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Many believe a major cause of the epidemic of clinician burnout is poorly designed electronic health records (EHRs). To determine which EHR design and use factors are associated with clinician stress and burnout and to identify other sources that contribute to this problem. This survey study of 282 ambulatory primary care and subspecialty clinicians from 3 institutions measured stress and burnout, opinions on EHR design and use factors, and helpful coping strategies. Linear and logistic regressions were used to estimate associations of work conditions with stress on a continuous scale and burnout as a binary outcome from an ordered categorical scale. The survey was conducted between August 2016 and July 2017, with data analyzed from January 2019 to May 2019. Clinician stress and burnout as measured with validated questions, the EHR design and use factors identified by clinicians as most associated with stress and burnout, and measures of clinician working conditions. Of 640 clinicians, 282 (44.1%) responded. Of these, 241 (85.5%) were physicians, 160 (56.7%) were women, and 193 (68.4%) worked in primary care. The most prevalent concerns about EHR design and use were excessive data entry requirements (245 [86.9%]), long cut-and-pasted notes (212 [75.2%]), inaccessibility of information from multiple institutions (206 [73.1%]), notes geared toward billing (206 [73.1%]), interference with work-life balance (178 [63.1%]), and problems with posture (144 [51.1%]) and pain (134 [47.5%]) attributed to the use of EHRs. Overall, EHR design and use factors accounted for 12.5% of variance in measures of stress and 6.8% of variance in measures of burnout. Work conditions, including EHR use and design factors, accounted for 58.1% of variance in stress; key work conditions were office atmospheres (β̂ = 1.26; P < .001), control of workload (for optimal control: β̂ = -7.86; P < .001), and physical symptoms attributed to EHR use (β̂ = 1.29; P < .001). Work conditions accounted for 36.2% of variance in burnout, where challenges included chaos (adjusted odds ratio, 1.39; 95% CI, 1.10-1.75; P = .006) and physical symptoms perceived to be from EHR use (adjusted odds ratio, 2.01; 95% CI, 1.48-2.74; P < .001). Coping strategies were associated with only 2.4% of the variability in stress and 1.7% of the variability in burnout. Although EHR design and use factors are associated with clinician stress and burnout, other challenges, such as chaotic clinic atmospheres and workload control, explain considerably more of the variance in these adverse clinician outcomes.

The COVID-19 pandemic accelerated the adoption of telehealth (TH) for substance use disorder (SUD) treatment, but disparities in TH access remain. Using data from the 2021 and 2022 National Survey on Drug Use and Health (NSDUH), we analyzed demographic and socioeconomic predictors of TH utilization among adults receiving SUD treatment. It should be noted that NSDUH 2021 and NSDUH 2022 are not comparable for the aims of the current study due to necessary changes to substance use treatment survey questions. In 2021, educational attainment showed a strong dose-response association with TH use. By 2022, significant disparities emerged based on age, race/ethnicity, insurance type, and employment status. Non-Hispanic Black and Non-Hispanic Asian/Hawaiians/Pacific Islanders individuals had significantly lower odds of receiving TH care, compared to non-Hispanic Whites, while Medicaid users had higher odds than those with private insurance. Our findings indicate that TH use for SUD treatment is shaped by evolving structural determinants. To promote equitable access, public health policies must address digital divides, improve cultural competence, and support infrastructure expansion. Targeted strategies are essential to avoid deepening existing inequities in SUD care.

Background The relationship between economic conditions and substance abuse is unclear, with few studies reporting drug-specific substance abuse. The present study examined the association between economic conditions and drug-specific substance abuse admissions. Methods State annual administrative data were drawn from the 1993–2016 Treatment Episode Data Set. The outcome variable was state-level aggregate number of treatment admissions for six categories of primary substance abuse (alcohol, marijuana/hashish, opiates, cocaine, stimulants, and other drugs). Additionally, we used a broader outcome for the number of treatment admissions, including primary, secondary, and tertiary diagnoses. We used a quasi-experimental approach -difference-in-difference model- to estimate the association between changes in economic conditions and substance abuse treatment admissions, adjusting for state characteristics. In addition, we performed two additional analyses to investigate (1) whether economic conditions have an asymmetric effect on the number of substance use admissions during economic downturns and upturns, and (2) the moderation effects of economic recessions (2001, 2008–09) on the relationship between economic conditions and substance use treatment. Results The baseline model showed that unemployment rate was significantly associated with substance abuse treatment admissions. A unit increase in state unemployment rate was associated with a 9% increase in treatment admissions for opiates (β = 0.087, p  < .001). Similar results were found for other substance abuse treatment admissions (cocaine (β = 0.081, p  < .001), alcohol (β = 0.050, p  < .001), marijuana (β = 0.036, p  < .01), and other drugs (β = 0.095, p <  .001). Unemployment rate was negatively associated with treatment admissions for stimulants (β = − 0.081, p  < .001). The relationship between unemployment rate and opioids treatment admissions was not statistically significant in models that adjusted for state fixed effects and allowed for a state- unique time trend. We found that the association between state unemployment rates and annual substance abuse admissions has the same direction during economic downturns and upturns. During the economic recession, the negative association between unemployment rate and treatment admissions for stimulants was weakened. Conclusion These findings suggest that economic hardship may have increased substance abuse. Treatment for substance use of certain drugs and alcohol should remain a priority even during economic downturns.

