Implementing a Teaching Rural Mobile Health Clinic: Challenges and Adaptations.

In 2022, Penn State College of Medicine launched the LION Mobile Clinic, a teaching mobile health clinic offering preventive health services in rural Snow Shoe, Pennsylvania. We outline four challenges the clinic team faced in implementation, along with adaptations made to tailor the model to Snow Shoe's needs and opportunities.

"We Got an Invite into the Fortress": VA-Community Partnerships for Meeting Veterans' Healthcare Needs.

Responding to identified needs for increased veterans' access to healthcare, in 2010 the United States Department of Veterans Affairs (VA) launched the Veteran Community Partnership (VCP) initiative to "foster seamless access to, and transitions among, the full continuum of non-institutional extended care and support services in VA and the community". This initiative represents an important effort by VA to promote collaboration with a broad range of community organizations as equal partners in the service of veteran needs. The purpose of the study is an initial assessment of the VCP program. Focus group interviews conducted in six sites in 2015 included 53 representatives of the local VA and community organizations involved with rural and urban VCPs across the US. Interview topics included the experiences and practices of VCP members, perceived benefits and challenges, and the characteristics and dynamics of rural and urban areas served by VCPs. Using a community-oriented conceptual framework, the analyses address VCP processes and preliminary outcomes, including VCP goals and activities, and VCP members' perceptions of their efforts, benefits, challenges, and achievements. The results indicate largely positive perceptions of the VCP initiative and its early outcomes by both community and VA participants. Benefits and challenges vary by rural-urban community context and include resource limitations and the potential for VA dominance of other VCP partners. Although all VCPs identified significant benefits and challenges, time and resource constraints and local organizational dynamics varied by rural and urban context. Significant investments in VCPs will be required to increase their impacts.

Privilege and Place: an exploratory study about healthcare bypass behavior.

AIM: Bypass, or utilizing healthcare outside of one's community rather than local health care, can have serious consequences on rural healthcare availability, quality, and outcomes. Previous studies of the likelihood of healthcare bypass used various individual and community characteristics. This study includes measures for individuals and communities, as well as place-based characteristics. The authors introduce the Social Vulnerability of Place Index (SoVI) - a well-established measure in disaster literature - into healthcare studies to further explain the impact of place on healthcare selection behavior. Additionally, with the use of open-ended questions, this study explains why people choose to bypass. By including each of these measures, this study provides a more nuanced and detailed understanding of how individual healthcare selection is affected by the privilege of the individual, community ties, place of residence, and primary motivator for bypass. METHODS: A systematic random sample of residents from 25 rural towns in the western US state of Utah were surveyed in 2017 in the Rural Utah Community Survey. After accounting for missing data, the total sample size was 1061. This study used logistic regression to better predict the likelihood of rural healthcare bypass behavior. Measures associated with community push factors (dissatisfaction with various local amenities), community pull factors (friends in community and length of residence), individual ability (demographics, self-reported health, and distance to a hospital), and SoVI, were added to the models to examine their impact on the likelihood of bypass. The SoVI was made using census data with variables that measure both social and place inequality. Each town in the study received a SoVI score and was then categorized as having low, mean, or high social vulnerability. Qualitative open-ended responses about healthcare selection were coded for explanations given for bypassing. RESULTS: The pooled model showed that bypass was more likely amongst residents who were dissatisfied with local health care and more likely for females. Breaking bypass down, according to SoVI, provides a more nuanced understanding of bypass. For people living in low socially vulnerable areas, privileges such as graduating college made them more likely to bypass. For high socially vulnerable areas, privilege did not help people bypass, but disadvantages such as aging made residents less likely to bypass. Thus, by introducing the SoVI into healthcare literature, this study can compare healthcare selection behaviors of residents in low vulnerable towns, average vulnerable towns, and highly vulnerable towns. Additionally, the analysis of open-ended responses showed patterns explaining why people bypass. CONCLUSION: Policymakers and public health workers can use the SoVI to better target their healthcare outreach. Reasons for bypass include quality, selection, consistency, cost of insurance, one-stop shop, and confidentiality. Rural clinics can help residents avoid the need to bypass by improving in these areas and thus gaining patients and minimizing the risk of closure. Healthcare policymakers should focus resources on high socially vulnerable places as well as underprivileged people in low socially vulnerable places.

