Do prescription drug monitoring programs encourage prescription - or illicit - opioid abuse?

BACKGROUND: Prescription drug monitoring programs (PDMPs) are tools that states can use to fight prescription opioid misuse within their jurisdiction. However, because PDMPs make prescription opioids more difficult to access, these programs may have the unintended consequence of increasing deaths related to illicit opioids. Methods: This study uses fixed effects models to estimate how PDMP regulatory strength is associated with both prescription opioid- and heroin-related deaths between 1999 and 2016. PDMP regulatory strength is measured by creating a score using multiple correspondence analysis (MCA). Additional models replace the MCA score with a binary indicator for the presence of one particular regulation requiring physicians to access the system before writing opioid prescriptions. Results: Results show that continuous measures of PDMP strength are not generally associated with prescription opioid- or heroin-related death rates. Yet, one model does show that PDMP scores are positively associated with the heroin-related death rate. The models using the binary mandatory access variable show a strong positive association with both prescription opioid and heroin deaths. Conclusions: This study supports the theory that more stringent state PDMPs are associated with higher rates of heroin-related deaths, potentially due to decreases in prescription opioid availability.

Are Rural Opioid Treatment Program (OTP) Facilities Associated with Lower Deaths?

Objectives: Rural areas have been particularly affected by the opioid epidemic in the U.S., with treatment options being scarce. This study focuses on opioid treatment programs (OTPs), which combine counseling services and opioid-related medication provision. In the South census region (comprised of 16 states and D.C.), 156 urban counties and 27 rural counties contained OTPs as of 2013. We examine whether their presence is associated with lower opioid-related death rates during 2014-2016. Methods: Coarsened exact matching (CEM) is used to match treated and untreated counties on demographic characteristics and opioid-related deaths from 2011 to 2013. Two treatments are considered: (1) if a county had an OTP in 2013; and (2) if an OTP existed in a neighboring county in 2013. The matched samples are then used in weighted least square regression models, with propensity score matching serving as a robustness check. Rural and urban counties are analyzed separately to determine if the impact of OTPs differs between these areas. Results: Results show that the presence of an OTP mostly has no statistical association with the rate of (or change in) future opioid deaths, in either rural or urban counties. Proximity to a neighboring county OTP displays a similar lack of association. Conclusions: The findings suggest that OTPs are not associated with fewer opioid-related deaths in the South over the near term, regardless of rural or urban location. These results could be attributed to outside factors that hinder this relationship. Continued assessment of varied approaches to the rural opioid crisis is encouraged.

State broadband policy: Impacts on availability.

We use a county-level panel dataset from 2012 to 2018 to assess the impacts of various state policies on total and rural broadband availability in the United States. The primary dependent variable is the percentage of residents with access to 25 Megabits per second (MBPS) download and 3 MBPS upload speeds via a fixed connection, with alternative specifications considering other aspects of availability such as technology type and competition. We control for the main determinants of Internet availability such as income, education, age, and population density. Our policy variables come from the newly released State Broadband Policy Explorer from the Pew Charitable Trusts and individual contacts from the nationwide State Broadband Leaders Network. Our primary policies of interest are those related to: (1) availability of state-level funding, (2) existence of a state-level broadband office/task force with full-time employees, and (3) restrictions on municipal/cooperative broadband provision. We find a positive and significant impact of state-level funding programs on general (and fiber) broadband availability, and a negative impact of municipal/cooperative restrictions. The findings are similar when the analysis is restricted to the rural portions of counties.

Community sociodemographics and rural hospital survival.

PURPOSE: To determine whether community sociodemographic factors are associated with the survival or closure of rural hospitals at risk of financial distress between 2010 and 2019. METHODS: We use a national sample of 985 rural hospitals at risk of financial distress to analyze the relationship between community sociodemographic characteristics and hospital survival or closure. We control for financial distress using the Financial Distress Index developed by the Sheps Center for Health Services Research. Community characteristics are retrieved from the Census and the Robert Wood Johnson Foundation. We first use Wilcoxon rank-sum tests to demonstrate annual sociodemographic differences between rural communities with financially distressed hospitals that closed between 2010 and 2019, and those that remained open. Multilevel Weibull proportional hazards regressions then uncover which sociodemographic factors are significantly associated with survival. FINDINGS: Our initial results confirm that closures of rural hospitals at risk of financial distress disproportionately affect communities with certain sociodemographic characteristics. However, most of these characteristics are not associated with higher rates of closure in the multivariate survival analysis. The final results suggest that financially distressed hospitals are more likely to experience closure if their communities have higher rates of unemployment (Hazard Ratio = 1.36, P < .05) or uninsured residents under 65 (Hazard Ratio = 1.13, P < .05). CONCLUSIONS: Among financially distressed rural hospitals, specific community-level sociodemographic characteristics (unemployment and uninsurance rates) are positively associated with the likelihood of closure. Social policies addressing these issues should emphasize their broader relationship with the local health sector.

