Faculty Bibliography

INTRODUCTION/BACKGROUND:Incident HIV infections persist in the United States (U.S.) among marginalized populations. Targeted and cost-efficient testing strategies can help in reaching HIV elimination. This analysis compares the effectiveness and cost of three HIV testing strategies in a high HIV burden area in the U.S. in identifying new HIV infections. METHODS:We performed a cost analysis comparing three HIV testing strategies in Chicago: (1) routine screening (RS) in an inpatient and outpatient setting, (2) modified partner services (MPS) among networks of the recently HIV infected and diagnosed, and (3) a respondent drive sampling (RDS)-based social network (SN) approach targeting young African-American men who have sex with men. All occurred at the same academic medical centre during the following times: routine testing, 2011 to 2016; MPS, 2013 to 2016; SN: 2013 to 2014. Costs were in 2016 dollars and included personnel, HIV testing, training, materials, overhead. Outcomes included cost per test, HIV-positive test and new diagnosis. Sensitivity analyses were performed to assess the impact of population demographics. RESULTS:The RS programme completed 57,308 HIV tests resulting in 360 (0.6%) HIV-positive tests and 165 new HIV diagnoses (0.28%). The MPS completed 146 HIV tests, resulting in 79 (54%) HIV-positive tests and eight new HIV diagnoses (5%). The SN strategy completed 508 HIV tests, resulting in 210 (41%) HIV-positive tests and 37 new HIV diagnoses (7.2%). Labour accounted for the majority of costs in all strategies. The estimated cost per new HIV diagnosis was $16,773 for the RS programme, $61,418 for the MPS programme and $15,683 for the SN testing programme. These costs were reduced for the RS and MPS strategies in sensitivity analyses limiting testing efficacy to the highest prevalence patient populations ($2,841 and $33,233 respectively). CONCLUSIONS:The SN strategy yielded the highest proportion of new diagnoses, followed closely by the MPS programme. Both the SN strategy and RS programme were comparable in the cost per new diagnosis. A simultaneous approach that consists of RS in combination with SN testing may be most effective for identifying new HIV infections in settings with heterogeneous epidemics with both high rates of HIV prevalence and HIV testing.

BACKGROUND:An individual's risk for future opioid overdoses is usually assessed using a 12-month "lookback" period. Given the potential urgency of acting rapidly, we compared the performance of alternative predictive models with risk information from the past 3, 6, 9, and 12 months. METHODS:We included 1,014,033 Maryland residents aged 18-80 with at least 1 opioid prescription and no recorded death in 2015. We used 2015 Maryland prescription drug monitoring data to identify risk factors for nonfatal opioid overdoses from hospital discharge records and investigated fatal opioid overdose from medical examiner data in 2016. Prescription drug monitoring program-derived predictors included demographics, payment sources for opioid prescriptions, count of unique opioid prescribers and pharmacies, and quantity and types of opioids and benzodiazepines filled. We estimated a series of logistic regression models that included 3, 6, 9, and 12 months of prescription drug monitoring program data and compared model performance, using bootstrapped C-statistics and associated 95% confidence intervals. RESULTS:For hospital-treated nonfatal overdose, the C-statistic increased from 0.73 for a model including only the fourth quarter to 0.77 for a model with 4 quarters of data. For fatal overdose, the area under the curve increased from 0.80 to 0.83 over the same models. The strongest predictors of overdose were prescription fills for buprenorphine and Medicaid and Medicare as sources of payment. CONCLUSIONS:Models predicting opioid overdose using 1 quarter of data were nearly as accurate as models using all 4 quarters. Models with a single quarter may be more timely and easier to identify persons at risk of an opioid overdose.

INTRODUCTION/BACKGROUND:Qualitative analysis of Twitter posts reveals key insights about user norms, informedness, perceptions, and experiences related to opioid use disorder (OUD). This paper characterizes Twitter message content pertaining to medications for opioid use disorder (MOUD) and Naloxone. METHODS:In-depth thematic analysis was conducted of 1,010 Twitter messages collected in June 2019. Our primary aim was to identify user perceptions and experiences related to harm reduction (e.g., Naloxone) and MOUD (e.g., sublingual and Extended-release buprenorphine, Extended-release naltrexone, Methadone). RESULTS:Tweets relating to OUD were most commonly authored by general Twitter users (43.8%), private residential or detoxification programs (24.6%), healthcare providers (e.g., physicians, first responders; 4.3%), PWUOs (4.7%) and their caregivers (2.9%). Naloxone was mentioned in 23.8% of posts and authored most commonly by general users (52.9%), public health experts (7.4%), and nonprofit/advocacy organizations (6.6%). Sentiment was mostly positive about Naloxone (73.6%). Commonly mentioned MOUDs in our search consisted of Buprenorphine-naloxone (13.8%), Methadone (5.7%), Extended-release naltrexone (4.1%), and Extended-release buprenorphine (0.01%). Tweets authored by PWUOs (4.7%) most commonly related to factors influencing access to MOUD or adverse events related to MOUD (70.8%), negative or positive experiences with illicit substance use (25%), policies related to expanding access to treatments for OUD (8.3%), and stigma experienced by healthcare providers (8.3%). CONCLUSION/CONCLUSIONS:Twitter is utilized by a diverse array of individuals, including PWUOs, and offers an innovative approach to evaluate experiences and themes related to illicit opioid use, MOUD, and harm reduction.

INTRODUCTION:In light of the COVID-19 pandemic, considerable effort is going into identifying and protecting those at risk. Criminalization, stigmatization and the psychological, physical, behavioural and economic consequences of substance use make people who inject drugs (PWID) extremely vulnerable to many infectious diseases. While relationships between drug use and blood-borne and sexually transmitted infections are well studied, less attention has been paid to other infectious disease outbreaks among PWID. DISCUSSION:COVID-19 is likely to disproportionally affect PWID due to a high prevalence of comorbidities that make the disease more severe, unsanitary and overcrowded living conditions, stigmatization, common incarceration, homelessness and difficulties in adhering to quarantine, social distancing or self-isolation mandates. The COVID-19 pandemic also jeopardizes essential for PWID services, such as needle exchange or substitution therapy programmes, which can be affected both in a short- and a long-term perspective. Importantly, there is substantial evidence of other infectious disease outbreaks in PWID that were associated with factors that enable COVID-19 transmission, such as poor hygiene, overcrowded living conditions and communal ways of using drugs. CONCLUSIONS:The COVID-19 crisis might increase risks of homelessnes, overdoses and unsafe injecting and sexual practices for PWID. In order to address existing inequalities, consultations with PWID advocacy groups are vital when designing inclusive health response to the COVID-19 pandemic.

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.