A tool to predict disparities in the timeliness of surgical treatment for breast cancer patients in the USA

Abstract

Purpose Breast cancer outcomes are impaired by both delays and disparities in treatment. This study was performed to assess their relationship and to provide a tool to predict patient socioeconomic factors associated with risk for delay. Methods The National Cancer Database was reviewed between 2004 and 2017 for patients with non-metastatic breast cancer managed with upfront surgery. Times to treatment were measured from the date of diagnosis. Patient, tumor, and treatment factors were assessed with attention paid to sociodemographic variables. Results 514,187 patients remained after exclusions, with 84.3% White, 10.8% Black, 3.7% Asian, and Hispanics comprising 5.6% of the cohort. Medicaid and uninsured patients had longer mean adjusted time to surgery (≥ 46 days) versus private (36.7 days), Medicare (35.9 days), or other governmental insurance (39.8 days). After adjustment, Black race and Hispanic ethnicity were most impactful, adding 6.0 and 6.4 preoperative days, 10.9 and 11.5 days to chemotherapy, 11.1 and 9.1 days to radiation, and 12.5 and 8.9 days to endocrine therapy, respectively. Income, education, and insurance, among other factors, also affected delay. A nomogram, including race and sociodemographic factors, was created to predict the risk of preoperative delay. Conclusion Significant disparities exist in timeliness of care for factors, including but not limited to, race and ethnicity. Although exact causes cannot be discerned, these data indicate population subsets whose intervals of care risk being longer than those specified by national quality standards. The nomogram created here may help direct resources to those at highest risk of incurring a treatment delay. Supplementary Information The online version contains supplementary material available at 10.1007/s10549-021-06460-9.

Reexamining Time From Breast Cancer Diagnosis to Primary Breast Surgery

“…We hypothesize that social determinants of health, represented by surrogate measures such as lower insurance status and neighborhood income, may negatively affect access to care and thus result in longer times to surgery. These findings are consistent with other studies reporting socioeconomic disadvantage to be associated with longer times to surgery . Verdone et al studied the NCDB to generate a nomogram for increased risk for surgical delay of greater than 60 days based on socioeconomic risk factors and found that Medicaid coverage was associated with increased risk of delay compared with private insurance.…”

“…These findings are consistent with other studies reporting socioeconomic disadvantage to be associated with longer times to surgery . Verdone et al studied the NCDB to generate a nomogram for increased risk for surgical delay of greater than 60 days based on socioeconomic risk factors and found that Medicaid coverage was associated with increased risk of delay compared with private insurance. Looking at Medicaid expansion, Obeng-Gyasi et al found that although the Affordable Care Act expansion increased breast conservation and reconstruction in Ohio, no difference in time to surgery was observed between those patients without insurance compared with those with Medicaid.…”

Race, geography, and risk of breast cancer treatment delays: A population‐based study 2004–2015

“…35 Individual sociodemographics certainly predict the risk of treatment delays at the patient level. 36 However, it is notable that in our study, adjustment for…”

“…Disparities in the quality of cancer care, whether by race or geography, are often attributed to the sociodemographic characteristics of marginalized patient populations, and it is common practice to include risk adjustment for sociodemographic characteristics in outcome‐based oncology quality measures 35 . Individual sociodemographics certainly predict the risk of treatment delays at the patient level 36 . However, it is notable that in our study, adjustment for patient‐level characteristics did not attenuate the observed geographic differences in treatment timeliness for either racial group.…”

Development of a Multilevel Model to Identify Patients at Risk for Delay in Starting Cancer Treatment

“…30,31 Our findings suggest that machine learning models can similarly be used to identify patients at risk of treatment delays and builds on our prior work in socioeconomically disadvantaged groups. 32 Our model selected neighborhood-level SDOH variables, which are not routinely available in the EHR as variables. However, their contribution to model performance is limited.…”