Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.

Objectives: The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data. Materials and Methods: Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets. Results: Our approach provides better prediction of localized case counts and indicates the impact of the dynamic elements of the pandemic on predictions, such as the influence of the mixture of viral variants in the population and variable testing and vaccination rates during various eras of the pandemic. Discussion: Utilizing real-time public health metrics, including estimated Rt from multiple SARS-CoV-2 variants, vaccination rates, and testing information, provided a significant increase in the accuracy of the model during the Omicron and Delta period, thus providing more precise forecasting of daily case counts at the county level. This work provides insights on the influence of various features on predictive performance in rural and non-rural areas. Conclusion: Our proposed framework incorporates available public health metrics with operational data on the impact of testing, vaccination, and current viral variant mixtures in the population to provide a foundation for combining dynamic public health metrics and ML models to deliver forecasting and insights in healthcare domains. It also shows the importance of developing and deploying ML frameworks in rural settings.

Comparing the effects of decreasing prescription opioid shipments and the release of an abuse deterrent OxyContin formulation on opioid overdose fatalities in WV: an interrupted time series study.

INTRODUCTION: The 2010 release of an abuse deterrent formulation (ADF) of OxyContin, a brand name prescription opioid, has been cited as a major driver for the reduction in prescription drug misuse and the associated increasing illicit opioid use and overdose rates. However, studies of this topic often do not account for changes in supplies of other prescription opioids that were widely prescribed before and after the ADF OxyContin release, including generic oxycodone formulations and hydrocodone. We therefore sought to compare the impact of the ADF OxyContin release to that of decreasing prescription opioid supplies in West Virginia (WV). METHODS: Opioid tablet shipment and overdose data were extracted from The Washington Post ARCOS (2006-2014) and the WV Forensic Drug Database (2005-2020), respectively. Locally estimated scatterplot smoothing (LOESS) was used to estimate the point when shipments of prescription opioids to WV began decreasing, measured via dosage units and morphine milligram equivalents (MMEs). Interrupted time series analysis (ITSA) was used to compare the impact LOESS-identified prescription supply changes and the ADF OxyContin release had on prescription (oxycodone and hydrocodone) and illicit (heroin, fentanyl, and fentanyl analogues) opioid overdose deaths in WV. Model fit was compared using Akaike Information Criteria (AIC). RESULTS: The majority of opioid tablets shipped to WV from 2006 to 2014 were generic oxycodone or hydrocodone, not OxyContin. After accounting for a 6-month lag from ITSA models using the LOESS-identified change in prescription opioid shipments measured via dosage units (2011 Q3) resulted in the lowest AIC for both prescription (AIC = -188.6) and illicit opioid-involved overdoses (AIC = -189.4), indicating this intervention start date resulted in the preferred model. The second lowest AIC was for models using the ADF OxyContin release as an intervention start date. DISCUSSION: We found that illicit opioid overdoses in WV began increasing closer to when prescription opioid shipments to the state began decreasing, not when the ADF OxyContin release occurred. Similarly, the majority of opioid tablets shipped to the state for 2006-2014 were generic oxycodone or hydrocodone. This may indicate that diminishing prescription supplies had a larger impact on opioid overdose patterns than the ADF OxyContin release in WV.

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.

Disparities in healthcare utilization by insurance status among patients with symptomatic peripheral artery disease.

BACKGROUND: Peripheral artery disease (PAD) is a common circulatory disorder associated with increased hospitalizations and significant health care-related expenditures. Among patients with PAD, insurance status is an important determinant of health care utilization, treatment of disease, and treatment outcomes. However, little is known about PAD-costs differences across different insurance providers. In this study we examined possible disparities in length of stay and total charge of inpatient hospitalizations among patients with PAD by insurance type. METHODS: We conducted a cross-sectional analysis of length of stay and total charge by insurance provider for all hospitalizations for individuals with PAD in South Carolina (2010-2018). Cross-classified multilevel modeling was applied to account for the non-nested hierarchical structure of the data, with county and hospital included as random effects. Analyses were adjusted for patient age, race/ethnicity, county, year of admission, admission type, all-patient refined diagnostic groups, and Charlson comorbidity index. RESULTS: Among 385,018 hospitalizations for individuals with PAD in South Carolina, the median length of stay was 4 days (IQR: 5) and the median total charge of hospitalization was $43,232 (IQR: $52,405). Length of stay and total charge varied significantly by insurance provider. Medicare patients had increased length of stay (IRR = 1.08, 95 CI%: 1.07, 1.09) and higher total charges (ß: 0.012, 95% CI: 0.007, 0.178) than patients with private insurance. Medicaid patients also had increased length of stay (IRR = 1.26, 95% CI: 1.24,1.28) but had lower total charges (ß: -0.022, 95% CI: -0.003. -0.015) than patients with private insurance. CONCLUSIONS: Insurance status was associated with inpatient length of stay and total charges in patients with PAD. It is essential that Medicare and Medicaid individuals with PAD receive proper management and care of their PAD, particularly in the primary care settings, to prevent hospitalizations and reduce the excess burden on these patients.

