Constance P Fontanet1,2, Niteesh K Choudhry1,2, Thomas Isaac3, Thomas D Sequist4, Chandrasekar Gopalakrishnan2, Joshua J Gagne2, Cynthia A Jackevicius5,6,7,8, Michael A Fischer2, Daniel H Solomon2,9, Julie C Lauffenburger1,2. 1. Center for Healthcare Delivery Sciences (C4HDS), Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. 2. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. 3. Department of Internal Medicine, Atrius Health, Newton, Massachusetts, USA. 4. Division of General Internal Medicine and Department of Health Care Policy, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. 5. Pharmacy Practice and Administration Department, Western University of Health Sciences, Pomona, California, USA. 6. Department of Cardiology, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA. 7. Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada. 8. ICES, University Health Network, Toronto, Ontario, Canada. 9. Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Abstract
OBJECTIVE: Medication nonadherence is linked to worsened clinical outcomes and increased costs. Existing system-level adherence interventions rely on insurer claims for patient identification and outcome measurement, yet suffer from incomplete capture and lags in data acquisition. Data from pharmacies regarding prescription filling, captured in retail dispensing, may be more efficient. DATA SOURCES: Pharmacy fill and insurer claims data. STUDY DESIGN: We compared adherence measured using pharmacy fill data to adherence using insurer claims data, expressed as proportion of days covered (PDC) over 12 months. Agreement was evaluated using correlation/validation metrics. We also explored the relationship between adherence in both sources and disease control using prediction modeling. DATA EXTRACTION METHODS: Large pragmatic trial of cardiometabolic disease in an integrated delivery network. PRINCIPAL FINDINGS: Among 1113 patients, adherence was higher in pharmacy fill (mean = 50.0%) versus claims data (mean = 47.4%), although they had moderately high correlation (R = 0.57, 95% CI: 0.53-0.61) with most patients (86.9%) being similarly classified as adherent or nonadherent. Sensitivity and specificity of pharmacy fill versus claims data were high (0.89, 95% CI: 0.86-0.91 and 0.80, 95% CI: 0.75-0.85). Pharmacy fill-based PDC predicted better disease control slightly more than claims-based PDC, although the difference was nonsignificant. CONCLUSIONS: Pharmacy fill data may be an alternative to insurer claims for adherence measurement.
OBJECTIVE: Medication nonadherence is linked to worsened clinical outcomes and increased costs. Existing system-level adherence interventions rely on insurer claims for patient identification and outcome measurement, yet suffer from incomplete capture and lags in data acquisition. Data from pharmacies regarding prescription filling, captured in retail dispensing, may be more efficient. DATA SOURCES: Pharmacy fill and insurer claims data. STUDY DESIGN: We compared adherence measured using pharmacy fill data to adherence using insurer claims data, expressed as proportion of days covered (PDC) over 12 months. Agreement was evaluated using correlation/validation metrics. We also explored the relationship between adherence in both sources and disease control using prediction modeling. DATA EXTRACTION METHODS: Large pragmatic trial of cardiometabolic disease in an integrated delivery network. PRINCIPAL FINDINGS: Among 1113 patients, adherence was higher in pharmacy fill (mean = 50.0%) versus claims data (mean = 47.4%), although they had moderately high correlation (R = 0.57, 95% CI: 0.53-0.61) with most patients (86.9%) being similarly classified as adherent or nonadherent. Sensitivity and specificity of pharmacy fill versus claims data were high (0.89, 95% CI: 0.86-0.91 and 0.80, 95% CI: 0.75-0.85). Pharmacy fill-based PDC predicted better disease control slightly more than claims-based PDC, although the difference was nonsignificant. CONCLUSIONS: Pharmacy fill data may be an alternative to insurer claims for adherence measurement.
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