Predictors of Adherence to Inhaled Medications among Veterans with COPD. John Huetsch
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1 Predictors of Adherence to Inhaled Medications among Veterans with COPD John Huetsch
2 Background Medication nonadherence is a problem common to the treatment of many chronic diseases Potential obstacles to adherence in COPD patients: Inhaled medications, polypharmacy, and medication use despite quiescent symptoms Limited exploration of medication adherence in patients with COPD
3 Research Questions Can we identify demographic, behavioral, or disease-related factors that are associated with medication adherence in patients with COPD? Does a patient s adherence to one inhaled medications predict their adherence to their other inhaled medications?
4 Methods Population: Patients who had PFTs performed between at two VA sites and who had post-bronchodilator FEV 1 /FVC < 0.7 Data Source: VA electronic records, including pharmacy records
5 Methods Measurements of variables Medications of interest: Inhaled corticosteroids (ICS) Long-acting beta agonists (LABA) Ipratropium bromide (IP) Outcome Measure: Medication adherence in the six months following the index date (date of the PFT) Adherence defined as ReComp score Exposure Variables: Medication adherence in the six months before the index date Demographic: Age, gender, race Behavioral: Smoking status, missed clinic visits COPD measures: FEV 1, number of outpatient COPD exacerbations Comorbid diseases
6 Associations between Patient Characteristics and Medication Adherence, I Outcome ICS Adherence Outcome LABA Adherence Outcome IP Adherence N=4971 Patients Total Adherent Nonadherent Adherent Nonadherent Adherent Nonadherent Demographic/Behavioral Mean age (years, sd) 65.5 (10.9) 66.4 (10.8) 65.2 (11.5) 67.1 (10.3) 65.2 (11.1) 66.3 (10.0) 66.4 (10.5) Male (%) Race (%) White Other Unknown Smoker within past year (%) Missed Clinic Visits (m, sd) (1.0) 0.5 (1.1) 0.6 (1.1) 0.5 (1.0) 0.7 (1.3) 0.5 (1.1) 0.5 (1.0)
7 Associations between Patient Characteristics and Medication Adherence, II Outcome ICS Adherence Outcome LABA Adherence Outcome IP Adherence N=4971 Patients Total Adherent Nonadherent Adherent Nonadherent Adherent Nonadherent COPD Severity FEV1 % predicted (m, sd) 54.0 (19.4) 45.8 (17.5) 48.1 (18.1) 42.5 (16.9) 46.1 (17.6) 43.8 (17.4) 50.0 (17.5) FEV1 Liters (m, sd) 1.9 (0.8) 1.6 (0.7) 1.7 (0.7) 1.5 (0.6) 1.6 (0.7) 1.5 (0.7) 1.8 (0.7) 1 inpatient COPD exacerbation (%) 1 outpatient COPD exacerbation (%)
8 Associations between Patient Characteristics and Medication Adherence, III Outcome ICS Adherence Outcome LABA Adherence Outcome IP Adherence N=4971 Patients Total Adherent Nonadherent Adherent Nonadherent Adherent Nonadherent Comorbidities (%) Lung cancer Depression Asthma Acute Coronary Syndrome Congestive Health Failure Diabetes Edema Hypertension Neuromuscular Disease Sleep Apnea Schizophrenia Medication classes (m, sd) 9.4 (6.9) 12.0 (6.4) 11.8 (6.8) 12.3 (6.5) 11.7 (7.0) 11.7 (6.7) 11.0 (6.8)
9 Associations between Patient Characteristics and Medication Adherence, IV Outcome ICS Adherence Outcome LABA Adherence Outcome IP Adherence N=4971 Patients Total Adherent Nonadherent Adherent Nonadherent Adherent Nonadherent Medication Adherence Number on ICS Baseline adherence to ICS (%) Number on LABA Baseline adherence to LABA (%) Number on IP Baseline adherence to IP (%)
10 Baseline adherence strongly predicts adherence to the same medication, but weakly for other medications Odds of ICS Adherence Odds of LABA Adherence Odds of IP Adherence Moderate ICS Adherence Good ICS Adherence Excellent ICS Adherence FEV 1 (Percent Predicted) No. missed appointments No. medication classes Moderate LABA Adherence Good LABA Adherence Excellent LABA Adherence FEV 1 (Percent Predicted) No. missed appointments No. medication classes Moderate IP Adherence Good IP Adherence Excellent IP Adherence FEV 1 (Percent Predicted) No. missed appointments No. medication classes All regression models also include age, sex, race, number of outpatient COPD exacerbations, lung cancer, and asthma as exposure variables. Error bars reflect 95% confidence interval
11 Conclusions Adherence rates to inhaled medications for COPD are low Adherence to an inhaled medication strongly predicts future adherence to the same medication Adherence to an inhaled medication only weakly predicts future adherence to other medications Severity of COPD is associated with better adherence to all classes of medication Other patient characteristics are less predictive of medication adherence
12 Implications The reasons why patients selectively adhere to different components of their medication regimen remains undetermined Physicians need to assess patient adherence to each class of medication separately Intervention studies that aim to improve medication adherence will likely require interventions focused on each inhaled medication class
13 Acknowledgments David Au Jane Uman Ed Udris VA Health Services Research and Development
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