Faculty Bibliography

BACKGROUND:Statins are the third most prescribed drug class in kidney transplant recipients as cardiovascular disease is the leading cause of death in this population. However, statins' safety profile remains unclear in kidney transplant recipients who are uniquely burdened by concomitant immunosuppression and comorbidities. We conducted a national study to characterize the association of statin use with adverse events in kidney transplant recipients. METHODS:We studied adult (18 years or older) single-organ kidney transplant recipients in 2006-2016 with Medicare as primary payer ( n =57,699). We used prescription drug claims to capture statin use and International Classification of Diseases 9/10 diagnosis codes to capture statin-related adverse events (post-transplant diabetes mellitus, hemorrhagic stroke, cataract, liver injury, and rhabdomyolysis). We conducted multivariable Cox regression for each outcome with statin use as a time-varying exposure. RESULTS:Post-transplant diabetes mellitus was the most common outcome (5-year Kaplan-Meier incidence; 43% in statin users versus 35% in nonusers), followed by cataract (22% versus 12%), liver injury (2% versus 3%), hemorrhagic stroke (1.9% versus 1.4%), and rhabdomyolysis (1.5% versus 0.9%). In our multivariable analysis, statin use was associated with higher hazard of post-transplant diabetes mellitus (adjust hazard ratio [aHR], 1.12; 95% confidence interval [95% CI], 1.07 to 1.18), cataract (aHR, 1.22; 95% CI, 1.14 to 1.31), and rhabdomyolysis (aHR, 1.37; 95% CI, 1.10 to 1.71) but lower hazard of liver injury (aHR, 0.82; 95% CI, 0.71 to 0.95). Statin use was not associated with hemorrhagic stroke (aHR, 1.04; 95% CI, 0.86 to 1.26). CONCLUSIONS:Statins seem to be generally well tolerated in kidney transplant recipients. However, statin use might be associated with slightly higher risk of post-transplant diabetes mellitus, cataract, and rhabdomyolysis.

IMPORTANCE:Appreciation for the effects of neighborhood conditions and community factors on perinatal health is increasing. However, community-level indices specific to maternal health and associations with preterm birth (PTB) have not been assessed. OBJECTIVE:To examine the association of the Maternal Vulnerability Index (MVI), a novel county-level index designed to quantify maternal vulnerability to adverse health outcomes, with PTB. DESIGN, SETTING, AND PARTICIPANTS:This retrospective cohort study used US Vital Statistics data from January 1 to December 31, 2018. Participants included 3 659 099 singleton births at 22 plus 0/7 to 44 plus 6/7 weeks of gestation born in the US. Analyses were conducted from December 1, 2021, through March 31, 2023. EXPOSURE:The MVI, a composite measure of 43 area-level indicators, categorized into 6 themes reflecting physical, social, and health care landscapes. Overall MVI and theme were stratified by quintile (very low to very high) by maternal county of residence. MAIN OUTCOMES AND MEASURES:The primary outcome was PTB (gestational age <37 weeks). Secondary outcomes were PTB categories: extreme (gestational age ≤28 weeks), very (gestational age 29-31 weeks), moderate (gestational age 32-33 weeks), and late (gestational age 34-36 weeks). Multivariable logistic regression quantified associations of MVI, overall and by theme, with PTB, overall and by PTB category. RESULTS:Among 3 659 099 births, 298 847 (8.2%) were preterm (male, 51.1%; female, 48.9%). Maternal race and ethnicity included 0.8% American Indian or Alaska Native, 6.8% Asian or Pacific Islander, 23.6% Hispanic, 14.5% non-Hispanic Black, 52.1% non-Hispanic White, and 2.2% with more than 1 race. Compared with full-term births, MVI was higher for PTBs across all themes. Very high MVI was associated with increased PTB in unadjusted (odds ratio [OR], 1.50 [95% CI, 1.45-1.56]) and adjusted (OR, 1.07 [95% CI, 1.01-1.13]) analyses. In adjusted analyses of PTB categories, MVI had the largest association with extreme PTB (adjusted OR, 1.18 [95% CI, 1.07-1.29]). Higher MVI in the themes of physical health, mental health and substance abuse, and general health care remained associated with PTB overall in adjusted models. While the physical health and socioeconomic determinant themes were associated with extreme PTB, physical health, mental health and substance abuse, and general health care themes were associated with late PTB. CONCLUSIONS AND RELEVANCE:The findings of this cohort study suggest that MVI was associated with PTB even after adjustment for individual-level confounders. The MVI is a useful measure for county-level PTB risk that may have policy implications for counties working to lower preterm rates and improve perinatal outcomes.

