Researchers at the North Carolina Rural Health Research Program released a brief on CMS Hospital Quality Star Ratings of rural hospitals.

The brief details how rural hospitals were more likely to be unrated than their urban counterparts (41.6% vs. 12.0%) and the percentage of unrated rural hospitals has increased dramatically between 2016 and 2021 (34.3% to 41.6%). Nearly all unrated rural hospitals are Critical Access Hospitals, and almost half of unrated rural hospitals are in the Midwest census region. Star ratings can give patients important information and help them compare hospitals locally and nationwide, but patients should consider a variety of factors when choosing a hospital – not just their star rating or lack thereof.

How much people moved around town predicted COVID-19 cases in a rural Pennsylvania county in 2020, according to a new study by researchers at Penn State. The researchers approximated movement during the initial stay-at-home orders and subsequent restricted phases by using data from traffic cameras and mobile devices. They confirmed that increases in movement preceded increases in COVID-19 cases in Centre County, Pennsylvania. The results also revealed general compliance with local regulations and suggest that these types of passive surveillance data could be used to monitor and improve behavioral intervention guidelines for outbreak management.

“With the emergence of the COVID-19 outbreak in 2019, local governments initially relied heavily on behavioral interventions like stay-at-home orders in order to limit transmission,” said Christina Faust, postdoctoral researcher at Penn State and first author of the study. “Knowing if people are willing to follow these kinds of interventions, and if these interventions do what they are intended to do, is important to future outbreak planning.”

The researchers approximated movement from March to August 2020 in Centre County, Pa, which is home to Penn State’s University Park campus, during a period when university students were primarily not residing in the area. This period encompassed the strictest restrictions in the county, including a 40-day red phase that involved a stay-at-home order except for life-sustaining businesses and activities; a 20-day yellow phase that stressed remote work and teaching and a preference for curbside retail; and the initial 78 days of a green phase that mandated reduced capacity at local businesses, mask wearing in public, and guidelines for additional businesses to reopen.

“Assessing the impact of intervention strategies is especially important in rural areas, where access to healthcare is often limited and under-resourced,” said Nita Bharti, Lloyd Huck Early Career Professor in Biology at Penn State and senior author of the paper. “Rural areas have limited health care capacity and struggle to manage the large numbers of patients we expect to see during outbreaks like this. Preventive strategies to limit transmission are critical.”

The research team used two data sources as proxies for movement. They collected real time images from 19 traffic cameras from across the county, including “connector” roads that provide links between towns and “internal” roads that measure movement within towns. They also studied anonymized location data from mobile devices from the company SafeGraph, which captured visits to over two thousand points of interest around the county, including grocery stores, coffee shops, gas stations, and locations on the Penn State campus. The team compared numbers of mobile visits recorded in the summer of 2020 to the pre-pandemic summer of 2019 to identify differences due to behavioral interventions. Their results appear in a paper published in the journal Epidemiology & Infection.

The research team found that, when moving from red phase to yellow and especially from yellow phase to green, traffic volume increased on both internal and connector roads. Although the numbers of visits to local points of interest were significantly lower than visit numbers from 2019, they increased as restrictions were lifted.

“During the strictest phases, movement was mostly internal, which is what we would hope to see in order to reduce opportunities for transmission between towns,” said Faust. “As restrictions eased, we saw a lot more traffic, particularly on connector roads, and more mobile visits to points of interest, which collectively suggests overall compliance with these intervention strategies. What is particularly reassuring is that, even though changes in phase regulations were announced 10 days before they were implemented, we did not see a change in movement until the new phase came into effect.”

Reported cases of COVID-19 in the county were related to movement collected from both data sources, with a 9 to 18-day lag depending on data type. The researchers believe this lag includes the incubation time of the virus — when an individual is infected but may not yet show symptoms — as well as in some cases considerable delays in accessing a test and receiving test results.

“Increases in movement reliably preceded increases in COVID-19 cases during the study period,” said Faust. “These results suggest that vehicle traffic and mobile visit data could be used in real time to monitor the outbreak. For example, if there is an uptick in movement, local governments could reinforce messaging and prepare to allocate resources for health care to high-movement areas.”

