Faculty Bibliography

BACKGROUND:Since 1999, the rate of fatal prescription opioid overdoses and of suicides has dramatically increased in the USA. These increases, which have occurred among similar demographic groups, have led to the hypothesis that the opioid epidemic contributed to increases in suicidal behavior, though the underlying association remains poorly defined. We examine the association between nonmedical use of prescription opioids/opioid use disorder and suicidal ideation/attempts. METHODS:We used longitudinal data from a national representative sample of the US adult population, the National Epidemiologic Survey on Alcohol and Related Conditions. Participants (n = 34 653) were interviewed in 2001-2002 (wave 1) and re-interviewed approximately 3 years later (wave 2). A propensity score analysis estimated the association between exposure to prescription opioids at wave 1 and prevalent/incident suicidal behavior at wave 2. RESULTS:Heavy/frequent (⩾2-3 times a month) prescription opioid use was associated with prevalent suicide attempts [adjusted risk ratio (ARR) = 2.75, 95% CI 1.35-5.60]. Prescription opioid use disorder was associated with prevalent (ARR = 1.98, 95% CI 1.20-3.28) and incident suicidal ideation (ARR = 2.59, 95% CI 1.25-5.37), and prevalent attempts (ARR = 4.19, 95% CI 1.71-10.27). None of the exposures was associated with incident suicide attempts. CONCLUSIONS:Heavy/frequent opioid use and related disorder were associated with prevalent suicide attempts; opioid use disorder was also associated with the incident and prevalent suicidal ideation. Given population increases in nonmedical use of prescription opioids and disorder, the opioid crisis may have contributed to population increases in suicidal ideation.

Importance/UNASSIGNED:Responding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems. Objective/UNASSIGNED:To develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data. Design, Setting, and Participants/UNASSIGNED:A cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019. Exposures/UNASSIGNED:Controlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters. Main Outcomes and Measures/UNASSIGNED:Fatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016. Results/UNASSIGNED:There were 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI, 2.10-7.11]), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI, 2.20-3.86]). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85. Conclusions and Relevance/UNASSIGNED:In this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.

Importance/UNASSIGNED:An important consequence of cannabis legalization is the potential increase in the number of cannabis-impaired drivers on roads, which may result in higher rates of traffic-related injuries and fatalities. To date, limited information about the effects of recreational cannabis laws (RCLs) on traffic fatalities is available. Objective/UNASSIGNED:To estimate the extent to which the implementation of RCLs is associated with traffic fatalities in Colorado and Washington State. Design, Setting, and Participants/UNASSIGNED:This ecological study used a synthetic control approach to examine the association between RCLs and changes in traffic fatalities in Colorado and Washington State in the post-RCL period (2014-2017). Traffic fatalities data were obtained from the Fatality Analysis Reporting System from January 1, 2005, to December 31, 2017. Data from Colorado and Washington State were compared with synthetic controls. Data were analyzed from January 1, 2005, to December 31, 2017. Main Outcome(s) and Measures/UNASSIGNED:The primary outcome was the rate of traffic fatalities. Sensitivity analyses were performed (1) excluding neighboring states, (2) excluding states without medical cannabis laws (MCLs), and (3) using the enactment date of RCLs to define pre-RCL and post-RCL periods instead of the effective date. Results/UNASSIGNED:Implementation of RCLs was associated with increases in traffic fatalities in Colorado but not in Washington State. The difference between Colorado and its synthetic control in the post-RCL period was 1.46 deaths per 1 billion vehicle miles traveled (VMT) per year (an estimated equivalent of 75 excess fatalities per year; probability = 0.047). The difference between Washington State and its synthetic control was 0.08 deaths per 1 billion VMT per year (probability = 0.674). Results were robust in most sensitivity analyses. The difference between Colorado and synthetic Colorado was 1.84 fatalities per 1 billion VMT per year (94 excess deaths per year; probability = 0.055) after excluding neighboring states and 2.16 fatalities per 1 billion VMT per year (111 excess deaths per year; probability = 0.063) after excluding states without MCLs. The effect was smaller when using the enactment date (24 excess deaths per year; probability = 0.116). Conclusions and Relevance/UNASSIGNED:This study found evidence of an increase in traffic fatalities after the implementation of RCLs in Colorado but not in Washington State. Differences in how RCLs were implemented (eg, density of recreational cannabis stores), out-of-state cannabis tourism, and local factors may explain the different results. These findings highlight the importance of RCLs as a factor that may increase traffic fatalities and call for the identification of policies and enforcement strategies that can help prevent unintended consequences of cannabis legalization.

