Economic Burden of Hypoglycemia With Basal Insulin in Type 2 Diabetes

ABSTRACT

Objectives: To assess the impact of hypoglycemia and potential underlying factors of economic burden in patients with type 2 diabetes (T2D) who are initiating basal insulin therapy.

Study Design: This retrospective cohort study combined commercial insurance and Medicare Advantage data from the Clinformatics Data Mart.

Methods: Adults with T2D on oral antidiabetes drugs initiating basal insulin (n = 18,918) were assessed at baseline (12 months prior to insulin initiation) and follow-up (1 and 2 years). The population was stratified by whether or not patients experienced hypoglycemia during year 1 after insulin initiation. Outcomes included hypoglycemia rate, complications, comorbidities, and adjusted economic burden (primary).

Results: There were 1683 (8.9%) patients in the hypoglycemia group and 17,235 (91.1%) in the no-hypoglycemia group. During year 1, the estimated rate of hypoglycemia events was 0.412 per member per year. Baseline hypoglycemia was the strongest predictor of subsequent hypoglycemia. The hypoglycemia group was older, with a significantly greater clinical and economic burden at baseline; these differences persisted during follow-up. In the hypoglycemia group, for every 100 members per year, 463 hypoglycemia episodes were recorded, with a mean cost per episode of $986. Hypoglycemia-related medical expenses accounted for 12.6% ($4563/$36,272) of total healthcare expenditure, with hypoglycemia-related hospitalizations accounting for 19.7% ($2602/$13,191) of total hospitalization expenditure.

Conclusions: Compared with patients with no hypoglycemia-related claims in year 1 after basal insulin initiation, patients with a hypoglycemia-related claim had a greater burden of complications and comorbidity associated with significantly higher healthcare utilization and cost at baseline; these persisted during follow-up.

Am J Manag Care. 2017;23(2):114-122

Takeaway Points

Patients with type 2 diabetes (T2D) who experienced hypoglycemia after adding basal insulin to oral antidiabetes drugs had a significantly greater clinical and economic healthcare burden at baseline, which persisted during follow-up.

• This analysis combined members of both commercial and Medicare Advantage plans, providing a representative sample of T2D in the United States.
• Consistent with other reports, we suggest hypoglycemia may be a marker for increased susceptibility to adverse healthcare outcomes.
• We speculate that earlier basal insulin initiation (ie, when disease burden is low) may incur a lower hypoglycemia risk, whereas delayed insulin (ie, when disease burden is high) may require closer monitoring. However, the current analysis was not designed to assess this, and prospective clinical trials are needed.

The economic burden of diabetes in the United States is estimated to be $245 billion annually (in 2012 US$), representing $176 billion in direct medical costs and $69 billion in lost productivity.¹ Type 2 diabetes (T2D) may initially be managed with noninsulin therapies, such as oral antidiabetes drugs (OADs); however, with disease progression, patients may require insulin therapy to achieve glycemic control.² If glycemic targets are not achieved after 3 months on metformin, adding basal insulin therapy is among the recommended management strategies and is considered to have the highest efficacy.³ Clinical evidence supports the conclusion that basal insulins—particularly longer-acting formulations—are associated with fewer hypoglycemia events than shorter-acting insulins^4,5; however, even with basal insulin, hypoglycemia is a concern.

Hypoglycemia is a major burden to the healthcare eco-system. Not only is it a barrier to achieving glycemic control in diabetes, but it also may be associated with increased clinical and economic healthcare burdens, and have a negative impact on patient quality of life and medication adherence.^6-10 Better insight into risk factors for hypoglycemia could help identify patients who are more likely to experience hypoglycemia and could thus help guide management.^11,12

This retrospective cohort study used commercial and Medicare Advantage data to allow inclusion of a broad population representative of adults with T2D in the United States. Study aims were to identify risk factors for hypoglycemia and to assess the clinical and economic burdens of hypoglycemia in patients with T2D who added basal insulin to OADs.

