Opioid overdose death following criminal justice involvement: Linking statewide corrections and hospital databases to detect individuals at highest risk.

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.

Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data.

Introduction: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids.

Methods: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016.

Predictors of Overdose Death Among High-Risk Emergency Department Patients With Substance-Related Encounters: A Data Linkage Cohort Study.

Study Objective: Persons with substance use disorders frequently utilize emergency department (ED) services, creating an opportunity for intervention and referral to addiction treatment and harm-reduction services. However, EDs may not have the appropriate tools to distinguish which patients are at greatest risk for negative outcomes. We link hospital ED and medical examiner mortality databases in one state to identify individual-level risk factors associated with overdose death among ED patients with substance-related encounters.

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients.

Background: Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences.

Methods: We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among buprenorphine patients (N = 25,487).

Zalutumumab in head and neck cancer.

Introduction: Over 90% of head and neck cancers overexpress EGFR. This correlates with advanced disease stage and worse prognosis. Strategies to inhibit the EGFR pathway have been developed over the last decade.