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Purpose: To explore adult community members' perspectives concerning barriers to mental health care that confront rural-dwelling youth., Methods: Group concept mapping, a participatory community-engaged research method, was used. Adult community members brainstormed and sorted statements describing barriers rural youth encounter in accessing mental health services. Point and cluster maps were created to visualize conceptual similarities between statements. Statements were rated according to their commonness and importance (1: low commonness/importance, 5: high commonness/importance)., Findings: Thirty-five adults sorted and/or rated 71 barriers facing rural youth in accessing mental health services. Seven conceptual clusters were identified: system-level barriers, knowledge and communication, youth concerns, parent/guardian concerns, parent/guardian barriers, costs and convenience, and school-level barriers. Within youth concerns, community members also identified a subcluster focused on stigma. Common and important statements related to limited after-school programs and community mental health support initiatives., Conclusions: Group concept mapping methodology provides structure for conceptualizing challenges facing rural youth in accessing mental health services. Policies should be informed by rural community concerns and priorities. After-school and support programs may align with mental health needs identified by rural communities., (© 2024 National Rural Health Association.)

Purpose: To examine factors associated with weight status underestimation and the relationship between weight status underestimation and weight loss as a weight management goal among adults living in the rural South., Methods: An anonymous survey was distributed at six primary care clinics and two churches in rural, South Carolina counties. Weight status underestimation was determined based on the difference between perceived weight status using standard body mass index (BMI) categories (underweight, normal weight, overweight, and obese) and BMI category from self-reported height and weight. Participants reported whether their weight management goal was to lose, maintain, or gain weight. Chi-square and binary logistic regressions were used for data analysis., Findings: A total of 185 respondents (76% female) at least partially completed the survey. Nearly 60% underestimated their weight status. Increasing BMI was associated with higher odds of weight status underestimation (OR: 1.10, 95%: 1.04, 1.15) and perceptions of being in fair or poor health was associated with lower odds of weight status underestimation relative to perceiving health as good or better (OR: 0.21, 95% CI: 0.06, 0.66). Among those with overweight and obesity, the association between underestimating weight status and having weight loss as a weight management goal was strong but not statistically significant (OR: 0.20, 95% CI: 0.04, 1.04)., Conclusions: Underestimating weight status was common among adults in the rural US South and was related to BMI and health status. Research is needed to determine if improving the accuracy of weight perceptions can promote weight management in the rural South., (© 2024 The Author(s). The Journal of Rural Health published by Wiley Periodicals LLC on behalf of National Rural Health Association.)

Purpose: Childhood obesity is more prevalent in rural compared to urban communities and may be related to urban-rural differences in environmental factors known to affect obesity. However, understanding of how environmental factors impact childhood obesity in rural settings remains limited. This study aimed to address this gap by exploring the relationship between obesogenic environments and childhood overweight/obesity rates, including variations across the urban-rural continuum., Methods: This study analyzed data for counties in the United States (N = 3140). Linear regression models were employed to examine the relationship between the Childhood Obesogenic Environment Index, which consisted of ten variables from a variety of sources associated with physical activity and healthy eating, and childhood overweight/obesity rates estimates derived from the 2016 National Survey of Children's Health. County rurality was categorized using Rural-Urban Continuum Codes and a moderation analysis was conducted to identify potential variations by rurality., Findings: There was a significant positive association between the COEI and childhood overweight/obesity rates nationally, with notable variations across the urban-rural continuum for specific index components. Neighborhood walkability showed a significant positive association across rurality, indicating that childhood overweight/obesity rates were higher in less walkable communities. Full-service restaurants exhibited an inverse relationship with childhood overweight/obesity rates across all RUCC levels., Conclusions: These results underscore the obesogenic environmental factors associated with childhood overweight/obesity rates nationally and how they vary across the urban-rural continuum. This study highlights the importance of considering these variations when designing interventions to address childhood obesity., (© 2024 The Author(s). The Journal of Rural Health published by Wiley Periodicals LLC on behalf of National Rural Health Association.)

Purpose: The National Institute of Health's All of Us Research Program represents a national effort to develop a database to advance health research, especially among individuals historically underrepresented in research, including rural populations. The purpose of this study was to describe the rural populations identified in the All of Us Research Program using the only proxy measure currently available in the dataset., Methods: Currently, the All of Us Research Program provides a proxy measure of rurality that identifies participants who self-reported delaying care due to far travel distances associated with living in rural areas. Using the All of Us Controlled Tier Dataset v6, we compared sociodemographic and health characteristics of All of Us rural participants identified via this proxy to rural US residents from nationally representative data sources using chi-squared tests., Results: 3.1% of 160,880 All of Us participants were rural, compared to 15%-20% of US residents based on commonly accepted rural definitions. Proportionally more rural All of Us participants reported fair or poor health status, history of cancer, and history of heart disease (P<.01)., Conclusions: The All of Us measure may capture a subset of underserved participants who live in rural areas and experience health care access barriers due to distance. Researchers who use this proxy measure to characterize rurality should interpret their findings with caution due to differences in population and health characteristics using this proxy measure rural compared to other commonly used rural definitions., (© 2024 The Authors. The Journal of Rural Health published by Wiley Periodicals LLC on behalf of National Rural Health Association.)

