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Leveraging Benefit Design for Better Diabetes Self-Management and A1C Control

Abiy Agiro, PhD; Yiqiong Xie, PhD; Kevin Bowman, MD; and Andrea DeVries, PhD
Patients with diabetes receiving insulin treatment with lower cost sharing for blood glucose testing strips were more likely to achieve glycemic control than those with higher cost sharing.
ABSTRACT

Objectives: To evaluate the relationship between cost sharing for blood glucose testing strips and glycemic control rates.

Study Design: A retrospective observational study using medical and pharmacy claims data integrated with laboratory glycated hemoglobin (A1C) values for patients using insulin and testing strips. A new user study design was utilized to identify individuals from 14 commercial US health plans who filled testing strips with assumed intention to monitor blood glucose.

Methods: Patients were divided into low (<20% of annual testing strip cost; n = 3575) and high (≥20%; n = 3580) cost-sharing categories. We compared the likelihood of patients in low and high cost-sharing categories achieving glycemic control (A1C <8.0%) through modified Poisson regression models.

Results: Patients with low cost sharing for testing strips had higher rates of control than those with high cost sharing (58.1% vs 50.3%; P <.001). Low cost sharing was associated with greater probability of glycemic control (adjusted risk ratio [aRR], 1.14; 95% CI, 1.09-1.20; P <.0001). Glycemic control was more likely for patients in areas with median household income greater than $60,000 versus less than $40,000 (aRR, 1.16; 95% CI, 1.07-1.25; P <.01) and greater than $80,000 versus less than $40,000 (aRR, 1.18; 95% CI, 1.06-1.32; P <.01).

Conclusions: We found a statistically significant correlation between cost sharing for testing strips and better A1C control for patients using insulin medication. Lower cost sharing for testing strips can remove a barrier to diabetes self-management and may lead to improved glycemic control at the population level. Future efforts should study the potential benefits of reducing diabetic complications and associated cost savings.

Am J Manag Care. 2018;24(2):e30-e36
Takeaway Points

Lower cost sharing for blood glucose testing strips can remove a barrier to self-management of diabetes and potentially improve glycemic control.
  • Patients with low cost sharing were likely to achieve glycemic control 58.1% of the time versus 50.3% for those with high cost sharing.
  • Despite filling more testing strips than patients with high cost sharing, those with low cost sharing had lower out-of-pocket costs.
  • Given these results and the benefits of glycemic control in patients with diabetes, payers may want to consider placing glucose testing strips in lower cost-sharing tiers and including monitoring of testing strip fills as part of the care management process.
Diabetes management aims to maintain glycated hemoglobin (A1C) levels as close to the normal range as possible, and self-monitoring of blood glucose (SMBG) levels is an important tool to help patients achieve this goal.1 Based on existing evidence describing the effectiveness of SMBG for managing glucose levels in patients with insulin-dependent diabetes,1-6 the American Diabetes Association and others recommend SMBG at least 3 times daily.7-10 However, up to 23% of patients with insulin-dependent diabetes do not regularly monitor their glucose levels.11

A recent claims analysis found that glucose test utilization varied according to patients’ type of health insurance, suggesting that differences in coverage and reimbursement affected SMBG.4 Out-of-pocket (OOP) costs for testing supplies, as an additional financial burden for patients with diabetes who may be taking multiple medications that are each subject to cost sharing, have been shown to be an important barrier to regular SMBG practices.12-15 However, a large cross-sectional study in Canada reported that implementing quantity limits for reimbursement of testing strips was not related to worsened short-term clinical outcomes.16 That study did not separately analyze the impact on insulin users.

Other than this study, few studies have quantified the relationship between cost sharing for testing strips and A1C values, most likely due to a lack of available data sources that include cost sharing information and A1C levels. Published cost analyses have focused on utilization of testing supplies and associated costs.6,12,13 The few studies that have focused on A1C levels were based on small patient samples.1-3 Our study evaluated the relationship between cost sharing for diabetic testing supplies and A1C levels among patients using insulin, because patients on insulin are more likely to benefit from SMBG than patients using oral antidiabetic drugs (OADs).6,14,15 We hypothesized that patients with low cost sharing for testing strips would be more likely to attain glycemic control.

METHODS

Data Sources

Administrative medical and pharmacy claims data integrated with laboratory test results were queried from the HealthCore Integrated Research Environment, a repository of fully adjudicated claims data for approximately 25 million Blue Cross and Blue Shield health plan members across the United States. The A1C laboratory values were obtained from national reference laboratories. All study data were kept de-identified to safeguard patient confidentiality; researchers only accessed a limited dataset, devoid of individual patient identifiers, which exempted this study from investigational review board review per 45 CFR 164.514(e), Other Requirements Relating to Uses and Disclosures of Protected Health Information.

