Surveillance of medication use: early identification of poor adherence

DSpace/Manakin Repository

Surveillance of medication use: early identification of poor adherence

Citable link to this page

 

 
Title: Surveillance of medication use: early identification of poor adherence
Author: Jonikas, Magdalena Anna; Mandl, Kenneth David

Note: Order does not necessarily reflect citation order of authors.

Citation: Jonikas, Magdalena A, and Kenneth D Mandl. 2012. Surveillance of medication use: early identification of poor adherence. Journal of the American Medical Informatics Association : JAMIA 19(4): 649-654.
Full Text & Related Files:
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.
Published Version: doi:10.1136/amiajnl-2011-000416
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384104/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10436291
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters