There were no significant differences in the risk of ambulatory care—sensitive condition hospitalization or mortality between patients who initiated analogue insulin compared with the neutral protamine Hagedorn.
ABSTRACT Background: Long-acting insulin analogues (eg, insulin glargine and insulin detemir) are an alternative to neutral protamine Hage-dorn (NPH) insulin for maintaining glycemic control in patients with diabetes. Clinical trials comparing analogue insulin and NPH have neither been adequately powered nor had sufficient follow-up to examine long-term health outcomes.
Objectives: To compare the effects of NPH and long-acting insulin analogues on long-term outcomes.
Study Design: This retrospective observational study relied on administrative data from the Veterans Health Administration and Medicare from 2000 to 2010. Local variations in analogue insulin prescribing rates were used in instrumental variable models to control for confounding. Outcomes were assessed using survival models.
Methods: The study population included US veterans dually enrolled in Medicare who received at least 1 prescription for oral diabetes medication and then initiated long-acting insulin be-tween 2001 and 2009. Outcomes included ambulatory care—sensi-tive condition (ACSC) hospitalizations and mortality.
Results: There was no significant relationship between type of insulin and ACSC hospitalization or mortality. The hazard ratio for mortality of individuals starting a long-acting analogue insulin was 0.97 (95% CI, 0.85-1.11), and was 1.05 (95% CI, 0.95-1.16) for ACSC hospitalization. Differences in risk remained insignificant when predicting diabetes-specific ACSC hospitalizations, but starting on long-acting analogue insulin significantly increased the risk of a cardiovascular-specific ACSC hospitalization.
Conclusions: We found no consistent difference in long-term health outcomes when comparing use of long-acting insulin analogues and NPH insulin. The higher cost of analogue insulin without demonstrable clinical benefit raises questions of its cost-effectiveness in the treatment of patients with diabetes.
Am J Manag Care. 2015;21(3):e235-e243
The progressive nature of type 2 diabetes mellitus requires many patients to initi-ate insulin. This research compares the effects of the neutral protamine Hagedorn (NPH) and analogue insulin on long-term outcomes. n Analogue insulin is significantly more expensive than NPH.
The progressive nature of type 2 diabetes mellitus requires many patients to use insulin to maintain glycemic control.1,2 Neutral protamine Hagedorn (NPH) insulin was the most commonly used intermediate- to long-acting insulin until the introduction of the long-act-ing insulin analogues: insulin glargine in 20002,3 and insulin detemir in 2005.4 The long-acting insulin analogues were designed with properties that prolonged absorption, and with a flattened activity peak that extended duration of ef-fect and also resulted in a reduced risk of hypoglycemia.5-7
The significantly higher cost of analogue insulin compared with NPH8,9 led researchers and policy makers to compare the efficacy and cost of these medications. Several reviews found no significant difference between analogue insulin and NPH on glycemic control or severe hypoglycemia (ie, low glucose level requiring assistance from another person) but did show reduced likelihood of nocturnal hypoglycemia for patients using analogue insulin.6,7,10,11
Despite these short-term differences, potential long-term benefits have been difficult to test due to the short time frame of studies comparing analogue insulin with NPH.2,7 Some cost-effectiveness studies were based on modeling that used clinical trial results as predictors of long-term treatment ef-fects (eg, Center for Outcomes Research Diabetes Model).1 Other cost-effectiveness studies used retrospective claims that may better reflect clinical settings outside of trials. Results from these studies are mixed, with some concluding analogue insulin was cost-effective1,5 and others concluding analogue insulin was not an efficient use of healthcare resources.12,13 However, an important limitation of these claims-based stud-ies was the assumption that no systematic, unobserved differ-ences existed between the groups being compared (eg, those that started on NPH compared with analogue insulin).
To bridge this gap in the evidence, we used national Vet-erans Health Administration (VA) records to systematically compare long-term outcomes for patients using NPH or ana-logue insulin. We used a novel approach to an established observational comparative effectiveness method, instru-mental variables, to obtain unbiased estimates despite the possibility of unobserved differences between groups.
Patient-level national data from the VA were used and supplemented with data from Medicare to ensure com-pleteness in the measures of outcomes, as VA patients often use non-VA facilities for hospital care.14 The study was reviewed and approved by the Institutional Review Board at the VA Boston Healthcare System.
All prescription claims for metformin, sulfonylurea, thiazolidinedione, and long-acting insulin (ie, NPH, insu-lin glargine and insulin detemir, and mixtures of short- and long-acting insulins) between 2000 and 2007 were extracted from VA pharmacy files (Figure). Eligible indi-viduals had a history of being prescribed at least 1 oral diabetes medication, and initiated long-acting insulin between February 1, 2001, and December 31, 2009. The initiation date for the long-acting insulin was the start of the study period (“index date”) for each patient and the prior 12 months was the baseline period. To ensure that all hospitalizations were recorded, we further limited the cohort to include only those enrolled in Medicare as well as the VA. The last index date permitted was at the end of 2009, to allow a minimum 12-month outcome period. This resulted in a cohort of 142,940 patients, including 118,878 who initiated NPH and 24,062 who initiated ana-logue insulin.
The main objective of the study was to compare the long-term effects of NPH and analogue insulin. Patients who initiated NPH or an NPH/regular in-sulin mixture were compared with patients who started glargine, detemir, or short-act-ing and long-acting mixtures that included analogue insulin. To improve comparabil-ity among patients, we attempted to isolate patients at a similar point in the progres-sion of diabetes. Consequently, patients were required to have a prescription for an oral diabetes medication during the base-line period before initiating insulin. Eighty percent of the individuals remained on the same insulin they started (NPH or analogue insulin). Among those who started on analogue insulin, 99% re-ceived glargine.
