Evidence map›Paper›PMID 34861189›Full record

ArticleThe Lancet. Public health2022

Clinical impact, costs, and cost-effectiveness of hospital-based strategies for addressing the US opioid epidemic: a modelling study.

Joshua A Barocas, Alexandra Savinkina, Joella Adams, Raagini Jawa, Zoe M Weinstein, Jeffrey H Samet, Benjamin P Linas

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in The Lancet. Public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07693270 (Identifying Biomarkers Related to Opioid Use and Pain Response Following Traumatic Injury and Surgery), which is not on this map. Cited by 32 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 2 pooled it
6.9field-weighted citation impact, top 3% of its field
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.

NCT07693270 recruitingnot on this mapstarted 2026, after this paper: background citation

Identifying Biomarkers Related to Opioid Use and Pain Response Following Traumatic Injury and Surgery

TypeobservationalSponsorUniversity of California, San DiegoRan2026 to 2027Enrolled100ConditionsOpioid Use Disorder, Opioid Consumption, Persistent Opioid Use, Persistent Postsurgical Pain
3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 1 country.

Joshua A BarocasSections of General Internal Medicine and Infectious Diseases, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. Electronic address: joshua.barocas@anschutz.edu.
Alexandra SavinkinaYale School of Public Health, New Haven, CT, USA.
Joella AdamsRTI, Durham, NC, USA.
Raagini JawaSection of Infectious Diseases, Boston Medical Center, Boston, MA, USA; Boston University School of Medicine, Boston, MA, USA.
Zoe M WeinsteinClinical Addiction Research and Education Unit, Section of General Internal Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA.
Jeffrey H SametBoston University School of Medicine, Boston, MA, USA; Clinical Addiction Research and Education Unit, Section of General Internal Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA.
Benjamin P LinasSection of Infectious Diseases, Boston Medical Center, Boston, MA, USA; Boston University School of Medicine, Boston, MA, USA.
Boston Medical Center · USBoston University · USRTI International · USUniversity of Colorado Anschutz Medical Campus · USYale University · US

Funding

Translational ScienceP30AI042853 · NIAID · MIRIAM HOSPITAL · PI CURT G BECKWITH, DEBBIE M. CHENG · 1998 to 2026
$51.7M
Utilization of health services after engagement with opioid use disorder (OUD) treatment provider by individuals with and without post-traumatic stress disorderP30DA040500 · NIDA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Yuhua Bao · 2015 to 2026
$20.7M
Researching Effective Strategies to Prevent Opioid Death (RESPOND)R01DA046527 · NIDA · BOSTON MEDICAL CENTER · PI Benjamin P. Linas · 2018 to 2026
$7.2M
BU Clinical HIV/AIDS Research Training Program (BU-CHART)T32AI052074 · NIAID · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Benjamin P. Linas · 2003 to 2026
$6.7M
Development of a novel community-based high-performance surveillance network for drug useDP2DA051864 · NIDA · UNIVERSITY OF COLORADO DENVER · PI BAROCAS, JOSHUA ADAM · 2020 to 2020
$2.3M
Strategies to reduce serious bacterial infections and overdose among people who inject drugsK01DA051684 · NIDA · UNIVERSITY OF COLORADO DENVER · PI BAROCAS, JOSHUA ADAM · 2020 to 2024
$809k
NIAID NIH HHS P30 AI042853NIAID NIH HHS T32 AI052074NIDA NIH HHS DP2 DA051864NIDA NIH HHS K01 DA051684NIDA NIH HHS P30 DA040500NIDA NIH HHS R01 DA046527
6 · The paper itself

Abstract

backgroundThe syndemic of injection drug use and serious injection-related infections is leading to increasing mortality in the USA. Although outpatient treatment with medications for opioid use disorder reduces overdose risk and recurrent infections, hospitalisation remains common. We evaluated the clinical impact, costs, and cost-effectiveness of hospital-based strategies to address the US opioid epidemic.

methodsWe developed a microsimulation model to compare the cost-effectiveness of: standard hospital care-detoxification for opioids, no addiction consult service (status quo); expanded inpatient prescribing of medications for opioid use disorder, including bridge prescriptions (ie, medication until they can see an outpatient provider) when possible (medications for opioid use disorder with bridge); implementation of addiction consult services within the hospital (addiction consult services alone); and a combined medication for opioid use disorder with addiction consult services strategy (combined). We used clinical trials and observational cohorts to inform model inputs. Outcomes were life-years, discounted costs, incremental cost-effectiveness ratios, hospitalisations, and deaths. We did deterministic sensitivity analyses on key model inputs related to costs and sequelae of drug use and probabilistic sensitivity analysis to further address uncertainty.

findingsAmong people who inject opioids in the USA, we estimated that expanding medications for opioid use disorder with bridge prescriptions would reduce hospitalisations and overdose deaths by 3·2% and 3·6%, respectively, and the combination of expanded medications with opioid use disorder along with addiction consult sevices would reduce hospitalisations and overdoses by 5·2% and 6·6%, respectively, compared with the status quo. Mean lifetime costs ranged from US$731 400 (95% credible interval 447 911-859 189 for the medications for opioid use disorder strategy) to $741 200 (470 930-868 551 for the combined strategy) per person. Assuming a willingness-to-pay threshold of $100 000 per life-year gained, medications for opioid use disorder with bridge and combined strategies were cost-effective ($7600 and $14 300, respectively). A scenario that assumed ideal access to harm reduction services came to the same conclusions as the base case and our results were robust in deterministic and probabilistic sensitivity analyses.

interpretationThe combined interventions of expanding hospital-based prescribing of medications for opioid use disorder and implementing addiction consult services could improve life expectancy, be cost-effective, and could be the basis for a comprehensive hospital-based strategy for addressing the opioid epidemic in the USA and countries with similar opioid epidemics.

fundingNational Institute on Drug Abuse and National Institute of Allergy and Infectious Diseases.

Indexed as

Cost-Benefit AnalysisDrug OverdoseHealth ServicesHospital AdministrationHospitalizationHumansModels, EconomicMonte Carlo MethodOpioid EpidemicOpioid-Related DisordersPatient Acceptance of Health CarePrescription DrugsReferral and ConsultationPrescription Drugs

Identifiers

PMID34861189
PMCPMC8756295
OpenAlexW3217211701

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.