The Internet is a common source of sleep information, but may be subject to commercial bias and misinformation. We compared the understandability, information quality, and presence of misinformation of popular YouTube videos on sleep to videos with credible experts. We identified the most popular YouTube videos on sleep/insomnia and 5 videos from experts. Videos were assessed for understanding and clarity using validated instruments. Misinformation and commercial bias were identified by consensus of sleep medicine experts. The most popular videos received on average 8.2 (±2.2) million views; the expert-led videos received on average 0.3 (±0.2) million views. Commercial bias was identified in 66.7% of popular videos and 0% of expert videos (p<0.012). The popular videos featured more misinformation than expert videos (p<0.001). The popular videos about sleep/insomnia on YouTube featured misinformation and commercial bias. Future research may explore methods for disseminating evidence-based sleep information.

OBJECTIVE:Electronic cigarettes are the most commonly used tobacco products by young adults. Measures of beliefs about outcomes of use (i.e., expectancies) can be helpful in predicting use, as well as informing and evaluating interventions to impact use. METHODS:We surveyed young adult students (N = 2296, Mean age=20.0, SD=1.8, 64 % female, 34 % White) from a community college, a historically black university, and a state university. Students answered ENDS expectancy items derived from focus groups and expert panel refinement using Delphi methods. Factor Analysis and Item Response Theory (IRT) methods were used to understand relevant factors and identify useful items. RESULTS:A 5-factor solution [Positive Reinforcement (consists of Stimulation, Sensorimotor, and Taste subthemes, α = .92), Negative Consequences (Health Risks and Stigma, α = .94), Negative Affect Reduction (α = .95), Weight Control (α = .92), and Addiction (α = .87)] fit the data well (CFI=0.95; TLI=0.94; RMSEA=0.05) and was invariant across subgroups. Factors were significantly correlated with relevant vaping measures, including vaping susceptibility and lifetime vaping. Hierarchical linear regression demonstrated factors were significant predictors of lifetime vaping after controlling for demographics, vaping ad exposure, and peer/family vaping. IRT analyses indicated that individual items tended to be related to their underlying constructs (a parameters ranged from 1.26 to 3.18) and covered a relatively wide range of the expectancies continuum (b parameters ranged from -0.72 to 2.47). CONCLUSIONS:A novel ENDS expectancy measure appears to be a reliable measure for young adults with promising results in the domains of concurrent validity, incremental validity, and IRT characteristics. This tool may be helpful in predicting use and informing future interventions. IMPLICATIONS:Findings provide support for the future development of computerized adaptive testing of vaping beliefs. Expectancies appear to play a role in vaping similar to smoking and other substance use. Public health messaging should target expectancies to modify young adult vaping behavior.

