Currently Viewing:
The American Journal of Managed Care June 2018
Prevalence and Predictors of Hypoglycemia in South Korea
Sun-Young Park, PhD; Eun Jin Jang, PhD; Ju-Young Shin, PhD; Min-Young Lee, PhD; Donguk Kim, PhD; and Eui-Kyung Lee, PhD
Initial Results of a Lung Cancer Screening Demonstration Project: A Local Program Evaluation
Angela E. Fabbrini, MPH; Sarah E. Lillie, PhD, MPH; Melissa R. Partin, PhD; Steven S. Fu, MD, MSCE; Barbara A. Clothier, MS, MA; Ann K. Bangerter, BS; David B. Nelson, PhD; Elizabeth A. Doro, BS; Brian J. Bell, MD; and Kathryn L. Rice, MD
A Longitudinal Examination of the Asthma Medication Ratio in Children
Annie Lintzenich Andrews, MD, MSCR; Daniel Brinton, MHA, MAR; Kit N. Simpson, DrPH; and Annie N. Simpson, PhD
Physician Practice Variation Under Orthopedic Bundled Payment
Joshua M. Liao, MD, MSc; Ezekiel J. Emanuel, MD, PhD; Gary L. Whittington, BSBA; Dylan S. Small, PhD; Andrea B. Troxel, ScD; Jingsan Zhu, MS, MBA; Wenjun Zhong, PhD; and Amol S. Navathe, MD, PhD
Simply Delivered Meals: A Tale of Collaboration
Sarah L. Martin, PhD; Nancy Connelly, MBA; Cassandra Parsons, PharmD; and Katlyn Blackstone, MS, LSW
Placement of Selected New FDA-Approved Drugs in Medicare Part D Formularies, 2009-2013
Bruce C. Stuart, PhD; Sarah E. Tom, PhD; Michelle Choi, PharmD; Abree Johnson, MS; Kai Sun, MS; Danya Qato, PhD; Engels N. Obi, PhD; Christopher Zacker, PhD; Yujin Park, PharmD; and Steve Arcona, PhD
Identifying Children at Risk of Asthma Exacerbations: Beyond HEDIS
Jonathan Hatoun, MD, MPH, MS; Emily K. Trudell, MPH; and Louis Vernacchio, MD, MS
Assessing Markers From Ambulatory Laboratory Tests for Predicting High-Risk Patients
Klaus W. Lemke, PhD; Kimberly A. Gudzune, MD, MPH; Hadi Kharrazi, MD, PhD, MHI; and Jonathan P. Weiner, DrPH
Satisfaction With Care After Reducing Opioids for Chronic Pain
Adam L. Sharp, MD, MS; Ernest Shen, PhD; Yi-Lin Wu, MS; Adeline Wong, MPH; Michael Menchine, MD, MS; Michael H. Kanter, MD; and Michael K. Gould, MD, MS
Currently Reading
Cost Sharing for Antiepileptic Drugs: Medication Utilization and Health Plan Costs
Nina R. Joyce, PhD; Jesse Fishman, PharmD; Sarah Green, BA; David M. Labiner, MD; Imane Wild, PhD, MBA; and David C. Grabowski, PhD

Cost Sharing for Antiepileptic Drugs: Medication Utilization and Health Plan Costs

Nina R. Joyce, PhD; Jesse Fishman, PharmD; Sarah Green, BA; David M. Labiner, MD; Imane Wild, PhD, MBA; and David C. Grabowski, PhD
Increased out-of-pocket costs for antiepileptic drugs were associated with decreased adherence, higher healthcare utilization, and higher spending among US commercial health plan beneficiaries with epilepsy.

In this study of more than 180,000 privately insured individuals with epilepsy, higher OOP cost sharing for AEDs was associated with a decrease in PDC and an increase in overall and epilepsy-related healthcare spending. These findings were consistent across several subgroup analyses, including variable definitions and model specification. Shifting costs to patients is one way that insurers can try to reduce spending. However, our results suggest that the unintended consequences of this approach may actually increase health plan spending in the long run. Thus, policies (eg, requirements to fail 1 treatment first) that penalize patients via higher OOP costs for not responding to a particular treatment financially disincentivize patients from taking a prescribed medicine that may be their best treatment. Additionally, they may detour physicians from their initial treatment selection, which was in the best interest of the patient.

