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  • Title: What can primary care prescribing data tell us about individual adherence to long-term medication?-comparison to pharmacy dispensing data.
    Author: Mabotuwana T, Warren J, Harrison J, Kenealy T.
    Journal: Pharmacoepidemiol Drug Saf; 2009 Oct; 18(10):956-64. PubMed ID: 19609958.
    Abstract:
    PURPOSE: To assess the predictive value of general practice electronic prescribing records with respect to adherence to long-term medications as compared to claims-based pharmacy dispensing data. METHODS: A total of 29772 electronic prescribing records relating to 2713 patients attending a New Zealand general medical practice were linked by national health identifier to 63 833 dispensing records used for community pharmacy reimbursement. Individual possession ratios-prescription possession ratio (PPR) for prescribing and medication possession ratio (MPR) for dispensing-were calculated for the 15-month period from 1 January 2006 to 30 March 2007 based on each data source for the common long-term medications simvastatin, metoprolol succinate, bendrofluazide, felodipine, cilazapril and metformin. RESULTS: Out of 646 patients prescribed at least one of the six medications by the practice during the 15-month period, 50% of patients maintained high adherence (MPR > or = 80%) to all (out of the 6) medications that they were prescribed over the period, with rates of high adherence to individual medications ranging from 68 (felodopine) to 55% (metformin). In 93% of 4043 cases where there was a prescription in the general practice data, a subsequent dispensing record for the same patient and drug was present with a time-stamp no more than seven days later. PPR < 80% demonstrated a positive predictive value (PPV) of 81.4% (95%CI 78-85%) and negative predictive value (NPV) of 76.3% (95%CI 73-79%) for MPR < 80%. CONCLUSION: There is potential for general practices to identify substantial levels of long-term medication adherence problems through their electronic prescribing records. Significant further adherence problems could be detected if an e-pharmacy network allowed practices to match dispensing against prescriptions.
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