Influence of Race, Insurance, Rurality, and Socioeconomic Status on Equity of Lung and Colorectal Cancer Care

Disparities in Cancer Stage of Diagnosis by Rurality in California, 2015 to 2019

BACKGROUND

Cancer rates in rural areas vary by insurance status, socioeconomic status, region, race, and ethnicity.

METHODS

California Cancer Registry data (2015-2019) were used to investigate the stage of diagnosis by levels of rurality for the five most common cancers. The percentage of residents in rural blocks within census tract aggregation zones was categorized into deciles up to 50%. Multivariable logistic regression was used to estimate associations with rurality, with separate models by cancer site, sex, race, and ethnicity (non-Hispanic White and Hispanic). Covariates included individual-level and zone-level factors.

RESULTS

The percentage of late-stage cancer diagnosis was 28% for female breast, 27% for male prostate, 77% for male lung, 71% for female lung, 60% for male colorectal, 59% for female colorectal, 7.8% for male melanoma, and 5.9% for female melanoma. Increasing rurality was significantly associated with increased odds of late-stage cancer diagnosis for female breast cancer (Ptrend < 0.001), male lung cancer (Ptrend < 0.001), female lung cancer (Ptrend < 0.001), and male melanoma (Ptrend = 0.01), after adjusting for individual-level and zone-level factors. The strength of associations varied by sex and ethnicity. For males with lung cancer, odds of late-stage diagnosis in areas with >50% rural population was 1.24 (95% confidence interval, 1.06-1.45) for non-Hispanic White patients and 2.14 (95% confidence interval, 0.86-5.31) for Hispanic patients, compared with areas with 0% rural residents.

CONCLUSIONS

Increasing rurality was associated with increased odds for late-stage diagnosis for breast cancer, lung cancer, and melanoma, with the strength of associations varying across sex and ethnicity.

IMPACT

Our findings will inform cancer outreach to these rural subpopulations.

Geographic and racial disparities in the quality of surgical care among patients with nonmetastatic uterine cancer

BACKGROUND

Although the rates of minimally invasive surgery and sentinel lymph node biopsy have increased considerably over time in the surgical management of early-stage uterine cancer, practice varies significantly in the United States, and there are disparities among low-volume centers and patients of Black race. A significant number of counties in the United States are without a gynecologic oncologist, and almost half of the counties with the highest gynecologic cancer rates lack a local gynecologic oncologist.

OBJECTIVE

This study aimed to evaluate the relationships of distance traveled and proximity to gynecologic oncologists with the receipt of and racial disparities in the quality of surgical care among patients who underwent a hysterectomy for nonmetastatic uterine cancer.

STUDY DESIGN

Patients who underwent a hysterectomy for nonmetastatic uterine cancer in Kentucky, Maryland, Florida, and North Carolina were identified in the 2012 to 2018 State Inpatient Database and the State Ambulatory Surgery Services Database files. County-to-county distances were used as the distances traveled to the nearest gynecologic oncologist. Factors associated with the receipt of minimally invasive surgery and lymph node dissection were analyzed using multivariable logistic regression models, as was the assessment of the interaction between travel for surgery and patient race.

RESULTS

Among 21,837 cases, 45.5% lived in a county without a gynecologic oncologist; overall, 55.5% traveled to another county for surgery, including 88% of those who lacked a local gynecologic oncologist. Patients who lacked access to a local gynecologic oncologist in their county who did not travel for surgery were more likely to receive open surgery and no lymph node dissection, and those in counties without access in any surrounding county were affected even more. Among patients in counties without a gynecologic oncologist, those who traveled for surgery had a similar likelihood of undergoing minimally invasive surgery (71%) but had a greater likelihood of undergoing lymph node dissection (64.7% vs 57.2%) than nontravelers. Among those in counties without a gynecologic oncologist, a longer distance traveled was associated with receipt of a lymph node assessment. When compared with non-Black patients, Black patients were less likely to undergo minimally invasive surgery (57.0% vs 74.1%). In adjusted regression models that controlled for a diagnosis of fibroids, Black race was an independent risk factor for the receipt of open surgery. There was a significant interaction between Black race and travel for surgery, and Black patients who lived in counties without a gynecologic oncologist who did not travel faced an incrementally lower likelihood of receiving minimally invasive surgery (odds ratio, 0.57 when compared with non-Black patients who traveled for surgery; odds ratio, 0.60 as interaction term; P<.001 for both). Similar disparities in surgical quality by race were noted for Black patients who lived in counties with a gynecologic oncologist who traveled out of county for surgery.

CONCLUSION

Patients, particularly those of Black race, who lacked local access to gynecologic oncologist specialty care benefitted from traveling to specialty centers to ensure access to high-quality surgery for nonmetastatic uterine cancer. Further work is needed to ensure equitable and universal access to high-quality care through patient travel or specialist outreach.

