title: Association of potentially inappropriate medication use with patient and prescriber characteristics in Medicare Part D.
abstract: OBJECTIVE
  The use of potentially inappropriate medications (PIMs) in older people is associated with increased risk of adverse drug events and hospitalization. This study aimed to determine the contribution of primary prescribers to variation in PIM use.
  
  
  METHODS
  This was a retrospective cohort study using 2008 Medicare Part D event files and claims data for a 100% sample of Texas beneficiaries. PIM use was defined as receiving any of 48 medications on the Beers 2003 list of PIMs. Patient characteristics associated with PIM use were determined using a multivariable model. A multilevel model for the odds of PIM use was constructed to evaluate the amount of variation in PIM use at the level of primary care prescriber, controlling for patient characteristics.
  
  
  RESULTS
  Of 677,580 patients receiving prescriptions through Part D in 2008, 31.9% received a PIM. Sex, ethnicity, low-income subsidy eligibility, and hospitalization in 2007 were associated with PIM use. The strongest associations with higher PIM use were increasing number of prescribers and increasing number of medications. The odds ratio for PIM use was 1.50 (95%CI 1.47-1.53) for ≥4 prescribers versus only 1 prescriber. In the multilevel model, the adjusted average percent of patients prescribed a PIM ranged from 17.5% for the lowest decile to 28.9% for the highest decile of prescribers.
  
  
  CONCLUSIONS
  PIM use was prevalent in Part D beneficiaries and varied among individual primary care prescribers. The association of PIM use with increasing numbers of prescribers suggests the need to reduce fragmentation of care to reduce inappropriate prescribing.
authors.affiliation: Department of General Internal Medicine, UT MD Anderson Cancer Center, Houston, TX, USA. hholmes@mdanderson.org
authors.forename: Holly M
authors.initials: HM
authors.lastname: Holmes
authors.forename: Ruili
authors.initials: R
authors.lastname: Luo
authors.forename: Yong-Fang
authors.initials: YF
authors.lastname: Kuo
authors.forename: Jacques
authors.initials: J
authors.lastname: Baillargeon
authors.forename: James S
authors.initials: JS
authors.lastname: Goodwin
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D057225:Data Mining; D016208:Databases, Factual / Q000706:statistics & numerical data; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D017723:Drug Utilization Review / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D057970:Inappropriate Prescribing / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D054524:Medicare Part D / Q000706:statistics & numerical data*; D015999:Multivariate Analysis; D016017:Odds Ratio; D017891:Pharmacoepidemiology; D060735:Pharmacovigilance; D058007:Physicians, Primary Care / Q000706:statistics & numerical data*; D019338:Polypharmacy; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012307:Risk Factors; D014481:United States
keywords: Beers criteria; Medicare Part D; elderly; inappropriate medication; pharmacoepidemiology; pharmacotherapy; polypharmacy