Excess Death Rates by State During the COVID-19 Pandemic: United States, 2020‒2023.

Objectives. To estimate state-level excess death rates during 2020 to 2023 and examine differences by region and partisan orientation. Methods. We modeled death and population counts from the Centers for Disease Control and Prevention to estimate excess death rates for the United States, 9 census divisions, and 50 states. We compared excess death rates for states with different partisan orientations, measured by the party of the seated governor and the level of partisan representation in state legislatures. Results. The United States experienced 1 277 697 excess deaths between March 2020 and July 2023. Almost 90% of these deaths were attributed to COVID-19, and 51.5% occurred after vaccines were available. The highest excess death rates first occurred in the Northeast and then shifted to the South and Mountain states. Between weeks ending June 20, 2020, through March 19, 2022, excess death rates were higher in states with Republican governors and greater Republican representation in state legislatures. Conclusions. Excess death rates during the COVID-19 pandemic varied considerably across the US states and were associated with partisan representation in state government, although the influence of confounding variables cannot be excluded. (Am J Public Health. 2024;114(9):882-891. https://doi.org/10.2105/AJPH.2024.307731).

Neighborhood Predictors of Poor Prenatal Care and Well-Child Visit Attendance.

PURPOSE: Women and children continue to miss preventive visits. Which neighborhood factors predict inadequate prenatal care (PNC) and well-child visit (WCV) attendance remain unclear. DESCRIPTION: In a retrospective case-control study at Virginia Commonwealth University Health System, mothers with less than 50% adherence or initiation after 5 months gestation were eligible as cases and those with ≥ 80% adherence and initiation before 5 months were eligible as controls. Children in the lowest quintile of adherence were eligible as cases and those with ≥ 80% of adherence were eligible as controls. Cases and controls were randomly selected at a 1:2 ratio and matched on birth month. Covariates were derived from the 2018 American Community Survey. A hotspot was defined as a zip code tabulation area (ZCTA) with a proportion of controls less than 0.66. ZCTAs with fewer than 5 individuals were excluded. Weighted quantile regression was used to determine which covariates were most associated with inadequate attendance. ASSESSMENT: We identified 38 and 35 ZCTAs for the PNC and WCV analyses, respectively. Five of 11 hotspots for WCV were also hotspots for PNC. Education and income predicted 51% and 34% of the variation in missed PNCs, respectively; language, education and transportation difficulties explained 33%, 29%, and 17% of the variation in missed WCVs, respectively. Higher proportions of Black residents lived in hotspots of inadequate PCV and WCV attendance. CONCLUSION: Neighborhood-level factors performed well in predicting inadequate PCV and WCV attendance. The disproportionate impact impact of inadequate PCV and WCV in neighborhoods where higher proportions of Black people lived highlights the potential influence of systemic racism and segregation on healthcare utilization.

Association Between Partisan Affiliation of State Governments and State Mortality Rates Before and During the COVID-19 Pandemic.

Policy Points The increasing political polarization of states reached new heights during the COVID-19 pandemic, when response plans differed sharply across party lines. This study found that states with Republican governors and larger Republican majorities in legislatures experienced higher death rates during the COVID-19 pandemic-and in preceding years-but these associations often lost statistical significance after adjusting for the average income and health status of state populations and for the policy orientations of the states. Future research may help clarify whether the higher death rates in these states result from policy choices or have other explanations, such as the tendency of voters with lower incomes or poorer health to elect Republican candidates. CONTEXT: Increasing polarization of states reached a high point during the COVID-19 pandemic, when the party affiliation of elected officials often predicted their policy response. The health consequences of these divisions are unclear. Prior studies compared mortality rates based on presidential voting patterns, but few considered the partisan orientation of state officials. This study examined whether the partisan orientation of governors or legislatures was associated with mortality outcomes during the COVID-19 pandemic. METHODS: Data on deaths and the partisan orientation of governors and legislators were obtained from the Centers for Disease Control and Prevention and the National Conference of State Legislatures, respectively. Linear regression was used to measure the association between Republican representation (percentage of seats held) in legislatures and (1) age-adjusted, all-cause mortality rates (AAMRs) in 2015-2021 and (2) excess death rates during three phases of the COVID-19 pandemic, controlling for median household income, the prevalence of four risk factors (obesity, chronic obstructive pulmonary disease, heart attack, stroke), and state policy orientation. Associations between excess death rates and the governor's party were also examined. FINDINGS: States with Republican governors or greater Republican representation in legislatures experienced higher AAMRs during 2015-2021, lower excess death rates during Phase 1 of the COVID-19 pandemic (weeks ending March 28, 2020, through June 13, 2020), and higher excess death rates in Phases 2 and 3 (weeks ending June 20, 2020, through April 30, 2022; p < 0.05). Most associations lost statistical significance after adjustment for control variables. CONCLUSIONS: Mortality was higher in states with Republican governors and greater Republican legislative representation before and during much of the pandemic. Observed associations could be explained by the adverse effects of policy choices, reverse causality (e.g., popularity of Republican candidates in states with lower socioeconomic and health status), or unmeasured factors that predominate in states with Republican leaders.

