Prevention Point Pittsburgh (PPP) is a public health advocacy organization that operates Allegheny County's only needle exchange program. In 2002, PPP implemented an Overdose Prevention Program (OPP) in response to an increase in heroin-related and opioid-related overdose fatalities in the region. In 2005, the OPP augmented overdose prevention and response trainings to include naloxone training and prescription. The objective of our study is to describe the experiences of 426 individuals who participated in the OPP between July 1, 2005, and December 31, 2008. Of these, 89 individuals reported administering naloxone in response to an overdose in a total of 249 separate overdose episodes. Of these 249 overdose episodes in which naloxone was administered, participants reported 96% were reversed. The data support findings from a growing body of research on similar programs in other cities. Community-based OPPs that equip drug users with skills to identify and respond to an overdose and prescribe naloxone can help users and their peers prevent and reverse potentially fatal overdoses without significant adverse consequences.

Since 2001, the National Drug Abuse Treatment Clinical Trials Network (CTN) has worked to put the results of its trials into the hands of community treatment

Preparing nursing students to apply an evidence-based screening and brief intervention approach with patients has the potential to reduce patients' risky alcohol and drug use. Responding to Mollica, Hyman, and Mann's article published in 2011, the current article describes implementation results of an Addiction Training for Nurses program of Screening, Brief Intervention, and Referral to Treatment (SBIRT) embedded within an undergraduate nursing curriculum. Results reveal that students in other schools of nursing would benefit from similar, significant training on substance use disorders and SBIRT. Training satisfaction surveys (N = 488) indicate students were satisfied with the quality of the training experience. More than 90% of students strongly agreed or agreed that the training was relevant to their nursing careers and would help their patients. Additional clinical practice and skill development may increase students' reported effectiveness in working with the topic area of substance use and SBIRT.

Objectives: We sought to understand how opioid treatment programs (OTPs) adapted OTP operations to the COVID-19 pandemic and new federal regulations around methadone and buprenorphine. Methods: In fall 2020, we conducted an online survey of all 103 OTPs licensed by the Pennsylvania Department of Drug and Alcohol Programs, including clinical directors. Survey domains included changes to methadone take-home and telehealth practices; overdose and diversion prevention tactics; perceptions regarding how such changes influence patient well-being; and financial/operational concerns related to the new policies and practices. We calculated descriptive statistics and conducted Chi-square test to test for differences between not-forprofit versus for-profit and large versus small OTPs. Results: Forty-seven percent (46%) OTPs responded to the survey. 10% and 25%, respectively, endorsed offering telephone and videobased telemedicine buprenorphine induction. Sixty-six percent endorsed extending take-home supplies of methadone, but most indicated that these extensions applied to a minority of their patients. Most respondents agreed that provision of buprenorphine via telehealth and extended take-home methadone reduced patient burden in accessing medications and prevented exposure to COVID-19, while not significantly increasing risk of overdose. We did not find major differences in COVID-19 practice modifications by nonprofit status or size of OTP. Conclusions: In Pennsylvania, the COVID-19 pandemic led to rapid changes in provision of opioid treatment services. Findings on relatively low uptake of longer methadone take-home regimens and virtual buprenorphine initiation despite general support for these practices imply a need to further develop guidelines for best clinical practices and understand/address barriers to their implementation.

We performed a retrospective cohort study that aimed to identify one or more groups that followed a pattern of chronic, high prescription use and quantify individuals’ time-dependent probabilities of belonging to a high-utilizer group. We analyzed data from 52,456 adults age 18–45 who enrolled in Medicaid from 2009–2017 in Allegheny County, Pennsylvania who filled at least one prescription for an opioid analgesic. We used group-based trajectory modeling to identify groups of individuals with distinct patterns of prescription opioid use over time. We found the population to be comprised of three distinct trajectory groups. The first group comprised 83% of the population and filled few, if any, opioid prescriptions after their index prescription. The second group (12%) initially filled an average of one prescription per month, but declined over two years to near-zero. The third group (6%) demonstrated sustained high opioid prescriptions utilization. Using individual patients’ posterior probability of membership in the high utilization group, which can be updated iteratively over time as new information become available, we defined a sensitive threshold predictive of sustained future opioid utilization. We conclude that individuals at risk of sustained opioid utilization can be identified early in their clinical course from limited observational data.

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This decision analytical model describes the use of a semisynthetic population to identify the distribution of excess cardiovascular death risk and its correlation with social and biological risk factors.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877–0.892 vs. C-statistic = 0.871; 95%CI = 0.863–0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.