title: Predictors of medication adherence postdischarge: the impact of patient age, insurance status, and prior adherence.
abstract: BACKGROUND
  Optimizing postdischarge medication adherence is a target for avoiding adverse events. Nevertheless, few studies have focused on predictors of postdischarge medication adherence.
  
  
  METHODS
  The Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL-CVD) study used counseling and follow-up to improve postdischarge medication safety. In this secondary data analysis, we analyzed predictors of self-reported medication adherence after discharge. Based on an interview at 30-days postdischarge, an adherence score was calculated as the mean adherence in the previous week of all regularly scheduled medications. Multivariable linear regression was used to determine the independent predictors of postdischarge adherence.
  
  
  RESULTS
  The mean age of the 646 included patients was 61.2 years, and they were prescribed an average of 8 daily medications. The mean postdischarge adherence score was 95% (standard deviation [SD] = 10.2%). For every 10-year increase in age, there was a 1% absolute increase in postdischarge adherence (95% confidence interval [CI] 0.4% to 2.0%). Compared to patients with private insurance, patients with Medicaid were 4.5% less adherent (95% CI -7.6% to -1.4%). For every 1-point increase in baseline medication adherence score, as measured by the 4-item Morisky score, there was a 1.6% absolute increase in postdischarge medication adherence (95% CI 0.8% to 2.4%). Surprisingly, health literacy was not an independent predictor of postdischarge adherence.
  
  
  CONCLUSIONS
  In patients hospitalized for cardiovascular disease, predictors of lower medication adherence postdischarge included younger age, Medicaid insurance, and baseline nonadherence. These factors can help predict patients who may benefit from further interventions.
authors.affiliation: Division of General Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA. mjcohen@partners.org
authors.forename: Marya J
authors.initials: MJ
authors.lastname: Cohen
authors.forename: Shimon
authors.initials: S
authors.lastname: Shaykevich
authors.forename: Courtney
authors.initials: C
authors.lastname: Cawthon
authors.forename: Sunil
authors.initials: S
authors.lastname: Kripalani
authors.forename: Michael K
authors.initials: MK
authors.lastname: Paasche-Orlow
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: Schnipper
mesh_terms: D017677:Age Distribution; D002318:Cardiovascular Diseases / Q000188:drug therapy* / Q000523:psychology; D005260:Female; D005544:Forecasting; D057220:Health Literacy; D006801:Humans; D019458:Insurance Coverage; D007348:Insurance, Health / Q000145:classification / Q000706:statistics & numerical data*; D007407:Interviews as Topic; D016014:Linear Models; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data; D055118:Medication Adherence / Q000523:psychology; D008875:Middle Aged; D010351:Patient Discharge / Q000592:standards* / Q000706:statistics & numerical data; D014481:United States; D016903:Drug Monitoring