The Impact of Medicaid Expansion on Patients with Cancer in the United States: A Review

Disparities in Cancer Stage Outcomes by Catchment Areas for a Comprehensive Cancer Center

Importance

The National Cancer Institute comprehensive cancer centers (CCCs) lack spatial and temporal evaluation of their self-designated catchment areas.

Objective

To identify disparities in cancer stage at diagnosis within and outside a CCC's catchment area across a 10-year period using spatial and statistical analyses.

Design, Setting, and Participants

This cross-sectional, population-based study conducted between 2010 and 2019 utilized cancer registry data for the Johns Hopkins Sidney Kimmel CCC (SKCCC). Eligible participants included patients with cancer in the contiguous US who received treatment for cancer, a diagnosis of cancer, or both at SKCCC. Patients were geocoded to zip code tabulation areas (ZCTAs). Individual-level variables included sociodemographic characteristics, smoking and alcohol use, treatment type, cancer site, and insurance type. Data analysis was performed between March and July 2023.

Exposures

Distance between SKCCC and ZCTAs were computed to generate a catchment area of the closest 75% of patients and outer zones in 5% increments for comparison.

Main Outcomes and Measures

The primary outcome was cancer stage at diagnosis, defined as early-stage, late-stage, or unknown stage. Multinomial logistic regression was used to determine associations of catchment area with stage at diagnosis.

Results

This study had a total of 94 007 participants (46 009 male [48.94%] and 47 998 female [51.06%]; 30 195 aged 22-45 years [32.12%]; 4209 Asian [4.48%]; 2408 Hispanic [2.56%]; 16 004 non-Hispanic Black [17.02%]; 69 052 non-Hispanic White [73.45%]; and 2334 with other or unknown race or ethnicity [2.48%]), including 47 245 patients (50.26%) who received a diagnosis of early-stage cancer, 19 491 (20.73%) who received a diagnosis of late-stage cancer , and 27 271 (29.01%) with unknown stage. Living outside the main catchment area was associated with higher odds of late-stage cancers for those who received only a diagnosis (odds ratio [OR], 1.50; 95% CI, 1.10-2.05) or only treatment (OR, 1.44; 95% CI, 1.28-1.61) at SKCCC. Non-Hispanic Black patients (OR, 1.16; 95% CI, 1.10-1.23) and those with Medicaid (OR, 1.65; 95% CI, 1.46-1.86) and no insurance at time of treatment (OR, 2.12; 95% CI, 1.79-2.51) also had higher odds of receiving a late-stage cancer diagnosis.

Conclusions and Relevance

In this cross-sectional study of CCC data from 2010 to 2019, patients residing outside the main catchment area, non-Hispanic Black patients, and patients with Medicaid or no insurance had higher odds of late-stage diagnoses. These findings suggest that disadvantaged populations and those living outside of the main catchment area of a CCC may face barriers to screening and treatment. Care-sharing agreements among CCCs could address these issues.

Addressing differences in cancer: a framework for synergistic programming in cancer prevention and control

Background

Cancer remains a leading cause of death worldwide and continues to disproportionately impact certain populations. Several frameworks have been developed that illustrate the multiple determinants of cancer. Expanding upon the work of others, we present an applied framework for cancer prevention and control designed to help clinicians, as well as public health practitioners and researchers, better address differences in cancer outcomes.

Methods

The framework was developed by the Cancer Prevention and Control Research Network's Health Behaviors Workgroup. An initial framework draft was developed based on workgroup discussion, public health theory, and rapid literature review on the determinants of cancer. The framework was refined through interviews and focus groups with Federally Qualified Health Center providers (n=2) and cancer patients (n=2); participants were asked to provide feedback on the framework's causal pathways, completeness, and applicability to their work and personal life.

Results

The framework provides an overview of the relationships between sociodemographic inequalities, social and structural determinants, and key risk factors associated with cancer diagnosis, survivorship, and cancer morbidity and mortality across the lifespan. The framework emphasizes how health-risk behaviors like cigarette smoking interact with psychological, psychosocial, biological, and psychosocial risk factors, as well as healthcare-related behavior and other chronic diseases. Importantly, the framework emphasizes addressing social and structural determinants that influence health behaviors to reduce the burden of cancer and improve health equity. Aligned with previous theory, our framework underscores the importance of addressing co-occurring risk factors and disease states, understanding the complex relationships between factors that influence cancer, and assessing how multiple forms of inequality or disadvantage intersect to increase cancer risk across the lifespan.

Conclusions

This paper presents an applied framework for cancer prevention and control to address cancer differences. Because the framework highlights determinants and factors that influence cancer risk at multiple levels, it can be used to inform the development, implementation, and evaluation of interventions to address cancer morbidity and mortality.

Reduced healthcare access contributes to delay of care in endometrial cancer

OBJECTIVE

We aimed to characterize delays to care in patients with endometrioid endometrial cancer and the role healthcare access plays in these delays.

METHODS

A chart review was performed of patients with endometrioid endometrial cancer who presented with postmenopausal bleeding at a diverse, urban medical center between 2006 and 2018. The time from symptom onset to treatment was abstracted from the medical record. This interval was subdivided to assess for delay to presentation, delay to diagnosis, and delay to treatment.

RESULTS

We identified 484 patients who met the inclusion criteria. The median time from symptom onset to treatment was 4 months with an interquartile range of 2 to 8 months. Most patients had stage I disease at diagnosis (88.6%). There was no significant difference in race/ethnicity or disease stage at time of diagnosis between different groups. Patients who had not seen a primary care physician or general obstetrician-gynecologist in the year before symptom onset were more likely to have significantly delayed care (27.7% vs 14.3%, p = 0.02) and extrauterine disease (20.2% vs 4.9%, p < 0.01) compared to those with established care. Black and Hispanic patients were more likely to experience significant delays from initial biopsy to diagnosis.

CONCLUSIONS

Delays exist in the evaluation of endometrial cancer. This delay is most pronounced in patients without an established outpatient primary care provider or obstetrician-gynecologist.