Associations between health insurance status, neighborhood deprivation, and treatment delays in women with breast cancer living in Georgia

Patient, Examination, and System Factors Associated with Completed Follow-up for Probably Benign (BI-RADS 3) Breast Findings in a Large, Complex Health Care System

RATIONALE AND OBJECTIVES

To investigate patient, examination, and system factors associated with follow-up completion for probably benign breast (BI-RADS 3) findings.

MATERIALS AND METHODS

Retrospective review identified all BI-RADS 3 mammography, ultrasound, and MRI reports (index studies) for unique patient encounters at a large multi-institution Health Care System Jan 1-Dec 31, 2021. Electronic health record supplied patient demographics and examination type; Radiology Information System supplied ordering and scheduling information. University of Wisconsin's Neighborhood Atlas was used to map patient home addresses to determine area deprivation index (ADI). Univariable and multivariable analyses identified variables associated with noncompleted BI-RADS 3 follow-up.

RESULTS

Among 8834 BI-RADS 3 examinations, 6778 (76.7%) had follow-up imaging completed within 15 months. Factors associated with incomplete follow-up on multivariable analysis included: ultrasound (Odds Ratio [OR] 0.22; 95% Confidence Interval [95%CI] 0.19-0.25); MRI (0.10, 95%CI 0.08-0.12); Asian race (0R 0.77; 95%CI 0.61-0.98); age< 40 years (OR 0.22; 95%CI 0.18-0.26); non-married status (single, OR 0.68; 95% CI 0.59-0.79; divorced OR 0.77; 95% CI 0.61-0.97; widowed OR 0.61; 95% CI 0.44-0.85); public insurance (OR 0.84; 95% CI 0.71-0.98), self-pay (OR 0.59; 95% CI 0.39-0.96); orders placed > 8 months after index examination (OR 0.20; 95%CI 0.14-0.29); ordering by non-primary care (PCP) (OR 0.51; 95%CI 0.36-0.72); and non-same day scheduling. Socioeconomic disadvantage (ADI) was not associated with incomplete BI-RADS 3 follow-up.

CONCLUSION

Non-completion of recommended BI-RADS 3 follow-up is associated with Asian race, age< 40 years, MRI or ultrasound (versus mammography), marital status, insurance coverage, delayed order placement or scheduling, and order placement by non-PCPs.

An innovative approach to the multidisciplinary treatment of uninsured breast cancer patients

PURPOSE

A significant proportion of many populations remain uninsured. The aim of the study was to assess differences in breast cancer outcomes before and after the implementation of an innovative approach to the multidisciplinary treatment of uninsured breast cancer patients.

METHODS

Retrospective review was performed of patients seen at a safety net hospital from January 2000 to December 2020. Beginning July 2006, an innovative approach was implemented to lower patient costs and facilitate care of uninsured patients.

RESULTS

The study included 1,797 patients, 661 patients before the changes (BCS), and 1136 patients after implementation of the new cost saving approach (ACS). The mean age was 53 years. The majority were uninsured (56%) or insured by Medicaid (31%). Only 18% underwent screening mammography. The ACS group had a higher rate of breast conservation (75% vs 47%, p < 0.001). A higher percentage of the ACS group received adjuvant therapy: Chemotherapy (91% vs 70%, p < 0.001), Radiation therapy (91% vs 70%, p < 0.001), and initiated endocrine therapy (87% vs 67%, p < 0.001). After follow-up of 8 years, these changes resulted in lower ipsilateral breast tumor recurrence (2% vs 16%, p < 0.001) and chest wall recurrence (5% versus 8%) and improvement in overall survival (90% vs 81%, p < 0.001).

CONCLUSION

Peer-reviewed literature is replete of studies documenting disparities in breast cancer treatment. The current study describes a successful cost-limiting method which takes advantage of existing financial assistance programs to improve care in uninsured patients.

Impact of patient, hospital, and operative characteristics relative to social determinants of health: Compliance with National Comprehensive Cancer Network guidelines for colon cancer

BACKGROUND

Despite an established association with improved patient outcomes, compliance with National Comprehensive Cancer Network (NCCN) guidelines remains suboptimal. We sought to assess the effect of patient characteristics (PCs), operative characteristics (OCs), hospital characteristics (HCs), and social determinants of health (SDoH) on noncompliance with NCCN guidelines for colon cancer.