METHODS

This retrospective cohort study used combined commercial insurance (patients of working age) and Part C Medicare Advantage (elderly patients) with Part D populations from the nationwide Clinformatics Data Mart Database (OptumInsight, Eden Prairie, Minnesota). Administrative data, medical and pharmacy claims, and some laboratory results were available. Data were submitted by managed care organizations, hospitals, Medicare supplement (ie, beneficiaries enrolled in Part C), and employers (small- to large-scale).

Patient Selection and Cohort Construction

Adults 18 years or older with T2D were identified from January 1, 2007, to September 30, 2014, via International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis codes: 250.xx, 357.2, 362.0x, 366.41. Additional criteria were used to “de-select” individuals with type 1 diabetes, as follows: a) if no OADs were prescribed within 1 year from the diagnosis of diabetes and the medication possession ratio (MPR) of any insulin was ≥80%, b) or if the patient was younger than 30 years and on insulin only, c) or if diabetic ketoacidosis was diagnosed and the MPR of any insulin was ≥80% in the following year. Gestational diabetes was excluded.

Eligible patients had been dispensed at least 2 prescriptions of basal insulin (insulin glargine, insulin detemir, or neutral protamine Hagedorn insulin) within 6 months of initiating basal insulin as an add-on to OADs. At least 36 months of continuous healthcare coverage were required (baseline defined as 12 months before basal insulin initiation [index]; follow-up defined as 12 and 24 months after initiation). Only patients with at least 2 prescriptions for OADs during baseline were included.

Outcome Measures

Clinical outcome measures included hypoglycemia (ICD-9-CM codes: 250.8, 251.0, 251.1, 251.2), glycated hemoglobin (A1C), diabetes complications (macrovascular: cardiovascular disease, peripheral arterial disease, and stroke; microvascular: retinopathy, kidney disease/nephropathy, and neuropathy, including foot infection and lower extremity amputation), comorbid conditions, and concomitant medications. ICD-9-CM codes for diabetes complications and comorbid conditions are provided in eAppendix A (eAppendices available at ajmc.com). The Charlson Comorbidity Index (CCI) score, predicting 10-year mortality by weighing 17 comorbid conditions, was calculated to measure disease burden and case mix.¹³

Healthcare utilization point-of-service (POS) settings included inpatient hospitalizations and procedures, emergency department (ED) visits, skilled nursing facility (SNF) stays, home care visits (eg, visiting nurses), and outpatient/office visits. Utilization was reported by annual rate and by number of episodes per member per year (PMPY). Hypoglycemia-related utilization was reported by episodes per 100 members per year. For healthcare cost, total PMPY spending (pharmacy and medical) and PMPY spending corresponding to each POS setting were calculated. Hypoglycemia-related medical costs were calculated for each POS setting.

Statistical Methods

χ

Descriptive statistics—mean (standard deviation [SD]) or median—and percentage and frequencies of various outcome measures were stratified by whether patients experienced hypoglycemia (hypoglycemia group) or did not experience hypoglycemia (no-hypoglycemia group) during year 1. To identify hypoglycemia risk factors, univariate analyses (Mann-Whitney test or t test for continuous variables, and Cochran-Mantel-Haenszel/² test for categorical variables) and a multivariate logistic regression model were employed. Patient baseline characteristics (ie, demographic/clinical characteristics, underlying conditions, medications, and hospital and ED utilization) were considered.

Unadjusted costs were presented as overall cost, diabetes-related cost, and hypoglycemia-related cost. Because the total expenditure was highly skewed to the right, assumption of constant variance was often violated, and inpatient hospitalization was uncommon, a generalized linear model assuming gamma distribution with log-link function was adapted to estimate total expenditure and inpatient hospital cost, adjusting for patient baseline characteristics. Adjusted cost was run on Statistical Analysis Software (SAS) version 9.2 (SAS Institute, Cary, North Carolina).

