Evidence map›Paper›PMID 34993865›Full record

ArticleJournal of general internal medicine2022

Latent Class Analysis of Prescribing Behavior of Primary Care Physicians in the Veterans Health Administration.

Alexis K Barrett, John P Cashy, Carolyn T Thorpe, Jennifer A Hale, Kangho Suh, Bruce L Lambert, William Galanter, Jeffrey A Linder, Gordon D Schiff, Walid F Gellad

Open access · bronzeAbstract read
In one paragraph

Article in Journal of general internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 30% 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.

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

9 citing papers in PubMed, 8 citations in OpenAlex.

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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

10 authors at 7 institutions in 1 country.

Alexis K BarrettVA Center for Medication Safety/Pharmacy Benefits Management Services, U.S. Department of Veteran Affairs, Hines, IL, USA. alexis.barrett@va.gov.ORCID 0000-0002-9339-5392
John P CashyCenter for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, USA.
Carolyn T ThorpeCenter for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, USA.
Jennifer A HaleCenter for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, USA.
Kangho SuhDepartment of Pharmacy and Therapeutics, University of Pittsburgh, Pittsburgh, PA, USA.
Bruce L LambertDepartment of Communication Studies, Center for Communication and Health, Northwestern University, Evanston, IL, USA.
William GalanterDepartment of Medicine, Department of Pharmacy Systems, Outcomes & Policy, University of Illinois at Chicago, Chicago, IL, USA.
Jeffrey A LinderDivision of General Internal Medicine and Geriatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Gordon D SchiffCenter for Patient Safety Research and Practice, Brigham and Women's Hospital, Boston, MA, USA.
Walid F GelladCenter for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, USA.
Northwestern University · USUniversity of Pittsburgh · USVA Pittsburgh Healthcare System · USBrigham and Women's Hospital · USMedication Management (United States) · USUniversity of Illinois Chicago · USUniversity of North Carolina at Chapel Hill · US

Funding

The Claude D. Pepper Older Americans Independence Center (OAIC) at Northwestern UniversityP30AG059988 · NIA · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LINDER, JEFFREY A., WOLF, MICHAEL S · 2020 to 2024
$7.1M
ToolBox Detect: Low Cost Detection of Cognitive Decline in Primary Care SettingsR01AG069762 · NIA · NORTHWESTERN UNIVERSITY AT CHICAGO · PI GERSHON, RICHARD, NOWINSKI, CINDY J. · 2020 to 2025
$6.4M
Use of Behavioral Economics in Repeat SARS-CoV-2 Antibody Testing in Disadvantaged CommunitiesR33AG057395 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI DOCTOR, JASON N., SULLIVAN, MARK D · 2019 to 2021
$3.3M
Improving Safe Antibiotic Prescribing in Telehealth: A Randomized TrialR01HS026506 · AHRQ · YALE UNIVERSITY · PI MEEKER, DANIELLA · 2018 to 2022
$2.1M
Behavioral Economics Applications to Geriatrics Leveraging EHRs (BEAGLE)R33AG057383 · NIA · NORTHWESTERN UNIVERSITY AT CHICAGO · PI PERSELL, STEPHEN · 2019 to 2021
$2.1M
The Role of Telehealth in COVID-19 ResponseR01HS028127 · AHRQ · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MEEKER, DANIELLA · 2021 to 2022
$961k
AHRQ HHS HHSP233201500020IAHRQ HHS R01 HS026506AHRQ HHS R01 HS028127NIA NIH HHS P30 AG059988NIA NIH HHS R01 AG069762NIA NIH HHS R33 AG057383NIA NIH HHS R33 AG057395
6 · The paper itself

Abstract

backgroundBenzodiazepines, opioids, proton-pump inhibitors (PPIs), and antibiotics are frequently prescribed inappropriately by primary care physicians (PCPs), without sufficient consideration of alternative options or adverse effects. We hypothesized that distinct groups of PCPs could be identified based on their propensity to prescribe these medications.

objectiveTo identify PCP groups based on their propensity to prescribe benzodiazepines, opioids, PPIs, and antibiotics, and patient and PCP characteristics associated with identified prescribing patterns.

designRetrospective cohort study using VA data and latent class regression analyses to identify prescribing patterns among PCPs and examine the association of patient and PCP characteristics with class membership.

participantsA total of 2524 full-time PCPs and their patient panels (n = 2,939,636 patients), from January 1, 2017, to December 31, 2018. MAIN MEASURES: We categorized PCPs based on prescribing volume quartiles for the four drug classes, based on total days' supply dispensed of each medication by the PCP to their patients (expressed as days' supply per 1000 panel patient-days). We used latent class analysis to group PCPs based on prescribing and used multinomial logistic regression to examine patient and PCP characteristics associated with latent class membership. KEY

resultsPCPs were categorized into four groups (latent classes): low intensity (23% of cohort), medium-intensity overall/high-intensity PPI (36%), medium-intensity overall/high-intensity opioid (20%), and high intensity (21%). PCPs in the high-intensity group were predominantly in the highest quartile of prescribers for all four drugs (68% in the highest quartile for benzodiazepine, 86% opioids, 64% PPIs, 62% antibiotics). High-intensity PCPs (vs. low intensity) were substantially less likely to be female (OR: 0.30, 95% CI: 0.21-0.42) or practice in the northeast versus other census regions (OR: 0.10, 95% CI: 0.06-0.17).

conclusionsVA PCPs can be classified into four clearly differentiated groups based on their prescribing of benzodiazepines, opioids, PPIs, and antibiotics, suggesting an underlying typology of prescribing. High-intensity PCPs were more likely to be male.

Indexed as

Analgesics, OpioidPhysicians, Primary CareAnti-Bacterial AgentsBenzodiazepinesFemaleHumansLatent Class AnalysisMalePharmaceutical PreparationsPractice Patterns, Physicians'Proton Pump InhibitorsRetrospective StudiesVeterans HealthAnalgesics, OpioidAnti-Bacterial AgentsBenzodiazepinesPharmaceutical PreparationsProton Pump InhibitorsLatent class analysisPrescriptionsPrimary careVA

Identifiers

PMID34993865
PMCPMC9550922
OpenAlexW4205878035

What OpenQuestion holds

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Registered trials

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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.