Faculty Bibliography

BACKGROUND:Substance use disorders (SUD) elevate the risk for COVID-19 hospitalization, but studies are inconsistent on the relationship of SUD to COVID-19 mortality. METHODS:Veterans Health Administration (VHA) patients treated in 2019 and evaluated in 2020 for COVID-19 (n=5,556,315), of whom 62,303 (1.1%) tested positive for COVID-19 (COVID-19+). Outcomes were COVID-19+ by 11/01/20, hospitalization, ICU admission, or death within 60 days of a positive test. Main predictors were any ICD-10-CM SUDs, with substance-specific SUDs (cannabis, cocaine, opioid, stimulant, sedative) explored individually. Logistic regression produced unadjusted and covariate-adjusted odds ratios (OR; aOR). RESULTS:Among COVID-19+ patients, 19.25% were hospitalized, 7.71% admitted to ICU, and 5.84% died. In unadjusted models, any SUD and all substance-specific SUDs except cannabis use disorder were associated with COVID-19+(ORs=1.06-1.85); adjusted models produced similar results. Any SUD and all substance-specific SUDs were associated with hospitalization (aORs: 1.24-1.91). Any SUD, cocaine and opioid disorder were associated with ICU admission in unadjusted but not adjusted models. Any SUD, cannabis, cocaine, and stimulant disorders were inversely associated with mortality in unadjusted models (OR=0.27-0.46). After adjustment, associations with mortality were no longer significant. In ad hoc analyses, adjusted odds of mortality were lower among the 49.9% of COVID-19+ patients with SUD who had SUD treatment in 2019, but not among those without such treatment. CONCLUSIONS:In VHA patients, SUDs are associated with COVID-19 hospitalization but not COVID-19 mortality. SUD treatment may provide closer monitoring of care, ensuring that these patients received needed medical attention, enabling them to ultimately survive serious illness.

BACKGROUND:Mandates for prescriber use of prescription drug monitoring programs (PDMPs), databases tracking controlled substance prescriptions, are associated with reduced opioid analgesic (OA) prescribing but may contribute to care discontinuity and chronic opioid therapy (COT) cycling, or multiple initiations and terminations. OBJECTIVE:To estimate risks of COT cycling in New York City (NYC) due to the New York State (NYS) PDMP mandate, compared to risks in neighboring New Jersey (NJ) counties. DESIGN/METHODS:We estimated cycling risk using Prentice, Williams, and Peterson gap-time models adjusted for age, sex, OA dose, payment type, and county population density, using a life-table difference-in-differences design. Failure time was duration between cycles. In a subgroup analysis, we estimated risk among patients receiving high-dose prescriptions. Sensitivity analyses tested robustness to cycle volume considering only first cycles using Cox proportional hazard models. PARTICIPANTS/METHODS:The cohort included 7604 patients dispensed 12,695 prescriptions. INTERVENTIONS/METHODS:The exposure was the August 2013 enactment of the NYS PDMP prescriber use mandate. MAIN MEASURES/METHODS:We used monthly, patient-level data on OA prescriptions dispensed in NYC and NJ between August 2011 and July 2015. We defined COT as three sequential months of prescriptions, permitting 1-month gaps. We defined recurrence as re-initiation of COT after at least 2 months without prescriptions. The exposure was enactment of the PDMP mandate in NYC; NJ was unexposed. KEY RESULTS/RESULTS:Enactment of the NYS PDMP mandate was associated with an adjusted hazard ratio (HR) for cycling of 1.01 (95% CI, 0.94-1.08) in NYC. For high-dose prescriptions, the risk was 1.16 (95% CI, 1.01-1.34). Sensitivity analyses estimated an overall risk of 1.01 (95% CI, 0.94-1.11) and high-dose risk of 1.09 (95% CI, 0.91-1.31). CONCLUSIONS:The PDMP mandate had no overall effect on COT cycling in NYC but increased cycling risk among patients receiving high-dose opioid prescriptions by 16%, highlighting care discontinuity.