IMPORTANCE:Treatment challenges exist for younger adults with type 1 (T1D) and type 2 diabetes (T2D). Health care coverage, access to, and use of diabetes care are not well delineated in these high-risk populations. OBJECTIVE:To compare patterns of health care coverage, access to, and use of diabetes care and determine their associations with glycemia among younger adults with T1D and with T2D. DESIGN, SETTING, AND PARTICIPANTS:This cohort study analyzed data from a survey that was jointly developed by 2 large, national cohort studies: the SEARCH for Diabetes in Youth (SEARCH) study, an observational study of individuals with youth-onset T1D or T2D, and the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study, a randomized clinical trial (2004-2011) followed by an observational study (2012-2020). The interviewer-directed survey was administered during in-person study visits in both studies between 2017 and 2019. Data analyses were performed between May 2021 and October 2022. MAIN OUTCOMES AND MEASURES:Survey questions addressed health care coverage, usual sources of diabetes care, and frequency of care use. Glycated hemoglobin (HbA1c) levels were assayed in a central laboratory. Patterns of health care factors and HbA1c levels were compared by diabetes type. RESULTS:The analysis included 1371 participants (mean [range] age, 25 [18-36] years; 824 females [60.1%]), of whom 661 had T1D and 250 had T2D from the SEARCH study and 460 had T2D from the TODAY study. Participants had a mean (SD) diabetes duration of 11.8 (2.8) years. More participants with T1D than T2D in both the SEARCH and TODAY studies reported health care coverage (94.7%, 81.6%, and 86.7%), access to diabetes care (94.7%, 78.1%, and 73.4%), and use of diabetes care (88.1%, 80.5%, and 73.6%). Not having health care coverage was associated with significantly higher mean (SE) HbA1c levels in participants with T1D in the SEARCH study (no coverage, 10.8% [0.5%]; public, 9.4% [0.2%]; private, 8.7% [0.1%]; P < .001) and participants with T2D from the TODAY study (no coverage, 9.9% [0.3%]; public, 8.7% [0.2%]; private, 8.7% [0.2%]; P = .004). Medicaid expansion vs without expansion was associated with more health care coverage (participants with T1D: 95.8% vs 90.2%; participants with T2D in SEARCH: 86.1% vs 73.9%; participants with T2D in TODAY: 93.6% vs 74.2%) and lower HbA1c levels (participants with T1D: 9.2% vs 9.7%; participants with T2D in SEARCH: 8.4% vs 9.3%; participants with T2D in TODAY: 8.7% vs 9.3%). The T1D group incurred higher median (IQR) monthly out-of-pocket expenses than the T2D group ($74.50 [$10.00-$309.00] vs $10.00 [$0-$74.50]). CONCLUSIONS AND RELEVANCE:Results of this study suggested that lack of health care coverage and of an established source of diabetes care were associated with significantly higher HbA1c levels for participants with T1D, but inconsistent results were found for participants with T2D. Increased access to diabetes care (eg, through Medicaid expansion) may be associated with improved health outcomes, but additional strategies are needed, particularly for individuals with T2D.

BACKGROUND AND OBJECTIVES/OBJECTIVE:To inform clinical practice, we identified subgroups of adults based on levels of depression symptomatology over time during opioid use disorder (OUD) treatment. METHODS:Participants were 474 adults in a 24-week treatment trial for OUD. Depression symptoms were measured using the 17-item Hamilton Depression Rating Scale (HAM-D) at nine-time points. This was a secondary analysis of the Clinical Trials Network Extended-Release Naltrexone versus Buprenorphine for Opioid Treatment (XBOT) trial using a growth mixture model. RESULTS:Three distinct depression trajectories were identified: Class 1 High Recurring-10% with high HAM-D with initial partial reductions (of HAM-D across time), Class 2 Persistently High-5% with persistently high HAM-D, and Class 3 Low Declining-85% of the participants, with low HAM-D with early sustained reductions. The majority (low declining) had levels of depression that improved in the first 4 weeks and then stabilized across the treatment period. In contrast, 15% (high recurring and persistently high) had high initial levels that were more variable across time. The persistently high class had higher rates of opioid relapse. DISCUSSION AND CONCLUSIONS/CONCLUSIONS:In this OUD sample, most depressive symptomatology was mild and improved after medication treatment for opioid use disorder (MOUD). Smaller subgroups had higher depressive symptoms that persisted or recurred after the initiation of MOUD. Depressive symptoms should be followed in patients initiating treatment for OUD, and when persistent, should prompt further evaluation and consideration of antidepressant treatment. SCIENTIFIC SIGNIFICANCE/CONCLUSIONS:This study is the first to identify three distinct depression trajectories among a large clinical sample of individuals in MOUD treatment.

