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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
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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.
Patient Characteristics

All analyses were adjusted for the individual’s age group (0-18, 19-45, or 46-65 years), gender, relationship to beneficiary (employee, spouse, or child/other), insurance plan type (comprehensive, exclusive provider organization, HMO, point of service [POS], preferred provider organization [PPO], POS with capitation, consumer-driven health plan, or high-deductible health plan), and Charlson Comorbidity Index (CCI) score (0, 1, 2, or ≥3),27 which was based on diagnoses in the year preceding the start of each 90-day period. In subgroup analyses, an epilepsy-specific comorbidity index28 was included; however, estimates were unchanged and, thus, data reflect models adjusted for the CCI score.28

OOP Costs

The objective of the analysis was to estimate how OOP costs for AEDs related to the proportion of days covered (PDC) and health plan spending for individuals with epilepsy. Because drug prices are largely uniform across plans, we focused on price differences introduced by a plan’s generosity. A commonly used method to construct a market-basket index of a set of representative AEDs was employed to calculate OOP costs.5,29,30 To construct the market basket of AEDs, all dispensings in a calendar year for a random sample of 100 individuals with at least 1 dispensing for an AED in that year were aggregated. Each AED in this market basket was assigned a weight equal to the relative frequency of dispensings for that AED. For example, if in 2009, lacosamide made up 40% of all AED dispensing claims across the sample of 100 people and phenytoin made up the remaining 60%, then the weights for lacosamide and phenytoin would be 0.4 and 0.6, respectively. The product of the weight and the average OOP cost, which included the patient’s deductible, coinsurance, and co-payment, for the AED were then summed across all AEDs for each plan-year. Thus, if in health plan A, the average OOP cost was $5 for lacosamide and $10 for phenytoin, then the market-basket value would be the weighted average of the 2: ($5 × 0.4) + ($10 × 0.6) = $8. Because prescriptions could range from 30 to 90 days’ supply, a standardized measure of the cost per day based on the days’ supply of AEDs was created (instead of using the number of dispensings).


Primary outcomes were measures of PDC, HCU, and healthcare spending in each 90-day period; the mean and median across all postindex 90-day periods were calculated for each outcome measure. PDC was chosen, as opposed to other commonly used measures of adherence (eg, medication possession ratio), based on a study of AED adherence that found PDC to be a more stable measure.31,32 PDC has also been used as a quality indicator for treatment of other chronic diseases.33 An algorithm for calculating PDC across short time periods using claims data was employed.34 The algorithm used shorter time periods as the denominator based on the pattern of drug dispensing to allow the PDC to vary, versus calculating the PDC over a set period of time, such as a year. For comparison with prior studies, the annual PDC was calculated according to standard methods.31 Only individuals with at least 1 AED dispensing in each quarter were included in the PDC measure. HCU was calculated as the total number of inpatient admissions, outpatient visits, and emergency department (ED) visits; epilepsy-specific HCU required claims with a primary diagnosis of epilepsy (ICD-9 code 345.xx or 780.39). Total outpatient, inpatient, and overall spending, as well as overall epilepsy-specific spending, were calculated as the sum of the deductible, co-pay, coinsurance, amount paid by insurance, and amount paid by coordination of benefits for each measure as identified from the adjudicated claims. Overall plan spending included total spending on all claims for patients with any primary diagnosis, whereas epilepsy-specific spending was defined as total spending on all claims for patients with a primary diagnosis of epilepsy.

Statistical Analysis

Multivariate linear models were used, first with and then without health plan fixed effects, to estimate the association between OOP spending for each plan and PDC and health plan spending in each 90-day period. For each 90-day period, a market-basket measure was used for the year in which the period began. Thus, the period beginning April 1, 2011, was assigned to the market-basket measure calculated for the calendar year 2011. Conducting the analysis over 90-day intervals is consistent with prior studies.26 A stable measure of the market basket was calculated over a 1-year time frame to avoid spikes in the prescribing of a single drug. Consequently, the market-basket distribution of AEDs was more likely to reflect a representative distribution of AEDs. Each model was adjusted for the patient’s age at the start of the period, gender, relationship to beneficiary, plan type, and calendar year. In sensitivity analyses, a categorical measure of OOP costs was used to further examine the linear relationship between healthcare spending and OOP costs. Categories were defined as low, medium, and high according to the tertile of OOP costs in that year.

Subgroup Analyses

To test model and study design assumptions, subgroup analyses were conducted. First, the cohort was limited to only those who were newly diagnosed with epilepsy, defined as beneficiaries with no AED dispensing or epilepsy diagnosis for at least 3 years. Second, the top and bottom 1% of spending values were excluded to limit the effect of spending outliers. In both cases, models were fit without health plan fixed effects. Last, 2-part models were used to account for skewed spending. However, because of the low prevalence of some of the outcomes (eg, inpatient hospitalizations), models failed to converge and are not included in the results.

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