Jennie H. Best, PhD; John A. Romley, PhD; Dana P. Goldman, PhD; Ryan M. Conrad, PhD; and Anne L. Peters, MD
To address measured potential confounders, propensity score matching was used to match patients who were given exenatide with patients using 1 of the other 3 therapies (TZD, basal insulin, or DPP4I). Matching was based on patient age, gender, race, pre-period CCI, comorbidities (including congestive heart failure [CHF], hyperlipidemia, hypertension, stroke, chronic obstructive pulmonary disease [COPD], and end-stage renal disease [ESRD]), year of treatment initiation, and prior therapy (ie, an SU and/or metformin used in the 90 days prior to initiation). Predicted probabilities from a multivariate logistic regression analysis of exenatide use were used to identify and match the “nearest neighbor” who received exenatide to each patient who did not.Statistical Analyses
Baseline characteristics, including demographics (age, race, sex), CCI, comorbidities (from CCI), prior diabetes therapy, and year of treatment initiation, were compared among patients who received exenatide and those who did not.
Mean medical spending in patients receiving exenatide was compared with that in patients receiving other add-on therapies. Additionally, results in patients not receiving exenatide were analyzed by individual treatment groups (ie, TZD, basal insulin, and DPP4I). Analyses were first completed using all patients who did not receive exenatide, then limited to the propensity score–matched sample.
Regression-adjusted models controlled for patient characteristics including age, sex, race, comorbidities (CHF, hypertension, stroke, COPD, hyperlipidemia, and ESRD), CCI, year of drug initiation, and prior therapy. With a large presented as regression-adjusted mean outcomes.21
Analyses were conducted using Stata version 11.1 software; results with a P
value less than .05 based on 2-tailed t
tests were considered significant.Results
A total of 23,966 patients met the criteria defining the population of interest. Of these, 1345 had exenatide added as a next-line treatment. Patients who did not receive exenatide (n = 22,621) were predominantly initiated on TZDs (n = 18,208), followed by basal insulin (n = 3823) or DPP4Is (n = 590). Figure 1
shows how the analysis sample was determined as the inclusion and exclusion criteria were applied.
Once matched, data were included for 1345 patients who received exenatide, and 1345 patients who received a TZD (n = 1061), basal insulin (n = 224), or DPP4I (n = 60), for a total sample size of 2690. Matched results including baseline characteristics, unadjusted outcomes, and adjusted mean outcomes were consistent with the unmatched results. Only unmatched results are presented below (matched results are presented in the Appendix
Baseline characteristics of the study populations are shown in Table 1
. Results in patients not receiving exenatide are also reported by individual therapy (eg, TZD, basal insulin, and DPP4I). Compared with patients who initiated another therapy (n = 22,621), those who initiated exenatide therapy (n = 1345) were a mean of 3.6 years younger (51.5 vs 47.9 years, respectively; P
<.01). The exenatide group had a higher proportion of female patients compared with the non-exenatide group (79% vs 66%, respectively; P
<.01). There were also differences in the racial composition of the 2 groups; 68% of patients receiving exenatide were white, compared with 51% of patients receiving other therapy (P
The exenatide group had a mean CCI score of 2.0, while the non-exenatide group had a mean score of 1.5 (P
<.01). Generally, patients receiving exenatide were significantly more likely than those receiving other treatments to have comorbid conditions: hypertension (49% vs 45%), hyperlipidemia (33% vs 24%), and COPD (23% vs 17%) (P
<.01 for all). However, compared with patients receiving other therapies, fewer patients receiving exenatide had ESRD (0.9% vs 0.3%, respectively, P
The baseline analysis also examined prescribing patterns of patients’ physicians. A significantly lower percentage of patients receiving exenatide were given an SU and/or metformin compared with those not receiving exenatide (SU, 58% vs 68%, respectively; metformin, 58% vs 63%; SU and metformin, 31% vs 39%; P
<.05 for all). Of the 22,621 patients in the non-exenatide group, 58% visited physicians who had prescribed exenatide for any of their Medicaid patients (42% visited physicians who had never prescribed exenatide).Unadjusted Outcomes
As shown in Table 2
, patients receiving exenatide averaged $11,830 (standard deviation [SD] = $15,986) in total annual healthcare expenditures, with a median of $7082 (data not shown). In comparison, patients receiving other add-on therapies averaged $15,459 (SD = $26,012) (P
<.01). Compared with the non-exenatide group, the exenatide group had lower inpatient costs ($3893 vs $1945, P
<.01) and prescription costs ($5714 vs $4505, P
<.01); outpatient costs were not significantly different ($5853 vs $5380). Focusing on specific therapies, the TZD and basal insulin groups had higher expenditures than the exenatide group. For example, total annual healthcare spending averaged $14,779 among those receiving a TZD compared with $11,830 for those receiving exenatide (P
Differences in utilization were more muted. Mean total days supplied within a year of initiation of therapy were not significantly different in the exenatide, DPP4I, and basal insulin groups (233.2 days, 235.9 days, and 229.1 days, respectively). Mean TZD use was higher (249.0 days, P
<.01 vs exenatide).Regression-Adjusted Results
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