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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.

To examine the association between health plan out-of-pocket (OOP) costs for antiepileptic drugs and healthcare utilization (HCU) and overall plan spending among US-based commercial health plan beneficiaries with epilepsy.

Study Design: Retrospective cohort.

Methods: The Truven MarketScan Commercial Claims database for January 1, 2009, to June 30, 2015, was used. Patients 65 years or younger with epilepsy and at least 12 months of continuous enrollment before index (date meeting first epilepsy diagnostic criteria) were included. Analyses were adjusted for age group, gender, beneficiary relationship, insurance plan type, and Charlson Comorbidity Index score. Primary outcomes included proportion of days covered (PDC), HCU, and healthcare spending in 90-day postindex periods. Associations between OOP costs and mean PDC, HCU, and plan healthcare spending per 90-day period were estimated.

Results: Across 5159 plans, 187,241 beneficiaries met eligibility criteria; 54.3% were female, 41.7% were aged 45 to 65 years, and 62.4% were in preferred provider organization plans. Across postindex 90-day periods, mean (SD) PDC, epilepsy-specific hospitalizations, outpatient visits, and emergency department visits were 0.85 (0.26), 0.02 (0.13), 0.34 (0.47), and 0.05 (0.22), respectively. Median (interquartile range) spending per 90-day period was $1488 ($459-$4705); median epilepsy-specific spending was $139 ($18-$623). Multivariable linear regression without health plan fixed effects revealed that higher OOP spending was associated with a decrease in PDC (coefficient, –0.008; 95% CI, –0.009 to –0.006; P <.001) and an increase in overall spending (218.6; 95% CI, 47.9-389.2; P = .012). Health plan fixed effects model estimates were similar, except for epilepsy-specific spending, which was significant (120.6; 95% CI, 29.2-211.9; P = .010).

Conclusions: Increases in beneficiaries’ OOP costs led to higher overall spending and lower PDC.

Am J Manag Care. 2018;24(6):e183-e189
Takeaway Points

Health plans have increasingly shifted costs of medical care and medications to their beneficiaries, resulting in repercussions for the management of chronic disease.
  • In beneficiaries with epilepsy, higher out-of-pocket (OOP) spending was associated with significant decreases in the proportion of days covered with an antiepileptic drug and in inpatient hospitalizations, and an increase in epilepsy-related outpatient visits.
  • Higher OOP costs were associated with increased total healthcare spending.
  • Greater formulary restriction has significant impact on beneficiaries and their management of epilepsy, with potentially severe consequences for patient outcomes.
Over the past 15 years, many healthcare plans have increased beneficiary cost sharing for prescription drugs as a mechanism to contain healthcare spending, and literature suggests that less generous prescription drug coverage reduces drug expenditures for healthcare plans.1-4 Some of this decrease simply shifts the drug acquisition costs, once paid by the plan, to the patients through higher out-of-pocket (OOP) costs in the form of increased deductibles, co-payments, or coinsurance. Similarly, individuals in high-deductible plans may have higher OOP spending. Higher cost sharing leads to lower medication utilization and adherence, especially among patients with chronic conditions.5,6 Studies across a number of chronic diseases (eg, rheumatoid arthritis, multiple sclerosis, hypertension, and hypercholesterolemia) report an association between higher OOP costs and lower drug utilization.7-10 Reduced utilization of prescription drugs with proven benefits could result in both worse health outcomes and higher overall costs.6,11-13

Epilepsy is a chronic brain disorder characterized by recurrent seizures. The Institute of Medicine (now the National Academy of Medicine) estimates that 1 in 26 individuals in the United States will have seizures during their lifetime and nearly 1% of the US population lives with active epilepsy.14 Epidemiologically, epilepsy has a bimodal distribution of incident cases occurring early and later in a patient’s life.15 In the aging population, this increased incidence of epilepsy often results from stroke, trauma, and other etiologies.16,17 Mortality associated with epilepsy is increased at all ages and is more common with poorly controlled seizures.18

Simply stated, the goal of epilepsy therapy is 2-fold: no seizures and no adverse effects (AEs). A wide range of antiepileptic drugs (AEDs) exist, and selecting the optimal treatment for an individual patient involves the consideration of many factors, including patient comorbidities and concomitant medications, risk of drug–drug interactions, and individual AED safety and efficacy profiles.19 AEDs have been shown to be effective in controlling seizures in up to two-thirds of patients who receive them.20,21 One-third of patients may not respond to initial AED monotherapy,20 necessitating modification of AED treatment. Breakthrough seizures may occur with suboptimal AED dosing due to intolerable AEs or titration or from inadequate efficacy. Patients who attain long-term (ie, ≥2 years) seizure freedom with medication may consider treatment cessation; however, this is a challenge,22 with studies reporting seizure recurrence in more than one-fourth of patients after a median of 41 months.23

Higher cost sharing in the form of OOP costs may discourage or delay patients’ access to the most appropriate medication that may control their seizures while minimizing AEs. As a consequence, treatment decisions driven by affordability rather than clinical appropriateness may lead to suboptimal treatment (from an efficacy or tolerability viewpoint). This may result in higher medical costs in both the short term (eg, due to injuries or hospitalizations) and the long term (eg, osteoporosis-related complications from long-term use of enzyme-inducing AEDs). To date, studies on the relationship between higher plan cost sharing for AEDs and the utilization of AEDs, overall healthcare utilization (HCU), and plan spending for enrollees with epilepsy have not been reported. The objective of this study was to examine how health plan cost sharing for AEDs relates to AED utilization, HCU, and overall plan spending among US-based commercial health plan beneficiaries with epilepsy.


Data and Study Design

A retrospective cohort study of individuals with epilepsy was conducted using data from the Truven MarketScan Commercial Claims research database for January 1, 2009, through June 30, 2015, and reporting aligned with STROBE guidelines for cohort studies.24 Individuals 65 years or younger were included in the analysis if they had at least 12 months of continuous enrollment prior to meeting 1 of the following diagnostic conditions: 2 or more inpatient or outpatient claims with an International Classification of Diseases, Ninth Revision (ICD-9) code of 345.xx; 1 or more claim with code 345.xx and 1 or more claim with code 780.39; 1 or more claim with code 345.xx and a pharmacy claim for an AED; and 2 or more claims with a code of 780.39 and a pharmacy claim for an AED. In cases requiring an ICD-9 code and AED pharmacy claim, both had to occur within a 12-month period. The index date was defined as the date on which the first of the diagnostic criteria was met after a period of 12 months of continuous enrollment. Individuals in capitated health maintenance organization (HMO) plans were excluded because claims are not submitted for each service provided; thus, measurement of their HCU and spending was not captured. The follow-up period began at the index date and was divided into 90-day periods until the beneficiary was no longer enrolled or was administratively censored on December 31, 2015. All periods were required to have the full 90 days; any partial period due to censoring was excluded from the analysis. A 90-day period was chosen based on prior studies of the association between cost sharing and healthcare spending that found that outcomes assessed over quarters are sensitive enough to identify changes over time.25,26 All patient characteristics, cost-sharing measures, and HCU and spending outcomes were assessed for each 90-day period.

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