Excess US Firearm Mortality During the COVID-19 Pandemic Stratified by Intent and Urbanization.

This cross-sectional study used time series forecasting to estimate excess firearm mortality in the US during the COVID-19 pandemic.

Prenatal care and infant outcomes of teenage births: a Project WATCH study.

INTRODUCTION: Infants of teenage births are known to have increased risk of poor infant outcomes. Adequate prenatal care (PNC) is essential to the overall health of infants and their birthing persons. While teenage births continue to be of concern in rural areas, little is known about the association between inadequate PNC and poor infant outcomes in teenage populations. PURPOSE: To determine the association between inadequate PNC (< 10 visits) and poor infant outcomes neonatal intensive care unit (NICU) stay, low APGAR score, small for gestational age (SGA) and length of stay (LOS). METHODS: The study used West Virginia (WV) Project WATCH population level data (May 2018-March 2022). Multiple logistic regressions and survival analysis were performed on infant outcomes; NICU stay, APGAR score, infant size, and infant length of stay (LOS) with PNC categories inadequate (< 10 PNC visits) vs adequate (10 or more) adjusting for covariates including maternal race, insurance status, parity, smoking status, substance use status, and diabetes status. RESULTS: Of births to teenagers, 14% received inadequate PNC. Teens who received inadequate PNC had increased odds of infant admitted to NICU (aOR: 1.84, CI:(1.41, 2.42), p < 0.0001), low 5- minute APGAR score (aOR: 3.26, CI:(2.03,5.22), p < 0.0001), and increased LOS (Est. = -0.33, HR: 0.72, CI:(0.65,0.81), p < 0.0001). CONCLUSIONS: Results demonstrated that infants of teenagers who received inadequate PNC are at increased risk of requiring a NICU stay, having a low APGAR score and requiring an increased LOS. PNC is particularly important for these groups as they are at increased risk of poor birth outcomes.

Characteristics of Fatal, Pedestrian-Involved, Motor Vehicle Crashes in West Virginia: A Cross-Sectional and Spatial Analysis.

Fatal, pedestrian-involved, motor vehicle collisions are increasing in the United States yet remain lower in rural states such as West Virginia. This study's purpose was to investigate the overall risk factors of pedestrian fatalities by rurality and sex in West Virginia. Data were obtained from the Fatality Analysis Reporting System. The fatality had to occur within West Virginia between 1 January 2009 and 31 December 2019. Risk factors of rural vs. urban and male vs. female crashes were determined using multivariable logistic regression models. Clustering of crash locations was analyzed using kernel density estimation and Ripley's K. Among the 254 fatalities, most victims were male (70%). Most crashes occurred at night (76%), on highways (73%), on level (71%), non-curved (84%), dry (82%) roads during fair weather conditions (82%). Nearly 34% of the victims tested positive for alcohol. Men were 2.5 times as likely to be hit in a rural area (OR = 2.5; 95% CI 1.2, 5.4), on curved roads, and 57% less likely (OR = 0.43; 95% CI 0.2, 0.9) to test positive for drugs compared to women. Crash characteristics, including location, were similar between the sexes. As many risk factors were modifiable behaviors, public health interventions to ensure pedestrian safety may be necessary.

Medical Expenditures Associated with Attention-Deficit/Hyperactivity Disorder Among Adults in the United States by Age, 2015-2019.

