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The American Journal of Managed Care June 2017
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Comparative Effectiveness and Costs of Insulin Pump Therapy for Diabetes
Ronald T. Ackermann, MD, MPH; Amisha Wallia, MD, MS; Raymond Kang, MA; Andrew Cooper, MPH; Theodore A. Prospect, FSA, MAAA; Lewis G. Sandy, MD, MBA; and Deneen Vojta, MD
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Judy A. Shea, PhD; Aderinola Adejare, BA; Kevin G. Volpp, MD, PhD; Andrea B. Troxel, ScD; Darra Finnerty, MPH; Karen Hoffer, BS; Thomas Isaac, MD, MPH, MBA; Meredith Rosenthal, PhD; Thomas D. Sequist
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Paul D. Jacobs, PhD, and Eamon Molloy, PhD
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Paul A. Heidenreich, MD, MS; Aimee Zapata, MS; Lisa Shieh, MD, PhD; Nancy Oliva, PhD, RN; and Anju Sahay, PhD
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Jennifer L. Cerully, PhD; Steven C. Martino, PhD; Lise Rybowski, MBA; Melissa L. Finucane, PhD; Rachel Grob, PhD; Andrew M. Parker, PhD; Mark Schlesinger, PhD; Dale Shaller, MPA; and Grant Martsolf, P
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Lucas Higuera, MA, and Caroline Carlin, PhD

Comparative Effectiveness and Costs of Insulin Pump Therapy for Diabetes

Ronald T. Ackermann, MD, MPH; Amisha Wallia, MD, MS; Raymond Kang, MA; Andrew Cooper, MPH; Theodore A. Prospect, FSA, MAAA; Lewis G. Sandy, MD, MBA; and Deneen Vojta, MD
Evaluation of healthcare utilization and costs over 3 years for adults with insulin-requiring diabetes who transition from multiple daily insulin injections to insulin infusion pumps.
ABSTRACT

Objectives:
Continuous subcutaneous insulin infusion (CSII), or “insulin pump” therapy, is an alternative to multiple daily insulin injections (MDII) for management of diabetes. This study evaluates patterns of healthcare utilization, costs, and blood glucose control for patients with diabetes who initiate CSII.

Study Design: Pre-post with propensity-matched comparison design involving commercially insured US adults (aged 18-64 years) with insulin-requiring diabetes who transitioned from MDII to CSII between July 1, 2009, and June 30, 2012 (“CSII initiators”; n = 2539), or who continued using MDI (n = 2539).

Methods: Medical claims and laboratory results files obtained from a large US-wide health payer were used to construct direct medical expenditures, hospital use, healthcare encounters for hypoglycemia, and mean concentration of glycated hemoglobin (A1C). We fit difference-in-differences regression models to compare healthcare expenditures for 3 years following the switch to CSII. Stratified analyses were performed for prespecified patient subgroups.

Results: Over 3 years, mean per-person total healthcare expenditures were $1714 (95% confidence interval [CI], $1184-$2244) higher per quarter for CSII initiators compared with matched MDII patients (total mean 3-year difference of $20,565). Compared with matched controls, mean A1C concentrations became lower for CSII initiators by 0.46% in year 2 (P = .0003) and by 0.32% in year 3 (= .047). CSII initiators also had a higher rate of hypoglycemia encounters in year 1 (P = .002).

Conclusions: For adults with insulin-requiring diabetes, transitioning from MDII to CSII was associated with modest improvements in A1C but more hypoglycemia encounters and increased healthcare expenditures, without significant improvement in other potentially offsetting areas of healthcare consumption.
Takeaway Points

  • Mean total healthcare expenditures were estimated to be $1714 higher per person per quarter for patients who initiate continuous subcutaneous insulin infusion (CSII) compared with matched multiple daily insulin injection (MDI) patients, equating to a total 3-year mean difference of about $20,565 per person. 
  • Subgroup analyses showed modest differences in glycated hemoglobin favoring CSII recipients in years 2 and 3, but also statistically significant differences in healthcare visits for hypoglycemia as CSII was being initiated. 
  • It may remain challenging for health payers or providers to develop policies regarding access or coverage for CSII when, in aggregate, CSII appears to add immediate costs with short-term benefits that are uncertain and/or are difficult to measure.
Diabetes affects 29 million Americans, imposing a substantial health and economic burdens on the US population.1 Intensive blood glucose management improves health outcomes for most patients,2 but achieving this goal requires them to monitor blood glucose more often, use more medications, including insulin; and to follow up regularly with healthcare providers. These activities increase healthcare expenditures and are perceived by many patients to reduce their quality of life (QOL).3 This has stimulated efforts to develop less demanding forms of insulin delivery to reduce patient burden, while attempting to limit adverse effects, such as hypoglycemia and weight gain.