Gauging Local Interest in Telemedicine Visits: Are Online Surveys Enough?

Telemedicine use surged during COVID-19, and a significant amount of recent research has relied solely on online surveys to assess patient perceptions. However, these surveys may be biased since they require an internet connection and digital literacy skills. We compare local perceptions of telemedicine visits in rural areas across two methods of data collection: online-only vs. paper surveys. We collected 100 paper and 108 online surveys in two rural counties with a total population of 10,000. The results show that significant differences exist in the demographics of people completing each type of survey and in the perceptions of telemedicine, with paper-based respondents generally demonstrating a higher degree of confidence in telemedicine. Ordered logistic regressions controlling for potentially influential underlying demographic characteristics (income, hours worked, and presence of children) show that paper-based respondents tend to have higher opinions of telemedicine, but that overall levels of comfort are similar across survey types.

Higher Electronic Health Record Functionality Is Associated with Lower Operating Costs in Urban-but Not Rural-Hospitals.

OBJECTIVES: The aim of the study is to examine the relationship between electronic health record (EHR) use/functionality and hospital operating costs (divided into five subcategories), and to compare the results across rural and urban facilities. METHODS: We match hospital-level data on EHR use/functionality with operating costs and facility characteristics to perform linear regressions with hospital- and time-fixed effects on a panel of 1,596 U.S. hospitals observed annually from 2016 to 2019. Our dependent variables are the logs of the various hospital operating cost categories, and alternative metrics for EHR use/functionality serve as the primary independent variables of interest. Data on EHR use/functionality are retrieved from the American Hospital Association's (AHA) Annual Survey of Hospitals Information Technology (IT) Supplement, and hospital operating cost and characteristic data are retrieved from the American Hospital Directory. We include only hospitals classified as "general medical and surgical," removing specialty hospitals. RESULTS: Our results suggest, first, that increasing levels of EHR functionality are associated with hospital operating cost reductions. Second, that these significant cost reductions are exclusively seen in urban hospitals, with the associated coefficient suggesting cost savings of 0.14% for each additional EHR function. Third, that urban EHR-related cost reductions are driven by general/ancillary and outpatient costs. Finally, that a wide variety of EHR functions are associated with cost reductions for urban facilities, while no EHR function is associated with significant cost reductions in rural locations. CONCLUSION: Increasing EHR functionality is associated with significant hospital operating cost reductions in urban locations. These results do not hold across geographies, and policies to promote greater EHR functionality in rural hospitals will likely not lead to short-term cost reductions.

Community Meetings on the Rural Opioid Crisis: Setting a Path Forward by Learning from Others.

OBJECTIVES: This study implemented a series of meetings in one rural community struggling with the opioid crisis. Set in the town of Ardmore, Oklahoma, these meetings presented local residents who dealt with the crisis as part of their jobs with four categories of programs aimed at preventing and treating opioid-related issues. The ultimate goal was for the participants to develop a consensus about where resources should be appropriated in the future, based off the needs of the area. METHODS: Three community meetings were held over a six-week period, with an average of 40 attendees. Data was collected through surveys, study circles, and a participant voting exercise. Surveys were distributed at the beginning and end of the meetings to determine if participant views changed over the course of the study. Study circles broke participants into small groups and prompted them with questions regarding the crisis to encourage group discussion. The participant voting exercise allowed participants to note where they would like future resources to be directed. FINDINGS: Listening to experts and holding group discussions led to changes in opinions for some participants. Most felt that the most pressing need was to provide accessible opioid treatment options in their community. Youth-based prevention efforts were also noted as being a program that the community should focus on. CONCLUSIONS: Local perceptions of the most appropriate strategies for combatting the rural opioid crisis can change with group discussions alongside others who are actively involved with this issue. Future research should actively involve affected communities in order to develop relevant and accepted action plans.