BACKGROUND: Attention-deficit hyperactivity disorder is a common disorder that affects both children and adults. However, for adults, little is known about ADHD-attributable medical expenditures. OBJECTIVE: To estimate the medical expenditures associated with ADHD, stratified by age, in the US adult population. DESIGN: Using a two-part model, we analyzed data from Medical Expenditure Panel Survey for 2015 to 2019. The first part of the model predicts the probability that individuals incurred any medical costs during the calendar year using a logit model. The second part of the model estimates the medical expenditures for individuals who incurred any medical expenses in the calendar year using a generalized linear model. Covariates included age, sex, race/ethnicity, geographic region, Charlson comorbidity index, insurance, asthma, anxiety, and mood disorders. PARTICIPANTS: Adults (18 +) who participated in the Medical Expenditure Panel Survey from 2015 to 2019 (N = 83,776). MAIN MEASURES: Overall and service specific direct ADHD-attributable medical expenditures. KEY RESULTS: A total of 1206 participants (1.44%) were classified as having ADHD. The estimated incremental costs of ADHD in adults were $2591.06 per person, amounting to $8.29 billion nationally. Significant adjusted incremental costs were prescription medication ($1347.06; 95% CI: $990.69-$1625.93), which accounted for the largest portion of total costs, and office-based visits ($724.86; 95% CI: $177.75-$1528.62). The adjusted incremental costs for outpatient visits, inpatient visits, emergency room visits, and home health visits were not significantly different. Among older adults (31 +), the incremental cost of ADHD was $2623.48, while in young adults (18-30), the incremental cost was $1856.66. CONCLUSIONS: The average medical expenditures for adults with ADHD in the US were substantially higher than those without ADHD and the incremental costs were higher in older adults (31 +) than younger adults (18-30). Future research is needed to understand the increasing trend in ADHD attributable cost.

Community risks for SARS-CoV-2 infection among fully vaccinated US adults by rurality: A retrospective cohort study from the National COVID Cohort Collaborative.

BACKGROUND: While COVID-19 vaccines reduce adverse outcomes, post-vaccination SARS-CoV-2 infection remains problematic. We sought to identify community factors impacting risk for breakthrough infections (BTI) among fully vaccinated persons by rurality. METHODS: We conducted a retrospective cohort study of US adults sampled between January 1 and December 20, 2021, from the National COVID Cohort Collaborative (N3C). Using Kaplan-Meier and Cox-Proportional Hazards models adjusted for demographic differences and comorbid conditions, we assessed impact of rurality, county vaccine hesitancy, and county vaccination rates on risk of BTI over 180 days following two mRNA COVID-19 vaccinations between January 1 and September 21, 2021. Additionally, Cox Proportional Hazards models assessed the risk of infection among adults without documented vaccinations. We secondarily assessed the odds of hospitalization and adverse COVID-19 events based on vaccination status using multivariable logistic regression during the study period. RESULTS: Our study population included 566,128 vaccinated and 1,724,546 adults without documented vaccination. Among vaccinated persons, rurality was associated with an increased risk of BTI (adjusted hazard ratio [aHR] 1.53, 95% confidence interval [CI] 1.42-1.64, for urban-adjacent rural and 1.65, 1.42-1.91, for nonurban-adjacent rural) compared to urban dwellers. Compared to low vaccine-hesitant counties, higher risks of BTI were associated with medium (1.07, 1.02-1.12) and high (1.33, 1.23-1.43) vaccine-hesitant counties. Compared to counties with high vaccination rates, a higher risk of BTI was associated with dwelling in counties with low vaccination rates (1.34, 1.27-1.43) but not medium vaccination rates (1.00, 0.95-1.07). Community factors were also associated with higher odds of SARS-CoV-2 infection among persons without a documented vaccination. Vaccinated persons with SARS-CoV-2 infection during the study period had significantly lower odds of hospitalization and adverse events across all geographic areas and community exposures. CONCLUSIONS: Our findings suggest that community factors are associated with an increased risk of BTI, particularly in rural areas and counties with high vaccine hesitancy. Communities, such as those in rural and disproportionately vaccine hesitant areas, and certain groups at high risk for adverse breakthrough events, including immunosuppressed/compromised persons, should continue to receive public health focus, targeted interventions, and consistent guidance to help manage community spread as vaccination protection wanes.

