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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Benzodiazepines, 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. Objective: To identify PCP groups based on their propensity to prescribe benzodiazepines, opioids, PPIs, and antibiotics, and patient and PCP characteristics associated with identified prescribing patterns. Design: Retrospective 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. Participants: A 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 Results: PCPs 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). Conclusions: VA 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.

Original languageEnglish (US)
JournalJournal of general internal medicine
DOIs
StateAccepted/In press - 2022

Keywords

  • Latent class analysis
  • Prescriptions
  • Primary care
  • VA

ASJC Scopus subject areas

  • Internal Medicine

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