Factors Associated With Surveillance Testing in Individuals With COVID-19 Symptoms During the Last Leg of the Pandemic: Multivariable Regression Analysis.

Background: Rural underserved areas facing health disparities have unequal access to health resources. By the third and fourth waves of SARS-CoV-2 infections in the United States, COVID-19 testing had reduced, with more reliance on home testing, and those seeking testing were mostly symptomatic. Objective: This study identifies factors associated with COVID-19 testing among individuals who were symptomatic versus asymptomatic seen at a Rapid Acceleration of Diagnostics for Underserved Populations phase 2 (RADx-UP2) testing site in West Virginia. Methods: Demographic, clinical, and behavioral factors were collected via survey from tested individuals. Logistic regression was used to identify factors associated with the presence of individuals who were symptomatic seen at testing sites. Global tests for spatial autocorrelation were conducted to examine clustering in the proportion of symptomatic to total individuals tested by zip code. Bivariate maps were created to display geographic distributions between higher proportions of tested individuals who were symptomatic and social determinants of health. Results: Among predictors, the presence of a physical (adjusted odds ratio [aOR] 1.85, 95% CI 1.3-2.65) or mental (aOR 1.53, 95% CI 0.96-2.48) comorbid condition, challenges related to a place to stay/live (aOR 307.13, 95% CI 1.46-10,6372), no community socioeconomic distress (aOR 0.99, 95% CI 0.98-1.00), no challenges in getting needed medicine (aOR 0.01, 95% CI 0.00-0.82) or transportation (aOR 0.23, 95% CI 0.05-0.64), an interaction between community socioeconomic distress and not getting needed medicine (aOR 1.06, 95% CI 1.00-1.13), and having no community socioeconomic distress while not facing challenges related to a place to stay/live (aOR 0.93, 95% CI 0.87-0.99) were statistically associated with an individual being symptomatic at the first test visit. Conclusions: This study addresses critical limitations to the current COVID-19 testing literature, which almost exclusively uses population-level disease screening data to inform public health responses.

Impact of stress and stress mindset on prevalence of cardiovascular disease risk factors among first responders.

BACKGROUND: Psychological stress is recognized as an important modifiable risk factor for cardiovascular disease (CVD). Despite its potential significance, few to no studies have evaluated the association between stress, stress mindset, and CVD risk factors among rural first responders. The objectives of this study were to identify relationships between general stress, stress mindset, and CVD risk factors. METHODS: The study sample (n = 148) included those 18 years or older and who currently serve as a first responder, defined as either EMS, firefighter, or law enforcement. Questionnaires captured information on demographics, years of work experience as a first responder, multiple first responder occupations, general stress, stress mindset, and self-reported CVD risk factors. Data were analyzed using regression analyses. RESULTS: Findings suggest that first responders with a stress-is-negative mindset have significantly higher general stress levels (ß = 2.20, p = 0.01). Of note, general stress was not a significant predictor of CVD risk factors (AOR = 1.00, 95%CI = 0.93, 1.08) included in our study. However, a negative stress mindset was statistically significant predictor of CVD risk factors (AOR = 2.82, 95%CI = 1.29, 6.41), after adjusting for general stress and other potential confounders. CONCLUSIONS: Findings suggest that stress mindset is an independent predictor of stress and CVD risk factors among rural first responders. These results have the potential to inform educational and organization level interventions targeting stress appraisal for this vulnerable sub population of workers.

Poverty and population health - The need for A Paradigm shift to capture the working poor and better inform public health planning.

BACKGROUND: Community-level socioeconomic disparities have a significant impact on an individual's health and overall well-being. However, current estimates for poverty threshold, which are often used to assess community-level socioeconomic status, do not account for cost-of-living differences or geography variability. The goals of this study were to compare geographic county-level overlap and gaps in access to care for households within poverty and working poor designations. METHODS: Data were obtained for 21 continental United States (US) states from the United Way's Asset Limited, Income Constrained, Employed (ALICE) households for 2021. Raw data contained the percentage of households at the federal poverty level, the percentage of households at the ALICE designations (working poor), and the total households at the county level. Local Moran's I tests for spatial autocorrelation were performed to identify the clustering of poverty and ALICE households. These clusters were overlaid with a 30-min drive time from critical access hospitals' physical addresses. FINDINGS: County-level clusters of ALICE (working poor) households occurred in different areas than the clustering of poverty households. Of particular interest, the extent to which the 30-min drive time to critical care overlapped with clusters of ALICE or poverty changed depending on the state. Overall, clustering in ALICE and poverty overlapped with 30-min drive times to critical care between 46 and 90% of the time. However, the specific states where disparities in access to care were prominent differed between analyses focused on households in poverty versus the working poor. INTERPRETATIONS: Findings highlight a disparity in equitable inclusion of individuals across the spectrum of socioeconomic status. Furthermore, they suggest that current public health programming and benefits which support low socioeconomic populations may be missing a vulnerable sub-population of working families. Future studies are needed to better understand how to address the health disparities facing individuals who are above the poverty threshold but still struggle economically to meet based needs.

Enhancing and Leveraging the West Virginia's Prescription Drug Monitoring Program (PDMP) for Public Health Surveillance and Clinical Decision Making: A Case Study.

West Virginia has struggled with an overdose epidemic for many years and continues to have the highest overdose death rate in the nation. However, through successful collaboration between the West Virginia Board of Pharmacy and the West Virginia Department of Health via its Violence and Injury Prevention Program, West Virginia has improved data quality, enhanced program development and implementation, and developed strategies to address the overdose epidemic. This multiagency collaboration plays an important role in addressing the overdose epidemic and promotes lasting interagency relationships. One strategy is overcoming barriers to maximizing and utilizing the Prescription Drug Monitoring Program, or PDMP. This strategy allows for a better understanding of a patient's prescription history and ensures safer prescribing practices. In addition, this strategic partnership facilitates the use of PDMP data for epidemiologic studies and public health surveillance, which results in sustainable analyses and dissemination of actionable data that are now driving public health action in West Virginia.

Prescription drug monitoring program policy reform: human and veterinary practitioner prescribing in West Virginia, 2008-2020.

No study has examined Prescription Drug Monitoring Program (PDMP) data for West Virginia or among specialty practices, such as veterinary medicine. The objectives of this study were to conduct time series modeling to describe the PDMP policy reform impact on prescribing rates for human and veterinary providers. We obtained data from the WV PDMP for 2008 through 2020 for the number of opioid prescriptions filled and providers. We estimated prescribing rates for human and veterinary providers separately based upon the top five opioids prescribed by veterinarians. We estimated temporal effects using a Bayesian log-normal time series model for humans and veterinarians separately. Throughout the study period prescribing rates increased significantly for veterinarians, and decreased for human providers, particularly during 2018 after implementation of the Opioid Reduction Act. Findings provide novel insight into the differential impact of policy on specialty practices and highlight decreasing human opioid prescribing observed elsewhere.