Higher hospitalization and mortality rates among SARS-CoV-2-infected persons in rural America.

PURPOSE: Rural communities are among the most underserved and resource-scarce populations in the United States. However, there are limited data on COVID-19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS-CoV-2-infected persons stratified by residential rurality. METHODS: This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS-CoV-2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all-cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90-day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan-Meier survival estimates and mixed-effects logistic regression. FINDINGS: Of 1,033,229 diagnosed COVID-19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban-adjacent and nonurban-adjacent rural dwellers with COVID-19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16-1.21 and aOR 1.29, CI 1.24-1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29-1.43 and 1.37, CI 1.26-1.50), respectively. All secondary outcomes were more likely among rural patients. CONCLUSIONS: Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID-19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

The association between socioeconomic distress communities index and amputation among patients with peripheral artery disease.

Background: Socioeconomic factors have been shown to be associated with amputation in peripheral artery disease (PAD); however, analyses have normally focused on insurance status, race, or median income. We sought to determine whether community-level socioeconomic distress was associated with major amputation and if that association differed by race. Materials and methods: Community-level socioeconomic distress was measured using the distressed communities index (DCI). The DCI is a zip code level compositive socioeconomic score (0-100) that accounts for unemployment, education level, poverty rate, median income, business growth, and housing vacancies. A distressed community was defined as a zip code with DCI of 40 or greater. We calculated one-year risk of major amputation by DCI score for individuals with peripheral artery disease in South Carolina, 2012-2017. Treating death as competing event, we reported Fine and Gray subdistribution hazards ratios (sdHR), adjusted for patient demographic and clinical comorbidities associated with amputation. Further analyses were completed to identify potential differences in outcomes within strata of race and DCI. Results: Among 82,848 individuals with peripheral artery disease, the one-year incidence of amputation was 3.5% (95% CI: 3.3%, 3.6%) and was significantly greater in distressed communities than non-distressed communities (3.9%; 95% CI: 3.8%, 4.1% vs. 2.4%; 95% CI: 2.2%, 2.6%). After controlling for death and adjusting for covariates, we found an increased hazard of amputation among individuals in a distressed community (sdHR: 1.25; 95% CI: 1.14, 1.37), which persisted across racial strata. However, regardless of DCI score, Black individuals had the highest incidence of amputation. Conclusion: Socioeconomic status is independently predictive of limb amputation after controlling for demographic characteristics and clinical comorbidities. Race continues to be an important risk factor, with Black individuals having higher incidence of amputation, even in non-distressed communities, than White individuals had in distressed communities.

Drive Times to Methadone Treatment among Medicaid Patients.

OBJECTIVE: To describe round-trip drive times and travel distance to methadone clinics among Medicaid enrollees in West Virginia, testing for differences between those in rural versus urban areas. FINDINGS: In this cross-sectional analysis of West Virginia Medicaid enrollees' claims from 2018-2019, methadone recipients on average traveled almost an hour round-trip to receive their daily treatment. The travel burden was substantially higher among those in more rural areas. Meaning. These findings indicate that it may be difficult for patients to adhere to their methadone treatment regimen, perhaps leading to suboptimal treatment rates and outcomes.

An ordinal severity scale for COVID-19 retrospective studies using Electronic Health Record data.

Objectives: Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data. Materials and Methods: An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal component analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis. Results: The data set used in this analysis consists of 2 880 456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period. Discussion: An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on the progression of COVID-19 disease severity over time. Conclusions: The OS provides a framework that can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.

Brief report: Cause of death among people discharged from infective endocarditis related hospitalization-West Virginia, 2016-2019.

BACKGROUND AND OBJECTIVES: Compare proportion of all-cause and cause-specific mortality among West Virginia Medicaid enrollees who were discharged from infective endocarditis (IE) hospitalization with and without opioid use disorder (OUD) diagnosis. METHODS: The proportions of cause-specific deaths among those who were discharged from IE-related hospitalizations were compared by OUD diagnosis. RESULTS: The top three underlying causes of death discharged from IE hospitalization were accidental drug poisoning, mental and behavioral disorders due to polysubstance use, and cardiovascular diseases. Of the total deaths occurring among patients discharged after IE-related hospitalization, the proportion has increased seven times from 2016 to 2019 among the OUD deaths while it doubled among the non-OUD deaths. DISCUSSION AND CONCLUSIONS: Of the total deaths occurring among patients discharged after IE-related hospitalization, the increase is higher in those with OUD diagnosis. OUD is becoming a significantly negative impactor on the survival outcome among IE patients. It is of growing importance to deliver medication for OUD treatment and harm reduction efforts to IE patients in a timely manner, especially as the COVID-19 pandemic persists.

