Early detection of poor adherers to statins: applying individualized surveillance to pay for performance.
BACKGROUND
Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS) now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold.


METHODS
This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction.


RESULTS
Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value.


CONCLUSIONS
To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection.
Children's Hospital Informatics Program at Harvard-Massachusetts Institute of Technology Health Sciences and Technology, Boston Children's Hospital, Boston, Massachusetts, United States of America ; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America.
Andrew J
AJ
Zimolzak
Claire M
CM
Spettell
Joaquim
J
Fernandes
Vincent A
VA
Fusaro
Nathan P
NP
Palmer
Suchi
S
Saria
Isaac S
IS
Kohane
Magdalena A
MA
Jonikas
Kenneth D
KD
Mandl
D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D008297:Male; D020398:Medicare Part C / Q000191:economics*; D008875:Middle Aged; D015233:Models, Statistical; D012052:Reimbursement, Incentive; D012189:Retrospective Studies; D013997:Time Factors; D014481:United States; D016903:Drug Monitoring
D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors