Toyya A. Pujol

Supplementary Figure 3. Simulation results for surveillance mammography positive predictive value overall and stratified by race and ethnicity in simulated populations of women with a personal history of breast cancer

Supplementary Table 2. Additional descriptive statistics for the Breast Cancer Surveillance Consortium cohort of surveillance mammograms

Supplementary Figure 2. Simulation results for surveillance mammography cancer detection rates overall and stratified by race and ethnicity in simulated populations of women with a personal history of breast cancer

Supplementary Table 1. Distributional assumptions and input parameter values for simulations. Model 1 includes no race and ethnicity variation, Model 2 includes variation in primary cancer and treatment characteristics by race and ethnicity, Model 3 includes variation in SDOH by race and ethnicity, Models 4 and 5 include variation in primary cancer and treatment characteristics as well as SDOH by race and ethnicity.}

Supplementary Table 3. Sensitivity and cancer risk prediction logistic regression model coefficients

Supplementary Figure 1. Simulation results for surveillance mammography false-negative rates overall and stratified by race and ethnicity in simulated populations of women with a personal history of breast cancer

Supplementary Methods 1: Simulation study design description

Background: Surveillance mammography is recommended for all women with a history of breast cancer. Risk-guided surveillance incorporating advanced imaging modalities based on individual risk of a second cancer could improve cancer detection. However, personalized surveillance may also amplify disparities. Methods: In simulated populations using inputs from the Breast Cancer Surveillance Consortium (BCSC), we investigated race- and ethnicity-based disparities. Disparities were decomposed into those due to primary breast cancer and treatment characteristics, social determinants of health (SDOH) and differential error in second cancer ascertainment by modeling populations with or without variation across race and ethnicity in the distribution of these characteristics. We estimated effects of disparities on mammography performance and supplemental imaging recommendations stratified by race and ethnicity. Results: In simulated cohorts based on 65,446 BCSC surveillance mammograms, when only cancer characteristics varied by race and ethnicity, mammograms for Black women had lower sensitivity compared to the overall population (64.1% vs 71.1%). Differences between Black women and the overall population were larger when both cancer characteristics and SDOH varied by race and ethnicity (53.8% vs 71.1%). Basing supplemental imaging recommendations on high predicted second cancer risk resulted in less frequent recommendations for Hispanic (6.7%) and Asian/Pacific Islander women (6.4%) compared to the overall population (10.0%). Conclusions: Variation in cancer characteristics and SDOH led to disparities in surveillance mammography performance and recommendations for supplemental imaging. Impact: Risk-guided surveillance imaging may exacerbate disparities. Decision-makers should consider implications for equity in cancer outcomes resulting from implementing risk-guided screening programs.

Supplementary Figure 2. Simulation results for surveillance mammography cancer detection rates overall and stratified by race and ethnicity in simulated populations of women with a personal history of breast cancer

Supplementary Table 1. Distributional assumptions and input parameter values for simulations. Model 1 includes no race and ethnicity variation, Model 2 includes variation in primary cancer and treatment characteristics by race and ethnicity, Model 3 includes variation in SDOH by race and ethnicity, Models 4 and 5 include variation in primary cancer and treatment characteristics as well as SDOH by race and ethnicity.}