The association among OOP costs, HCU, and spending has been previously examined.25,26 Although higher OOP costs are generally associated with lower utilization and higher spending, these associations are often weaker in patients with a chronic condition receiving ongoing care, reflecting a lower price sensitivity than in patients receiving intermittent medications, such as nonsteroidal anti-inflammatory drugs or antihistamines.5 Thus, although we found a negative association between OOP spending and HCU, the magnitude of those associations may reflect this trade-off. In addition, our estimates reflect the association over a 90-day period, in which small estimates may translate into substantial changes over longer periods of time. For example, these results indicate that a $10 increase in OOP spending is associated with a decrease in PDC of 0.16 per year. Furthermore, although the magnitude of our estimates may appear small relative to previous studies, the high costs associated with the treatment of epilepsy suggest that the impact in absolute terms may still be very high. The Institute of Medicine has estimated the total direct and indirect annual costs associated with epilepsy to be $15.5 billion.35 Thus, if all health plans capped their OOP spending at the median value of $0.30 per day, one would expect to save $140 per person per year among privately insured individuals, translating to an estimated savings of $121 million per year, or 0.8% of the total costs associated with epilepsy (eAppendix D).

To our knowledge, this study is the first to examine the association among OOP costs, HCU, and spending in a population of privately insured individuals with epilepsy. Several strengths are worth mentioning. First, the use of a market-basket index to measure OOP spending5 allowed us to standardize the comparisons across plans, thereby avoiding the potential for one plan’s benefit design to bias the comparison. A second strength of the study is the large sample size. Although epilepsy is the fourth most common neurological condition in the United States,36 it is still rare enough that large cohorts of individuals with epilepsy can be difficult to find and expensive to follow.14 The Truven MarketScan database has more than 40 million covered lives, which provided a large enough sampling to identify and follow more than 180,000 individuals with epilepsy, providing the statistical power to test hypotheses related to rare outcomes, such as inpatient hospitalization. A third study strength is the use of a newly published validated algorithm for calculating PDC over shorter time periods, such as the 90-day periods used in the current analysis.34 PDC measured over this shorter period provided a granularity in the measures that would not be possible over the more commonly used longer periods of time (eg, 1 year).


Although this is the first study to examine the effect of OOP costs on HCU and spending for individuals with epilepsy, there were several limitations. First, this was a privately insured population 65 years and younger; therefore, our results may not be generalizable to uninsured individuals, those with Medicaid or Medicare, or individuals with a lower socioeconomic status, for whom the cost-sharing structures may differ greatly. Second, although the use of a 90-day period over which to measure outcomes provided the sensitivity to detect smaller changes over time, some outcomes, such as epilepsy-related hospitalizations, were rare and resulted in zero-inflated counts in each period, which limited the options for model specification. Third, patients varied with respect to the length of time since their initial epilepsy diagnosis, and individuals’ experiences prior to inclusion in the study were not captured. Fourth, we did not look at treatment patterns, persistence, or rate of abandonment, which could also have been factored into our models as HCU measures or as outcomes. In subgroup analyses, the sample was limited to beneficiaries with new diagnoses, but because of the dramatic reduction in sample size and the highly skewed distribution of outcomes, estimates were not stable.


As healthcare costs continue to rise, payers may seek out alternative methods of limiting spending, including transferring some of the cost burden to patients. However, our findings suggest that, for patients with epilepsy, the long-term effect of this decision may be counterproductive, as patients reduce their use of preventive services and medications, which may translate into costlier care later on. Furthermore, given the potentially severe consequences of reducing medication adherence, such as greater seizure frequency that can lead to death, payers should carefully consider the unintended consequences of increased cost sharing for patients with epilepsy. 


Editorial support was provided by Lynne Isbell, PhD, CMPP, of Evidence Scientific Solutions (Philadelphia, Pennsylvania) and was funded by UCB Pharma.

Author Affiliations: Department of Health Care Policy, Harvard Medical School (NRJ, DCG), Boston, MA; Department of Health Services Research, Policy and Practice, Brown University (NRJ), Providence, RI; UCB Pharma (JF, IW), Smyrna, GA; Precision Health Economics (SG), Los Angeles, CA; Department of Neurology, The University of Arizona Health Sciences Center (DML), Tucson, AZ.

Source of Funding: This study was sponsored by UCB Pharma.

Author Disclosures: Dr Joyce and Dr Grabowski report a consultancy for Precision Health Economics, which received payment for this project from UCB Pharma. Dr Fishman reports employment with and stock ownership in UCB Pharma. Ms Green reports employment with Precision Health Economics. Dr Labiner reports grants pending for a drug study from MonoSol and grants received for drug studies from Upsher-Smith and UCB Pharma. Dr Wild reports employment with UCB Pharma at the time of the study. 