Cell-free next-generation sequencing impacts diagnosis and antimicrobial therapy in immunocompromised hosts: A retrospective study.

BACKGROUND: Cell-free next-generation sequencing (cfNGS) may have a unique role in the diagnosis of infectious complications in immunocompromised hosts. The rapid turnaround time and non-invasive nature make it a promising supplement to standard of care. METHODS: This retrospective, observational single-center study at a tertiary care medical center in Virginia investigated the use of cfNGS in clinical practice. Patients over age 18 years with cfNGS performed for any indication were included. The primary outcome was detection of bacteria and/or fungi on cfNGS. The secondary outcomes were concordance, and abundance of fungal and bacterial organism concentration detected over time from symptom onset, and clinical impact. RESULTS: Thirty-six patients (92% immunosuppressed) were identified and included. Twenty-one (58%) tests detected one to five organisms (14/21 bacteria, 8/21 fungi, and 6/21 viruses). The clinical impact of cfNGS was positive in 52.8% of cases, negative in 2.8%, and negligible in 44.4%. Positive tests prompted therapy changes in 12 of 21 patients; six of 20 bacteria and seven of eight fungi identified were considered clinically pathogenic. Three bacteria identifications and six fungi identifications prompted targeted treatment. When fungal species were not identified by cNFGS, antifungal de-escalation occurred in seven patients. CONCLUSION: cfNGS assisted in critical management changes, including initiation of treatment for identified organisms and antimicrobial de-escalation. Its non-invasive nature and rapid turnaround time make this an important adjunct to standard of care testing that may assist in providing earlier, targeted therapy, especially when opportunistic pathogens remain high on the differential diagnosis.

High dose valacyclovir for cytomegalovirus prophylaxis following allogeneic hematopoietic cell transplantation.

INTRODUCTION: Cytomegalovirus (CMV) is one of the most common and clinically significant viral infections following allogeneic hematopoietic cell transplantation (HCT). Currently available options for CMV prophylaxis and treatment present challenges related to side effects and cost. METHODS: In this retrospective medical record review, the incidence of clinically significant CMV infection (CMV disease or reactivation requiring preemptive treatment) following allogeneic HCT was compared in patients receiving valacyclovir 1 g three times daily versus acyclovir 400 mg every 12 h for viral prophylaxis. RESULTS: Forty-five patients who received valacyclovir were matched based on propensity scoring to 35 patients who received acyclovir. All patients received reduced-intensity conditioning regimens containing anti-thymocyte globulin. Clinically significant CMV infection by day + 180 was lower in the valacyclovir group compared to the acyclovir group (18% vs. 57%, p = 0.0004). Patients receiving valacyclovir prophylaxis also had less severe infection evidenced by a reduction in CMV disease, lower peak CMV titers, delayed CMV reactivation, and less secondary neutropenia. CONCLUSION: Prospective evaluation of valacyclovir 1 g three times daily for viral prophylaxis following allogeneic HCT is warranted. Due to valacyclovir's favorable toxicity profile and affordable cost, it has the potential to benefit patients on a broad scale as an option for CMV prophylaxis.

The Virginia primary care workforce: Final results of a novel method of analysis.