Over the past 2 decades, there has been an increase in the use of continuous subcutaneous insulin infusion (CSII) therapy, or “insulin pumps,” for the treatment of diabetes.4-6 An insulin pump is a small programmable device that uses a computer algorithm to administer insulin both continuously and by patient-initiated bolus infusion through a small catheter that is left under the skin. The American Diabetes Association and American Association of Clinical Endocrinologists recommend intensive blood glucose management using either multiple daily insulin injections (MDII) or CSII, but advise CSII primarily for well-educated and motivated patients who are unable to achieve optimal glycemic control with MDII.6-8

Prior research has suggested some potential advantages of CSII over MDII therapy, such as reduced pain, less stigma, lower frequency and severity of hypoglycemia, and overcoming some barriers to adherence.9,10 However, a recent meta-analysis of trials directly comparing MDII with CSII found that CSII did not reduce hypoglycemia or weight gain and had inconsistent effects on QOL and blood glucose control, with the results of only 1 trial showing greater improvement in glycated hemoglobin (A1C) among adults with type 1 diabetes (T1D) who had comparatively higher A1C levels at baseline.11 Recent reports have called for clearer evidence-based guidelines for CSII indications and more transparent reporting of safety data by pump manufacturers.8

Insulin infusion pumps have been reported to cost about $4500, with additional costs for supplies exceeding $1500 per person per year.10 Because health insurance typically pays for 80% to 90% of CSII costs, access to insurance and coverage policies naturally play a strong role in whether patients choose to initiate this approach. Health payers generally offer insurance coverage for CSII to patients who meet specific clinical criteria and demonstrate good self-management practices and participation with healthcare visits. Medicare provides coverage for CSII in patients who have evidence of either: a) prior use of CSII with adherence to glucose self-testing prior to Medicare enrollment, or b) persistent hyperglycemia, recurring hypoglycemia, or other forms of poor blood glucose control despite completion of a comprehensive diabetes education program and adherence to glucose self-monitoring and MDII for at least 6 months.12 Patients must then complete regular follow-up visits with a supervising physician at least every 3 months to continue receiving health payer coverage for CSII.

Although prior research has demonstrated the high acquisition costs for insulin infusion pumps and supplies, it is not yet known if those higher upfront costs can be recovered through improvements in health that translate into higher QOL and/or lower utilization of healthcare services. A related important question is whether those outcomes might vary among different subgroups of patients.9 To address these gaps, we designed this study to evaluate patterns of healthcare utilization and costs for adults who primarily have T1D and transition to CSII in practice, comparing them with otherwise similar patients who continued MDII therapy.

METHODS

Overall Design and Study Setting


We used a pre-post with propensity-matched comparison group quasi-experimental study design with difference-in-differences estimation to evaluate patterns of healthcare costs, utilization, and blood glucose control associated with the initiation of CSII by adults with insulin-requiring diabetes who received health insurance coverage from a single, large, nationwide commercial health insurer. The study design is depicted in Figure 1 and described further below.

Study Sample and Exposures

The evaluation sample included patients with insulin-requiring diabetes who were aged 18 to 64 years. Insulin-requiring diabetes was defined as at least 1 encounter with an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 250.XX and at least 1 dispensing event for MDII, but no previous use of an oral hypoglycemic class medication. This exclusion made it more likely, but not certain, that individuals included in the study had T1D. CSII initiators were defined by a new dispensing event for an infusion pump (Healthcare Common Procedure Coding System billing code E0784 or A9274) any time between July 1, 2009, and June 30, 2012. Additional CSII-related utilization and supplies were identified using specific procedural and encounter codes (see eAppendix Table 1 for details [eAppendix available at ajmc.com]). The date of the first occurrence of one of these codes within 270 days before the pump device dispensing date was defined as the index date for our pre-post analysis (Figure 1). This date occurred a median of 145 days before the pump dispensing date, and the time between these 2 dates was referred to as the “pump onboarding” period. In follow-up analyses, CSII initiators were considered to have stopped CSII if they had 120 days of enrollment without any claims for CSII-related supplies or services and had at least 2 basal insulin refills during that same period.