Authorship Information: Concept and design (NRJ, JF, SG, DML, IW, DCG); acquisition of data (JF, SG); analysis and interpretation of data (NRJ, JF, DML, IW, DCG); drafting of the manuscript (NRJ, JF, DML, IW, DCG); critical revision of the manuscript for important intellectual content (NRJ, JF, DML, IW, DCG); statistical analysis (NRJ, DCG); obtaining funding (JF); administrative, technical, or logistic support (JF, SG); and supervision (DCG).

Address Correspondence to: Nina R. Joyce, PhD, Brown University School of Public Health, Box G-S121-6, 121 South Main St, Providence, RI 02198. Email:

1. Joyce GF, Escarce JJ, Solomon MD, Goldman DP. Employer drug benefit plans and spending on prescription drugs [erratum in JAMA. 2002;288(19):2409. doi: 10.1001/jama.288.19.2409]. JAMA. 2002;288(14):1733-1739. doi: 10.1001/jama.288.14.1733.

2. Goldman DP, Joyce GF, Zheng Y. Prescription drug cost sharing: associations with medication and medical utilization and spending and health. JAMA. 2007;298(1):61-69. doi: 10.1001/jama.298.1.61.

3. Huskamp HA, Deverka PA, Landrum MB, Epstein RS, McGuigan KA. The effect of three-tier formulary adoption on medication continuation and spending among elderly retirees. Health Serv Res. 2007;42(5):1926-1942. doi: 10.1111/j.1475-6773.2007.00722.x.

4. Chandra A, Gruber J, McKnight R. Patient cost-sharing and hospitalization offsets in the elderly. Am Econ Rev. 2010;100(1):193-213. doi: 10.1257/aer.100.1.193.

5. Goldman DP, Joyce GF, Escarce JJ, et al. Pharmacy benefits and the use of drugs by the chronically ill. JAMA. 2004;291(19):2344-2350. doi: 10.1001/jama.291.19.2344.

6. Hsu J, Price M, Huang J, et al. Unintended consequences of caps on Medicare drug benefits. N Engl J Med. 2006;354(22):2349-2359. doi: 10.1056/NEJMsa054436.

7. Hopson S, Saverno K, Liu LZ, et al. Impact of out-of-pocket costs on prescription fills among new initiators of biologic therapies for rheumatoid arthritis. J Manag Care Spec Pharm. 2016;22(2):122-130. doi: 10.18553/jmcp.2016.14261.

8. Gleason PP, Starner CI, Gunderson BW, Schafer JA, Sarran HS. Association of prescription abandonment with cost share for high-cost specialty pharmacy medications. J Manag Care Pharm. 2009;15(8):648-658. doi: 10.18553/jmcp.2009.15.8.648.

9. Doshi JA, Li P, Ladage VP, Pettit AR, Taylor EA. Impact of cost sharing on specialty drug utilization and outcomes: a review of the evidence and future directions. Am J Manag Care. 2016;22(3):188-197.

10. Huskamp HA, Deverka PA, Epstein AM, et al. Impact of 3-tier formularies on drug treatment of attention-deficit/hyperactivity disorder in children. Arch Gen Psychiatry. 2005;62(4):435-441. doi: 10.1001/archpsyc.62.4.435.

11. Tamblyn R, Laprise R, Hanley JA, et al. Adverse events associated with prescription drug cost-sharing among poor and elderly persons. JAMA. 2001;285(4):421-429. doi: 10.1001/jama.285.4.421.

12. Heisler M, Langa KM, Eby EL, Fendrick AM, Kabeto MU, Piette JD. The health effects of restricting prescription medication use because of cost. Med Care. 2004;42(7):626-634. doi: 10.1097/

13. Goldman DP, Joyce GF, Lawless G, Crown WH, Willey V. Benefit design and specialty drug use. Health Aff (Millwood). 2006;25(5):1319-1331. doi: 10.1377/hlthaff.25.5.1319.

14. CDC. Epilepsy in adults and access to care—United States, 2010. MMWR Morb Mortal Wkly Rep. 2012;61(45):909-913.

15. Kotsopoulos IA, van Merode T, Kessels FG, de Krom MC, Knottnerus JA. Systematic review and meta-analysis of incidence studies of epilepsy and unprovoked seizures. Epilepsia. 2002;43(11):1402-1409.

16. Hauser WA. Seizure disorders: the changes with age. Epilepsia. 1992;33(suppl 4):S6-S14.

17. Graham NS, Crichton S, Koutroumanidis M, Wolfe CD, Rudd AG. Incidence and associations of poststroke epilepsy: the prospective South London Stroke Register. Stroke. 2013;44(3):605-611. doi: 10.1161/STROKEAHA.111.000220.

