Faculty Bibliography

INTRODUCTION/BACKGROUND:Kratom is a plant with partial opioid agonist effects, and its use has become popular to ameliorate symptoms of opioid withdrawal. However, use has been linked to thousands of poisonings, although most have involved use of other drugs. Little is known regarding prevalence and correlates of use in the general U.S. POPULATION/METHODS/: METHODS:Data were examined from the 2019 National Survey on Drug Use and Health, a nationally representative probability sample of non-institutionalized individuals aged ≥12 years in the U.S. (N=56,136). Prevalence and correlates of past-year kratom use were estimated. Data were analyzed in 2020. RESULTS:An estimated 0.7% (95% CI=0.6, 0.8) of individuals in the U.S. have used kratom in the past year. Past-year proxy diagnosis of prescription opioid use disorder was associated with increased odds for kratom use (AOR=3.20, 95% CI=1.38, 7.41), with 10.4% (95% CI=6.7, 15.9) of those with use disorder reporting use. Opioid misuse not accompanied with use disorder was not associated with kratom use. Those reporting past-year cannabis use both with (AOR=4.33, 95% CI=2.61, 7.19) and without (AOR=4.57, 95% CI=3.29, 6.35) use disorder and those reporting past-year cocaine use (AOR=1.69, 95% CI=1.06, 2.69) and prescription stimulant misuse (AOR=2.10, 95% CI=1.44, 3.05) not accompanied with use disorder were at higher odds for kratom use. CONCLUSIONS:Kratom use is particularly prevalent among those with prescription opioid use disorder, but it is also prevalent among people who use other drugs. Research is needed to determine reasons for use and potential dangers associated with adding kratom to drug repertoires.

PURPOSE/OBJECTIVE:Studies have examined trends in cannabis vaping, but research is needed to examine trends in more frequent use as this may increase risk for adverse health outcomes. METHODS:Data were from 12,561 high school seniors participating in the Monitoring the Future national study. Prevalence of self-reported frequent vaping of cannabis (defined as using ≥10 times in the past month) was compared between 2018 and 2019 cohorts. RESULTS:Frequent vaping of cannabis significantly increased from 2.1% to 4.9%, a 131.4% increase. This increase was larger than the increase for any vaping of cannabis (which increased 85.9%). Notable significant increases occurred among students aged ≥18 years (a 154.9% increase), female students (a 183.5% increase), those who go out 4-7 evenings per week (a 163.0% increase), and those reporting past-year nonmedical prescription opioid use (a 184.7% increase). CONCLUSIONS:Frequent vaping of cannabis is increasing among adolescents in the United States, particularly among selected subgroups.

CONTEXT/BACKGROUND:In a previous study we reported that anti-Müllerian hormone (AMH), a marker of ovarian reserve, is positively associated with breast cancer risk, consistent with other studies. OBJECTIVE:Assess whether risk factors for breast cancer are correlates of AMH concentration. DESIGN/METHODS:Cross-sectional. PARTICIPANTS/METHODS:3831 healthy premenopausal women (aged 21-57, 87% aged 35-49). SETTING/METHODS:Ten cohort studies, general population. RESULTS:Adjusting for age and cohort, we observed positive associations of AMH with age at menarche (p<0.0001) and parity (p=0.0008), and an inverse association with hysterectomy/partial oophorectomy (p=0.0008). Compared to women of normal weight (BMI 18.5-24.9 kg/m 2, AMH was lower (relative geometric mean difference 27%, p<0.0001) among women who were obese (BMI>30). Current oral contraceptive use and current/former smoking were associated with lower AMH concentration than never use (40% and 12% lower, respectively, p<0.0001). We observed higher AMH concentrations among women who had had a benign breast biopsy (15% higher, p=0.03), a surrogate for benign breast disease, an association that has not been reported. In analyses stratified by age (<40/≥40), associations of AMH with BMI and oral contraceptives were similar in younger and older women, while associations with the other factors (menarche, parity, hysterectomy/partial oophorectomy, smoking, and benign breast biopsy) were limited to women ≥40 (p-interaction<0.05). CONCLUSION/CONCLUSIONS:This is the largest study of AMH and breast cancer risk factors among women from the general population (not presenting with infertility), and suggests that most of the associations are limited to women over 40, who are approaching menopause and whose AMH concentration is declining.

Intimate partner violence (IPV) is prevalent among young sexual and gender minorities assigned male at birth (YSGM-AMAB). However, few studies have examined the chronicity or distinguished between minor and severe forms of IPV among YSGM-AMAB. Furthermore, while past research has documented differences in IPV by race/ethnicity, sexual identity, gender identity, income, and education in other populations, few studies have examined these sociodemographic characteristics in relation to IPV in YSGM-AMAB. Thus, the present study aims to: (1) estimate past year prevalence and chronicity of minor and severe forms of IPV victimization and perpetration in a diverse sample of (N = 665) YSGM-AMAB in New York City, and (2) examine differences in IPV prevalence and chronicity by the aforementioned sociodemographic characteristics. Cross-sectional data from [BLINDED] informed these descriptive and inferential analyses. Nearly half of all participants reported past year IPV victimization and approximately 40% reported perpetration. Psychological violence was the most common form of victimization, followed by sexual, physical, and injury victimization. Psychological violence was the most common form of perpetration, followed by physical, sexual, and injury perpetration. Regarding sociodemographic differences in last year IPV prevalence, bisexual, transgender, and lower income YSGM-AMAB were more likely to report several subtypes of IPV victimization. Whereas Asian/API, bisexual, transgender, and lower income participants were more likely to report several subtypes of IPV perpetration. Regarding last year IPV chronicity, non-graduate YSGM-AMAB reported more instances of two subtypes of IPV victimization, while Black, White, cisgender, upper income, non-graduate participants reported more instances of several subtypes of IPV perpetration. These findings may be used to develop IPV prevention and intervention programs, inform future research endeavors, and develop and strengthen policies that reduce sociodemographic inequalities and promote more favorable sociopolitical conditions for YSGM-AMAB.