Association of State Social and Environmental Factors With Rates of Self-injury Mortality and Suicide in the United States.

Importance: Self-injury mortality (SIM) combines suicides and the preponderance of drug misuse-related overdose fatalities. Identifying social and environmental factors associated with SIM and suicide may inform etiologic understanding and intervention design. Objective: To identify factors associated with interstate SIM and suicide rate variation and to assess potential for differential suicide misclassification. Design, Setting, and Participants: This cross-sectional study used a partial panel time series with underlying cause-of-death data from 50 US states and the District of Columbia for 1999-2000, 2007-2008, 2013-2014 and 2018-2019. Applying data from the Centers for Disease Control and Prevention, SIM includes all suicides and the preponderance of unintentional and undetermined drug intoxication deaths, reflecting self-harm behaviors. Data were analyzed from February to June 2021. Exposures: Exposures included inequity, isolation, demographic characteristics, injury mechanism, health care access, and medicolegal death investigation system type. Main Outcomes and Measures: The main outcome, SIM, was assessed using unstandardized regression coefficients of interstate variation associations, identified by the least absolute shrinkage and selection operator; ratios of crude SIM to suicide rates per 100 000 population were assessed for potential differential suicide misclassification. Results: A total of 101 325 SIMs were identified, including 74 506 (73.5%) among males and 26 819 (26.5%) among females. SIM to suicide rate ratios trended upwards, with an accelerating increase in overdose fatalities classified as unintentional or undetermined (SIM to suicide rate ratio, 1999-2000: 1.39; 95% CI, 1.38-1.41; 2018-2019: 2.12; 95% CI, 2.11-2.14). Eight states recorded a SIM to suicide rate ratio less than 1.50 in 2018-2019 vs 39 states in 1999-2000. Northeastern states concentrated in the highest category (range, 2.10-6.00); only the West remained unrepresented. Least absolute shrinkage and selection operator identified 8 factors associated with the SIM rate in 2018-2019: centralized medical examiner system (ß = 4.362), labor underutilization rate (ß = 0.728), manufacturing employment (ß = -0.056), homelessness rate (ß = -0.125), percentage nonreligious (ß = 0.041), non-Hispanic White race and ethnicity (ß = 0.087), prescribed opioids for 30 days or more (ß = 0.117), and percentage without health insurance (ß = -0.013) and 5 factors associated with the suicide rate: percentage male (ß = 1.046), military veteran (ß = 0.747), rural (ß = 0.031), firearm ownership (ß = 0.030), and pain reliever misuse (ß = 1.131). Conclusions and Relevance: These findings suggest that SIM rates were associated with modifiable, upstream factors. Although embedded in SIM, suicide unexpectedly deviated in proposed social and environmental determinants. Heterogeneity in medicolegal death investigation processes and data assurance needs further characterization, with the goal of providing the highest-quality reports for developing and tracking public health policies and practices.

Bridging the Gap-Building Surgical Subspecialty Telemedicine Clinics in the Rural Setting.

INTRODUCTION: Pediatric urology is a much-needed subspecialty with a breadth of complex disorders that can often prove challenging to diagnose and manage. Exacerbating this need is the minimal exposure medical trainees receive to pediatric urology. Pediatric urology arrived in West Virginia in 1983 but the subspecialty has been inconsistently represented since then. Currently there are 2 fellowship-trained pediatric urologists in the state of West Virginia, which has an area of approximately 24,038 square miles. We review our experience with the use of telemedicine in providing outreach to the wider parts of our medically underserved state and ultimately evaluate its efficacy from a patient-centric cost analysis and diagnosis concordance perspective. We hypothesized that the use of telemedicine would be cost and time-effective for patients in our rural state. METHODS: We retrospectively reviewed our series of patients presenting from outside telemedicine "referral centers" in Martinsburg, Parkersburg and Wheeling for pediatric urological consultation. We evaluated reason for consultation, geographic driving distance, drive time and travel cost saved from telemedicine consultation. RESULTS: A total of 92 patients presented to outside designated telemedicine centers from August 2018 to April 2020. The mean driving time saved utilizing telemedicine consultation was 4 hours and 46 minutes, and mean driving distance saved was 299.8 miles. Travel costs saved in terms of fuel averaged $173.88 per patient. The most common reason for consultation was undescended testis, followed by recurrent urinary tract infection and nocturnal enuresis. Of the 23 patients who required surgery, only 2 (8.7%) had an initial diagnosis that was not concordant with their operating room examination. CONCLUSIONS: While modest, our data indicate a modern solution to a historical need in our state. Our high diagnosis concordance rate (91.3%) shows that a well-trained advanced practice provider can adequately perform an operative evaluation via telemedicine.

