title: Surveillance of medication use: early identification of poor adherence.
abstract: BACKGROUND
  We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence.
  
  
  METHODS
  Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence.
  
  
  RESULTS
  All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point.
  
  
  CONCLUSIONS
  Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted.
authors.affiliation: Children's Hospital Informatics Program, Children's Hospital Boston, Boston, Massachusetts 02115, USA.
authors.forename: Magdalena A
authors.initials: MA
authors.lastname: Jonikas
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Mandl
mesh_terms: D000368:Aged; D000465:Algorithms; D000959:Antihypertensive Agents; D002908:Chronic Disease / Q000188:drug therapy*; D016903:Drug Monitoring / Q000379:methods*; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents; D000960:Hypolipidemic Agents; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D050316:Medical Order Entry Systems; D008875:Middle Aged; D010349:Patient Compliance; D011159:Population Surveillance / Q000379:methods*; D012189:Retrospective Studies; D014481:United States
chemical_list: D000959:Antihypertensive Agents; D007004:Hypoglycemic Agents; D000960:Hypolipidemic Agents
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