Baseline characteristics, including those that were statistically significant in multivariate logistic regression, were added into the cost model and included: age at basal insulin initiation (≥65/<65 years), hypoglycemia (yes/no), endocrinologist visits (yes/no), cardiologist visits (yes/no), metformin (yes/no), sulfonylureas (yes/no), dipeptidyl peptidase-4 inhibitors (yes/no), any antihypertension medication (yes/no), CCI score, nephropathy exam (yes/no), retinopathy (yes/no), neuropathy (yes/no), chronic kidney disease (yes/no), any pancreatitis (yes/no), hyperlipidemia (yes/no), hypertension (yes/no), any macrovascular disease (yes/no), any gastrointestinal disorders (yes/no), any cardiovascular procedures (yes/no), obesity (yes/no), mental illness (yes/no), foot infection (yes/no), any inpatient visit (yes/no), any ED visit (yes/no), and total cost.

Sensitivity analyses were conducted to assess the potential impact of sulfonylureas, prandial insulin, and/or beta-blockers, since these medication classes can cause hypoglycemia or mask hypoglycemia symptoms and influence hypoglycemia rates. A subgroup analysis by A1C achieved during year 1 of follow-up was conducted. Baseline characteristics were also stratified by health plan (commercial vs Medicare Advantage) and age (≥65 years vs <65 years).

RESULTS

Patient Population

A total of 18,918 basal insulin initiators were identified (eAppendix B). The mean age was 64 years (SD = 13) and 1732 (9.2%) patients initiated basal insulin in the hospital. In the 12 months before initiating basal insulin, 3.9% (741/18,918) of patients had experienced hypoglycemia. During year 1 after basal insulin initiation, 8.9% of patients experienced hypoglycemia. There were 7792 hypoglycemia events in year 1, corresponding to an estimated rate of 0.412 events PMPY in the overall population. When stratified by hypoglycemia during year 1 (ie, hypoglycemia [n = 1683] and no-hypoglycemia [n = 17,235] groups), gender distribution was comparable; however, the hypoglycemia group had more patients 65 years or older versus the no-hypoglycemia group (62.2% vs 51.6%; P <.001) (Table 1). Additional baseline differences included more patients experiencing hypoglycemia (16.2% vs 2.7%) prior to insulin initiation, a higher prevalence of microvascular (60.0% vs 39.8%) and macrovascular (49.2% vs 30.2%) complications, and greater CCI score (4.2 [2.6] vs 3.0 [2.3]) in the hypoglycemia versus no-hypoglycemia group (all P <.001). Baseline healthcare utilization and cost were significantly higher in the hypoglycemia group (Tables 2 and 3).

Predicting Hypoglycemia During Year 1 After Basal Insulin Initiation

The strongest predictor of hypoglycemia during year 1 was hypoglycemia at baseline (OR, 5.49; 95% CI, 4.79-6.28) (Figure 1). Additional significantly predictive baseline factors included foot infection, neuropathy, inpatient hospitalization, macrovascular complications, chronic kidney disease, ED visits, and being at least 65 years.

Clinical Outcomes

Baseline differences observed between the hypoglycemia versus the no-hypoglycemia group remained significant during years 1 and 2 of follow-up (Table 1 and eAppendix C). In both groups, the median number of OADs decreased from 2 to 1 during year 1 and remained at 1 during year 2. Reductions were seen for all OAD classes. For example, in the hypoglycemia group, sulfonylurea use declined from 71% at baseline to 46% during year 1 and to 32% during year 2. Declines in sulfonylurea use in the no-hypoglycemia group appeared comparable.

Microvascular and macrovascular complications and comorbidities appeared to increase to a greater degree in the hypoglycemia group versus the no-hypoglycemia group. In line with these observations, in the hypoglycemia group, the CCI score changed from 4.2 (2.6) at baseline to 5.0 (2.8) and 4.8 (2.9) in years 1 and 2, respectively; whereas in the no-hypoglycemia group, the CCI score changed from 3.0 (2.3) to 3.2 (2.4) and 3.4 (2.5).

Glycemic control data at baseline and follow-up were available for a subset of patients (n = 2512; 13.3%). A1C decreased by 0.5% and 0.7% in the hypoglycemia and no-hypoglycemia groups, respectively, during year 1, and by an additional 0.1% in both groups during year 2. The proportion of patients with A1C ≥9% decreased from 41% at baseline to 32% during year 1 in the hypoglycemia group and from 52% to 39% in the no-hypoglycemia group; reductions were maintained in year 2.