Background: In order to evaluate the capacity of primary care to provide comprehensive, coordinated, continuous care, a description of the primary care workforce is necessary. There have been prior efforts to describe the distribution of primary care, however, an accurate and timely description of which clinicians are in what location has been lacking. Further, the scope of care provided has not been fully explored. This research aimed to describe the primary care workforce distribution and specialty across the state of Virginia. Methods: The Virginia All-Payers Claims Database (APCD) and National Plan and Provider Enumeration System (NPPES) we queried. We identified all physicians (MD and DO) through NPPES and then cross referenced these clinicians with the APCD to identify those with an active claim in 2018. The NPPES was used to identify the physician specialty. Results: In 2018, there were 20,779 active physicians, of which 3,054 were family medicine, 1,794 were pediatricians, 1, 079 were OB/Gyns, and 5,247 were internal medicine or general medicine clinicians. The number of clinicians remained relatively stable between 2015-2018. Physicians were also categorized by ambulatory, inpatient, and mixed care. Discussion: This is the first evaluation that identifies clinician characteristics by using both active practice and NPPES taxonomy of type of clinicians. This will provide insight into the way care is provided across the state and establishes a foundation for evaluation of continuous care, comprehensive care and coordinated care in the future. As claims can be used to identify individuals over the course of time, it will be possible to identify scope, continuity and additional characteristics of clinicians. Additionally, we hope to identify advanced practice providers and their scope of care as well as clinicians migration and evolution during their career.

Identifying bright spot communities: Socioecological, workforce, and healthcare delivery factors influencing opioid mortality.

Context: There were 50,000 U.S. opioid overdose deaths in 2019. Millions suffer from opioid addiction. Identifying protective factors for low community opioid mortality may have important implications for addressing the opioid epidemic. This study was funded through the Virginia (VA) Department of Medical Assistance Services (DMAS) through a SUPPORT Act Grant. Objective: To identify "Bright Spot" communities in Virginia with protective factors associated with reduced opioid mortality and morbidity. Study Design: Ecologic study. Dataset: Virginia All Payer Claims Database (APCD), Virginia Department of Health (VDH) statewide medical examiner registry, and American Community Survey (ACS). Time Period: 2016-2019; 2019 data cited here. Population Studied: APCD includes VA residents with medical claims through commercial, Medicaid, and Medicare coverage. VDH data includes fatal drug overdoses. ACS surveys all VA residents. Outcome Measures: Primary outcome: fatal opioid overdoses. Secondary outcomes: emergency room visits for overdoses and opioid-related diagnoses, outpatient diagnoses for opioid-related disorder, prescription rate for opioids, and prescription rate for buprenorphine. Results: Opioid mortality was associated with higher rates of community poverty (r=.38, p<.0001) and disability (r=.52, r<.0001). Opioid mortality was associated with inequality, with higher Gini index associated with higher opioid mortality (r=.23, p<.0001). A higher percentage of black residents was associated with increased fatal opioid overdoses (r=.37, p<.0001) and ED visits for overdoses (r=.30, p<.0001). A higher percentage of white residents correlated with increased outpatient visits for opioid use disorder (r=.24, p<.0001) and higher rates of buprenorphine (r=.34, p<.0001) and opioid prescriptions (r=.31, p <.0001). Conclusions: These findings suggest significant racial disparities in opioid outcomes. Communities with a higher percentage of black residents are more likely to have higher opioid mortality and a lower rate of outpatient treatment. This association may be affected by the time period used in the analysis (2015-2019), as nationally there has been an increasing rate of synthetic opioid deaths in Black communities. These measures have been incorporated into a multivariate analysis to identify Bright Spot communities, which will be discussed during the presentation.

Using State All-Payer Claims Data to Identify the Active Primary Care Workforce: A Novel Study in Virginia.