Introduction Disruption of daily routines (employment, social interaction, health behaviors) during the COVID-19 pandemic has contributed to psychological distress (worry, rumination), likely impacting sleep-related behaviors. This study evaluated change in psychological adjustment and insomnia symptoms during the COVID-19 pandemic. Methods The sample included 192 adults from Utah who completed three data collection cycles across 9 consecutive months to assess self-reported depressive, anxiety, and insomnia symptoms. Anxiety and depressive symptoms were assessed via the Generalized Anxiety Disorder scale (GAD-7) and Patient Health Questionnaire depression scale (PHQ-8). Insomnia was measured by the Insomnia Severity Index (ISI). Data were analyzed using mixed-effect modeling and adjusted for anxiety and depression to determine their independent effects on insomnia symptoms. Spaghetti plots examined mean changes over time and significance was set at p<0.05. Average anxiety, depression, and insomnia severity scores were aggregated for each month. Results As participants’ symptoms of anxiety and depression increased in severity, insomnia symptoms increased similarly. Over half of participants reported clinically significant ISI scores (59.38%). In both the random intercept and random slope models, there were significant independent effects of anxiety on insomnia severity (F=20.69; p<0.0001) and significant effects of depression on insomnia severity (F=87.44, p<0.0001). While the change in insomnia severity over time was on the boundary of statistical significance (F=3.54; p=0.0618), dropping from 15.17 (April) to 12.58 (December), our longitudinal analyses revealed no significant difference for the effect of anxiety or depression in predicting insomnia severity over time. Participants’ monthly averages varied for sleep and psychological scores (ISI) from 12.58 to 16.07 (SD=3.76 to 6.34 for December and September, respectively), (GAD-7) from 3.47 to 6.39 (SD=3.36 to 5.26 for December and June, respectively), and (PHQ-8) 4.47 to 6.10 (SD=4.65 to 4.39 for December and June, respectively). Conclusion Results demonstrate high prevalence of insomnia symptoms during the COVID-19 pandemic and underscore the importance of examining mental health functioning and psychological resiliency on sleep in order to enhance prevention efforts in response to a significant stressor. Support (if any):

Background Pregnant women are potentially a high-risk population during infectious disease outbreaks such as COVID-19, because of physiologic immune suppression in pregnancy. However, data on the morbidity and mortality of COVID-19 among pregnant women, compared to nonpregnant women, are sparse and inconclusive. We sought to assess the impact of pregnancy on COVID-19 associated morbidity and mortality, with particular attention to the impact of pre-existing comorbidity. Methods We used retrospective data from January through June 2020 on female patients aged 18–44 years old utilizing the Cerner COVID-19 de-identified cohort. We used mixed-effects logistic and exponential regression models to evaluate the risk of hospitalization, maximum hospital length of stay (LOS), moderate ventilation, invasive ventilation, and death for pregnant women while adjusting for age, race/ethnicity, insurance, Elixhauser AHRQ weighted Comorbidity Index, diabetes history, medication, and accounting for clustering of results in similar zip-code regions. Results Out of 22,493 female patients with associated COVID-19, 7.2% ( n  = 1609) were pregnant. Crude results indicate that pregnant women, compared to non-pregnant women, had higher rates of hospitalization (60.5% vs. 17.0%, P  < 0.001), higher mean maximum LOS (0.15 day vs. 0.08 day, P  < 0.001) among those who stayed < 1 day, lower mean maximum LOS (2.55 days vs. 3.32 days, P  < 0.001) among those who stayed ≥1 day, and higher moderate ventilation use (1.7% vs. 0.7%, P  < 0.001) but showed no significant differences in rates of invasive ventilation or death. After adjusting for potentially confounding variables, pregnant women, compared to non-pregnant women, saw higher odds in hospitalization (aOR: 12.26; 95% CI (10.69, 14.06)), moderate ventilation (aOR: 2.35; 95% CI (1.48, 3.74)), higher maximum LOS among those who stayed < 1 day, and lower maximum LOS among those who stayed ≥1 day. No significant associations were found with invasive ventilation or death. For moderate ventilation, differences were seen among age and race/ethnicity groups. Conclusions Among women with COVID-19 disease, pregnancy confers substantial additional risk of morbidity, but no difference in mortality. Knowing these variabilities in the risk is essential to inform decision-makers and guide clinical recommendations for the management of COVID-19 in pregnant women.

