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Predicting Adherence Trajectory Using Initial Patterns of Medication Filling
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Predicting Adherence Trajectory Using Initial Patterns of Medication Filling

Jessica M. Franklin, PhD; Alexis A. Krumme, MS; William H. Shrank, MD, MSHS; Olga S. Matlin, PhD; Troyen A. Brennan, MD, JD, MPH; and Niteesh K. Choudhry, MD, PhD
Initial medication filling during the first 2 to 4 months following initiation of a statin strongly predicted adherence patterns during the following year.
Among patients with an initial statin fill greater than 30 days, prediction accuracy when using only baseline clinical predictors was strongest when predicting trajectory group 6, but even this had only modest discriminative ability (C = 0.67). Prediction of this group was near perfect when adding in any initial adherence variables. All other trajectories or trajectory groupings were predicted poorly when using baseline clinical characteristics and up to 3 months of initial adherence indicators (C ≤0.59). However, when using 4 months of initial adherence indicators, discrimination was greatly improved for all adherence groupings, ranging from 0.65 for prediction of trajectory 3 versus all others, up to 0.84 for prediction of trajectories 1 through 3 versus 4 through 6.

In this cohort of Medicare beneficiaries initiating treatment with statins, we found that initial adherence during the first few months after initiation strongly predicted the 12-month adherence trajectory. Prediction was best when predicting consistent medication use (trajectory 1) and consistent nonuse (trajectory 6) or when predicting the combination of groups 1 through 3 versus 4 through 6. Among patients with an index prescription longer than 30 days, accurate predictions for most trajectories required observing adherence for 4 months after initiation—likely because this window provides an opportunity to observe the presence or absence of a refill among patients with a 90-day index prescription. In all cases, prediction using initial adherence observations was much stronger than prediction from baseline clinical characteristics alone.

These results confirm and extend results previously reported in studies of adherence. Identified adherence trajectories were very similar to those observed in other cohorts of statin initiators.11 Poor prediction of adherence from baseline characteristics alone has also been observed across a range of medications, especially in the study of statins.6,7,11,23,24 The high accuracy of adherence predictions based on initial filling behavior is similar to that recently observed when predicting whether patients will be optimally adherent over the 12-month period following initiation based on a PDC threshold of ≥0.8.13 However, accuracy when predicting the most extreme trajectories (1 and 6) was even stronger than accuracy in predicting optimal adherence. In addition, prior research did not evaluate the effect of including a fourth month of adherence observations to the predictions, which appears to be crucial for patients with 90-day index fills.

Predicting adherence trajectory rather than PDC may be useful for focusing interventions on patients with moderate medication use—for example, patients falling in trajectories 2 through 5. These patients are not perfect adherers, but they also have not completely discontinued their medication. Therefore, patients with these dynamic patterns may be most susceptible to potential interventions that encourage adherence and those that help moderate adherers refill more regularly or avert discontinuation.

In addition, interventions may be especially effective if deployed at specific times (eg, just prior to or in the early stages of nonadherence). Unlike PDC, trajectory groupings differentiate between patients who struggle with adherence at different times during their medication use. For instance, patients in group 4 were identified well after observing 4 months of initial adherence. For these patients, this time coincides with a steep decline in adherence, followed by a period of sporadic medication use. Targeted interventions for patients predicted to be in group 4 could be implemented at this time, and these interventions may be systematically different in structure and timing than those targeted to patients predicted to be in group 6. Filling behavior from the first 3 or 4 months would therefore be highly actionable, even in the presence of administrative delays in the receipt of claims data.


Our study was restricted to patients who demonstrated active use of the healthcare system and who remained enrolled in both Medicare and their Part D drug plan for 180 days before and 365 days after their initial statin dispensing. This group may not be representative of all statin initiators in Medicare though, since the patients in our study maintained stable drug coverage for at least 18 months. Prediction performance observed in our study may also not hold in a younger working population receiving statins or when predicting adherence to other chronic disease medications.

As in prior studies of medication adherence, our study is also limited by the accuracy of assessing adherence from pharmacy claims data, which may misclassify the adherence of patients who fill prescriptions but do not actually take them. However, we expect this issue to be of less practical importance in patients with short, frequent dispensings. The potential for misclassification is also diminished as the period of adherence follow-up is lengthened, since patients who do not take their medications generally do not continue to fill those medications. The use of pharmacy claims additionally prevents us from evaluating the reason for nonadherence, including clinically appropriate discontinuation due to side effects; however, based on prior research, we expect this number to be low.2

In this study, 12-month trajectories of statin use were well predicted by observations of adherence during the first 2 to 4 months after initiation, but could not be predicted accurately by clinical characteristics measured at baseline. Therefore, physicians, pharmacy benefit managers, or other providers with timely access to patient refill data could easily implement a dynamic prediction system for adherence trajectories. The trajectories observed in this study were similar to those observed previously, but individual providers may wish to optimize their prediction system by re-estimating the trajectory models in their specific patient population and with a specific number of groups corresponding to different adherence interventions. Because both the trajectory model and the prediction model methodology are relatively simple and require little beyond pharmacy refill data, highly accurate predictions are possible for a wide spectrum of patients at providers with varying resources. 