METHODS

Patients treated for stage I to III colon cancer from 2004 to 2017 were identified from the National Cancer Database. Multilevel multivariate regression analysis was performed to identify factors associated with receipt of NCCN-compliant care and quantify the proportion of variance explained by PCs, OCs, HCs, and SDoH.

RESULTS

Among 468,097 patients with colon cancer treated across 1319 hospitals, 1 in 4 patients did not receive NCCN-compliant care (122,170 [26.1%]). On regression analysis, older age (odds ratio [OR], 0.96; 95% CI, 0.96-0.96), female sex (OR, 0.97; 95% CI, 0.96-0.99), Black race (OR, 0.96; 95% CI, 0.94-0.98), higher Charlson-Deyo score (OR, 0.84; 95% CI, 0.82-0.86), tumor stage ≥II (OR, 0.42; 95% CI, 0.40-0.44), and tumor grade ≥ 3 (OR, 0.33; 95% CI, 0.32-0.34) were associated with lower odds of receiving NCCN-compliant care (all P values <.05). Higher hospital volume (OR, 1.02; 95% CI, 1.02-1.03), minimally invasive or robotic surgical approach (OR, 1.26; 95% CI, 1.23-1.29), adequate (≥12) lymph node assessment (OR, 3.46; 95% CI, 3.38-3.53), private insurance status (OR, 1.33; 95% CI, 1.26-1.40), Medicare insurance status (OR, 1.42; 95% CI, 1.35-1.49), and higher educational status (OR, 1.06; 95% CI, 1.02-1.09) were associated with higher odds of receiving NCCN-compliant care (all P values <.05). Overall, PCs contributed 36.5%, HCs contributed 1.3%, and OCs contributed 12.9% to the variation in guideline-compliant care, while SDoH contributed only 3.6% of the variation in receipt of NCCN-compliant care.

CONCLUSION

The variation in NCCN-compliant care among patients with colon cancer was largely attributable to patient- and surgeon-level factors, whereas SDoH were associated with a smaller proportion of the variation.

Real-world Trends, Rural-urban Differences, and Socioeconomic Disparities in Utilization of Narrow versus Broad Next-generation Sequencing Panels

Advances in genetic technology have led to the increasing use of genomic panels in precision oncology practice, with panels ranging from a couple to hundreds of genes. However, the clinical utilization and utility of oncology genomic panels, especially among vulnerable populations, is unclear. We examined the association of panel size with socioeconomic status and clinical trial matching. We retrospectively identified 9,886 eligible adult subjects in the Mayo Clinic Health System who underwent genomic testing between January 1, 2016 and June 30, 2020. Patient data were retrieved from structured and unstructured data sources of institutional collections, including cancer registries, clinical data warehouses, and clinical notes. Socioeconomic surrogates were approximated using the Area Deprivation Index (ADI) corresponding to primary residence addresses. Logistic regression was performed to analyze relationships between ADI or rural/urban status and (i) use of genomic test by panel size; (ii) clinical trial matching status. Compared with patients from the most affluent areas, patients had a lower odds of receiving a panel test (vs. a single-gene test) if from areas of higher socioeconomic deprivation [OR (95% confidence interval (CI): 0.71 (0.61-0.83), P < 0.01] or a rural area [OR (95% CI): 0.85 (0.76-0.96), P < 0.01]. Patients in areas of higher socioeconomic deprivation were less likely to be matched to clinical trials if receiving medium panel tests [(OR) (95% CI): 0.69 (0.49-0.97), P = 0.03]; however, there was no difference among patients receiving large panel tests (P > 0.05) and rural patients were almost 2x greater odds of being matched if receiving a large panel test [(OR) (95% CI): 1.76 (1.21-2.55), P < 0.01].

SIGNIFICANCE

We identified socioeconomic and rurality disparities in the use of genomic tests and trial matching by panel size, which may have implications for equal access to targeted therapies. The lack of association between large panel tests and clinical trial matching by socioeconomic status, suggests a potential health equity impact, while removing barriers in access to large panels for rural patients may improve access to trials. However, further research is needed.