The researchers note the importance of using multiple types of data; individual data sources may measure different types of behaviors and reflect certain subsets of a population. For example, they believe the vehicle data may represent permanent residents while mobile visits may better reflect students. While urban areas may have more data sources available, this study demonstrates that the combination of existing data sources in rural areas — vehicle traffic and mobile data — provide important information.

“Rural areas typically experienced delayed introductions to the virus and delayed outbreaks, but statewide regulations were largely based on outbreaks in urban areas, where the bulk of cases occurred,” said Bharti. “Local oversight, when paired with federal and statewide response and relief, can more effectively serve outbreak response, management, and planning efforts. Here we show that measuring local population movements through passive approaches can help assess the effectiveness of intervention strategies and inform policies that target transmission prevention.”

In addition to Faust and Bharti, the research team at Penn State includes Brian Lambert, computational scientist; Cale Kochenour, spatial analyst; and Anthony Robinson, associate professor of geography.

A report from HHS shows that insurance coverage and access to care improved significantly for Latinos between 2013 and 2016, but they still have among the highest uninsured rate of any racial or ethnic group within the U.S.  To build on progress, HHS launched a Spanish version of its QuestionBuilder app, which can help Latino patients prepare for their in-person or telehealth appointments.  The Agency for Healthcare Research and Quality built the app to help users improve their interaction with clinicians, providing questions they might want to ask with links to helpful resources.  The 2020 Census reports that Hispanics are the second most prevalent racial or ethnic group in rural America, comprising 10.4 percent of the rural population.

See more at New Spanish Language App Helps Latinos With Health Care.

 

In an open-access article from the Journal of the American Medical Association (JAMA), researchers report that, among more than 34 million hospital births in the U.S., 37.4 percent of the hospitals were low volume.  Among low-volume hospitals, 18.9 percent were isolated and 58.4 percent of these were rural.

See JAMA: Birth Volume and Geographic Distribution of U.S. Obstetric Hospitals, 2010 – 2018 for the article.

Age-friendly policies and programs have typically occurred at the local level but are becoming increasingly common at the state level.  This is of particular importance for rural communities as the share of older adults is increasing in rural areas faster than in urban areas, yet funding of and access to aging supports in rural areas may be more limited or disjointed. This policy brief from the University of Minnesota Rural Health Research Center identifies these state-level initiatives and the extent to which such programs have an explicit rural focus.

For more information, see Statewide Age-Friendly Initiatives: An Environmental Scan.

HIV Prevention Among Persons Who Inject Drugs: Rural and Urban Differences. In an open access article, researchers examined how stigma operates in rural and urban settings in relation to PrEP, a medicine for people who are at high risk for HIV from sex or injection drug use.  The investigation showed willingness to use PrEP in both urban and rural settings. However, stigma against drug use, HIV, and sexualities were identified as barriers to PrEP uptake, particularly in the rural setting.

CDC: Binge Drinking Among Adults by Select Characteristics and State.  The study analyzed data from the 2018 Behavioral Risk Factor Surveillance System to determine prevalence of binge drinking by select demographic characteristics (age, gender, race, income level) and by state.  The finding that the prevalence of binge drinking was lower in the most rural counties than in the most urban counties is consistent with earlier reports. However, adults in the most rural counties who binge drank did so more frequently and at higher intensity than did adults in the most urban counties. The report points to recommendations from the Community Preventive Services Task Force to regulate alcohol sales as a population health approach.  The U.S. Preventive Services Task Force recommends increased screening and counseling in primary care settings.

The most sparsely populated regions of the American West often are unable to provide local treatment for opioid use disorder. Long driving distances can be a barrier for people who need treatment, so the issue has ramifications for the health and wellness of many residents across the most rural areas of the country.

A team of researchers from Penn State and JG Research and Evaluation recently examined the effectiveness of a successful model for rural treatment of opioid use disorder in Montana, one of the nation’s most sparsely populated states.

Opioids are highly addictive, and opioid use disorder is difficult to treat. Fortunately, many people who experience opioid use disorder can reach recovery. Most treatment programs, however, are very intensive and require specialized care, highly regulated medication, and daily or weekly clinical visits. Because of this intensive specialization, people in rural areas who experience opioid use disorder often lack access to local treatment.