Author Affiliations: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School (JMF, AAK, NKC), Boston, MA; CVS Health (WHS, OSM, TAB), Woonsocket, RI.

Source of Funding: This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital.

Author Disclosures: Drs Shrank, Brennan, and Matlin are employees of CVS Health, which builds and supplies proprietary algorithms predicting medication adherence; Drs Brennan and Matlin are also CVS stockholders. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JMF, WHS, TAB, NKC); acquisition of data (NKC, TAB); analysis and interpretation of data (JMF, AAK, OSM, NKC); drafting of the manuscript (JMF, AAK); critical revision of the manuscript for important intellectual content (JMF, AAK, WHS, OSM, TAB, NKC); statistical analysis (JMF, AAK); obtaining funding (OSM); administrative, technical, or logistic support (OSM); and supervision (WHS).

Address correspondence to: Jessica M. Franklin, PhD, 1620 Tremont St, Ste 3030, Boston, MA 02120. E-mail:

1. Avorn, J, Monette J, Lacour A, et al. Persistence of use of lipid-lowering medications: a cross-national study. JAMA. 1998;279(18):1458-1462.

2. Benner JS, Glynn RJ, Mogun H, Neumann PJ, Weinstein MC, Avorn J. Long-term persistence in use of statin therapy in elderly patients. JAMA. 2002;288(4):455-461.

3. Choudhry NK, Setoguchi S, Levin R, Winkelmayer WC, Shrank WH. Trends in adherence to secondary prevention medications in elderly post-myocardial infarction patients. Pharmacoepidemiol Drug Saf. 2008;17(12):1189-1196.

4. DiMatteo MR, Giordani PJ, Lepper HS, Croghan TW. Patient adherence and medical treatment outcomes: a meta-analysis. Med Care. 2002;40(9):794-811.

5. Roebuck MC, Liberman JN, Gemmill-Toyama M, Brennan TA. Medication adherence leads to lower health care use and costs despite increased drug spending. Health Aff (Millwood). 2011;30(1):91-99.

6. Raebel MA, Ellis JL, Carroll NM, et al. Characteristics of patients with primary non-adherence to medications for hypertension, diabetes, and lipid disorders. J Gen Intern Med. 2012;27(1):57-64.

7. Chan DC, Shrank WH, Cutler D, et al. Patient, physician, and payment predictors of statin adherence. Med Care. 2010;48(3):196-202.

8. Caldeira KM, O’Grady KE, Garnier-Dykstra LM, Vincent KB, Pickworth WB, Arria AM. Cigarette smoking among college students: longitudinal trajectories and health outcomes. Nicotine Tob Res. 2012;14(7):777-785.

9. Conklin CA, Perkins KA, Sheidow AJ, Jones BL, Levine MD, Marcus MD. The return to smoking: 1-year relapse trajectories among female smokers. Nicotine Tob Res. 2005;7(4):533-540.

10. Hong SI, Hasche L, Bowland S. Structural relationships between social activities and longitudinal trajectories of depression among older adults. Gerontologist. 2009;49(1):1-11.

11. Franklin JM, Shrank WH, Pakes J, et al. Group-based trajectory models: a new approach to classifying and predicting long-term medication adherence. Med Care. 2013;51(9):789-796.

12. Cutrona, SL, Choudhry NK, Fischer MA, et al. Targeting cardiovascular medication adherence interventions. J Am Pharm Assoc (2003). 2012;52(3):381-397.

13. Franklin JM, Shrank WH, Lii J, et al. Observing versus predicting: initial patterns of filling predict long-term adherence more accurately than high-dimensional modeling techniques. Health Serv Res. 2015. doi:10.1111/1475-6773.12310.

14. Mann DM, Woodward M, Muntner P, Falzon L, Kronish I. Predictors of nonadherence to statins: a systematic review and meta-analysis. Ann Pharmacother. 2010;44(9):1410-1421.

15. Kulik A, Shrank WH, Levin R, Choudhry NK. Adherence to statin therapy in elderly patients after hospitalization for coronary revascularization. Am J Cardiol. 2011;107(10):1409-1414.

16. Choudhry NK, Glynn RJ, Avorn J, et al. Untangling the relationship between medication adherence and post-myocardial infarction outcomes: medication adherence and clinical outcomes. Am Heart J. 2014;167(1):51-58.e5.

17. Medicare health & drug plan quality and performance ratings: 2013 Part C & Part D technical notes. CMS website. Published 2013. Accessed October 15, 2014.

18. HEDIS measures. National Committee for Quality Assurance website. Published 2014. Accessed October 15, 2014.

19. Twisk J, Hoekstra T. Classifying developmental trajectories over time should be done with great caution: a comparison between methods. J Clin Epidemiol. 2012;65(10):1078-1087.

20. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361-87.

21. Jones BL, Nagin DS. Advances in group-based trajectory modeling and an SAS procedure for estimating them. Sociol Methods Res. 2007;35(4):542-571.

22. Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774-781.

23. Steiner JF. Can we identify clinical predictors of medication adherence... and should we? Med Care. 2010;48(3):193-195.

24. Steiner JF, Ho PM, Beaty BL, et al. Sociodemographic and clinical characteristics are not clinically useful predictors of refill adherence in patients with hypertension. Circ Cardiovasc Qual Outcomes. 2009;2(5):451-457. 
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