PURPOSE: Primary care is the foundation of the health care workforce and the only part that extends life and improves health equity. Previous research on the geographic and specialty distribution of physicians has relied on the American Medical Association's Masterfile, but these data have limitations that overestimate the workforce. METHODS: We present a pragmatic, systematic, and more accurate method for identifying primary care physicians using the National Plan and Provider Enumeration System (NPPES) and the Virginia All-Payer Claims Database (VA-APCD). Between 2015 and 2019, we identified all Virginia physicians and their specialty through the NPPES. Active physicians were defined by at least 1 claim in the VA-APCD. Specialty was determined hierarchically by the NPPES. Wellness visits were used to identify non-family medicine physicians who were providing primary care. RESULTS: In 2019, there were 20,976 active physicians in Virginia, of whom 5,899 (28.1%) were classified as providing primary care. Of this primary care physician workforce, 52.4% were family medicine physicians; the remaining were internal medicine physicians (18.5%), pediatricians (16.8%), obstetricians and gynecologists (11.8%), and other specialists (0.5%). Over 5 years, the counts and relative percentages of the workforce made up by primary care physicians remained relatively stable. CONCLUSIONS: Our novel method of identifying active physicians with a primary care scope provides a realistic size of the primary care workforce in Virginia, smaller than some previous estimates. Although the method should be expanded to include advanced practice clinicians and to further delineate the scope of practice, this simple approach can be used by policy makers, payers, and planners to ensure adequate primary care capacity.

Are primary care providers' nutrition care and food insecurity screening practices associated with their perceptions of team-based care?

BACKGROUND: Screening for food insecurity (FI) and providing nutrition care are important management strategies for chronic diseases, but rates are low. Aspects of team-based care and providers' nutrition competence may help inform interventions to improve these services. The objectives of this study were to describe US primary care providers' FI screening and nutrition care practices (counselling, referrals, and time spent counselling) and test for associations with scored measures of their perceptions of team-based care (care continuity, patient-centredness, coordination with external providers and resources) and nutrition competence (confidence counselling and attitudes towards nutrition). METHODS: Cross-sectional online survey data of primary care providers were described and analysed for associations using Wilcoxon rank sum tests. RESULTS: Of provider respondents (N = 92), 35% (n = 32) worked in clinics that screen for FI and had higher team perceptions (P = 0.006) versus those who do not. Those who reported counselling >30% patients about nutrition (57%, n = 52) and referring >10% patients to nutrition professionals (24%, n = 22) had significantly better attitudes towards nutrition (P = 0.013 and P = 0.04, respectively) compared with those with lower counselling and referral rates. Half (n = 46) of the providers reported spending >3-min counselling each patient about nutrition and had higher patient-centred care (P = 0.004) and nutrition competence (P < 0.001) compared with those who spent less time counselling. CONCLUSION: Providers in clinics that screen for FI had higher overall perceptions of team-based care, but their nutrition competence was not significantly different. Meanwhile, reported more time counselling was associated with a culture of patient-centredness. Promoting team-based care may be a mechanism for improving FI screening and nutrition care.

Community, Social, and Facility Factors and Long-stay Antipsychotic Use.

OBJECTIVES: Compare Virginia nursing homes in the top- and bottom-quintiles of antipsychotic use for variation in community, social, and facility factors. METHODS: 2018 CMS data ascertained Virginia nursing homes in the top and bottom quintiles for antipsychotic use. The Virginia Health Department provided social determinant of health (SDOH) statistics for each facility's county/city while claims identified facility demographics. Chi square and independent two-sample t-tests compared quintiles for regional, social, and demographic differences. RESULTS: Quintiles averaged 3000 residents and 56 facilities. Facilities with the lowest rates of antipsychotic use were more likely to be privately owned and had fewer African-American and minority residents and more white residents. All 18 SDOH statistics were superior for the communities of facilities with the lowest antipsychotic rates. Nine of these differences were statistically significant, including the aggregated "Health Opportunity Index." CONCLUSIONS: The antipsychotic prevalence rate for facilities in the top-quintile of antipsychotic use is fivefold the bottom-quintile's rate. Antipsychotic prescribing in nursing homes is associated with regional, demographic, and social factors not addressed by existing antipsychotic reduction measures, with vulnerable populations at greatest risk. CLINICAL IMPLICATIONS: The efficacy of measures aimed at curbing long-stay antipsychotic prescribing could be improved by addressing SDOH including economic opportunities.

Unreported Antipsychotic Use Increasing in Nursing Homes: The Impact of Quality-Measure Exclusions on the Percentage of Long-Stay Residents Who Got an Antipsychotic Medication Quality-Measure.