Residential Redlining, Neighborhood Trajectory, and Equity of Breast and Colorectal Cancer Care

OBJECTIVE

To determine the influence of structural racism, vis-à-vis neighborhood socioeconomic trajectory, on colorectal and breast cancer diagnosis and treatment.

BACKGROUND

Inequities in cancer care are well-documented in the United States but less is understood about how historical policies like residential redlining and evolving neighborhood characteristics influence current gaps in care.

METHODS

This retrospective cohort study included adult patients diagnosed with colorectal or breast cancer between 2010 and 2015 in 7 Indiana cities with available historic redlining data. Current neighborhood socioeconomic status was determined by the Area Deprivation Index. Based on historic redlining maps and the current Area Deprivation Index, we created 4 "neighborhood trajectory" categories: advantage stable, advantage reduced, disadvantage stable, and disadvantage reduced. Modified Poisson regression models estimated the relative risks (RRs) of neighborhood trajectory on cancer stage at diagnosis and receipt of cancer-directed surgery (CDS).

RESULTS

A final cohort derivation identified 4862 cancer patients with colorectal or breast cancer. Compared with "advantage stable" neighborhoods, "disadvantage stable" neighborhood was associated with a late-stage diagnosis for both colorectal and breast cancer [RR = 1.30 (95% CI: 1.05-1.59); RR = 1.41 (1.09-1.83), respectively]. Black patients had a lower likelihood of receiving CDS in "disadvantage reduced" neighborhoods [RR = 0.92 (0.86-0.99)] than White patients.

CONCLUSIONS

Disadvantage stable neighborhoods were associated with late-stage diagnoses of breast and colorectal cancer. "Disadvantage reduced" (gentrified) neighborhoods were associated with racial inequity in CDS. Improved neighborhood socioeconomic conditions may improve timely diagnosis but could contribute to racial inequities in surgical treatment.

Emergency Free School Meal Distribution During the COVID-19 Pandemic in High-Poverty Urban Settings

BACKGROUND

The coronavirus disease 2019 pandemic triggered nationwide school closures in March 2020, putting millions of children in the United States who were reliant on subsidized school meals at risk of experiencing hunger. In response, the US Department of Agriculture mobilized the Summer Food Service Program and Seamless Summer Option program to provide emergency free school meals. There is a need to investigate the effectiveness of these programs in covering underresourced communities during the pandemic.

OBJECTIVE

This study assessed associations between meal distribution and census tract demographics (ie, poverty level, race/ethnicity, and deprivation level based on social deprivation index score).

DESIGN

An observational study using longitudinal meal distribution data collected over an 18-month period following school closures (March 2020 to August 2021).

PARTICIPANTS AND SETTING

Monthly meal distribution data were collected for community sites serving 142 census tracts within 4 urban New Jersey cities predominantly populated by people with low incomes and from racial and ethnic minority groups.

MAIN OUTCOME MEASURES

Main outcome measures were the number of meals served monthly by Summer Food Service Program and Seamless Summer Option meal sites.

STATISTICAL ANALYSES PERFORMED

A 2-part multivariable regression approach was used to analyze the data.

RESULTS

In the first step, logistic regression models showed that high-deprivation tracts were more likely to serve meals during the observed period (odds ratio 3.43, 95% CI 1.001 to 11.77; P = 0.0499). In the second step, among tracts that served any meals during the observed period, mixed effects negative binomial regression models showed that high-poverty and high-deprivation tracts served comparatively more meals (incidence rate ratio [IRR] 2.83, 95% CI 2.29 to 3.51; P < 0.001 and IRR 1.94, 95% CI 1.65 to 2.28; P < 0.001, respectively).

CONCLUSIONS

Findings show that meal distribution during the pandemic was higher within census tracts with higher poverty and deprivation levels, indicating that underresourced communities with higher need had more free meals available during this unprecedented public health emergency.

Inequity in Cancer and Cancer Care Delivery in the United States

Inequity exists along the continuum of cancer and cancer care delivery in the United States. Marginalized populations have later stage cancer at diagnosis, decreased likelihood of receiving cancer-directed care, and worse outcomes from treatment. These inequities are driven by historical, structural, systemic, interpersonal, and internalized factors that influence cancer across the pathologic and clinical continuum. To ensure equity in cancer care, interventions are needed at the level of policy, care delivery, interpersonal communication, diversity within the clinical workforce, and clinical trial accessibility and design.

Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums

PURPOSE

To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities.

METHODS

An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities.

RESULTS

Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, year of diagnosis, age, proximity to superfund sites, and primary payer. Spatio-temporal clusters highlighted geographic areas with a statistically significant high probability of late-stage diagnoses, emphasizing the need for targeted healthcare interventions.

