title: What can primary care prescribing data tell us about individual adherence to long-term medication?-comparison to pharmacy dispensing data.
abstract: OBJECTIVE
  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%.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Science, The University of Auckland, New Zealand.
authors.forename: Thusitha
authors.initials: T
authors.lastname: Mabotuwana
authors.forename: Jim
authors.initials: J
authors.lastname: Warren
authors.forename: Jeff
authors.initials: J
authors.lastname: Harrison
authors.forename: Timothy
authors.initials: T
authors.lastname: Kenealy
mesh_terms: D002317:Cardiovascular Agents / Q000627:therapeutic use*; D003157:Community Pharmacy Services / Q000706:statistics & numerical data; D011307:Drug Prescriptions; D055695:Electronic Prescribing / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D007004:Hypoglycemic Agents / Q000627:therapeutic use*; D007349:Insurance, Health, Reimbursement; D007356:Insurance, Pharmaceutical Services; D016014:Linear Models; D008297:Male; D009520:New Zealand; D011320:Primary Health Care / Q000706:statistics & numerical data; D013997:Time Factors; D016903:Drug Monitoring
chemical_list: D002317:Cardiovascular Agents; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D007004:Hypoglycemic Agents