OBJECTIVE: Excluded from reporting to CMS's Percentage of long-stay residents who got an antipsychotic medication quality-measure are antipsychotics prescribed to nursing home patients with schizophrenia, Tourette's, or Huntington's. Over the 4 years following its 2012 debut, the quality-measure calculated a 27% reduction in inappropriate antipsychotic use but also an 18.3% increase in exclusion claims. This study evaluated the impact of these exclusions on the measure's findings. METHODS: Claims data for the years 2011-2016 retrospectively identified the prevalence of schizophrenia, Tourette's, and Huntington's in quarterly cohorts of Virginia long-stay residents prescribed antipsychotics. Annualized diagnoses in 2011 were compared with subsequent years using simple logistic regression. RESULTS: In 2016, 29% of the antipsychotics prescribed in Virginia nursing homes were to residents with diagnoses of schizophrenia, Tourette's, and Huntington's, a significant 32% increase from 2011. CONCLUSION: Almost 30% of the antipsychotics employed in Virginia nursing homes are excluded from CMS's long-stay antipsychotic quality-measure.

What is the association between student well-being and high-stakes examination scores?

INTRODUCTION: As educators seek to improve medical student well-being, it is essential to understand the interplay between distress and important outcomes. Performance on Step 1 of the United States Medical Licensing Examination has played a significant role in selection for postgraduate residency positions in the United States and consequently has been a source of great stress for medical students. The purpose of this study was to examine whether student well-being correlates with performance on a high stakes licensing examination. METHODS: Between 2014 and 2016, three sequential cohorts of medical students at the University of Michigan Medical School completed the Medical Student Well-Being Index (MSWBI) at the end of their 2nd-year curriculum, shortly before taking Step 1. Associations between well-being and Step 1 scores were investigated while adjusting for MCAT scores and cumulative second-year course scores. RESULTS: In total, 354 students were included in the analysis (68.1% of potential responders). On bivariate analysis, poor student well-being (0 = low distress [high well-being], 7 = high distress [poor well-being]) was associated with lower Step 1 examination scores (slope = -2.10, P < .01), and well-being accounted for 5% of overall Step 1 score variability (R2  = .05). However, after adjustment for MCAT scores and cumulative GPA (full model R2  = .51), the relationship between well-being and Step 1 score was no longer significant (slope = -0.70, P-value = .06). CONCLUSIONS: When controlling for metrics of academic performance, student well-being prior to taking Step 1 was not associated with how well students performed on Step 1 for the study sample.

A natural lead-in approach to response-adaptive allocation for continuous outcomes.

Response-adaptive (RA) allocation designs can skew the allocation of incoming subjects toward the better performing treatment group based on the previously accrued responses. While unstable estimators and increased variability can adversely affect adaptation in early trial stages, Bayesian methods can be implemented with decreasingly informative priors (DIP) to overcome these difficulties. DIPs have been previously used for binary outcomes to constrain adaptation early in the trial, yet gradually increase adaptation as subjects accrue. We extend the DIP approach to RA designs for continuous outcomes, primarily in the normal conjugate family by functionalizing the prior effective sample size to equal the unobserved sample size. We compare this effective sample size DIP approach to other DIP formulations. Further, we considered various allocation equations and assessed their behavior utilizing DIPs. Simulated clinical trials comparing the behavior of these approaches with traditional Frequentist and Bayesian RA as well as balanced designs show that the natural lead-in approaches maintain improved treatment with lower variability and greater power.

Killer Immunoglobulin-Like Receptor-Ligand Interactions Predict Clinical Outcomes following Unrelated Donor Transplantations.

Killer immunoglobulin-like receptor (KIR) and KIR ligand (KIRL) interactions play an important role in natural killer (NK) cell-mediated graft-versus-leukemia effect following hematopoietic cell transplantation (HCT). However, there is considerable heterogeneity in the KIR gene and KIRL content in individuals, making it difficult to estimate the full clinical impact of NK cell reconstitution following HCT. Here we present a novel adaptive mathematical model designed to quantify these interactions to better assess the influence of NK cell-mediated alloreactivity on transplant outcomes. Ninety-eight HLA- matched unrelated donor (URD) HCT recipients were studied retrospectively. The KIR-KIRL interactions were quantified using a system of matrix equations. Unit values were ascribed to each KIR-KIRL interaction, and the directionality of interactions was denoted by either a positive (activating) or negative (inhibition) symbol; these interactions were then summed. The absolute values of both the missing KIRL and inhibitory KIR-KIRL interactions were significantly associated with overall survival and relapse. These score components were initially used to develop a weighted score (w-KIR score) and subsequently a simplified, nonweighted KIR-KIRL interaction score (IM-KIR score). Increased w-KIR score and IM-KIR score were predictive of all-cause mortality and relapse (w-KIR score: hazard ratio [HR], .37 [P = .001] and .44 [P = .044], respectively; IM-KIR score: HR, .5 [P = .049] and .44 [P = .002], respectively). IM-KIR score was also associated with NK cell reconstitution post-HCT. KIR-KIRL interactions as reflected by the w-KIR and IM-KIR scores influence both relapse risk and survival in recipients of HLA-matched URD HCT with hematologic malignancies.