Interest in rural medicine among osteopathic residents and medical students.

INTRODUCTION: This study examines US osteopathic residents' and medical students' attitudes and willingness to practice in rural medicine. The multiple aims of this study were to determine: (1) if there are any significant differences in interest in rural medicine among various levels of training; (2) the relative age, gender, and race of those who are intending to pursue a career in rural health; and (3) whether a number of demographic characteristics (age, race, year of study) or participation in a rural elective significantly impacted the students' and residents' interest in practicing in a rural area. In particular, differences between osteopathic students and residents are emphasized, because few previous studies have focused on this topic. METHODS: De-identified, cross-sectional, descriptive techniques utilizing 2 distinct web-based electronic surveys were used in this study. Each survey was sent electronically to medical students and physicians-in-training. Statistical methods included means, frequencies, and t-tests to determine significant differences among groups. Logistic regression was used to determine the impact of various factors on overall rural interest for each group. RESULTS: A total of 161 students from two osteopathic colleges completed and submitted the survey as well as 51 residents/fellows from a variety of training programs. Approximately 43% of the student respondents and 67% of residents expressed an intention of practicing rural medicine. Several notable differences were found among the opinions of students and residents, particularly regarding the perceived prestige of rural physicians. Among medical students, overall interest in rural practice decreased in years 2 to 4; however, there was a positive influence if the students were aged 34 years or over. As expected, being raised in a rural area had a positive impact on rural interest. Additional findings included the lack of significance for gender or race, and the positive influence of taking a rural elective. For residents, some results are similar, although interest in rural medicine actually increased with time. CONCLUSION: It is imperative that osteopathic medical schools recruit individuals who will be most likely to pursue rural medicine, and then train them to provide health access for rural populations. Further, financial incentives are important to both students and residents, suggesting that 'loan forgiveness' programs or scholarships may be useful in promoting rural location. In order to facilitate the training of individuals who will likely pursue rural medicine, there must be institutional dedication to this goal.

The Influence of the Degree of Rurality on EMR Adoption, by Physician Specialty.

OBJECTIVE: To explore the influence of varying degrees of remoteness on practice-level electronic medical record (EMR) adoption, including whether the effect differs across practice specialty. DATA SOURCES: Survey data on over 270,000 office-based physician practices (representing over 1,250,000 providers) collected by SK&A information services during 2012. STUDY DESIGN: This study examined differences in EMR adoption by practices located across the nine-category rural-urban continuum. Logistic regressions and associated marginal effects are used to assess how much a move up or down the rural-urban continuum code impacts the likelihood of EMR adoption, after controlling for characteristics likely to affect EMR adoption such as practice size and specialty. PRINCIPAL FINDINGS: Overall practice-level EMR adoption rates generally increase with the degree of rurality and range from 47 percent in the most urban counties to over 60 percent in the most rural. Moving from the most urban county to the most rural corresponded to a 7 percent increase in the likelihood of EMR adoption (p < .01). CONCLUSIONS: EMR adoption rates do vary significantly across nonmetropolitan counties, and they generally increase as a practice becomes more rural. From a policy perspective, this suggests that urban practices may in fact be the lowest hanging remaining fruit for increasing EMR adoption rates.

What Can the National Broadband Map Tell Us About the Health Care Connectivity Gap?

PURPOSE: Internet connection speeds are generally slower in rural areas, and this issue is rising in importance for health care facilities as technologies such as Electronic Health Records and Health Information Exchanges become more common. However, the extent of the rural-urban divide in terms of health care connectivity has not been fully quantified. This report uses data compiled from the National Broadband Map (NBM) to compare levels of health care facility connectivity across metropolitan and nonmetropolitan counties. METHODS: The number of health and medical entries in the Community Anchor Institution (CAI) data collected as part of the NBM grew from 35,000 to 63,000 between 2010 and 2014. About one-fifth provided information on the speed of their connections in 2014. Comparisons across metro and nonmetro counties and over time provide insight into trends associated with the health care connectivity gap. FINDINGS: The data clearly show that health-related institutions in nonmetro counties connect with lower speeds than do their more urban counterparts. At the aggregate level, over 55% of metro institutions who provided speed information had download speeds in excess of 50 megabytes per second in 2014, compared with only 12% of nonmetro institutions (P < .001). More importantly, the connectivity gap has grown significantly during 2010-2014, particularly for nonhospital facilities. CONCLUSIONS: The NBM CAI data are a publicly available and easy to use asset that rural health advocates should be aware of. The fact that the connectivity gap increased during 2010-2014, despite policies focusing on this issue, is a cause for concern.

