Has the United States Reached a Plateau in Overdoses Caused by Synthetic Opioids After the Onset of the COVID-19 Pandemic? Examination of Centers for Disease Control and Prevention Data to November 2021

Brown, Kate; Chen, Carina Y.; Dong, Deanna; Lake, Kimberly J.; Butelman, Eduardo R.

doi:10.3389/fpsyt.2022.947603

Cited by 12 publications

(9 citation statements)

References 49 publications

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“…As mentioned in the Introduction, there have been increases in overdose mortality for both synthetic opioids and psychostimulants during the COVID-19 pandemic period [1,4,51]. Furthermore, mortality due to COVID-19 is also greater in men versus women [50,104], and substance use disorders have been detected as a comorbidity with this viral infection [51,56].…”

Section: Limitationsmentioning

confidence: 99%

“…Overdose deaths due to the use of opioids [-opioid receptor (MOP-r) agonists such as fentanyl and heroin], or psychostimulants such as methamphetamine and cocaine, have increased considerably in recent years in the United States [1][2][3]. Starting in 2020, and following the onset of the COVID-19 pandemic, there has been a further increase in overdose mortality for some substances (e.g., synthetic opioids such as fentanyl or the psychostimulant methamphetamine), but not others (e.g., heroin and other opiates) [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, there has been an increase in overdoses due to synthetic opioids during recent years, especially 2020 and thereafter, coinciding with the COVID-19 pandemic [1,4,48]. COVID-19 mortality, which is greater in men versus women, also involves respiratory distress [49-51], and the presence of specific SUD is also noted as a risk factor for COVID-19 infection [51,52].…”

Section: Introductionmentioning

confidence: 99%

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Overdose mortality rates for opioids or stimulants are higher in males than females, controlling for rates of drug misuse: State-level data

Butelman¹,

Huang²,

Goldstein³

et al. 2023

Preprint

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Drug overdoses are an escalating cause of mortality in the United States, with potential sex differences across the lifespan. The objective of this study was to use state-level nationally representative data that includes the COVID-19 pandemic period to determine overdose mortality for specific drug categories across the lifespan of men and women. We used Centers for Disease Control and Prevention (CDC) Multiple Cause of Death 2020-2021 data on overdose mortality, for 50 states and District of Columbia, across 10-year age bins (age range: 15-74). The outcome measure was sex-specific crude overdose death rate (per 100,000) for: synthetic opioids excluding methadone (ICD-10 code: T40.4; e.g., fentanyl), heroin (T40.1), psychostimulants with abuse potential (T43.6; e.g., methamphetamine), and cocaine (T40.5). Multiple regression analyses adjusted for ethnic-cultural background and household net worth from Census data, and sex-specific rate of misuse of the relevant substances, from the National Survey on Drug Use and Health (2019-2020). For each of these major drug categories, men had greater overall overdose mortality than women. Although overall rates of mortality differed across jurisdictions, the sex ratio of mortality for each drug category was relatively stable (≈2- to 3-fold greater mortality in men vs women). These findings survived adjustment for state-level ethnic-cultural and economic variables, and for sex-specific misuse of each drug type (especially in the 25-34, 35-44, 45-54 and 55-64 age bins). These findings underscore the need for research into sex- and gender-based mechanisms underlying differential vulnerability in overdose mortality for these drugs, based on their diverse pharmacodynamics and pathophysiology.

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Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Overdose mortality rates for opioids or stimulants are higher in males than females, controlling for rates of drug misuse: State-level data

Butelman¹,

Huang²,

Goldstein³

et al. 2023

Preprint

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“…In 2020, overdose deaths from opioid misuse soared to an all-time high, with a record 93,000 deaths nationwide during the pandemic year [5]. Substance misuse ranks second among the principal diagnoses for unplanned 7-day hospital readmission rates [6].…”

Section: Introductionmentioning

confidence: 99%

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults

Afshar¹,

Adelaine²,

Resnik³

et al. 2023

JMIR Med Inform

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Background The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery. Objective We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool. Methods The pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan. Results The pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR. Conclusions The components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence–driven CDS. Trial Registration ClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480

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“…The COVID-19 pandemic has exposed major gaps in health care delivery with limited resources and staffing. In 2020, deaths related to drug overdose reached an all-time high with a record 93,000 deaths nationwide during the pandemic year [ 1 ]. The number of substance use–related hospital visits outpaces visits for heart disease and respiratory failure [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design

et al. 2022

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Background Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. Objective This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use. Methods A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models. Results The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025. Conclusions The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. Trial Registration ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804 International Registered Report Identifier (IRRID) DERR1-10.2196/42971

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Has the United States Reached a Plateau in Overdoses Caused by Synthetic Opioids After the Onset of the COVID-19 Pandemic? Examination of Centers for Disease Control and Prevention Data to November 2021

Cited by 12 publications

References 49 publications

Overdose mortality rates for opioids or stimulants are higher in males than females, controlling for rates of drug misuse: State-level data

Overdose mortality rates for opioids or stimulants are higher in males than females, controlling for rates of drug misuse: State-level data

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults

The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design

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