In the United States, combined stimulant/opioid overdose mortality has risen dramatically over the last decade. These increases may particularly affect non-Hispanic Black and Hispanic populations. We used death certificate data from the US National Center for Health Statistics (2007-2019) to compare state-level trends in overdose mortality due to opioids in combination with 1) cocaine and 2) methamphetamine and other stimulants (MOS) across racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Asian American/Pacific Islander). To avoid unstable estimates from small samples, we employed principles of small area estimation and a Bayesian hierarchical model, enabling information-sharing across groups. Black Americans experienced severe and worsening mortality due to opioids in combination with both cocaine and MOS, particularly in eastern states. Cocaine/opioid mortality increased 575% among Black people versus 184% in White people (Black, 0.60 to 4.05 per 100,000; White, 0.49 to 1.39 per 100,000). MOS/opioid mortality rose 16,200% in Black people versus 3,200% in White people (Black, 0.01 to 1.63 per 100,000; White, 0.09 to 2.97 per 100,000). Cocaine/opioid overdose mortality rose sharply among Hispanic and Asian Americans. State-group heterogeneity highlighted the importance of data disaggregation and methods to address small sample sizes. Research to understand the drivers of these trends and expanded efforts to address them are needed, particularly in minoritized groups.

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

BACKGROUND AND AIM/OBJECTIVE:In Uruguay, residents age 18 and older seeking legal cannabis must register with the government and choose one of three supply mechanisms: self-cultivation, non-profit cannabis clubs or pharmacies. This is the first paper to measure the association between type of legal cannabis supply mechanism and traffic crashes involving injuries. DESIGN/METHODS:Ecological study using ordinary least squares regression to examine how department-level variation in registrations (overall and by type) is associated with traffic crashes involving injuries. SETTING/METHODS:Uruguay. CASES/METHODS:532 department-quarters. MEASUREMENTS/METHODS:Quarterly cannabis registration counts at the department level and incident-level traffic crash data were obtained from government agencies. The analyses controlled for department-level economic and demographic characteristics and, as a robustness check, we included traffic violations involving alcohol for departments reporting this information. Department-level data on crashes, registrations and alcohol violations were denominated by the number of residents ages 18 and older. FINDINGS/RESULTS:From 2013 to 2019, the average number of registrations at the department-quarter level per 10 000 residents age 18 and older for self-cultivation, club membership and pharmacy purchasing were 17.7 (SD = 16.8), 3.6 (SD = 8.6), and 25.1 (SD = 50.4), respectively. In our multivariate regression analyses, we did not find a statistically significant association between the total number of registrations and traffic crashes with injuries (β = -0.007; P = 0.398; 95% CI = -0.023, 0.01). Analyses focused on the specific supply mechanisms found a consistent, positive and statistically significant association between the number of individuals registered as self-cultivators and the number of traffic crashes with injuries (β = 0.194; P = 0.008; 95% CI = 0.058, 0.329). Associations for other supply mechanisms were inconsistent across the various model specifications. CONCLUSIONS:In Uruguay, the number of people allowed to self-cultivate cannabis is positively associated with traffic crashes involving injuries. Individual-level analyses are needed to assess better the factors underlying this association.

BACKGROUND:Being bullied online is associated with being bullied in school. However, links between online bullying and violence-related experiences are minimally understood. We evaluated potential disparities in these associations to illuminate opportunities to reduce school-based violence. METHODS: = 73 074). We used survey-weighted logistic and multinomial models to examine links between online bullying and five school-based violence-related experiences: offline bullying, weapon carrying, avoiding school due to feeling unsafe, being threatened/injured with a weapon, and physical fighting. We examined interactions by sex, race/ethnicity, and sexual identity. RESULTS:Being bullied online was positively associated with all offline violence-related behaviors. Groups with stronger associations between online bullying and physical fighting, including boys, adolescents whose sexual identity was gay/lesbian or unsure, and many adolescents of color (Black, Hispanic/Latino, and Asian/Pacific Islander adolescents), had stronger associations between online bullying and either weapon carrying or avoiding school. CONCLUSIONS:Online bullying is not an isolated harmful experience; many marginalized adolescents who experience online bullying are more likely to be targeted in school, feel unsafe, get in fights, and carry weapons. Reduction of online bullying should be prioritized as part of a comprehensive school-based violence prevention strategy.