To assess the risk of new-onset type 1 diabetes mellitus (T1D) diagnosis following COVID-19 diagnosis and the impact of COVID-19 diagnosis on the risk of diabetic ketoacidosis (DKA) in patients with prior T1D diagnosis. Retrospective data consisting of 27,292,879 patients from the Cerner Real-World Data were used. Odds ratios, overall and stratified by demographic predictors, were calculated to assess associations between COVID-19 and T1D. Odds ratios from multivariable logistic regression models, adjusted for demographic and clinical predictors, were calculated to assess adjusted associations between COVID-19 and DKA. Multiple imputation with multivariate imputation by chained equations (MICE) was used to account for missing data. The odds of developing new-onset T1D significantly increased in patients with COVID-19 diagnosis (OR: 1.42, 95% CI: 1.38, 1.46) compared to those without COVID-19. Risk varied by demographic groups, with the largest risk among pediatric patients ages 0-1 years (OR: 6.84, 95% CI: 2.75, 17.02) American Indian/Alaskan Natives (OR: 2.30, 95% CI: 1.86, 2.82), Asian or Pacific Islanders (OR: 2.01, 95% CI: 1.61, 2.53), older adult patients ages 51-65 years (OR: 1.77, 95% CI: 1.66, 1.88), those living in the Northeast (OR: 1.71, 95% CI: 1.61, 1.81), those living in the West (OR: 1.65, 95% CI: 1.56, 1.74), and Black patients (OR: 1.59, 95% CI: 1.47, 1.71). Among patients with diagnosed T1D at baseline (n = 55,359), 26.7% (n = 14,759) were diagnosed with COVID-19 over the study period. The odds of developing DKA for those with COVID-19 were significantly higher (OR 2.26, 95% CI: 2.04, 2.50) than those without COVID-19, and the largest risk was among patients with higher Elixhauser Comorbidity Index. COVID-19 diagnosis is associated with significantly increased risk of new-onset T1D, and American Indian/Alaskan Native, Asian/Pacific Islander, and Black populations are disproportionately at risk. In patients with pre-existing T1D, the risk of developing DKA is significantly increased following COVID-19 diagnosis.

BackgroundTobacco use is a long-standing epidemic that has caused millions of premature deaths. Electronic cigarette use is rising among young adults, yet few studies have included combustible tobacco, e-cigarettes and their dual use in an analysis of binge drinking among college students. This study aims to calculate the associations between these three forms of tobacco use and binge drinking among college students.MethodsThis cross-sectional study used data from 332 721 college students from the American College Health Association-National College Health Assessment survey. Tobacco use was organised into four groups: no tobacco use, e-cigarette use only, combustible tobacco use only and dual use of e-cigarettes and combustible tobacco. Binge drinking was a binary indication of whether students had engaged in binge drinking in the last 2 weeks. Multiple logistic regression was employed to examine the relationship between tobacco use and binge drinking, adjusting for demographic factors and constructs of the integrated behavioural model.ResultsEach tobacco use group had significantly higher odds of binge drinking compared with students who did not use tobacco. Students who engaged in dual tobacco use had significantly higher odds of binge drinking compared with exclusive combustible tobacco use (adjusted OR, aOR=2.41, 95% CI: 2.29, 2.53) and exclusive e-cigarette use (aOR=1.79, 95% CI: 1.71, 1.86).ConclusionThe strong relationship between dual tobacco use and binge drinking among college students warrants further investigation into the aetiology and clinical manifestations of this emerging coupled substance use behaviour among the next generation of adults.