18. Tomson T, Walczak T, Sillanpaa M, Sander JW. Sudden unexpected death in epilepsy: a review of incidence and risk factors. Epilepsia. 2005;46(suppl 11):54-61. doi: 10.1111/j.1528-1167.2005.00411.x.

19. Glauser T, Ben-Menachem E, Bourgeois B, et al. ILAE treatment guidelines: evidence-based analysis of antiepileptic drug efficacy and effectiveness as initial monotherapy for epileptic seizures and syndromes. Epilepsia. 2006;47(7):1094-1120. doi: 10.1111/j.1528-1167.2006.00585.x.

20. Kwan P, Brodie MJ. Early identification of refractory epilepsy. N Engl J Med. 2000;342(5):314-319. doi: 10.1056/NEJM200002033420503.

21. Brodie MJ, Barry SJ, Bamagous GA, Norrie JD, Kwan P. Patterns of treatment response in newly diagnosed epilepsy. Neurology. 2012;78(20):1548-1554. doi: 10.1212/WNL.0b013e3182563b19.

22. Hixson JD. Stopping antiepileptic drugs: when and why? Curr Treat Options Neurol. 2010;12(5):434-442. doi: 10.1007/s11940-010-0083-8.

23. Lossius MI, Hessen E, Mowinckel P, et al. Consequences of antiepileptic drug withdrawal: a randomized, double-blind study (Akershus Study). Epilepsia. 2008;49(3):455-463. doi: 10.1111/j.1528-1167.2007.01323.x.

24. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies [erratum in Ann Intern Med. 2008;148(2):168. doi: 10.7326/0003-4819-148-2-200801150-00020]. Ann Intern Med. 2007;147(8):573-577. doi: 10.7326/0003-4819-147-8-200710160-00010.

25. Romley J, Goldman D, Eber M, Dastani H, Kim E, Raparla S. Cost-sharing and initiation of disease-modifying therapy for multiple sclerosis. Am J Manag Care. 2012;18(8):460-464.

26. Philipson TJ, Mozaffari E, Maclean JR. Pharmacy cost sharing, antiplatelet therapy utilization, and health outcomes for patients with acute coronary syndrome. Am J Manag Care. 2010;16(4):290-297.

27. Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173(6):676-682. doi: 10.1093/aje/kwq433.

28. St Germaine-Smith C, Liu M, Quan H, Wiebe S, Jette N. Development of an epilepsy-specific risk adjustment comorbidity index. Epilepsia. 2011;52(12):2161-2167. doi: 10.1111/j.1528-1167.2011.03292.x.

29. Johnsrud M, Lawson KA, Shepherd MD. Comparison of mail-order with community pharmacy in plan sponsor cost and member cost in two large pharmacy benefit plans. J Manag Care Pharm. 2007;13(2):122-134. doi: 10.18553/jmcp.2007.13.2.122.

30. Karaca-Mandic P, Swenson T, Abraham JM, Kane RL. Association of Medicare Part D medication out-of-pocket costs with utilization of statin medications. Health Serv Res. 2013;48(4):1311-1333. doi: 10.1111/1475-6773.12022.

31. Goodman MJ, Durkin M, Forlenza J, Ye X, Brixner DI. Assessing adherence-based quality measures in epilepsy. Int J Qual Health Care. 2012;24(3):293-300. doi: 10.1093/intqhc/mzs017.

32. Karve S, Cleves MA, Helm M, Hudson TJ, West DS, Martin BC. Good and poor adherence: optimal cut-point for adherence measures using administrative claims data. Curr Med Res Opin. 2009;25(9):2303-2310. doi: 10.1185/03007990903126833.

33. PQA performance measures. Pharmacy Quality Alliance website. Accessed February 8, 2017.

34. Bijlsma MJ, Janssen F, Hak E. Estimating time-varying drug adherence using electronic records: extending the proportion of days covered (PDC) method. Pharmacoepidemiol Drug Saf. 2016;25(3):325-332. doi: 10.1002/pds.3935.

35. England MJ, Liverman CT, Schultz AM, Strawbridge LM. Epilepsy across the spectrum: promoting health and understanding. a summary of the Institute of Medicine report. Epilepsy Behav. 2012;25(2):266-276. doi: 10.1016/j.yebeh.2012.06.016.

36. Hirtz D, Thurman DJ, Gwinn-Hardy K, Mohamed M, Chaudhuri AR, Zalutsky R. How common are the “common” neurologic disorders? Neurology. 2007;68(5):326-337. doi: 10.1212/01.wnl.0000252807.38124.a3.
Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up