CONCLUSIONS

This research underlines the potential of ML in enhancing the prognostic predictions in oncology, particularly in CRC. The gradient boosting model, with its robust performance, holds promise for deployment in healthcare systems to aid early detection and formulate localized cancer prevention strategies. The study's methodology demonstrates a significant step toward utilizing AI in public health to mitigate disparities and improve cancer care outcomes.

Insurance status impacts survival of hepatocellular carcinoma patients after liver resection

BACKGROUND

This study intends to examine the effect of public insurance status on survival outcomes of HCC patients after liver resection in China.

METHODS

We divided 2911 HCC patients after liver resection included in our study into the Urban Employed-based Medical Insurance group (UEBMI group, n = 1462) and the non-Urban Employed-based Medical Insurance group (non-UEBMI group, n = 1449). A propensity score matching (PSM) analysis was used to control confounding factors. Overall survival (OS) was estimated by Kaplan-Meier curves and Cox proportional hazard models based on variables screened by Lasso regression. Competing risk analysis was used to analyze cancer-specific survival (CSS).

RESULTS

UEBMI group had more male patients (p = 0.031), patients in the UEBMI group were older (p < 0.001) and had lower Charlson Comorbidity Index scores (CCI score, p < 0.001). Meanwhile, patients in the UEBMI group had better liver function (albumin-bilirubin grade I [ALBI I], p < 0.001) and lower tumor burden (α-fetoprotein [AFP], p = 0.009; Barcelona Clinic Liver Cancer stage [BCLC], p = 0.026; Milan criteria, p < 0.001; tumor size, p < 0.001; microvascular invasion [MVI], p = 0.030; portal vein tumor thrombosis [PVTT], p = 0.002). More patients in the UEBMI group received laparoscopic surgery (p = 0.024) and adjuvant transarterial chemoembolization (TACE, p < 0.001). After PSM, patients in the two matched groups had similar characteristics. Patients with recurrent HCC in the UEBMI were more likely to receive curative therapy (p < 0.001) and less likely to receive supportive care (p < 0.001). HCC patients after liver resection in the non-UEBMI group had a worse OS before (p < 0.0001) and after PSM (p = 0.002). [Correction added on August 16, 2023 after first online publication. The p value has been updated in the preceding sentence.] In our Lasso-Cox risk regression model, public health insurance status was an independent factor linked with OS (non-UEBMI vs. UEBMI, hazard ratio [HR]: 1.27; 95% confidence interval [CI]: 1.12-1.46; p < 0.001). In the competing risk analysis, patients in the UEBMI group had a lower cumulative incidence of CSS before (p < 0.001) and after PSM (p = 0.001), and public insurance status of HCC patients after liver resection remained independently associated with CSS (non-UEBMI vs. UEBMI; HR:1.36; 95% CI: 1.18-1.58; p < 0.001).

CONCLUSIONS

Underinsured HCC patients after liver resection had worse survival outcomes. Less access to care for underinsured patients may explain the difference in survival, but the corresponding conclusions need to be further explored.

Disparities in Access to Thoracic Surgeons among Patients Receiving Lung Lobectomy in the United States

Objective: Lung lobectomy is the standard of care for early-stage lung cancer. Studies have suggested improved outcomes associated with lobectomy performed by specialized thoracic surgery providers. We hypothesized that disparities would exist regarding access to thoracic surgeons among patients receiving lung lobectomy for cancer. Methods: The Premier Hospital Database was used to identify adult inpatients receiving lung lobectomy from 2009 to 2019. Patients were categorized as receiving their lobectomy from a thoracic surgeon, cardiovascular surgeon, or general surgeon. Sample-weighted multivariable analysis was performed to identify factors associated with provider type. Results: When adjusted for sampling, 121,711 patients were analyzed, including 71,709 (58.9%) who received lobectomy by a thoracic surgeon, 36,630 (30.1%) by a cardiovascular surgeon, and 13,373 (11.0%) by a general surgeon. Multivariable analysis showed that thoracic surgeon provider type was less likely with Black patients, Medicaid insurance, smaller hospital size, in the western region, and in rural areas. In addition, non-thoracic surgery specialty was less likely to perform minimally-invasive (MIS) lobectomy (cardiovascular OR 0.80, p < 0.001, general surgery OR 0.85, p = 0.003). Conclusions: In this nationally representative analysis, smaller, rural, non-teaching hospitals, and certain regions of the United States are less likely to receive lobectomy from a thoracic surgeon. Thoracic surgeon specialization is also independently associated with utilization of minimally invasive lobectomy. Combined, there are significant disparities in access to guideline-directed surgical care of patients receiving lung lobectomy.

