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Erschienen in:
01.04.2010 | Original Article
Primary Medication Non-Adherence: Analysis of 195,930 Electronic Prescriptions
Erschienen in: Journal of General Internal Medicine | Ausgabe 4/2010
Einloggen, um Zugang zu erhaltenABSTRACT
BACKGROUND
Non-adherence to essential medications represents an important public health problem. Little is known about the frequency with which patients fail to fill prescriptions when new medications are started (“primary non-adherence”) or predictors of failure to fill.
OBJECTIVE
Evaluate primary non-adherence in community-based practices and identify predictors of non-adherence.
PARTICIPANTS
75,589 patients treated by 1,217 prescribers in the first year of a community-based e-prescribing initiative.
DESIGN
We compiled all e-prescriptions written over a 12-month period and used filled claims to identify filled prescriptions. We calculated primary adherence and non-adherence rates for all e-prescriptions and for new medication starts and compared the rates across patient and medication characteristics. Using multivariable regressions analyses, we examined which characteristics were associated with non-adherence.
MAIN MEASURES
Primary medication non-adherence.
KEY RESULTS
Of 195,930 e-prescriptions, 151,837 (78%) were filled. Of 82,245 e-prescriptions for new medications, 58,984 (72%) were filled. Primary adherence rates were higher for prescriptions written by primary care specialists, especially pediatricians (84%). Patients aged 18 and younger filled prescriptions at the highest rate (87%). In multivariate analyses, medication class was the strongest predictor of adherence, and non-adherence was common for newly prescribed medications treating chronic conditions such as hypertension (28.4%), hyperlipidemia (28.2%), and diabetes (31.4%).
CONCLUSIONS
Many e-prescriptions were not filled. Previous studies of medication non-adherence failed to capture these prescriptions. Efforts to increase primary adherence could dramatically improve the effectiveness of medication therapy. Interventions that target specific medication classes may be most effective.