Purpose: To examine racial and gender differences in treatment acceptance and relapse potential among rural residents admitted to a substance use disorder (SUD) treatment program., Methods: A cross-sectional study using data collected from a sample of 1850 rural residents admitted to a South Carolina state-run SUD treatment center between the years of 2018 and 2020. Chi-square and t-tests were used to compare treatment acceptance and relapse potential by race and gender. Multivariate logistic regression analyses was conducted to further examine the relationship of race and gender with treatment acceptance and relapse potential after adjusting for potential confounding variables., Findings: Approximately 50% of participants were classified as being accepting of their treatment and committed to changing their substance use, and there were no racial or gender differences in the bivariate and multivariate analyses. Approximately 25% of participants were classified as having low/no potential risk for relapsing, and there were no racial or gender differences in the bivariate analysis. However, the adjusted odds ratio of relapsing risk were lower among White compared to Black adults [AOR = 0.49 with 95% CI (0.31-0.77)]., Conclusion: This study suggests there are no gender or racial differences in treatment acceptance for SUD but that Black adults are at greater risk of relapsing relative to White adults. Additional research is needed to identify factors that increase Black adults' risk for relapse to inform interventions that can improve SUD treatment outcomes in this population., (© 2024 National Rural Health Association.)

Purpose: Rural residents may be at higher risk for loneliness than urban residents due to factors such as social isolation, poorer health, and socioeconomic disadvantage. To date, there have been few studies examining rural-urban differences in loneliness among adults in the United States. We examined differences in loneliness across the rural-urban continuum among adult residents living in Washington State., Methods: Stratified random sampling was used to select 2,575 adults from small rural, large rural, suburban, and urban areas who were invited to complete a survey on factors affecting health. Data were obtained from 616 adults (278 from small rural, 100 from large rural, 98 from suburban, and 140 from urban areas) from June 2018 through October 2019. Loneliness was measured using the UCLA Loneliness Scale (3rd version). Multivariable linear and logistic regressions were used to examine geographic differences in loneliness (measured continuously and dichotomously)., Findings: Mean unadjusted loneliness scores were lower in suburban compared to urban areas (35.06 vs 38.57, P = .03). The prevalence of loneliness was 50.7%, 59.0%, 40.8%, and 54.3% in small rural, large rural, suburban, and urban areas, respectively. Suburban living was associated with lower odds for being lonely compared to urban living (unadjusted OR = 0.58; 95% CI = 0.34-0.98), but this association was not statistically significant in the adjusted model (OR = 0.63; 95% CI = 0.33-1.19)., Conclusion: Loneliness is a prevalent health issue across the rural-urban continuum among Washington State adults., (© 2020 National Rural Health Association.)

Objectives: To compare health care costs and service utilization associated with mild traumatic brain injury (mTBI) in rural and urban commercially insured children., Data Source: MarketScan Commercial Claims and Encounters Data, 2007-2011., Study Design: We compared health care costs and outpatient encounters for physical/occupational therapy, speech therapy, and psychiatry/psychology encounters 180 days after mTBI among rural versus urban children (<18 years)., Principal Findings: A total of 387 846 children had mTBI, with 13 percent residing in rural areas. Adjusted mean total health care costs in the 180 days after mTBI were $2778 (95% CI: 2660-2897) among rural children, compared to $2499 (95% CI: 2471-2528) among urban children (adjusted cost ratio 1.11, 95% CI 1.06-1.16). Rural-urban differences in utilization for specific services were also found., Conclusions: Total health care costs were higher for rural compared to urban children despite lower utilization of certain services. Differences in health service utilization may exacerbate geographic disparities in adverse outcomes associated with mTBI., (© Health Research and Educational Trust.)

Purpose: This study was conducted to (1) compare diet quality among depressed and nondepressed overweight and obese rural-dwelling adults and (2) determine whether body mass index (BMI) category moderates the relationship between depressive symptoms and overall diet quality., Methods: Rural adults in Kentucky (n = 907) completed the 9-item Patient Health Questionnaire (PHQ-9) that assessed depressive symptoms and a food frequency questionnaire that generated 2005 Healthy Eating Index (HEI) scores. Participants were grouped into overweight (BMI 25-29.9 kg/m 2 ) and obese (≥30 kg/m 2 ), and nondepressed (PHQ-9 < 10) and depressed (PHQ-9 ≥ 10) groups. Bootstrapped ANCOVAs were used to compare diet quality among the 4 groups. Ordinary least-squares regression using PROCESS was used to determine whether BMI category (overweight vs obese) moderated the association between depressive symptoms and overall diet quality., Results: Overall diet quality was poorer in the obese depressed group than in the obese nondepressed group. Intake of fruit and dark green/orange vegetables and legumes was lower in the obese depressed group than in the overweight nondepressed group. Depressive symptoms predicted poor overall diet quality (B = -0.287, P < .001) and the relationship was moderated by BMI category (coefficient of BMI category * depressive symptom interaction term = 0.355, P < .049). A significant inverse relationship between depressive symptoms and overall diet quality was observed in the overweight group but not in the obese group., Conclusion: Components of diet quality vary according to BMI category and depressive symptom status. The relationship between depressive symptoms and diet quality is influenced by BMI category., (© 2017 National Rural Health Association.)