To identify a comparison group with similar baseline characteristics, we used a nearest-neighbor propensity-matching approach.13,14 The patient-level propensity model used logistic regression to predict the odds of CSII initiation and included baseline variables relating to the outcomes of interest, for which prior research results have demonstrated associations with diabetes treatment choice or intensity. Propensity model predictive variables included patient sex, age category, Charlson Comorbidity Index score, presence/absence of a prior obesity-related ICD-9-CM code, most recent A1C test result ≥ or <8% (or “value missing” if not available), and each of the following utilization criteria within 180 days before the CSII index date: total encounters for a hypoglycemia diagnosis, total encounters with an endocrinologist, and the presence or absence of at least 1 billing code suggestive of poorly controlled diabetes (eAppendix Table 1). To enable direct comparisons between groups, matched control clients were assigned the same index date as the CSII initiator to whom they were paired. To construct baseline variables for the match, individuals in both groups were required to have continuous health plan enrollment for at least 3 months before and 3 months after the index date.

Measures and Outcomes

Study outcomes included direct medical costs, categorized as inpatient, outpatient, pharmacy, and total healthcare costs; emergency department (ED) visits; encounters for hypoglycemic events; and, when available, A1C values. Total healthcare costs, including both health plan and patient cost share components, were assessed equally across all patient groups by applying a standardized price for each claim. To minimize the effect of extreme outliers on mean cost estimates, we replaced costs above the 95th percentile with the 95th percentile value.15

Data Sources

Data sources included national member enrollment files, medical inpatient and ambulatory claims, and pharmacy claims made available by a large US-based commercial health insurer. Although the completion of all laboratory tests (including A1C tests) could be determined for all patients in both groups (ie, based on laboratory claims), a subset of national commercial laboratory vendors also included the laboratory result with each claim submitted to the payer. A1C test results were available for about 40% of submitted A1C laboratory claims for both CSII- and MDII-treated patients. Because the availability of an A1C result was determined by where the test was perfomed rather than by a patient’s individual characteristics or form of diabetes treatment, this subsample of individuals with A1C test results enabled a unique opportunity for the analysis of glycemic control within an unbiased subset of patients nationally. The Northwestern University Institutional Review Board reviewed the parent study and determined that this work involved the use of coded, nonidentifiable data and was not classifiable as human subjects research.

Statistical Analysis

Univariate and bivariate descriptive statistics for baseline characteristics were calculated for both CSII initiators and the matched MDII comparison group. Student t tests were used to compare continuous variables; χ2 tests were used to compare categorical variables. Means of continuous outcomes were plotted for each 90-day period before and after the index date to observe comparability of time trends between groups.

To minimize the potential for bias introduced by the nonrandomized study design, we used a pre-post with propensity-matched comparison group design. We estimated mean between-group differences in quarterly outcomes over different time horizons (year 1, year 2, year 3) using enrollee-level difference-in-differences random effects regression models that included a dummy variable for group (CSII initiator versus MDII), calendar year indicators (ie, calendar year of the index date), exposure year (ie, year 1, 2, or 3 relative to the index date), and group-by-exposure-year interaction terms. Means and 95% confidence intervals (CIs) were estimated using a generalized method of moments approach. For estimating continuous outcomes, we used linear models because they provide estimates in natural units of the outcome variable and have been shown to produce reliable and unbiased estimates of mean cost differences and CIs when sample sizes are large.16 For count outcomes (eg, hypoglycemic encounters or ED visits), estimations used negative binomial distributional assumptions. Analyses were conducted on the overall sample as well as across prespecified patient subgroups, including different pump-qualifying diagnoses—hypoglycemia encounters or evidence of poor glycemic control within 180 days of the index date—and whether or not the CSII initiator also used real-time continuous glucose monitoring (rt-CGM) (see eAppendix Table 1).

Because our analysis assumed a health payer perspective, each patient who was no longer enrolled with the health plan was censored from future measurement periods, rather than imputing missing data or assuming them to have zero costs. To avoid potential bias from differential dropout rates across the 2 groups, we censored both the CSII initiator and matched control patient at any point when either one was no longer enrolled in the health plan. This reduced sample sizes but ensured comparability throughout the evaluation period.

RESULTS

 
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