Taking another look at the sense of community index: Six confirmatory factor analyses.

Previous studies focusing on the effects of the social aspects of community have often used the Sense of Community Index (SCI), despite other research showing that it is not a good-fit measure for its expected dimensions. Using a sample of students from Brigham Young University, we performed confirmatory factor analysis of the SCI to assess 1-factor, 4-factor, 1-factor revised, 3-factor revised, 1-factor revised, 4-factor revised, and 1-factor revised models. Our study resulted in mixed findings: models were neither a poor-fit nor a good fit. Although the 4-factor revised model was the best fit, it did not measure the intended dimensions well. Our analysis indicates that future research investigating sense of community should use measures other than the SCI.

The Louisiana Community Oil Spill Survey (COSS) dataset.

This article presents an overview of the Louisiana Community Oil Spill Survey (COSS), the dataset used in "Community Sentiment following the Deepwater Horizon Oil Spill Disaster: A Test of Time, Systemic Community, and Corrosive Community Models" [1] as well as elsewhere [2-6]. The COSS, administered by the Louisiana State University's Public Policy Research Laboratory, consists of five waves of cross-sectional trend data attuned to the characteristics and effects of the 2010 BP Deepwater Horizon (BP-DH) oil spill on those coastal Louisiana residents most affected by the disaster. Respondents were randomly drawn from a list of nearly 6,000 households in the coastal Louisiana zip codes located in Lafourche Parish, Plaquemines Parish, Terrebonne Parish, and the community of Grand Isle. COSS data were initially collected in June 2010 when oil was still flowing from the wellhead, with additional data waves, collected in October 2010, April 2011, April 2012, and April 2013. The respective response rates were: June 2010, 20%; October 2010, 24%; April 2011, 25%; April 2012, 20%; and April 2013, 19%.

Infants without health insurance: Racial/ethnic and rural/urban disparities in infant households' insurance coverage.

In order to gain insights into how the effects of the uneven adoption of Medicaid expansion varies across the rural/urban spectrum and between racial/ethnic groups in the United States, this research used the fertility question in the 2011-2015 American Community Survey to link infants' records to their mothers' household health insurance status. This preliminary exploration of the Medicaid expansion used logistic regression to examine the probability that an infant will be born without health insurance coverage. Overall, the states that adopted Medicaid expansion improved the health insurance coverage for households with infants. However, rural households with infants report lower percentages of coverage than urban households with infants. Furthermore, the rural/urban gap in health insurance coverage is wider in states that adopted the Medicaid expansion. Additionally, Hispanic infants remain significantly less likely to have health insurance coverage compared to Non-Hispanic White infants. Understanding infant health insurance coverage across ethnic/racial groups and the rural/urban spectrum will become increasingly important as the U.S. population transitions to a minority-majority and also becomes more urban. Although not a perfect solution, our findings showed that the Medicaid expansion of health insurance coverage had a mainly overall positive effect on the percentage of U.S. households with infants who have health insurance coverage.

Migration and Mental Health in the Aftermath of Disaster: Evidence from Mt. Merapi, Indonesia.

Migration is a standard survival strategy in the context of disasters. While prior studies have examined factors associated with return migration following disasters, an area that remains relatively underexplored is whether moving home to one's original community results in improved health and well-being compared to other options such as deciding to move on. In the present study, our objective is to explore whether return migration, compared to other migration options, results in superior improvements to mental health. We draw upon data from a cross-sectional pilot study conducted 16 months after a series of volcanic eruptions in Merapi, Indonesia. Using ordinal logistic regression, we find that compared to respondents who were still displaced (reference category), respondents who had "moved home" were proportionally more likely to report good mental health (proportional odds ratios (POR) = 2.02 [95% CI = 1.05, 3.91]) compared to average or poor mental health. Likewise, respondents who had "moved on" were proportionally more likely to report good mental health (POR = 2.64 [95% CI = 0.96, 7.77]. The results suggest that while moving home was an improvement from being displaced, it may have been better to move on, as this yielded superior associations with self-reported mental health.