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Telehealth has been proposed as a strategy to reduce disparities in access to mental health counseling in rural areas. The objective of this study was to compare interest in telehealth between rural and urban communities and to identify the factors that impact interest in using telehealth services. A sample of 2,010 respondents completed a cross-sectional survey addressing issues of access to mental counseling and interest in telehealth in both rural and urban areas. Survey data were collected in October-November 2018 from a five-county area in central Illinois. Path modeling was used to test the relationship between rural residence, access to mental health counseling, and interest in telehealth. Rural residents had less access to mental health counseling (85.1% vs. 90.0%) and the internet than their urban counterparts (81.1% vs. 91.7%). However, people in rural communities had more interest in telehealth (β = .45, p < .01) and access to mental health counseling was a predictor for interest in telehealth (β = −.53, p < .01). This study found that rural residents, especially younger adults and those who face barriers to accessing mental health counseling, are the most interested in telehealth. Policies regarding availability of broadband platforms and expanding telehealth in rural areas may help alleviate issues related to access to mental health counseling in rural areas. Public Health Significance StatementThis study describes the use of telehealth and finds that people living in rural communities are interested in use of these services for mental health care.

Objective The study examined the impact that the Medicaid expansion in Illinois had upon insurance rates, access to medical care, dental care, pharmaceuticals, and mental‐health counseling between rural and urban counties. Design and Sample A serial cross‐sectional design was used to assess the health perceptions of adults living in Illinois. Measures Survey data were collected in 2012 (n = 6,149) before the Medicaid expansion in Illinois and in 2015 (n = 3,532) after the expansion from rural (n = 4) and urban counties (n = 4). Intervention Medicaid expansion reduced the uninsured rate in both rural (16.39%–4.87%) and urban counties (17.05%–5.2%) and improved self‐reported health. It also increased access to all types of healthcare, with the biggest increase in dental coverage. Results Path analysis indicated that the Medicaid expansion β = −1.03 (p < .01) and poor versus not poor β = −1.50 (p < .01) were a significant predictor to no healthcare access. Rural verses urban location was not significant (β = 0.04); however, race/ethnicity was significantly different (p < .01). Conclusion Findings suggest that although the expansion has increased access to care overall, those who are the most vulnerable are still not benefiting equally from the expansion. Therefore, strategies to assist high‐risk adults in enrolling and using their Medicaid coverage need to developed and implemented.

Objectives Over 50 million people in the USA are enrolled in a Medicaid Managed Care plan. If they do not select a primary care provider, they are auto-assigned to one. The impact of auto-assignment has largely been understudied outside the context of patient satisfaction with the insurance plan. The purpose of the study was to explore the association between auto-assignment and flu vaccination use, which will contribute to our understanding of factors influencing the COVID-19 vaccine uptake. Methods Retrospective data from the Enterprise Data Warehouse of a health system were obtained for adult Medicaid enrolees assigned to a Midwestern health system in 2019. Descriptive statistics, independent t-tests and tetrachoric correlations were used to explore the relationship between auto-assignment and flu vaccine receipt among a large sample of Illinois residents (N = 7224). The sample was then divided into those who chose their provider (n = 6027) and those who were auto-assigned (n = 1197). Key findings Individuals who selected their provider were deemed to have flu vaccine coverage over those who were auto-assigned (33.2% vs. 6.6%). Furthermore, among those who were auto-assigned, age, number of office visits and having chronic morbidities, including chronic obstructive pulmonary disease (P &lt; 0.01), diabetes (P &lt; 0.01) and heart failure (P &lt; 0.01), were positively associated with flu vaccine receipt. Conclusions Individuals who are auto-assigned to a primary care provider are less likely to be flu vaccine recipients than those who choose their provider. This suggests that auto-assignment is a risk factor that influences vaccine receipt. This research provides perspectives for outreach efforts that target individuals who are auto-assigned to a provider.

Mammography screening rates are typically lower in those with less economic advantage (EA). This study, conducted at an integrated health care system covering a mixed rurality population, assessed the ability of interventions (text messages linking to a Web microsite, digital health care workers, and a community health fair) to affect mammography screening rates and disparity in those rates among different EA populations. Payor type served as a proxy for greater (commercially insured) versus lower (Medicaid insured) EA. 4,342 subjects were included across the preintervention (“Pre”) and postintervention (“Post”) periods. Interventions were prospectively applied to all Medicaid subjects and randomly selected commercial subjects. Applying interventions only to lower EA subjects reversed the screening rate disparity (2.6% Pre vs. −3.7% Post, odds ratio [OR] 2.4 P < 0.01). When intervention arms (“Least,” “More,” “Most”) were equally applied, screening rates in both EA groups significantly increased in the More arm (Medicaid OR = 2.04 P = 0.04, Commercial OR = 3.08 P < 0.01) and Most arm (Medicaid OR 2.57 P < 0.01, Commercial OR 2.33 P < 0.01), but not in the Least (text-only) arm (Medicaid OR 1.83 P = 0.11, Commercial OR 1.72 P = 0.09), although this text-only arm was inadequately powered to detect a difference. In summary, targeting interventions to those with lower EA reversed screening rate disparities, text messaging combined with other interventions improved screening rates in both groups, and future research is needed to determine whether interventions can simultaneously improve screening rates for all without worsening the disparity.