Purpose While smoking prevalence may be declining in the general population, health disparities in tobacco use remain a public health priority. This study examined national, sociodemographic, and geographic trends in American Indians and Alaska Natives (AIs/ANs) smoking prevalence from 1992/1993 to 2014/2015. Additionally, correlates of cigarette smoking were examined among this group. Methods Data were drawn from the 1992–2015 Tobacco Use Supplement to the Current Population Survey. Cochran–Armitage tests were used to assess changes in the prevalence of smoking over time in the population, as well by sociodemographic characteristics. Multivariable logistic regression was conducted to examine the correlates of cigarette smoking for AIs/ANs in 2014/2015. Results The trend analysis indicated that the prevalence of smoking, among AIs/ANs, decreased significantly from 39.1% in the 1992/1993 cycle to 20.9% in the 2014/2015. This decrease was seen in both males and females, with the prevalence of smoking decreasing from 43.6% and 35.4%, respectively, in 2006/2007 to 23.8% and 18.3% in 2014/2015. The decreasing trend was also found for all subgroups, except for the 55+ age group. Multivariable analysis showed higher odds of smoking among males, those with low income compared to those with median or higher income, and those living in non-metropolitan areas. Those aged 25–54 were more likely to be smokers compared with the 55+ age group. Conclusions Results indicate a recent decrease in AIs/ANs smoking prevalence, although these populations still experience a high prevalence of smoking compared to the general population. Our findings highlight the need for a comprehensive tobacco control strategy that includes working with stakeholders within the AI/AN community.

Objective This study examines the relationship between opioid use disorder (OUD) and healthcare use, especially regarding length of stay, total charges, and costs in U.S. hospitals from 2005 to 2020. Methods We used the Healthcare Cost & Utilization Projects (HCUP) National Inpatient Sample (NIS) data to compare these outcomes between patients with and without OUD. We applied generalized linear modeling (GLM) with gamma distribution and log link to assess the effect of OUD on the three outcomes. Results Our results show that hospital stays for patients with OUD were significantly longer, while total charges and costs were lower than those without OUD. Over time, there was a tendency towards convergence between total charges and costs for OUD and non-OUD patients. The study also revealed that the severity of illness was strongly related to length of stay, total charge, and total cost, and OUD patients with greater illness severity and comorbid conditions demonstrated increased outcomes compared to those without OUD, with increased total costs and charges in 2020. Conclusions Our results offer important insights into the healthcare impact of OUD. Future studies should use patient-level data to better understand the overall healthcare use per person rather than per hospital stay, as well as more recent years of data to study greater Covid-19 specific impacts. Implications The study emphasizes the need for more efforts to decrease the prevalence of OUD in the U.S. to help ease the pressure on the healthcare system. It also demonstrates the potential influence of the severity of illness and comorbidity on healthcare use, suggesting a need for specific interventions for patients with severe conditions.

Cytokine storm syndrome in patients with COVID-19 is mediated by pro-inflammatory cytokines resulting in acute lung injury and multiorgan failure. Elevation in serum ferritin and D-dimer is observed in COVID-19 patients. To determine prognostic values of optimal serum cutoff with trajectory plots for both serum ferritin and D-dimer in COVID-19 patients with invasive ventilator dependence and in-hospital mortality. We used retrospective longitudinal data from the Cerner COVID-19 de-identified cohort. COVID-19 infected patients with valid repeated values of serum ferritin and D-dimer during hospitalization were used in mixed-effects logistic-regression models. Among 52,411 patients, 28.5% (14,958) had valid serum ferritin and 28.6% (15,005) D-dimer laboratory results. Optimal cutoffs of ferritin (714 ng/mL) and D-dimer (2.1 mg/L) revealed AUCs ≥ 0.99 for in-hospital mortality. Optimal cutoffs for ferritin (502 ng/mL) and D-dimer (2.0 mg/L) revealed AUCs ≥ 0.99 for invasive ventilator dependence. Optimal cutoffs for in-house mortality, among females, were lower in serum ferritin (433 ng/mL) and D-dimer (1.9 mg/L) compared to males (740 ng/mL and 2.5 mg/L, respectively). Optimal cutoffs for invasive ventilator dependence, among females, were lower in ferritin (270 ng/mL) and D-dimer (1.3 mg/L) compared to males (860 ng/mL and 2.3 mg/L, respectively). Optimal prognostic cutoffs for serum ferritin and D-dimer require considering the entire trajectory of laboratory values during the disease course. Females have an overall lower optimal cutoff for both serum ferritin and D-dimer. The presented research allows health professionals to predict clinical outcomes and appropriate allocation of resources during the COVID-19 pandemic, especially early recognition of COVID-19 patients needing higher levels of care.