Rural EMR adoption rates overtake those in urban areas.

OBJECTIVE: To assess rural-urban differences in electronic medical record (EMR) adoption among office-based physician practices in the United States. METHODS: Survey data on over 270 000 office-based physician sites (representing over 1 280 000 physicians) in the United States from 2012 was used to assess differences in EMR adoption rates among practices in rural and urban areas. Logistic regression tests for differences in the determinants of EMR adoption by geography, and a nonlinear decomposition is used to quantify how much of the rural-urban gap is due to differences in measureable characteristics (such as type of practice or affiliation with a health system). RESULTS: Overall EMR adoption rates were significantly higher for practices in rural areas (56%) vs those in urban areas (49%) in 2012 (P < 0.001). Twenty-nine states had statistically significantly different adoption rates between rural and urban areas, with only two states demonstrating higher rates in urban areas. EMR adoption continues to be higher for primary care practices when compared to specialists (51% vs 49%, P < 0.001), and state-level rural-urban differences in adoption are more pronounced for specialists. The decomposition technique finds that only 14% of the rural-urban gap can be explained by differences in measurable characteristics between practices. CONCLUSIONS: At the national level, rates of EMR adoption are higher for rural practices than for their urban counterparts, reversing earlier trends. This suggests that outreach efforts, namely the Regional Extension Centers created by the Office of the National Coordinator, have been particularly effective in increasing EMR adoption in rural areas.

Electronic medical record adoption in Oklahoma practices: rural-urban differences and the role of broadband availability.

PURPOSE: Most recent research has not found significant differences in electronic medical record (EMR) adoption rates between rural and urban physicians. However, few studies have assessed rural/urban differences at a lower level--for instance, by specialty or size of practice. Determinants of EMR adoption by physician practices in Oklahoma are explored, including the potential role of broadband availability (which is required for EMR interoperability). METHODS: Surveys of 2,800 unique Oklahoma physician practices in 2011 were meshed with data from the National Broadband Map for that same year. Summary statistics from the survey data allowed for comparison of EMR adoption rates by sub category. Logistic regressions were used to tease out the impact of location, specialty, and broadband availability on the EMR adoption decision. FINDINGS: Similar overall EMR adoption rates in rural and urban practices masked significant differences among specific subcategories. In particular, solo practices in rural areas are much more likely to adopt EMRs than are their urban counterparts (41% vs 33%, P < .01); rural psychiatric practices also have measurably higher adoption rates (59% vs 25%, P < .01). Logistic regression results demonstrate that determinants of adoption do vary between rural and urban practices. No statistical relationship between EMR adoption and measures of broadband availability was found. CONCLUSIONS: Measurable differences in EMR adoption rates do exist between rural and urban practices for specific physician categories in Oklahoma. Targeted policies may be important for increasing EMR adoption, but policy efforts focusing solely on broadband availability for private practices are likely misguided.

Critical access hospitals and retail activity: an empirical analysis in Oklahoma.

PURPOSE: This paper takes an empirical approach to determining the effect that a critical access hospital (CAH) has on local retail activity. Previous research on the relationship between hospitals and economic development has primarily focused on single-case, multiplier-oriented analysis. However, as the efficacy of federal and state-level rural health subsidies come under increasing scrutiny, more comprehensive investigations can provide support for continued funding. METHODS: Data from 105 rural Oklahoma communities are used to explore whether the presence of a CAH impacts several measures of retail activity. The measures are: total retail sales, total number of retail establishments, and number of micro and small retail establishments. Ordinary least squares regression is used to evaluate the impact of a CAH after controlling for a host of other factors influencing retail activity such as local demographics, unemployment rates, and the presence of a Wal-Mart. FINDINGS: The presence of a CAH has a positive and significant influence on each measure of retail activity. The parameter estimates suggest that a CAH has a similar influence on rural retail sales as a Wal-Mart, increasing total retail sales by approximately 28% over towns without a CAH. Other model results indicate that a CAH presence significantly increases the number of total retail establishments and the number of micro and small business establishments. CONCLUSIONS: The positive results provide additional evidence on the far-reaching economic development impacts of CAHs. The results also emphasize the importance of continued support for these rural institutions, including federal and state subsidies.