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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.
DISCUSSION

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).

Limitations

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.

CONCLUSIONS

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. 

Acknowledgments

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: nina_joyce@brown.edu.
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