Study Design and Population

This retrospective cohort analysis focused on patients with diabetes using insulin. The study population consisted of commercially insured patients with 2 years of continuous medical and pharmacy eligibility during the study period (January 1, 2009, through December 31, 2013). Patients with diabetes were identified if they had at least 1 medical claim for International Classification of Diseases, Ninth Revision, Clinical Modification diabetes diagnosis codes (eAppendix Table 1 [eAppendices available at ajmc.com]) during the study period.4

Patients were identified as new users of diabetic testing strips if they had at least 1 testing strip prescription filled in the intake period from January 1, 2010, through December 31, 2012, with no prior fill within the previous year. As our study aim was to examine the impact on glycemic control of low versus high cost sharing for testing strips vis-à-vis initiation of testing strip use, we employed the new user study design for 2 reasons.16 First, prevalent users of testing strips are “survivors” of early SMBG and their inclusion could introduce bias, as risk for outcome (glycemic control) varies with time. Second, covariates for testing strip users at study entry could plausibly be affected by the use of the testing strips themselves. Because observational studies cannot exhaustively identify and adjust for all possible covariates, inclusion of prevalent users of testing strips could introduce confounding. Patients were required to have at least 1 year of continuous medical and pharmacy eligibility both before (baseline) and after (follow-up) the initial testing strip fill. Additional age- (18-75 years) and condition-specific inclusion and exclusion criteria were adapted from the Comprehensive Diabetes Care guidelines from the National Committee for Quality Assurance (NCQA) (n = 164,456).8,17

Among these patients, those with at least 1 pharmacy claim with an insulin medication fill during the study period and an insulin fill within 30 days from the start date of testing strip fill (index date) were identified (n = 30,445). Patients were required to have at least 1 A1C test result within the 1-year follow-up (postindex) period as the primary measure of glycemic control. A total of 7155 (23.5% of 30,445) patients met these criteria, forming the final study sample.

Cohort Assignment

Patient cost sharing for blood glucose testing strips was calculated as the OOP cost percentage of total testing strip costs, created by dividing OOP costs by total testing strip costs and multiplying by 100%. The OOP cost was the sum of co-pays, coinsurances, and deductibles paid by the patient over the 1-year period from the first testing strip fill. The total testing strip cost was the sum of OOP and health plan–paid amounts over the same period. We chose to use cost-sharing percentages rather than dollar amounts because the latter would reflect more on adherence differences instead of benefit design differences, as patients with more testing fills (better adherence) are likely to pay more out of pocket than those with fewer fills (lower adherence).

Using the median cost-sharing percentage (20%) in our final sample, we generated 2 study groups: the low cost-sharing group (OOP cost percentage below the median) and the high cost-sharing group (OOP cost percentage at or above the median).

Study Variables

Existing comorbid conditions and resource utilization were assessed during the baseline period; A1C outcomes, utilization, and cost measures were assessed during the follow-up period. The primary clinical outcome was the most recent A1C laboratory value during the follow-up period. The number and percentage of patients achieving glycemic control per NCQA guidelines (ie, A1C <8.0%) were analyzed. For the subset of patients whose baseline lab results were available, we also reported their baseline A1C lab values.

Statistical Analysis

Descriptive statistics, including means (± SD) and frequencies, were reported for continuous and categorical data, respectively. Differences in descriptive characteristics between the low versus high cost-sharing groups were assessed with Pearson’s χ2 tests for categorical data and t tests or nonparametric tests for numeric data. All statistical analyses were conducted with SAS 9.4 software (SAS Institute; Cary, North Carolina). Alpha was set at 0.05.

The association between cost sharing and glycemic control was first examined in the aforementioned descriptive (bivariate) analysis, followed by multivariable analysis in all patients and then a sensitivity analysis in a subset of patients. In the multivariable analysis in all patients, the likelihood of patients achieving glycemic control in the low cost-sharing group was compared with that in the high cost-sharing group through relative risk ratios, estimated from the modified Poisson model with the associated standard errors obtained from the sandwich method.18,19 Patient characteristics, such as age, gender, and area median income, were adjusted for in the multivariable model. In the sensitivity analysis, among the subset of patients with available baseline A1C values, we modeled the change in the proportion of patients achieving glycemic control before and after initiation of testing strip use with a difference-in-differences (DID) analysis through generalized estimating equations.

As an exploration of the impact of testing strip use on glycemic results, we examined the proportions of patients achieving glycemic control in groups with different numbers of testing strip fills. This was conducted in the subset of patients with available baseline A1C values.

RESULTS

Patient Baseline Characteristics

A total of 7155 patients were included in the analysis: 3575 in the low cost-sharing group and 3580 in the high cost-sharing group (Table 1). The mean age of patients in the 2 groups was similar (49 years and 50 years, respectively; P <.01), although a higher proportion of those in the high cost-sharing group were aged 45 to 64 years (67.3% of high cost-sharing vs 58.7% of low cost-sharing). The low cost-sharing group resided in areas with a higher median household income (based on the zip codes for the areas in which the patients resided) compared with the high cost-sharing group (mean = $56,473 vs $53,025, respectively; P <.0001).

Baseline use of medications for metabolic disorders was high in both groups (74.9% high and 72.1% low cost-sharing; P <.05). A greater proportion of patients with high cost sharing had baseline OAD use compared with those with low cost sharing (57.4% high vs 48.6% low cost sharing; P <.001) (Table 1).

Testing Strip Utilization and Other Postindex Characteristics

 
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