Healthcare Utilization and Cost

At baseline, the hypoglycemia group was associated with significantly higher healthcare utilization versus the no-hypoglycemia group in all areas except outpatient/office visits (Table 2). Differences remained significant in both follow-up years, during which outpatient/office visits were also significantly higher in the hypoglycemia group. The inpatient hospitalization rate was 80% higher in the hypoglycemia versus no-hypoglycemia group (45.5% vs 25.1%; P <.001) at baseline and more than 90% higher at follow-up time points. During year 1, the number and duration of hospitalizations, number of ED visits, and number of SNF stays decreased in the no-hypoglycemia group but increased in the hypoglycemia group. During year 2, utilization appeared to be comparable with or below baseline in both groups. Hypoglycemia-related facility utilization was significantly higher for the hypoglycemia group versus the no-hypoglycemia group, both at baseline and during year 2.

Total healthcare expenditure at baseline was significantly higher in the hypoglycemia group versus the no-hypoglycemia group ($25,022 [$37,303] vs $15,649 [$30,034]; P <.001) (Table 3). Pharmacy costs were comparable between groups, thus the difference was solely driven by medical costs; these were approximately double in the hypoglycemia group versus the no-hypoglycemia group in all areas. Total expenditure during follow-up appeared relatively unchanged in the no-hypoglycemia group, but increased by $11,250 in the hypoglycemia group during year 1; major contributors to this increased cost were outpatient visits followed by pharmacy expenditure. In both groups, pharmacy costs increased during follow-up, with diabetes-related pharmacy cost doubling. In the hypoglycemia group, diabetes-related medical costs increased by approximately $3000 during year 1 of follow-up, but declined to below baseline levels during year 2; in the no-hypoglycemia group, diabetes-related medical costs declined slightly during follow-up.

In the hypoglycemia group, for every 100 members per year, 463 hypoglycemia episodes were recorded, with a mean cost per episode of $986. The annual cost PMPY was higher among patients who experienced multiple episodes of hypoglycemia (Figure 2). Among patients experiencing hypoglycemia (n = 1683), hypoglycemia-related medical expenses ($4563) accounted for 12.6% and 15.6% of the total healthcare expenditure ($36,272) and medical expenditure ($29,207), respectively, with hypoglycemia-related hospitalizations accounting for 7.2% ($2602/$36,272) of total healthcare expenditure and 19.7% ($2602/$13,191) of total hospitalization expenditure.

Sensitivity/Subgroup Analysis

Subgroup analyses data are provided in the eAppendix. Briefly, significant baseline differences were noted when the population was stratified by health plan (eAppendix D) or by age (eAppendix E). Findings from sensitivity analyses based on sulfonylurea, beta-blocker, and/or prandial insulin use were consistent with those in the overall patient population (eAppendix F).

In the A1C sub-analysis, patients who achieved A1C less than 7% during year 1 were older and had lower baseline A1C, a higher baseline CCI score, a higher follow-up hypoglycemia event rate (11.2% vs 8.7%), and higher baseline and follow-up hospitalization rates versus patients with A1C ≥7% (eAppendix G). Regardless of A1C level, patients with hypoglycemia had greater clinical burden.

DISCUSSION

This retrospective cohort analysis of 18,918 patients with T2D on OADs who initiated basal insulin therapy showed that patients who experienced hypoglycemia during year 1 of follow-up had significantly greater baseline comorbidity versus patients who did not. Compared with the no-hypoglycemia group, the hypoglycemia group had higher healthcare utilization, which was associated with a 60% higher total healthcare expenditure at baseline. This increased economic burden among patients with hypoglycemia is not surprising given the apparent inherently greater disease burden. Baseline differences in clinical and economic burden between the hypoglycemia and no-hypoglycemia groups remained significant during follow-up. Hypoglycemia-related medical expenditure represented more than 10% of total healthcare expenditure in the hypoglycemia group, with hypoglycemia-related hospitalizations accounting for almost 20% of total hospitalization cost.

