Multivariable regression models were used to assess the relationship between 1-month follow-up and 6-month medication use. Variables entered into these models included study site; baseline demographics (age, sex, race, marital status); socioeconomic factors (education, insurance status); patients' avoidance of medications or care owing to cost; psychosocial factors (baseline depression score,19 patient-perceived social support20); medical history, including history of MI, percutaneous coronary intervention, prior coronary artery bypass grafting, congestive heart failure, diabetes mellitus, hypercholesterolemia, hypertension, tobacco use, peripheral arterial disease, prior cerebral vascular accident, lung disease, renal failure, and cancer; clinical status on admission (heart rate, systolic blood pressure, anemia, glucose level, estimated glomerular filtration rate); participation in cardiac rehabilitation; and MI characteristics (number of diseased vessels, primary reperfusion, any revascularization during admission). The multivariable models also adjusted for discharge prescription of each of the medications. In addition, interactions between provider follow-up status and age, sex, race, insurance status, and cost barriers to obtaining medications were tested for each medication and not found to be significant (P >.05 for all interaction terms). Because medication usage rates were not rare events, relative risks (RRs) were estimated directly using a modified Poisson regression model21 rather than logistic regression, which overestimates RRs. In secondary analyses by provider specialty, P values and confidence intervals (CIs) were adjusted for multiple comparisons using simulation-based methods to account for correlations between the different tests.22