BACKGROUND:Persons who interact with criminal justice and hospital systems are particularly vulnerable to negative health outcomes, including overdose. However, the relationship between justice involvement, healthcare utilization and overdose risk is not well-understood. This data linkage study seeks to improve our understanding of the link between different types of justice involvement as well as hospital interaction and risk of fatal opioid overdose among persons with incarcerations, arrests and parole/probation records for drug and property crimes in Maryland. METHODS:Maryland statewide criminal justice records were obtained for 2013-2016. Data were linked at the person-level to an all-payer hospitalization database and overdose death records for the same years. Logistic regression was performed to determine which criminal justice and hospital characteristics were associated with greatest risk of overdose death. RESULTS:89,591 adults had criminal-justice records and were included in the study. During the 2013-2016 study period, 4108 (4.59 %) were hospitalized for a non-fatal opioid overdose, and 519 (0.58 %) died of opioid overdose. Strongest risk factors for death included being older, being white, having had an inpatient or emergency hospitalization, having had more arrests, having been arrested for a drug charge (vs. property charge), having a misdemeanor drug charge (vs. a felony charge), and having been released from incarceration during the study period. CONCLUSION/CONCLUSIONS:Linking corrections and healthcare information can help advance understanding of risk and target overdose prevention interventions directed at justice-involved individuals with greatest need.

INTRODUCTION/BACKGROUND:Special populations, including veterans, pregnant and postpartum women, and adolescents, benefit from opioid use disorder treatment tailored to their specific needs, but access to such services is poorly described. This study identifies the availability of opioid use disorder treatment facilities that use medications and have special programming and contextualizes facilities amid counties' opioid-related overdose mortality. METHODS:Data were compiled on 15,945 U.S. treatment facilities using medications for opioid use disorder listed in the Behavioral Health Services Treatment Locator in 2018. Facilities with programs tailored to special populations (veterans, pregnant and postpartum women, and adolescents) were identified and geocoded. Counties with such facilities were characterized. Cold spots (county clusters with poor treatment availability) were identified using Getis-Ord Gi* statistics. Data were extracted in October 2018 and analyzed from October 2018 to May 2019. RESULTS:Of all 3,142 U.S. counties, 1,889 (60.1%) had opioid use disorder treatment facilities. Facilities with tailored programs for veterans, pregnant and postpartum women, and adolescents were located in 701 (22.3%), 918 (29.2%), and 1,062 (33.8%) of the counties, respectively. Specific medications provided for opioid use disorder varied, with only a minority of facilities offering methadone (among facilities with tailored programs for veterans, 6.0%; pregnant and postpartum women, 13.2%; adolescents, 1.3%). Many counties reporting opioid-related overdose deaths lacked programs for special populations (veterans, 72.6%; pregnant and postpartum women, 54.8%; adolescents, 30.6%). Cold spots were located throughout the Midwest, U.S. Southeast, and portions of Texas. CONCLUSIONS:Facilities using medications for opioid use disorder with tailored programs for veterans, pregnant and postpartum women, and adolescents are limited. There is a need for improved access to evidence-based programs that address the unique treatment needs of special populations.

PURPOSE/OBJECTIVE:After years of stable or declining HIV prevalence and declining incidence among people who inject drugs (PWID) in the United States, some rapidly emerging outbreaks have recently occurred in new areas (e.g., Scott County, Indiana). However, to our knowledge, trends over time in HIV prevalence among PWID in US metropolitan statistical areas (MSAs) across all major regions of the country have not been systematically estimated beyond 2002, and the extent to which HIV prevalence may be increasing in other areas is largely unknown. This article estimates HIV prevalence among PWID in 89 of the most populated US MSAs, both overall and by geographic region, using more recent surveillance and HIV testing data. METHODS:We computed MSA-specific annual estimates of HIV prevalence (both diagnosed and undiagnosed infections) among PWID for these 89 MSAs, for 1992-2013, using several data series from the Centers for Disease Control and Prevention's (CDC) National HIV Surveillance System and National HIV Prevention Monitoring and Evaluation data; Holmberg's (1997) estimates of 1992 PWID population size and of HIV prevalence and incidence among PWID; and research estimates from published literature using 1992-2013 data. A mixed effects model, with time nested within MSAs, was used to regress the literature review estimates on all of the other data series. Multiple imputation was used to address missing data. Resulting estimates were validated using previous 1992-2002 estimates of HIV prevalence and data on antiretroviral (ARV) prescription volumes and examined for patterns based on geographic region, numbers of people tested for HIV, and baseline HIV prevalence. RESULTS:Mean (across all MSAs) trends over time suggested decreases through 2002 (from approximately 11.4% in 1992 to 9.2% in 2002), followed by a period of stability, and steep increases after 2010 (to 10.6% in 2013). Validation analyses found a moderate positive correlation between our estimates and ARV prescription volumes (r = 0.45), and a very strong positive correlation (r = 0.94) between our estimates and previous estimates by Tempalski et al. (2009) for 1992-2002 (which used different methods). Analysis by region and baseline prevalence suggested that mean increases in later years were largely driven by MSAs in the Western United States and by MSAs in the Midwest that had low baseline prevalence. Our estimates suggest that prevalence decreased across all years in the Eastern United States. These trends were particularly clear when MSAs with very low numbers of people tested for HIV were removed from analyses to reduce unexplained variability in mean trajectories. CONCLUSIONS:Our estimates suggest a fairly large degree of variation in 1992-2013 trajectories of PWID HIV prevalence among 89 US MSAs, particularly by geographic region. They suggest that public health responses in many MSAs (particularly those with larger HIV prevalence among PWID in the early 1990s) were sufficient to decrease or maintain HIV prevalence over time. However, future research should investigate potential factors driving the estimated increase in prevalence after 2002 MSAs in the West and Midwest. These findings have potentially important implications for program and/or policy decisions, but estimates for MSAs with low HIV testing denominators should be interpreted with caution and verified locally before planning action.