Practice facilitation to promote evidence-based screening and management of unhealthy alcohol use in primary care: a practice-level randomized controlled trial.

BACKGROUND: Unhealthy alcohol use is the third leading cause of preventable death in the United States. Evidence demonstrates that screening for unhealthy alcohol use and providing persons engaged in risky drinking with brief behavioral and counseling interventions improves health outcomes, collectively termed screening and brief interventions. Medication assisted therapy (MAT) is another effective method for treatment of moderate or severe alcohol use disorder. Yet, primary care clinicians are not regularly screening for or treating unhealthy alcohol use. METHODS AND ANALYSIS: We are initiating a clinic-level randomized controlled trial aimed to evaluate how primary care clinicians can impact unhealthy alcohol use through screening, counseling, and MAT. One hundred and 25 primary care practices in the Virginia Ambulatory Care Outcomes Research Network (ACORN) will be engaged; each will receive practice facilitation to promote screening, counseling, and MAT either at the beginning of the trial or at a 6-month control period start date. For each practice, the intervention includes provision of a practice facilitator, learning collaboratives with three practice champions, and clinic-wide information sessions. Clinics will be enrolled for 6-12 months. After completion of the intervention, we will conduct a mixed methods analysis to identify changes in screening rates, increase in provision of brief counseling and interventions as well as MAT, and the reduction of alcohol intake for patients after practices receive practice facilitation. DISCUSSION: This study offers a systematic process for dissemination and implementation of the evidence-based practice of screening, counseling, and treatment for unhealthy alcohol use. Practices will be asked to implement a process for screening, counseling, and treatment based on their practice characteristics, patient population, and workflow. We propose practice facilitation as a robust and feasible intervention to assist in making changes within the practice. We believe that the process can be replicated and used in a broad range of clinical settings; we anticipate this will be supported by our evaluation of this approach. TRIAL REGISTRATION: ClinicalTrials.gov, ClinicalTrials.gov Identifier: NCT04248023, Registered 5 February 2020.

Versatility in multiple mini-interview implementation: Rater background does not significantly influence assessment scoring.

The medical school admissions process seeks to assess a core set of cognitive and non-cognitive competencies that reflect professional readiness and institutional mission alignment. The standardized format of multiple mini-interviews (MMIs) can enhance assessments, and thus many medical schools have switched to this for candidate interviews. However, because MMIs are resource-intensive, admissions deans use a variety of interviewers from different backgrounds/professions. Here, we analyze the MMI process for the 2018 admissions cycle at the VCU School of Medicine, where 578 applicants were interviewed by 126 raters from five distinct backgrounds: clinical faculty, basic science faculty, medical students, medical school administrative staff, and community members. We found that interviewer background did not significantly influence MMI evaluative performance scoring, which eliminates a potential concern about the consistency and reliability of assessment.

Loneliness in Primary Care Patients: A Prevalence Study.

