Faculty Bibliography

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.

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: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.

BACKGROUND AND AIMS/OBJECTIVE:In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. DESIGN/METHODS:Randomized, population-based, community intervention trial. SETTING/METHODS:Rhode Island, USA. PARTICIPANTS/METHODS:All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. INTERVENTION/METHODS:Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. MEASUREMENTS/METHODS:The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities. COMMENTS/CONCLUSIONS:The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.

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.

ABSTRACT/UNASSIGNED:Chronic pain is associated with mental and physical health difficulties and is prevalent among veterans. Cannabis has been put forth as a treatment for chronic pain, and changes in laws, attitudes, and use patterns have occurred over the last two decades. Differences in prevalence of non-medical cannabis use and cannabis use disorder (CUD) were examined across two groups: veterans/non-veterans and those reporting/not reporting recent pain. Data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (2012-2013; n=36,309) were analyzed using logistic regression. Prevalence Differences (PD) for three cannabis outcomes: (1) past-year non-medical cannabis use, (2) frequent (≥3 times a week) non-medical use, and (3) DSM-5 CUD were estimated for those reporting recent moderate-severe pain (veterans/non-veterans), and veterans reporting/not reporting recent pain. Difference in differences were calculated to investigate prevalence differences on outcomes associated with residence in a state with medical cannabis laws (MCLs). Associations between physical and mental health and cannabis variables were tested. Results indicated that the prevalence of recent pain was greater among veterans (PD=7.25%, 95% CI [4.90, 9.60]). Among veterans, the prevalence of frequent cannabis use was greater among those with pain (PD=1.92%, 98% CI [0.21, 3.63]), and, among veterans residing in a state with MCLs, the prevalence of CUD was greater among those reporting recent pain (PD=3.88%, 98% CI [0.36, 7.39]). Findings failed to support the hypothesis that cannabis use improves mental or physical health for veterans with pain. Providers treating veterans with pain in MCL states should monitor such patients closely for CUD.

Over the past decade, US adolescents' depressive symptoms have increased, and changing religious beliefs and service attendance may be contributing factors. We examined the contribution of religious factors to depressive symptoms among 417,540 US adolescents (grades: 8, 10, 12), years:1991-2019, in survey-weighted logistic regressions. Among adolescents who felt religion was personally important, those who never attended services had 2.23 times higher odds of reporting depressive symptoms compared to peers attending weekly. Among adolescents who did not feel that religion was important, the pattern was reversed. Among adolescents, concordance between importance of religion and religious service attendance may lower risk of depressive symptoms. Overall, we estimate that depressive symptom trends would be 28.2% lower if religious factors had remained at 1991 levels.