Literature DB >> 34601275

Literal text analysis of poly-class and polydrug overdose deaths in North Carolina, 2015-2019.

Kristin Y Shiue1, Anna E Austin2, Scott Proescholdbell3, Mary E Cox3, Michelle Aurelius4, Rebecca B Naumann5.   

Abstract

BACKGROUND: The literal text on death certificates was leveraged to enhance the examination of trends in the specific drugs and drug combinations involved in North Carolina (NC) overdose deaths from 2015 to 2019.
METHODS: Using NC death certificate data, overdose deaths included those with a drug poisoning as the underlying ICD-10 cause-of-death code (n = 10,117). The literal text from three death certificate fields were searched for drug mentions by integrating a tool developed by the Council of State and Territorial Epidemiologists Overdose Subcommittee with search terms originating from a National Center for Health Statistics/Food and Drug Administration collaboration. Descriptive statistics were calculated to evaluate substance classes, specific drugs, and drug combinations most frequently involved in these deaths over time.
RESULTS: From 2015-2019, polydrug involvement in NC overdose deaths increased (71% in 2015 to 75% in 2019). During the study period, opioid involvement shifted from heroin and/or oxycodone in 2015 to predominantly fentanyl in 2019, with fentanyl involvement increasing from 15% to 58%. Psychostimulant involvement increased for both cocaine (2015: 21%, 2019: 35%) and methamphetamine (2015: 3%, 2019: 13%). Benzodiazepine involvement, including alprazolam and clonazepam, declined during the study period, while the involvement of alcohol and antiepileptics/sedative-hypnotics, specifically gabapentin, remained stable. The top polydrug combinations in 2019 were fentanyl + cocaine (15% of all overdose deaths), fentanyl + heroin (10%), fentanyl + cocaine + heroin (6%), and fentanyl + methamphetamine (4%).
CONCLUSIONS: Incorporation of literal text methodology into ongoing overdose surveillance can facilitate the identification of specific, emerging drugs and combinations and inform targeted overdose prevention approaches.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Benzodiazepines; Opioids; Overdose; Polydrug; Polysubstance; Stimulants

Mesh:

Substances:

Year:  2021        PMID: 34601275     DOI: 10.1016/j.drugalcdep.2021.109048

Source DB:  PubMed          Journal:  Drug Alcohol Depend        ISSN: 0376-8716            Impact factor:   4.492


  1 in total

1.  Development and Validation of Machine Models Using Natural Language Processing to Classify Substances Involved in Overdose Deaths.

Authors:  David Goodman-Meza; Chelsea L Shover; Jesus A Medina; Amber B Tang; Steven Shoptaw; Alex A T Bui
Journal:  JAMA Netw Open       Date:  2022-08-01
  1 in total

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