PURPOSE: Loneliness has important health consequences. Little is known, however, about loneliness in primary care patient populations. This study describes the prevalence of loneliness in patients presenting for primary care and associations with self-reported demographic factors, health care utilization, and health-related quality of life. METHODS: We conducted cross-sectional surveys of adults presenting for routine care to outpatient primary care practices in 2 diverse practice-based research networks. The 3-item University of California, Los Angeles Loneliness Scale was utilized to determine loneliness. RESULTS: The prevalence of loneliness was 20% (246/1,235). Loneliness prevalence was inversely associated with age (P <.01) and less likely in those who were married (P <.01) or employed (P <.01). Loneliness was more common in those with lower health status (P <.01), including when adjusting for employment and relationship status (odds ratio [OR] = 1.05; 95% CI, 1.03-1.07). Primary care visits (OR = 1.07; 95% CI, 1.03-1.10), urgent care/emergency department visits (OR = 1.24; 95% CI, 1.12-1.38), and hospitalizations (OR = 1.15; 95% CI, 1.01-1.31) were associated with loneliness status. There was no significant difference in rates of loneliness between sexes (P = .08), racial categories (P = .57), or rural and urban respondents (P = .42). CONCLUSIONS: Our findings demonstrate that loneliness is common in primary care patients and is associated with adverse health consequences including poorer health status and greater health care utilization. Further work is needed to understand the value of screening for and using interventions to treat loneliness in primary care.

Geographic Characteristics of Loneliness in Primary Care.

Loneliness is associated with poor health outcomes, and there is growing attention on loneliness as a social determinant of health. Our study sought to determine the associations between community factors and loneliness. The Three-Item Loneliness Scale and zip codes of residence were collected in primary care practices in Colorado and Virginia. Living in zip codes with higher unemployment, poor access to health care, lower income, higher proportions of blacks, and poor transportation was associated with higher mean loneliness scores. Future studies that examine interventions addressing loneliness may be more effective if they consider social context and community characteristics.

Chronic Opioid Prescribing in Primary Care: Factors and Perspectives.

BACKGROUND: Primary care clinicians write 45% of all opioid prescriptions in the United States, but little is known about the characteristics of patients who receive them and the clinicians who prescribe opioids in primary care settings. Our study aimed to describe the patient and clinician characteristics and clinicians' perspectives of chronic opioid prescribing in primary care. METHODS: Using a mixed methods approach, we completed an analysis of 2016 electronic health records from 21 primary care practices to identify patients who had received chronic opioids, which we defined as in receipt of an opioid prescription for at least 3 consecutive months. We compared those receiving chronic opioids with those not in terms of their demographics, prescribing clinician characteristics, and risk factors for opioid-related harms, as identified by the Centers for Disease Control and Prevention Guideline on Opioid Prescribing for Chronic Pain. We then interviewed 16 primary care clinicians about their perspectives on chronic opioid prescribing. RESULTS: Of 84,029 patients, 1.1% (902/84,929) received chronic opioid prescriptions. Characteristics associated with being prescribed chronic opioids include being female, being of black or African American race, and having risks for opioid-related harms, such as mental health diagnoses, substance use disorder, and concurrent benzodiazepine use. Clinicians report multiple difficulties in weaning patients from chronic opioids, including medical contraindications of nonopioid alternatives and difficulty justifying weaning by stable long-term patients. CONCLUSION: Although patients prescribed opioids in primary care have higher risks of opioid-related harms, clinicians report multiple barriers in deprescribing chronic opioids. Future studies should examine strategies to mitigate these harms and engage patients in shared decision making about their chronic opioid use.

A state-level study of opioid use disorder treatment access and neonatal abstinence syndrome.

BACKGROUND: Adult opioid use and neonatal abstinence syndrome (NAS) are growing public health problems in the United States (U.S.). Our objective was to determine how opioid use disorder treatment access impacts the relationship between adult opioid use and NAS. METHODS: We conducted a cross-sectional state-level ecologic study using 36 states with available Healthcare Cost and Utilization Project State Inpatient Databases in 2014. Opioid use disorder treatment access was determined by the: 1) proportion of people needing but not receiving substance use treatment, 2) density of buprenorphine-waivered physicians, and 3) proportion of individuals in outpatient treatment programs (OTPs). The incidence of NAS was defined as ICD-9 code 779.5 (drug withdrawal syndrome in newborn) from any discharge diagnosis field per 1000 live births in that state. RESULTS: Unmet need for substance use disorder treatment correlated with NAS (r = 0.54, 95% CI: 0.26-0.73). The correlation between adult illicit drug use/dependence and NAS was higher in states with a lower density of buprenorphine-waivered physicians and individuals in OTPs. CONCLUSIONS: Measures of opioid use disorder treatment access dampened the correlation between illicit drug use/dependence and NAS. Future studies using community- or individual-level data may be better poised to answer the question of whether or not opioid use disorder treatment access improves NAS relative to adult opioid use.