Faculty Bibliography

OBJECTIVE/UNASSIGNED:In the United States, adult cannabis use has increased over time, but less information is available on time trends in cannabis use disorder. The authors used Veterans Health Administration (VHA) data to examine change over time in cannabis use disorder diagnoses among veterans, an important population subgroup, and whether such trends differ by age group (<35 years, 35-64 years, ≥65 years), sex, or race/ethnicity. METHODS/UNASSIGNED:VHA electronic health records from 2005 to 2019 (range of Ns per year, 4,403,027-5,797,240) were used to identify the percentage of VHA patients seen each year with a cannabis use disorder diagnosis (ICD-9-CM, January 1, 2005-September 30, 2015; ICD-10-CM, October 1, 2015-December 31, 2019). Trends in cannabis use disorder diagnoses were examined by age and by race/ethnicity and sex within age groups. Given the transition in ICD coding, differences in trends were tested within two periods: 2005-2014 (ICD-9-CM) and 2016-2019 (ICD-10-CM). RESULTS/UNASSIGNED:In 2005, the percentages of VHA patients diagnosed with cannabis use disorder in the <35, 35-64, and ≥65 year age groups were 1.70%, 1.59%, and 0.03%, respectively; by 2019, the percentages had increased to 4.84%, 2.86%, and 0.74%, respectively. Although the prevalence of cannabis use disorder was consistently higher among males than females, between 2016 and 2019, the prevalence increased more among females than males in the <35 year group. Black patients had a consistently higher prevalence of cannabis use disorder than other racial/ethnic groups, and increases were greater among Black than White patients in the <35 year group in both periods. CONCLUSIONS/UNASSIGNED:Since 2005, diagnoses of cannabis use disorder have increased substantially among VHA patients, as they have in the general population and other patient populations. Possible explanations warranting investigation include decreasing perception of risk, changing laws, increasing cannabis potency, stressors related to growing socioeconomic inequality, and use of cannabis to self-treat pain. Clinicians and the public should be educated about the increases in cannabis use disorder in general in the United States, including among patients treated at the VHA.

BACKGROUND:The United States overdose crisis continues unabated. Despite efforts to increase capacity for treating opioid use disorder (OUD) in the U.S., how actual treatment receipt compares to need remains unclear. In this cross-sectional study, we estimate progress in addressing the gap between OUD prevalence and OUD treatment receipt at the national and state levels from 2010 to 2019. METHODS:We estimated past-year OUD prevalence rates based on the U.S. National Survey on Drug Use and Health (NSDUH), using adjustment methods that attempt to account for OUD underestimation in national household surveys. We used data from specialty substance use treatment records and outpatient pharmacy claims to estimate the gap between OUD prevalence and number of persons receiving medications for opioid use disorder (MOUD) during the past decade. RESULTS:Adjusted estimates suggest past-year OUD affected 7,631,804 individuals in the U.S. in (2,773 per 100,000 adults 12+), relative to only 1,023,959 individuals who received MOUD (365 per 100,000 adults 12+). This implies approximately 86.6% of individuals with OUD nationwide who may benefit from MOUD treatment do not receive it. MOUD receipt increased across states over the past decade, but most regions still experience wide gaps between OUD prevalence and MOUD receipt. CONCLUSIONS:Despite some progress in expanding access to MOUD, a substantial gap between OUD prevalence and treatment receipt highlights the critical need to increase access to evidence-based services.

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.

BACKGROUND:Substance-related interactions with the criminal justice system are a potential touchpoint to identify people at risk for firearm violence. We used an agent-based model to simulate the change in firearm violence after disqualifying people from owning a firearm given prior alcohol- and drug-related misdemeanors. METHODS:We created a population of 800,000 agents reflecting a 15% sample of the adult New York City population. RESULTS:Disqualification from purchasing firearms for 5 years after an alcohol-related misdemeanor conviction reduced population-level rates of firearm homicide by 1.0% [95% CI 0.4-1.6%] and suicide by 3.0% [95% CI 1.9-4.0%]. Disqualification based on a drug-related misdemeanor conviction reduced homicide by 1.6% [95% CI 1.1-2.2%] and suicide by 4.6% [95% CI 3.4-5.8%]. Reductions were generally 2 to 8 times larger for agents meeting the disqualification criteria. CONCLUSIONS:Denying firearm access based on a history of drug and alcohol misdemeanors may reduce firearm violence among the high-risk group. Enactment of substance use-related firearms denial criteria needs to be balanced against concerns about introducing new sources of disenfranchisement among already vulnerable populations.

Opioid-related overdose deaths have increased since 2010 in the U.S., but information on trends in opioid use disorder (OUD) prevalence is limited due to unreliable data. Multiplier methods are a classical epidemiological technique to estimate prevalence when direct estimation is infeasible or unreliable. We used two different multiplier approaches to estimate OUD prevalence from 2010 to 2019. First, we estimated OUD in National Survey on Drug Use and Health (NSDUH), and based on existing capture-recapture studies, multiplied prevalence by 4.5x. Second, we estimated the probability of drug poisoning death among people with OUD (Meta-analysis indicates 0.52/100,000), and divided the number of drug poisoning deaths in the US by this probability. Estimates were weighted to account for increase in drug-related mortality in recent years due to fentanyl. Estimated OUD prevalence was lowest when estimated in NSDUH with no multiplier, and highest when estimated from vital statistics data without adjustment. Consistent findings emerged with two methods: NSDUH data with multiplier correction, and vital statistics data with multiplier and adjustment. From these two methods, OUD prevalence increased from 2010 to 2014; then stabilized and slightly declined annually (survey data with multiplier, highest prevalence of 4.0% in 2015; death data with a multiplier and correction, highest prevalence of 2.35% in 2016). The number of US adolescent and adult individuals with OUD in 2019 was estimated between 6.7-7.6 million. When multipliers and corrections are used, OUD may have stabilized or slightly declined after 2015. Nevertheless, it remains highly prevalent, affecting 6-7 million US adolescents and adults.

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 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.