Evidence map›Paper›PMID 42633260›Full record

ArticleEClinicalMedicine2026

Longitudinal patterns of fentanyl utilisation (medical versus illicit sources) in the United States 2015-2023: a retrospective cohort study.

Seungyeon Lee, Wenyu Song, David W Bates, Richard D Urman, Ping Zhang

Abstract read
In one paragraph

Article in EClinicalMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Seungyeon LeeDepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
Wenyu SongDepartment of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
David W BatesDepartment of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Richard D UrmanDepartment of Anesthesiology, College of Medicine, The Ohio State University, Columbus, OH, USA.
Ping ZhangDepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.

Funding

Opioid and SUD Data Enclave (O-SUDDEn): Bringing real-time data to the opioid crisisR01DA057668 · NIDA · OHIO STATE UNIVERSITY · PI Naleef Fareed, Soledad A Fernandez · 2022 to 2026
$3.6M
Linking Genetic and Clinical Data to Optimize Surgical Opioid Analgesic Prescribing and Predict Risks of Opioid-Related Adverse Drug EventsK01DA059572 · NIDA · BRIGHAM AND WOMEN'S HOSPITAL · PI Wenyu Song · 2024 to 2026
$581k
NIDA NIH HHS K01 DA059572NIDA NIH HHS R01 DA057668
6 · The paper itself

Abstract

Background: Over the last decade, fentanyl use in the U.S. has experienced a dramatic shift, largely driven by a rise in illicit fentanyl and its role in the opioid overdose crisis. Characterizing individual-level illicit fentanyl exposure poses significant challenges, as such use often occurs outside clinical settings and is not directly captured in clinical data, making it difficult to measure its true scale and impact. Methods: We conducted a retrospective cohort study using Epic Cosmos, a large U.S. electronic health record dataset comprising over 300 million patients across inpatient and outpatient settings nationwide (January 1, 2015, to December 31, 2023). The dataset provides individual-level clinical data, including diagnoses, medication records, and laboratory testing data, enabling longitudinal characterization of fentanyl exposure. To infer sources of fentanyl exposure, we linked urine drug testing (UDT) results to fentanyl medication records using a sequential time window screening method (e.g., UDT positive with pre-fentanyl records or without). Temporal and Cox proportional hazards analyses were used to examine longitudinal patterns and risks of opioid-related harmful outcomes associated with medical-source versus illicit-source fentanyl exposure. Regional variation was explored. Confounding was adjusted using stabilized inverse probability weighting based on demographics, social vulnerability index, and 37 baseline conditions. Subgroup analyses tested effects of underlying clinical burden. Sensitivity analyses evaluated alternative UDT screening windows and follow-up periods to assess sensitivity to exposure and outcome definitions. Findings: Our study cohort included 295,728 patients, consisting of 85,535 (28.9%) in the medical-source cohort (MSC) and 210,193 (71.1%) in the illicit-source cohort (ISC). From 2015 to 2023, the prevalence of nonfatal opioid overdose was consistently higher in the ISC. Overdose prevalence in the ISC increased markedly over time, reaching 18.6%, whereas only a modest increase was observed in the MSC, reaching 4.3%. For opioid dependence and abuse, the ISC had higher prevalence, and steeper year-over-year increases, versus the MSC until 2020. After 2020, prevalence in the ISC declined, particularly for dependence, but remained consistently higher than in the MSC throughout the study period. Region-stratified temporal patterns followed the overall cohort-level patterns. Illicit-source fentanyl initiation was associated with significantly elevated risk of 30-day opioid-related harmful outcomes, with adjusted hazard ratios of 2.99 (95% CI, 2.71-3.29; Interpretation: Illicit-source fentanyl exposure is associated with a markedly higher risk of opioid-related harmful outcomes than medical-source exposure, providing evidence that illicit fentanyl is a driver of adverse patient outcomes. Whilst acknowledging the limitations of this analysis, these findings underscore the substantial contribution of illicit fentanyl to the ongoing opioid epidemic and highlight the need for deeper investigation of how medical and illicit fentanyl use interact over time. Further research is warranted. Funding: National Institute on Drug Abuse (NIDA) and National Science Foundation (NSF).

Indexed as

Electronic health recordsFentanyl exposureIllicit fentanyl useOpioid overdoseUrine drug testing

Identifiers

PMID42633260
PMCPMC13499189

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.