BACKGROUND:To examine trends in state-level policy support for sexual minorities and HIV outcomes among MSM. METHODS:This longitudinal analysis linked state-level policy support for sexual minorities (N=94 Metropolitan Statistical Areas [MSAs] in 38 states) to 7 years of data (2008-2014) from CDC on HIV outcomes among MSM. Using latent growth mixture modeling, we combined 11 state-level policies (e.g., non-discrimination laws including sexual orientation as a protected class) from 1999-2014, deriving 3 latent groups: consistently low policy support; consistently high policy support; and increasing trajectory of policy support. Outcomes were HIV diagnoses per 10,000 MSM; late diagnoses (number of deaths within 12 months of HIV diagnosis and AIDS diagnoses within three months of HIV diagnosis) per 10,000 MSM; AIDS diagnoses per 10,000 MSM with HIV; and AIDS-related mortality per 10,000 MSM with AIDS. RESULTS:Compared to MSAs in states with low levels and increasing policy support for sexual minorities, MSAs in states with the highest level of policy support had lower risks of HIV diagnoses (Risk Difference [RD]=-37.9, 95% Confidence Interval [CI]: -54.7, -21.0), late diagnoses (RD=-12.5, 95% CI: -20.4, -4.7), and AIDS-related mortality (RD=-33.7, 95% CI: -61.2, -6.2), controlling for time and 7 MSA-level covariates. In low policy support states, 27% of HIV diagnoses, 21% of late diagnoses, and 10% of AIDS deaths among MSM were attributable to policy climate. CONCLUSION/CONCLUSIONS:State-level policy climate related to sexual minorities was associated with HIV health outcomes among MSM and could be a potential public health tool for HIV prevention and care.

BACKGROUND:Unintentional injury, a leading cause of morbidity among adolescents, may also be a risk factor for deliberate self-harm. To inform clinical and public health prevention efforts in adolescent populations, we examined whether distinct subtypes of unintentional injury were differentially associated with deliberate self-harm. METHODS:Statewide, all-payer, individually linkable administrative data on adolescent patients presenting to any California emergency department (ED) in 2010 (n = 490,071) were used to investigate longitudinal associations between subtypes of unintentional injury and deliberate self-harm. Adolescents aged 10-19 years presenting with unintentional drug poisoning, other poisoning, fall, suffocation, or cutting/piercing injuries formed the exposure groups; adolescents presenting with unintentional strike injuries formed the primary referent group. Study patients were followed back in time (2006-2009) to compare the groups' odds of a prior ED visit for deliberate self-harm, as well as forwards in time (2010-2015) to compare their risks of subsequent self-harm. RESULTS:Unintentional drug-poisoning injury was strongly associated with increased likelihood of ED visits for deliberate self-harm, assessed both retrospectively (adjusted OR = 4.52; 95% confidence interval [CI] = 3.08, 6.64) and prospectively (adjusted RR = 3.74; 95% CI = 3.03, 4.60). Positive associations with odds of prior self-harm and/or risk of subsequent self-harm were also observed for patients with unintentional non-drug poisoning, suffocation, and cutting/piercing injuries. CONCLUSIONS:Certain subtypes of unintentional injury, particularly drug poisoning, are strongly associated with risk for deliberate self-harm among adolescents, a finding with implications for targeting clinical assessment and intervention in emergency department settings. More research is needed to understand the mechanisms underlying these associations.

BACKGROUND:In 2013, Uruguay became the first country in the world to legalize recreational cannabis, instituting a non-commercial state regulatory model of production and supply. This study provides the first empirical evidence on its impacts on adolescent use of cannabis and related risks. METHODS:We use a generalization of the synthetic control method (SCM) to estimate the impact of legalization in Uruguay on adolescent past year and month cannabis use, perceived availability of cannabis and perceived risk of cannabis use. We compare biennial high school student self-reported survey data from Montevideo and regions in the interior of Uruguay post-legalization (2014-2018) and post initial implementation (2015-2018) to a synthetic counterfactual constructed using a weighted combination of 15 control regions in Chile. RESULTS:We find no evidence of an impact on cannabis use or the perceived risk of use. We find an increase in student perception of cannabis availability (58% observed vs. 51% synthetic control) following legalization. CONCLUSION/CONCLUSIONS:Our findings provide some support for the thesis that Uruguay's state regulatory approach to cannabis supply may minimize the impact of legalization on adolescent cannabis use. At the same time, our study period represents a period of transition: pharmacy access, by far the most popular means of access, was not available until the summer of 2017. Additional study will be important to assess the longer-term impacts of the fully implemented legalization regime on substance use outcomes.