BACKGROUND:Drug use is prevalent among people who attend electronic dance music (EDM) parties at nightclubs or festivals. This population can serve as a sentinel population to monitor trends in use of party drugs and new psychoactive substances (NPS) that may diffuse through larger segments of the population. METHODS:We surveyed adults entering randomly selected EDM parties at nightclubs and dance festivals in New York City about their drug use in 2017 (n=954), 2018 (n=1,029), 2019 (n=606), 2021 (n=229), and 2022 (n=419). We estimated trends in past-year and past-month use of 22 drugs or drug classes based on self-report from 2017-2022 and examined whether there were shifts pre- vs. post-COVID (2017-2019 vs. 2021-2022). RESULTS:Between 2017 and 2022, there were increases in past-year and past-month use of shrooms (psilocybin), ketamine, poppers (amyl/butyl nitrites), synthetic cathinones ("bath salts"), and novel psychedelics (lysergamides and DOx series), increases in past-year cannabis use, and increases in past-month use of 2C series drugs. Between 2017 and 2022, there were decreases in past-year heroin use and decreases in past-month cocaine use, novel stimulant use, and nonmedical benzodiazepine use. The odds of use of shrooms, poppers, and 2C series drugs significantly increased after COVID, and the odds of use of cocaine, ecstasy, heroin, methamphetamine, novel stimulants, and prescription opioids (nonmedical use) decreased post-COVID. CONCLUSIONS:We estimate shifts in prevalence of various drugs among this sentinel population, which can inform ongoing surveillance efforts and public health response in this and the general populations.

Optimal cardiovascular health is an essential component of human health and well-being across the life course. Heart healthy practices around diet, physical activity, and sleep early in childhood have the potential to greatly improve lifespan and quality (Mehta et al., 2020). Early childhood routines, defined as functional practices that are predictable and repeatable, predict positive growth and development across the lifecourse (Fiese et al., 2002; Ferretti and Bub, 2017; Spagnola and Fiese, 2007). The American Heart Association has identified key heart healthy routines, such as daily regular activities including diet, physical activity, and sleep that promote cardiovascular health (Lloyd-Jones et al., 2022). Integrating the strength-based relational aspects of routines with the acquisition of cardiovascular health development capabilities allows children to establish their own optimal cardiovascular health trajectory early on. A systematic life course approach to supporting heart healthy routines in early childhood would inform clinical, research, and policy strategies to promote long-term cardiovascular health, and contribute to reducing inequalities in cardiovascular outcomes.

BACKGROUND:At the start of 2020, the kidney waiting list consisted of 2526 candidates with a calculated panel reactive antibody (CPRA) of 99.9% or greater, a cohort demonstrated in published research to have meaningfully lower than average access to transplantation even under the revised kidney allocation system (KAS). METHODS:This was a retrospective analysis of US kidney registrations using data from the OPTN [Reference (https://optn.transplant.hrsa.gov/data/about-data/)]. The period-prevalent study cohort consisted of US kidney-alone registrations who waited at least 1 day between April 1, 2016, when HLA DQ-Alpha and DP-Beta unacceptable antigen data became available in OPTN data collection, to December 31, 2019. Poisson rate regression was used to model deceased donor kidney transplant rates per active year waiting and using an offset term to account for differential at-risk periods. Median time to transplant was estimated for each IRR group using the Kaplan-Meier method. Sensitivity analyses were included to address geographic variation in supply-to-demand ratios and differences in dialysis time or waiting time. RESULTS:In this study, we found 1597 additional sensitized (CPRA 50-<99.9%) candidates with meaningfully lower than average access to transplant when simultaneously taking into account CPRA and other factors. In combination with CPRA, candidate blood type, Estimated Post-Transplant Survival Score (EPTS), and presence of other antibody specificities beyond those in the current, 5-locus CPRA were found to influence the likelihood of transplant. CONCLUSION:In total, this suggests approximately 4100 sensitized candidates are on the waiting list who represent a community of disadvantaged patients who may benefit from progressive therapies and interventions to facilitate incompatible transplantation. Though associated with higher risks, such interventions may nevertheless be more attractive than remaining on dialysis with the associated accumulation of mortality risk over time.