This study examined racial/ethnic differences in health/life insurance denial due to cancer among cancer survivors after the passage of the Affordable Care Act (ACA). Behavioral Risk Factor Surveillance System data were obtained from 2012–2020. The dependent variable asked: “Were you ever denied health insurance or life insurance coverage because of your cancer?” Cancer survivors were included if they were diagnosed with cancer after the Affordable Care Act (N = 14,815). Unadjusted and adjusted logistic regressions for age, sex, income, and employment provided odds ratios of insurance denial due to cancer across racial/ethnic groups: Non-Hispanic White, Black, and Other/mixed race; and Hispanic. Statistically significant differences (p < 0.05) were found between those who were denied or not denied insurance across sex, age, race/ethnicity, income, and employment. Adjusted regressions found significantly higher odds ratios of insurance denial for Blacks (OR: 3.00, 95% CI: 1.77, 5.08), Other/mixed race (OR: 2.16, 95% CI: 1.16, 4.02), and Hispanics (OR: 2.13, 95% CI: 1.02, 4.42) compared to Whites. Differences were observed across sex, income, and employment. Cancer survivors report racial/ethnic disparities in health and life insurance denial due to their cancer despite policy changes. This may be harmful for those who are already financially vulnerable due to their cancer diagnosis and exacerbate racial/ethnic cancer disparities.

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Introduction: While cancer deaths have decreased nationally, declines have been much slower in rural areas than in urban areas. Previous studies on rural cancer service capacity are limited to specific points along the cancer care continuum (eg screening, diagnosis or treatment) and require updating to capture the current rural health landscape since implementation of the 2010 Affordable Care Act in the USA. The association between current rural cancer service capacity across the cancer care continuum and cancer incidence and death is unclear. This cross-sectional study explored the association between breast cancer service capacity and incidence and mortality in Arizona's low populous counties. Methods: To measure county-level cancer capacity, clinical organizations operating within low populous areas of Arizona were surveyed to assess on-site breast cancer services provided (screening, diagnosis and treatment) and number of healthcare providers were pulled from Centers for Medicare and Medicaid Services National Provider Identifier database. The number of clinical sites and healthcare providers were converted to countylevel per capita rates. Rural-Urban Continuum codes were used to designate rural or urban county status. Age-adjusted county-level breast cancer incidence and death rates from 2010 to 2016 were obtained from the Arizona Department of Health Services, Arizona Cancer Registry. Descriptive statistics were used to summarize the results. Multivariate regression was used to evaluate the association between cancer service capacity and incidence and mortality in 13 out of Arizona's 15 counties. Results: Rural counties had more per capita clinical sites (20.4)than urban counties (8.9) (p=0.02). Urban counties had more per capita pathologists (1.0) than rural counties (0) (p≤0.01). In addition to zero pathologists, rural counties had zero medical oncologists. Rural county status was associated with a decrease in breast cancer incidence (β=-20.1, 95% confidence interval:-37.2-3.1). Conclusion: WhileArizona's sparsely populated rural counties may have more physical infrastructure per capita, these services are dispersed over vast geographic areas. They lack specialists providing cancer services. Non-physician clinical providers may be more prevalent in rural areas and represent opportunities for improving access to cancer preventive services and care. Compared to urban counties, rural county status was associated with lower detected breast cancer incidence rates although there were no statistically significant differences in breast cancer mortality. Other factors may contribute to rural-urban differences in breast cancer incidence. Future research should explore these factors and the association between cancer capacity and local resources because the use of county-level data represents a challenge in Arizona, where counties average over 19 425 km (7500 square miles).

Compared with urban residents, rural Americans have seen slower declines in cancer deaths, have lower incidence but higher death rates from cancers that can be prevented through screening, have lower screening rates, are more likely to present with later-stage cancers, and have poorer cancer outcomes and lower survival. Rural health provider shortages and lack of cancer services may explain some disparities. The literature was reviewed to identify factors contributing to rural health care capacity shortages and propose policy recommendations for improving rural cancer care. Uncompensated care, unfavorable payer mix, and low patient volume impede rural physician recruitment and retainment. Students from rural areas are more likely to practice there but are less likely to attend medical school because of lower graduation rates, grades, and Medical College Admission Test (MCAT) scores versus urban students. The cancer care infrastructure is costly and financially challenging in rural areas with high proportions of uninsured and publicly insured patients. A lack of data on oncology providers and equipment impedes coordinated efforts to address rural shortages. Graduate Medical Education funding greatly favors large, urban, tertiary care teaching hospitals over residency training in rural, critical access and community-based hospitals and clinics. Policies have the potential to transform rural health care. This includes increasing advanced practice provider postgraduate oncology training opportunities and expanding the scope of practice; improving health workforce and services data collection and aggregation; transforming graduate medical education subsidies to support rural student recruitment and rural training opportunities; and expanding federal and state financial incentives and payments to support the rural cancer infrastructure.

BackgroundDistribution of tobacco cessation medications through state quitlines increases service utilization and quit outcomes. However, some state quitlines have moved to models in which callers are instructed to obtain quit medications through their health insurance pharmaceutical benefit. We aimed to investigate the impact of this policy on medication access and quit outcomes in the state quitline setting for clients who must obtain covered medications through the state Medicaid program. We hypothesized that clients with Medicaid who were referred by their healthcare provider would be more likely to report using quit medication and have higher quit rates compared to clients with Medicaid who engaged the quitline on their own.MethodsAn observational, retrospective study was conducted using state quitline clients with Medicaid health insurance who were ineligible for quitline provided cessation medications. Clients were stratified by referral type: self-referred, passively referred, and proactively referred. Unadjusted and adjusted logistic regression was used to estimate the effect of referral type on both quit status and cessation medication use.ResultsProactively referred clients were less likely to use quit medication (53.6%) compared to self (56.9%) and passively referred clients (61.1%). Proactively referred clients had lower quit rates (31.4%), as compared to passively referred (36.0%) and self-referred (35.1%). In adjusted models, proactively referred clients were significantly less likely to be quit than passively referred clients (OR = 0.75, 95% CI: 0.56, 0.99). There were no statistically significant differences in medication use or number of coaching sessions among proactive, passive, and self-referred clients in adjusted models.ConclusionsIn adjusted models, medication use did not significantly differ by mode of entry in this population of Medicaid beneficiaries. Psychosocial factors such as intention to quit in the next 30 days, social support for quitting, education level, race, and ethnicity impacted quit status and differed by mode of entry. Quitlines should use tailored strategies to increase engagement and reduce barriers among proactively referred clients.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5923-6) contains supplementary material, which is available to authorized users.