Although statistical comparisons from baseline to follow-up were not conducted, it appears that being in the hypoglycemia group was associated with relatively greater increases in clinical burden, particularly macrovascular complications. This is consistent with a retrospective cohort study that found that patients with hypoglycemia after initiation of antidiabetes treatments (n = 761) had a significantly increased risk of microvascular complications and macrovascular events compared with propensity score-matched controls.¹⁴

The potential negative impact of hypoglycemia in T2D was raised by the ACCORD (Action to Control Cardiovascular Risk in Diabetes), ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation), and VADT (Veterans Affairs Diabetes Trial) studies in which intensive glycemic control was associated with higher rates of severe hypoglycemia than less intensive control.^15-17 None of these studies achieved their primary endpoint of reduced cardiovascular events, and in the case of ACCORD, significantly higher cardiovascular mortality was reported with intensive control.

Whether severe hypoglycemia contribute to excess cardiovascular mortality has been a subject of debate.¹⁸ In an epidemiological analysis of ACCORD (n = 10,251), symptomatic, severe hypoglycemic events were associated with higher mortality risk; however, patients with hypoglycemia in intensive therapy had a lower mortality risk than those in the standard group. Thus, the authors speculated that susceptibility to hypoglycemia may be a marker for underlying factor(s)/condition(s) that increase mortality risk even among patients with controlled T2D.¹⁹ Similarly, an analysis of ADVANCE (n = 11,140) reported a strong association between severe hypoglycemia and adverse outcomes, but could not conclusively attribute direct causality, suggesting that hypoglycemia may be a “marker of vulnerability to a wide range of adverse clinical outcomes.”²⁰

Our results are consistent with this concept of hypoglycemia being a marker of increased susceptibility to adverse clinical outcomes. Compared with the no-hypoglycemia group, the hypoglycemia group had a significantly higher clinical burden prior to insulin initiation, which appeared to further increase during follow-up. This lends support to the argument for earlier initiation of basal insulin when patients have a lower overall disease burden in terms of disease progression and comorbid conditions.²¹ Such an approach is consistent with treatment guidelines; however, the claims data in this analysis show that insulin therapy is often delayed until patients are already on 2 or more agents.

Another consideration is that customized strategies, such as closer monitoring and gradual dose titration, may be warranted when initiating insulin in patients with more advanced disease/greater overall disease burden. It is of interest to note that many of the increases in healthcare utilization and expenditure that were observed in the hypoglycemia group in the first year after initiation of basal insulin appeared to decline and/or return to baseline levels in the second year; thus, although patients in the hypoglycemia group were significantly older and more severe at baseline, they still derived benefit from initiation of basal insulin.

In the current analysis, the presence of hypoglycemia-related claims prior to basal insulin initiation was associated with a 5 times greater likelihood of hypoglycemia during year 1 of follow-up. Consistent with other analyses of hypoglycemia predictors, older age and the presence of complications such as neuropathy and chronic kidney disease were associated with an elevated risk of hypoglycemia.^22,23 Although sulfonylurea use did not emerge as a predictive factor, it is well known that these drugs can contribute to increased hypoglycemia and that patients initiating insulin while on sulfonylureas should be monitored carefully.

Initiation of basal insulin therapy was associated with a median reduction in OAD classes from 2 to 1 in this report. Of interest is a pooled analysis of 11 clinical trials showing lower hypoglycemia rates when basal insulin (glargine) was added to a single OAD (biguanide or sulfonylurea) than when it was added to 2 OADs.²⁴ Symptomatic hypoglycemia rates ranged an average of 4.05 events per patient-year for those on 1 OAD and an average of 7.18 events for those on 2 OADs. In the current analysis, more than 60% of patients in both the hypoglycemia and no-hypoglycemia groups were on at least 2 OADs at baseline, which may have contributed to the relatively high rate of hypoglycemia.

