 2.

Cumulative excess breast cancer mortality according to exemplar model (University of Wisconsin-Madison and Harvard Medical School model) over time when the pandemic-related disruptions last for 12 months. A) The number of cumulative excessive deaths when each disruption is modeled separately. B) The number of excessive deaths when disruptions are combined.

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