As opioid prescribing has declined, it is unclear how the landscape of prescription pain treatment across the US has changed. We used nationally-representative data from the Medical Expenditure Health Survey, 2014-2018 to examine trends in prescriptions for opioid and non-opioid pain medications, including acetaminophen, non-steroidal anti-inflammatory drugs (NSAIDs), gabapentinoids, and antidepressants among US adults with self-reported pain. Overall, from 2014-2018, the percentage of participants receiving a prescription for opioids declined, (38.8% vs. 32.8%), remained stable for NSAIDs (26.8% vs. 27.7%), and increased for acetaminophen (1.6% vs. 2.3%), antidepressants (9.6% vs. 12.0%) and gabapentinoids (13.2% vs. 19.0%). In this period, the adjusted odds of receiving an opioid prescription decreased (aOR=0.93, 95% CI=0.90-0.96), while the adjusted odds of receiving antidepressant, gabapentinoid and acetaminophen prescriptions increased (antidepressants: aOR=1.08, 95% CI=1.03-1.13 gabapentinoids: aOR=1.11, 95% CI=1.06-1.17; acetaminophen: aOR=1.10, 95% CI: 1.02-1.20). Secondary analyses stratifiying within the 2014-2016 and 2016-2018 periods revealed particular increases in prescriptions for gabapentinoids (aOR=1.13, 95% CI=1.05-1.21) and antidepressants (aOR=1.23, 95% CI=1.12-1.35) since 2016.

OBJECTIVE:Availability of medications for opioid use disorder (MOUD) remains sparse. To date, there has been no national, state-by-state comparison of patient MOUD utilization relative to treatment availability and burden of overdose deaths. We aimed to quantify, for each state, the number of MOUD patients relative to (1) office-based buprenorphine providers and opioid treatment programs (OTPs) and (2) overdose deaths. METHODS:We conducted a spatial analysis of patients receiving MOUD from OTPs or buprenorphine providers in March 2017 across all 50 states and Washington, DC. For each state, we calculated the number of patients receiving MOUD from OTPs and buprenorphine prescriptions, relative to available OTPs and buprenorphine providers; as well as ratios of number of patients receiving MOUD relative to overdose deaths. RESULTS:In March 2017, 942,368 patients attended an OTP (410,288) or received a buprenorphine prescription (486,318). Patient to OTP ratio was highest in West Virginia, Delaware, Washington, DC, New Jersey, New Hampshire, Connecticut and Ohio, ranging from 91 to 193 patients per OTP in the first quintile to 430 to 648 in the fifth. Patient to buprenorphine provider ratio was highest in Kentucky and West Virginia, ranging from 3 to 7 patients per provider in the first quintile to 19 to 28 in the fifth. Median MOUD patients per overdose death was 21 (IQR:14.9-28.2). Of high overdose states, Washington, DC, New Jersey, and Ohio had the smallest number of patients on MOUD relative to deaths. CONCLUSIONS:High patient volume relative to treatment availability in overdose-burdened areas may indicate strain on MOUD providers and OTPs. Promoting greater utilization while expanding MOUD providers and programs is critical.

Adolescent internalizing symptoms have increased since 2010, while adequate sleep has declined for several decades. It remains unclear how self-reported sleep attainment has impacted internalizing symptoms trends. Using 1991-2019 MTF data (N~390,000), we estimate age-period-cohort effects in adolescent internalizing symptoms (loneliness, self-esteem, self-derogation, depressive affect) and the association with yearly prevalence of a survey-assessed, self-reported measure of ≥ 7 hours sleep attainment. We focus our main analysis on loneliness and use median odds ratios (MORs), measures of variance in loneliness associated with period differences. We observed limited signals for cohort effects and modeled only period effects. Loneliness increased by 0.83% per year; adolescents in 2019 had 0.68 (95% CI: 0.49, 0.87) increased log-odds of loneliness compared with the mean, consistent by race/ethnicity and parental education. Girls experienced steeper increases than boys (p<0.0001). The period effect MOR for loneliness was 1.16 (variance=0.09; 95% CI: 0.06, 0.17) before adjusting for self-reported frequency of ≥ 7 hours sleep vs. 1.07 (variance=0.02; 95% CI: 0.01, 0.03) after adjusting. Adolescents across cohorts are experiencing worsening internalizing symptoms. Self-reported frequency of <7 hours sleep partially explains increases in loneliness, indicating the need for feasibility trials to study the effect of increasing sleep attainment on internalizing symptoms.