Limitations

Several limitations to the current analysis are evident. First, the analysis was not designed for statistical comparisons from baseline to follow-up; therefore, although it appears that the clinical and economic burdens increased to a greater degree from baseline to year 1 in the hypoglycemia group, these are descriptive findings and potential implications should be applied with caution. Although the economic data were adjusted by baseline characteristics, the clinical data were unadjusted. Another limitation is the paucity of A1C data, making it difficult to assess the relationship between intensity of glycemic control and hypoglycemia, although this has been well documented in other investigations.^15,17 Inadequate A1C data may explain why observed A1C reductions were less than expected. It was also not possible to obtain accurate insulin dose/titration information, thus the impact of insulin dose on A1C and/or hypoglycemia could not be assessed. In addition, details allowing precise definition/classification of the types of hypoglycemia events were not available due to the nature of documentation in the claims database; however, events that resulted in hypoglycemia codes were likely to be symptomatic, clinically relevant, and/or severe. The database did not capture whether or not the hypoglycemia event(s) required third-party assistance.

Another issue is that the analysis did not capture adequate information on other potentially important factors identified in previous reports. For example, hypoglycemia has been associated with an elevated risk of falls,²⁵ but hypoglycemia-related falls and related economic consequences could not be evaluated in our report. Although gender, race, and socioeconomic status have been associated with hypoglycemia risk,²⁶ the current analysis did not find any association with gender and data on race and socioeconomic status were not available. It also was not possible to determine whether patient cognitive or behavioral factors^11,27,28 contributed to hypoglycemia events, although mental illness was significantly more common in the hypoglycemia group. In addition, hypoglycemia may lead to reduced workplace productivity²⁹; however, such indirect cost data were not captured. Finally, cost, as described in this report, could vary by healthcare plan and was based on billed charges; these may differ from actual amounts paid.

Despite these limitations, these claims data have advantages relative to electronic health records (EHRs) in that they capture drugs that were truly dispensed and provide data on healthcare utilization and expenses. Although EHR data are mostly limited to be collected in provider’s office, this database, like EHRs, also included point-of-service settings that offer a broader view of a patient’s care (eg, SNFs and home care that are not typically represented). The nationwide sample of commercial and Medicare Advantage plans provided a robust representation of the overall T2D diabetes population.

Although we speculated that adding basal insulin earlier in the treatment of patients with T2D—when the disease burden is relatively low&mdash;may be associated with a lower risk of hypoglycemia and the associated economic burden, a prospective clinical trial would be needed for confirmation (ie, the current analysis was not designed to make this assessment). Prospective randomized clinical trials are ideal for treatment assessment; however, our claims analysis provides insights into real-world patient outcomes and underscores the clinical and economic burdens associated with hypoglycemia.

CONCLUSIONS

This retrospective cohort study of patients with T2D who initiated basal insulin found that patients experiencing hypoglycemia during year 1 of follow-up had a significantly greater clinical and economic burden prior to insulin initiation than those with no hypoglycemia. The observed baseline differences between groups persisted during follow-up. These data suggest that certain patients may be inherently more susceptible to hypoglycemia. Notably, among patients who experienced hypoglycemia, hypoglycemia-related medical expenditures and hospitalizations were major contributors to total healthcare expenditure.

Acknowledgments

This study and editorial support were funded by Sanofi. The authors would like to acknowledge Kulvinder K. Singh, PharmD, for medical writing support.

Author Affiliations: Tulane University Health Sciences Center (VF), New Orleans, LA; Sanofi (EC, CG), Bridgewater, NJ; TechData Service Company, LLC (HC), King of Prussia, PA

Source of Funding: Sanofi.

Author Disclosures: Dr Fonseca has received grants for research support from Novo Nordisk, Asahi, Eli Lilly, Abbott, Endo Barrier, and Gilead Sciences, and has received honoraria for consulting and lectures from Takeda, Novo Nordisk, Sanofi, Eli Lilly, Pamlabs, Astra-Zeneca, Abbott, Amgen, Boehringer Ingelheim, and Jansen. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (VF, EC, HC, CG); analysis and interpretation of data (VF, EC, HC, CG); drafting of the manuscript (VF, EC, HC, CG); critical revision of the manuscript for important intellectual content (VF, EC, HC, CG); statistical analysis (EC, HC); obtaining funding (CG); and supervision (CG).

Address Correspondence to: Vivian Fonseca, MD, Tulane University Health Sciences Center, 1430 Tulane Ave — SL 53, New Orleans, LA 70112. E-mail: vfonseca@tulane.edu.

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