Disparities in treatment and survival for patients with isolated colorectal liver metastases

BACKGROUND

Surgical resection improves survival for patients with isolated colorectal liver metastasis. National studies on the disparities related to this topic are limited; therefore, we investigated factors that affect surgical treatment and survival.

METHODS

We queried the National Cancer Database (2010-2017) for patients with isolated synchronous colorectal liver metastasis. Multivariable logistic regression and Cox proportional hazard regressions were used to identify factors associated with surgical resection, treatment at high-volume facilities, and overall survival.

RESULTS

Of 34,050 patients with isolated colorectal liver metastasis, surgical resection (n = 7,810; 23.0%) was more likely among patients who were of high socioeconomic status (odds ratio = 1.16; 95% confidence interval, 1.04-1.31), traveled long distance for treatment (odds ratio = 1.48; 95% confidence interval, 1.31-1.66), and were treated at high-volume facilities (odds ratio = 4.86; 95% confidence interval, 14.45-5.30). Black patients were less likely to undergo resection (odds ratio = 0.75; 95% confidence interval, 0.69-0.82). Treatment at high-volume facility was more common among patients who were Black (odds ratio = 1.14; 95% confidence interval, 1.07-1.21), were of high socioeconomic status (socioeconomic status index 7: odds ratio = 1.21; 95% confidence interval, 1.11-1.31), and traveled long distance (odds ratio = 4.03; 95% confidence interval, 3.63-4.48) and less likely for nonmetropolitan residents and those of low socioeconomic status (P < .05). Patients of high socioeconomic status and those who traveled long distance, were treated at high-volume facilities, underwent surgical resection, and received perioperative chemotherapy had an associated survival advantage (P < .05 for all), whereas Black race was associated with poorer overall survival (P < .05).

CONCLUSION

Nonmedical patient factors, such as race, socioeconomic status, and geography, are associated with treatment and survival for isolated colorectal liver metastases. Disparities persist after adjusting for surgical resection and treatment facility. These barriers must be addressed to improve care for vulnerable cancer patients.

social determinants of health in multiple sclerosis

Social determinants of health are the conditions in which people are born, grow, live, work and age. These circumstances are the non-medical factors that influence health outcomes. Evidence indicates that health behaviours, comorbidities and disease-modifying therapies all contribute to multiple sclerosis (MS) outcomes; however, our knowledge of the effects of social determinants — that is, the ‘risks of risks’ — on health has not yet changed our approach to MS. Assessing and addressing social determinants of health could fundamentally improve health and health care in MS; this approach has already been successful in improving outcomes in other chronic diseases. In this narrative Review, we identify and discuss the body of evidence supporting an effect of many social determinants of health, including racial background, employment and social support, on MS outcomes. It must be noted that many of the published studies were subject to bias, and screening tools and/or practical interventions that address these social determinants are, for the most part, lacking. The existing work does not fully explore the potential bidirectional and complex relationships between social determinants of health and MS, and the interpretation of findings is complicated by the interactions and intersections among many of the identified determinants. On the basis of the reviewed literature, we consider that, if effective interventions targeting social determinants of health were available, they could have substantial effects on MS outcomes. Therefore, funding for and focused design of studies to evaluate and address social determinants of health are urgently needed.

Here, the authors discuss the potential effects of social determinants of health on multiple sclerosis risk and outcomes. They suggest that addressing these determinants of health could substantially improve the lives of individuals with multiple sclerosis and call for more research.

Addressing an individual’s social determinants of health — that is, the conditions under which they are born, grow, live, work and age — could provide opportunities to reduce the burden of living with multiple sclerosis (MS).

Individual factors that may influence MS-related outcomes include sex, gender and sexuality, race and ethnicity, education and employment, socioeconomic status, and domestic abuse.

Societal infrastructures, including access to food, health care and social support, can also affect MS-related outcomes.

Awareness of the specific circumstances of a patient with MS might help neurologists deliver better care.

Social determinants of health are not static and can change according to wider sociopolitical contexts, as highlighted by the COVID-19 pandemic.

Rigorous studies of interventions to ameliorate the effects of poor social determinants on people with MS are urgently needed.

Cancer Disparities Experienced by People with Disabilities

People with disabilities, who represent a rapidly growing and seriously disadvantaged segment of the U.S. population, face unremitting barriers to equal and accessible healthcare and a high prevalence of chronic health conditions. A slowly growing body of research suggests multiple cancer-related disparities between people with and without disabilities. This commentary identifies multiple aspects of the cancer experience and highlights ways cancer is impacted by disability. This includes vulnerabilities to risk factors, barriers to accessing healthcare, and disparities in screening, diagnosis, and treatment. The authors offer six essential pathways for reducing cancer disparities faced by people with disabilities. It is clear that reducing cancer health disparities experienced by people with disabilities will require the commitment and cooperation of a wide range of stakeholders.

Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.