Assessing the impact of social distancing measures implemented during COVID-19 pandemic on medications for opioid use disorder in West Virginia.

INTRODUCTION: This study evaluates if social distancing measures instituted during the novel coronavirus SARS-CoV-2 (COVID-19) pandemic were associated with a reduction in Medication for Opioid Use Disorder (MOUD) prescribing in West Virginia. The COVID-19 pandemic necessitated the quick implementation of public health interventions such as social distancing. This led to the use of telemedicine in the clinical setting however implementing telemedicine involves system level and infrastructure level changes within a healthcare environment. This could cause a barrier to MOUD delivery as it is often provided concomitantly with other face to face substance use and mental health services. The purpose of this study is to determine whether social distancing was associated with a reduction in MOUD prescribing in West Virginia, with the goal of adding to the knowledge of how COVID-19 and COVID-19-related mitigation strategies have impacted patients with OUD. METHODS: Prescription monitoring data were requested from the West Virginia Board of Pharmacy. We applied interrupted time series modeling to investigate MOUD prescribing practices before and after social distancing took effect. Gabapentin prescriptions were utilized as a control for comparison. RESULTS: Our study assessed state-wide buprenorphine and Suboxone prescriptions as compared to a control medication and found an increase in dosage of both medications and an increase in number of buprenorphine prescriptions, but a small decrease in buprenorphine/naloxone prescription number related to the dates of implementation of social distancing. Taken together, overall this indicates an increase in prescription number of MOUD prescriptions as well as an increase in dosage. CONCLUSION: This study suggests that social distancing measures were associated with an increase in both the number of MOUD prescriptions and the number of doses in each prescription. Significant alterations to MOUD delivery in the clinical setting were implemented in a short timeframe with the COVID-19 pandemic. Understanding the implementation of clinical measures to accommodate social distancing measures may provide benefit to transformation of future delivery of MOUD.

Spatial epidemiology: An empirical framework for syndemics research.

Syndemics framework describes two or more co-occurring epidemics that synergistically interact with each other and the complex structural social forces that sustain them leading to excess disease burden. The term syndemic was first used to describe the interaction between substance abuse, violence, and AIDS by Merrill Singer. A broader range of syndemic studies has since emerged describing the framework's applicability to other public health scenarios. With syndemic theory garnering significant attention, the focus is shifting towards developing robust empirical analytical approaches. Unfortunately, the complex nature of the disease-disease interactions nested within several social contexts complicates empirical analyses. In answering the call to analyze syndemics at the population level, we propose the use of spatial epidemiology as an empirical framework for syndemics research. Spatial epidemiology, which typically relies on geographic information systems (GIS) and statistics, is a discipline that studies spatial variations to understand the geographic landscape and the risk environment within which disease epidemics occur. GIS maps provide visualization aids to investigate the spatial distribution of disease outcomes, the associated social factors, and environmental exposures. Analytical inference, such as estimation of disease risks and identification of spatial disease clusters, can provide a detailed statistical view of spatial distributions of diseases. Spatial and spatiotemporal models can help us to understand, measure, and analyze disease syndemics as well as the social, biological, and structural factors associated with them in space and time. In this paper, we present a background on syndemics and spatial epidemiological theory and practice. We then present a case study focused on the HIV and HCV syndemic in West Virginia to provide an example of the use of GIS and spatial analytical methods. The concepts described in this paper can be considered to enhance understanding and analysis of other syndemics for which space-time data are available.