Purpose: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention.
Methods: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models.
Results: We found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals.
Conclusions: Stratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11117432 | PMC |
| http://dx.doi.org/10.1016/j.annepidem.2024.04.011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
CHANTER syndrome: an under-recognized cause of reversible hydrocephalus after opioid overdose.
Neurol Sci
December 2024
Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA, 02115, USA.
Escalation of intravenous fentanyl self-administration and assessment of withdrawal behavior in male and female mice.
Psychopharmacology (Berl)
December 2024
Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, 47904, USA.
Rationale: The rise in overdose deaths from synthetic opioids, especially fentanyl, necessitates the development of preclinical models to study fentanyl use disorder (FUD). While there has been progress with rodent models, additional translationally relevant models are needed to examine excessive fentanyl intake and withdrawal signs.
Objective: The current study aimed to develop a translationally relevant preclinical mouse model of FUD by employing chronic intravenous fentanyl self-administration (IVSA).
Tramadol as a fentanyl adulterant: Prevalence and management in a ToxIC Fentalog study prospective cohort.
Am J Emerg Med
December 2024
Icahn School of Medicine at Mount Sinai, Center for Research on Emerging Substances, Poisoning, Overdose, and New Discoveries (RESPOND), NYC Health + Hospitals/Elmhurst, New York, NY, USA.
Background: Tramadol is an adulterant of illicit opioids. As it is a serotonin-norepinephrine reuptake inhibitor as well as a μ-opioid agonist, tramadol adulteration may worsen overdose signs and symptoms or affect the amount of naloxone patients receive.
Methods: This is a multicenter, prospective cohort of adult patients with suspected opioid overdoses who presented to one of eight United States emergency departments and were included in the Toxicology Investigators Consortium's Fentalog Study.
#Opioids: A Mixed Methods Examination of Top Opioid-Related Content on TikTok.
J Stud Alcohol Drugs
December 2024
Center for Alcohol and Addiction Studies, Brown University School of Public Health, Providence, RI.
Objective: Despite an abundance of public discourse about the opioid crisis in the media, there is little research characterizing opioid-related content on TikTok, a popular video-based social media platform. This study sought to examine how opioids are portrayed on TikTok.
Methods: This study used mixed-methods to analyze top opioid-related posts marked with the hashtag "#opioids" collected in May 2023.
Non-fatal drug overdose surveillance using hospital discharge data: a comparison between the Drug Overdose Surveillance and Epidemiology (DOSE) system and the Healthcare Cost and Utilisation Project, 18 states, 2018-2020.
Inj Prev
December 2024
Division of Overdose Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Background: The Centers for Disease Control and Prevention's Drug Overdose Surveillance and Epidemiology (DOSE) system captures non-fatal overdose data from health departments' emergency department (ED) and inpatient hospitalisation discharge data; however, these data have not been compared with other established state-level surveillance systems, which may lag by several years depending on the state. This analysis compared non-fatal overdose rates from DOSE discharge data with rates from the Healthcare Cost and Utilization Project (HCUP) in order to compare DOSE data against an established dataset.
Methods: DOSE discharge data case definitions (ie, International Classification of Diseases, 10th revision, Clinical Modification codes) for non-fatal unintentional/undetermined intent all drug, all opioid-involved, heroin-involved and stimulant-involved overdoses were applied to HCUP's 2018-2020 State Emergency Department Databases (SEDD) and State Inpatient Databases (SID).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!