To address the lack of services for people with opioid use disorder in rural areas, researchers and clinicians in Vermont developed a model of care for opioid treatment. People with opioid use disorders from remote areas are stabilized at addiction care facilities in more populous areas and then receive ongoing care at rural primary care clinics that have established partnerships with these addiction care facilities. Based on this model’s success in Vermont, it has been deployed in many rural areas across the nation.

Danielle Rhubart, assistant professor of biobehavioral health at Penn State, co-authored an article in the journal Substance Abuse: Research and Treatment that evaluated the application of the Vermont model in Montana.

“There are people in Montana who have to drive 100 or even 200 miles one way to reach a physician who can prescribe medical treatments for opioid use disorder,” Rhubart explained. “This is fundamentally very different from Vermont, which is only about 80 miles wide. The model that is used in Vermont has been very successful, and a lot of good science has validated it. We needed to know, however, whether what worked in Vermont was applicable in a state as remote as Montana.”

The researchers found that the Vermont model was not successfully adopted in Montana. Addiction care facilities in Montana were often unsuccessful at forming partnerships with rural primary care offices. The rural providers who were interviewed for this research were concerned about a variety of interrelated issues.

Geographic concerns were one of the reasons that primary care physicians were reluctant to enter into addiction-care partnerships. The total area of Montana is nearly 150,000 square miles, while the area of Vermont is less than 10,000 square miles. Though Montana is home to more people, there are between nine and 10 times as many people per square mile in Vermont as there are in Montana. Prior research has shown that there are important differences between rural areas that are adjacent to urban areas compared with rural areas that are distant from urban areas. People in more remote rural areas, like most of Montana, are much less likely to have access to a variety of services. Physicians were concerned that the lack of trained staff who lived in their area would make implementing the program impractical.

The lack of available staff was cited by some rural primary care physicians as a reason not to participate in opioid use disorder treatment programs. According to the researchers, behavioral health services are more widely available in Vermont than in rural Montana. This lack of medical staff and support services led many rural health care providers in Montana to believe that they would be unable to recruit and retain staff to run an opioid use disorder treatment program.

Some rural providers in Montana reported that they did not want to participate in treatment because they were concerned that the demand would overwhelm their capacity to provide high quality care. Primary care facilities in some of the most rural and remote portions of the state cited the lack of available behavioral health staff in the area as a reason to suspect that, if they started an opioid use disorder program, they would not be able to address their patients’ needs.

In addition, there is a stigma associated with treating people with opioid use disorder in some rural areas, and some physicians expressed fear that they would lose patients if they prescribed these medications.

“The differences between Vermont and Montana go beyond population density,” Rhubart explained. “Cultures differ too. In our study, we found that some rural physicians’ offices in Montana preferred an informal relationship with addiction-treatment facilities to a formal partnership. Rural providers welcomed technical assistance, but were hesitant to formalize long-term partnerships.

One of the most significant barriers to treating opioid use disorder in rural areas is the special license required for prescribing the appropriate medication. There is concern that the medication could be abused as a street drug, so it is highly regulated. In addition, physicians expressed concerns that the Vermont model would not be financially viable for their practices.

This research shows that for treatment of opioid use disorder — and other health issues — there is no one-size-fits-all solution for rural areas.

“When states develop treatment models for opioid use disorder, public health officials must account for local variations in culture, stigma, and access to resources so that rural physicians are not overwhelmed by the prospect of treating people in need,” Rhubart explained. “Program and partnership buy-in from physicians requires attention to the geographic, economic, and cultural norms of a community. These factors are essential for developing care models that effectively support those with opioid use disorder.”

The Flex Monitoring Team has released a new policy brief, The Association Between System Affiliation and Financial Performance in Critical Access Hospitals.

There is growing evidence to suggest that affiliation with a health system may have important implications for rural hospitals. However, most studies have not investigated the effects of system affiliation on CAHs, specifically, and it is important for hospitals and state Flex programs to anticipate the consequences and potential impact of system affiliation.

The purpose of this study is to examine the association between system affiliation and the financial indicators included in the Critical Access Hospital Measurement and Performance Assessment System (CAHMPAS).