Study Design and Data Sources
A retrospective cohort study was conducted using administrative claims from a large, US health plan affiliated with Optum during July 2009 through September 2013. The administrative claims database includes demographic information as well as medical data from physician and facilities or hospitals and pharmacy data in the form of prescription medication claims. There were approximately 18.5 million commercially insured adult enrollees covered during the study period. Individuals included in the database are geographically diverse across the US, with the greatest representation in the South and Midwest regions. Claims include International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes, Current Procedural Terminology procedure codes, Healthcare Common Procedure Coding System codes, site of service codes, and health plan and patient costs. Outpatient pharmacy data includes National Drug Codes for dispensed medications, quantity dispensed, drug strength, days’ supply, and costs. Outpatient lab results (including A1C) are available in linked laboratory data for a subset of the population.
Adult commercial health plan members with T2D who were treated with liraglutide were included in the study. Specifically, those with at least one pharmacy claim for liraglutide between January 01, 2010 and September 30, 2012 were identified, and the index date was defined as the date of the first liraglutide claim. An indication of T2D was based on ICD-9-CM diagnosis codes or claims for OADs during the 180 days prior to the index date (see “Appendix 1” for the algorithm). Additionally, subjects were required to be at least 18 years old as of the index year and have continuous enrollment in the health plan with medical and pharmacy benefits for 180 days prior to the index date (baseline period) and for 365 days following the index date (follow-up period). Subjects with claims for GLP-1 agents during the baseline period or evidence of pregnancy or gestational diabetes during either the baseline or follow-up periods were excluded. Finally, subjects were required to have at least one A1C lab result during the period 45 days prior to the index date through 7 days after the index date and at least one A1C lab result between 275 and 455 days after the index date (365 days post-index ±90 days).
Adherence to liraglutide was based on the proportion of days covered (PDC), which has gained favor as the preferred adherence measure. The PDC is used by the Pharmacy Quality Alliance (PQA) in its most recent quality measures , and the Centers for Medicare and Medicaid Services (CMS) use PDC as a quality measure of Part D plans in their Quality Evaluation System —a measure endorsed by the National Quality Forum. The PDC is calculated as the number of days the medication is available to the patient divided by the number of days in the follow-up period . For this study, the PDC was dichotomized into ≥0.80 (adherent) and <0.80 (non-adherent). The threshold of 0.80 is commonly used, including by both the PQA and CMS [18, 19]. For completeness, as well as for use in sensitivity analyses, the Medication Possession Ratio (MPR) was also calculated  and dichotomized into adherent (MPR ≥0.80) and non-adherent (<0.80) binary measures. Persistence to liraglutide was defined by the continuation or discontinuation of the medication as measured via the days’ supply reported on pharmacy claims. Discontinuation was defined as a gap in therapy of at least 90 days and represented non-persistence. The time to discontinuation was calculated as the number of days from the index date to the run-out date of the last fill before the gap in therapy. Adherence and persistence were defined separately and represent different aspects of medication usage; each subject in the study cohort was defined as either adherent or non-adherent and also as either persistent or non-persistent.
Demographic characteristics of subjects included their age as of the index year, gender, and geographic region. Also collected were patient paid amounts for their index prescription fill and whether or not their index fill was obtained through a mail order. Baseline clinical characteristics included A1C at index, use of antidiabetic medications, and comorbid conditions. The baseline A1C was captured from laboratory results on claims during the 45 days prior to the index date through 7 days post-index. If multiple values were present, the A1C result closest to the index date was used. The dose of the index fill of liraglutide was obtained, as well as the specialty of the prescribing physician. Fills for insulin and OAD medications during the baseline period were identified and counted at the class level, and the overall baseline antidiabetic regimen was categorized into several groups (no therapy, OAD monotherapy, OAD combination therapy, insulin monotherapy, and insulin with OADs). Comorbid conditions during the baseline period were defined using the Clinical Classification Software managed by the Agency of Healthcare Research and Quality , which generates indicator variables for specific disease conditions based on ICD-9-CM diagnoses. The comorbidities of dyslipidemia, hypertension, renal disease, and non-alcohol fatty liver disease were identified during the baseline period based on diagnosis, procedure, and revenue codes appearing on medical claims. The Diabetes Complications Severity Index (DCSI) at baseline was created using ICD-9-CM codes as described in Young et al.  and Chang et al. .
During follow-up, several A1C outcomes were analyzed. The follow-up A1C result occurring within 90 days of the end of the 1-year follow-up period was captured from claims. If multiple A1C measures were available during this period, the one closest to the end of the 1-year follow-up period was retained. This A1C result was used to create indicator variables for A1C <7.0% and ≤6.5% achievement at follow-up. Additionally, the absolute change in A1C from baseline was calculated, and patients with a reduction of ≥1.0% from baseline to follow-up were identified.
Diabetes-related healthcare resource utilization during the baseline and follow-up periods was characterized by binary indicators and counts of ambulatory visits, emergency room (ER) visits, and inpatient (IP) stays related to diabetes. Visits were considered diabetes-related if they had an ICD-9-CM diagnosis code of 250.xx in any position. Consumer Price Index  adjusted diabetes-related healthcare costs were computed as the combined health plan and patient paid amounts. Total healthcare costs were calculated as the sum of medical costs (categorized into ambulatory visit costs, emergency services costs, IP costs, and other costs) and pharmacy costs. For medical claims, services were defined as diabetes related if they had a diagnosis of 250.xx in any position, and pharmacy costs included oral and injectable diabetes medications.
All study variables, including baseline and outcome measures, were analyzed descriptively. Numbers and percentages were calculated for dichotomous and polychotomous variables, while means, medians, and standard deviations (SD) were calculated for continuous variables. Results were stratified by the dichotomous adherence and persistence measures. Bivariate comparisons were conducted, and appropriate tests for significance were performed based on the distribution of the variable.
Multivariate analysis of the study outcomes was conducted using appropriate regression models. Ordinary least squares regression was used to analyze the absolute reduction in A1C, while logistic regression was used to analyze dichotomous A1C outcomes (e.g., A1C goal attainment). To analyze diabetes-related costs, generalized linear models with a gamma distribution and log link were employed, utilizing Manning and Mullahy’s formulation . All multivariate analyses were adjusted for key covariates, including age group, gender, health plan region, index prescriber specialty, mail-order status, patient paid amount for index fill, baseline DCSI, baseline comorbidities of interest (dyslipidemia, hypertension, renal disease, non-alcohol fatty liver disease), baseline count of OAD classes, baseline insulin use, baseline diabetes-related utilization, baseline diabetes-related costs (not included in cost models), and baseline A1C. Adjusted outcomes and average costs were predicted and bootstrapped 95% confidence intervals were estimated. Data extraction and statistical analysis were performed using SAS version 9.2 (SAS Institute Inc., Cary, NC).
Statement of Ethics
No identifiable protected health information was extracted or accessed during the course of this study; hence, no Institutional Review Board approval was required.
This article does not contain any new studies with human or animal subjects performed by any of the authors.