Objectives/UNASSIGNED:To understand how patients and family members experience dehumanizing or humanizing treatment when in the ICU. Design/UNASSIGNED:Qualitative study included web-based focus groups and open-ended surveys posted to ICU patient/family social media boards. Focus groups were audio recorded and transcribed. Social media responses were collected and organized by stakeholder group. Data underwent qualitative analysis. Setting/UNASSIGNED:Remote focus groups and online surveys. Patients/UNASSIGNED:ICU patient survivors, family members, and ICU teams. Interventions/UNASSIGNED:Not available. Measurements and Main Results/UNASSIGNED:Semi-structured questions and open-ended survey responses. We enrolled 40 patients/family members and 31 ICU team members. Focus groups and surveys revealed three primary themes orienting humanizing/dehumanizing ICU experiences: 1) communication, 2) outcomes, and 3) causes of dehumanization. Dehumanization occurred during "communication" exchanges when ICU team members talked "over" patients, made distressing remarks when patients were present, or failed to inform patients about ICU-related care. "Outcomes" of dehumanization were associated with patient loss of trust in the medical team, loss of motivation to participate in ICU recovery, feeling of distress, guilt, depression, and anxiety. Humanizing behaviors were associated with improved recovery, well-being, and trust. "Perceived causes" of dehumanizing behaviors were linked to patient, ICU team, and healthcare system factors. CONCLUSIONS:Behaviors of ICU clinicians may cause patients and families to feel dehumanized when in the ICU. Negative behaviors are noticed by patients and families, possibly contributing to poor outcomes including mental health, recovery, and lack of trust in ICU teams. Supporting ICU clinicians may enable a more empathic environment and in turn more humanizing clinician-patient encounters.

As the world faced the devastation of the COVID-19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID-19 encountered at participating sites. It has become clear that it might take several more COVID-19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient-level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta-analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID-19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

Rationale: Only nine studies in the current literature focus on the effects of air pollution exposures among patients with Interstitial Lung Diseases (ILD), most of which focus on idiopathic pulmonary fibrosis (IPF). Even less is known about the effects of air pollution exposure on non-IPF ILDs and particularly in populations that are not in major metropolitan regions in the US, where there is a sparse distribution of air pollution monitoring sites.
Method(s): A study cohort of 1,365 patients with ILD (including non-IPF ILDs) living across the Intermountain West region of the US were followed at the University of Utah, with hospitalization data collected from 2009 to 2018. Daily concentrations of major pollutants (PM2.5, NO2, and O3) were calculated using two methods of exposure assessment: 1) weighted mean concentrations from all EPA monitors within a 20km radius of the patient with missing data filled in using imputed values, and 2) using a validated spatiotemporal model, the Optimal Interpolation (OI) model, that integrates satellite remote sensing data and EPA monitoring data with modeled concentrations. A time-stratified, case-crossover study was performed using a 14-day interval to estimate the association between short-term exposure to air pollution and hospitalizations. Conditional logistic regression models were used to estimate adjusted odds ratios (aORs) per interquartile range (IQR) of the pollutant and reported for a 4-day average lag structure from lag days 0 through 3, controlling for daily apparent temperature and holidays. Results were stratified by season, where season was determined by high (March to October) and low (November to February) ozone months.
Result(s): We evaluated 1,277 hospitalizations, and neither exposure assessment model found those to be significantly associated with increased concentrations of PM2.5 or O3. However, the OI model found that in the colder season, an IQR increase in daily average NO2 exposure is associated with 1.5 times the odds of being hospitalized (aOR: 1.53, 95% CI: 1.03, 2.35). These odds are significantly more pronounced in males (aOR: 2.50, 95% CI: 1.14, 5.50).
Conclusion(s): This analysis provides important insight on the impacts of short-term air pollution exposure on hospitalizations in patients with ILD. Improved exposure assessment using an integrated pollution model was critical to identifying these associations due to the sparse distribution of monitors, particularly for pollutants like NO2. Further research using ILD patient populations will help transition clinical guidance regarding air pollution exposures from common sense recommendations to evidence-based guidance

OBJECTIVE:To characterize the general health status of infertile men in the United States using a nationally representative sample of men. METHODS:Using the National Survey for Family Growth from 2011 to 2017, infertile subgroups were created using a range of inclusion criteria. Univariate and multivariate analyses were conducted comparing these men to fertile men. RESULTS:Using population estimates, 6.5 million men with reduced fertility potential were compared to 26 million fertile men. After controlling for demographic and healthcare utilization factors, these groups did not have significantly different rates of key medical co-morbidities, including cancer, obesity, and overall disability. Looking at the subset of men who had received a specific infertility diagnosis, estimated as a population of nearly 600,000 men, this pattern held, in that there were no significant differences in the rates of medical co-morbidities. Notably, the rate of male infertility evaluation among potentially infertile men was only 50%. These findings also persisted after a propensity-matched analysis. CONCLUSIONS:In this cohort, there was no significant relationship between infertility and specific medical co-morbidities. We must consider the influence of sample selection as we continue to investigate the relationship between medical co-morbidities and reduced fertility potential. Given the persistent low rates of infertility evaluation, even among men who seek medical advice to conceive, we must continue to search for ways to characterize the infertile male population while simultaneously working to improve access.