Provider Practice Pattern as Instrumental Variable
The principal threat to a simple comparison of out-comes is that the selection of treatment by patients and providers could be influenced by unmeasured differences in patient risk (selection bias or confounding by indica-tion). To address this, we developed instrumental vari-ables models, which identify a factor (the instrumental variable [IV]) that influences treatment but is effectively random with respect to patient risk and other potential confounders.15,16 The statistical model isolates the com-ponent of treatment variation attributable to the IV and measures the relationship between this component and outcomes.17 The success of the approach depends on the effective randomness of the IV (controlling for all the other variables in the model) as well as the strength of the IV’s influence on treatment status.16,18
VA patients are assigned to their primary care physi-cian by variable and often arbitrary methods, so in this study, the IV was the proportion of long-acting insulin prescriptions written for analogue insulin by each pro-vider during an individual’s baseline period19—the 12 months before the index date on which analogue insulin was prescribed. The instrument assigned to each individ-ual was the proportion of analogue insulin prescriptions written by the provider who prescribed the initial insulin prescription on the index date.
eAppendix Figure A1
For example, if an individual started on long-acting in-sulin on January 1, 2003, their baseline period was January 1, 2002, to December 31, 2002 (see ). We identified the provider that prescribed insulin on Jan-uary 1, 2003, and calculated their proportion of analogue insulin prescriptions in that baseline period (January to December 2002). An individual’s insulin prescription does not contribute to their instrumental variable calcu-lation, eliminating concerns about selection bias. Provid-ers and patients were aligned based on the index date to minimize confounding that could occur if patients later switched providers.
If a provider prescribed insulin to less than 10 unique patients during the baseline period (53% of the time), the rate at the community-based outpatient clinic (CBOC) or VA medical center (VAMC) at which the provider prac-ticed was used.
Provider Quality Controls
The IV will not be effective if correlated with other provider characteristics that might also affect outcomes, causing biased estimates of the treatment-outcome rela-tionship. Therefore, we included 3 provider-level process quality variables: percent of glycated hemoglobin (A1C) labs ≥9%,20,21 percent of blood pressure readings ≥140/90 mm Hg,22 and percent of low-density lipoprotein choles-terol labs >100 mg/dL.22 These variables were computed at the same provider, CBOC, or VAMC level, and time periods as the IV prescribing rate.
Additional control variables computed at baseline included patient age, sex, race, A1C, serum creatinine, urine microalbumin, body mass index (see Table 1 for cat-egorizations), 29 indicator variables for comorbidities,23 8 indicator variables for the components of the Young dia-betes severity index,24 and indicator variables for calen-dar years corresponding to index dates. Individuals can be diagnosed with multiple comorbidities (eg, obesity and congestive heart failure [CHF]).
Outcomes included mortality and hospital admission (VA or Medicare) for any of 13 ambulatory care—sensitive conditions (ACSCs) as defined by the Agency for Health-care Research and Quality.25,26 These hospitalizations are hypothesized to be preventable with high-quality outpa-tient care and include several diabetes and cardiovascular complications such as uncontrolled diabetes, short- and long-term complications of diabetes, CHF, and chronic obstructive pulmonary disease. Sensitivity analyses es-timated models for specific ACSC hospitalization types most closely related to diabetes and cardiovascular dis-ease. The VA Vital Status File, which determines the date of death from VA, Medicare, and Social Security Admin-istration data, was used to determine mortality.27 Patients were censored either at the end of 2010, or when they ex-perienced either outcome (mortality or ACSC hospitaliza-tion), or the date they first switched between long-acting insulin types (NPH and analogue insulin). Consequently, the modeled outcome was the amount of time between the index date and each individual’s censoring date.
We used Stata version 10 (StataCorp LP, College Sta-tion, Texas) to estimate the effects of analogue insulin use on the risks of outcomes using Cox proportional hazards models. This model was chosen for 2 reasons: first, a time-to-event analysis has more statistical power than a logistic regression, because more information is used. In a logistic regression, estimates are based on dif-ferences between individuals who had events and those who did not. In a time-to-event model, estimates are based on differences in time to event as well. The second reason to use a Cox model was to mimic a clinical trial study design. In this study, individuals started analogue or NPH insulin and we then pre-dicted time until death or an ACSC hospitaliza-tion, using the provider’s prescribing pattern as an instrumental variable to control for selection bias. This is analogous to individuals starting on the treatment or placebo in a randomized clinical trial.
The IV approach estimates a pair of simultane-ous equations: one for the likelihood of receiving analogue insulin compared with NPH, and the sec-ond for the likelihood of the outcome. The treat-ment equation modeled the likelihood of receiving analogue insulin as a function of the provider-level prescribing rate and control variables. The outcome equations related treatment and control variables to probabilities of ACSC hospitalization and death. Because the outcome equations were nonlinear, we used the 2-stage residual inclusion technique28 to es-timate IV specifications. Two-stage models typically require computationally intensive bootstrapping to calculate accurate standard errors.29 We bootstrapped standard errors for the mortality model and deter-mined that the large samples and strong instruments involved resulted in bootstrapped values that were virtually identical to the asymptotic values generated automatically by the statistical software.
The Cox models assume that analogue insulin treatment increases or decreases outcome risk by a constant proportion over time. We tested this as-sumption using scaled Schoenfeld residuals from the mortality and hospitalization equations.30
Finally, to control for facility quality differences, we included a facility-level fixed effect in the treat-ment equation and a facility-level random effect in the outcome equations. Including a fixed effect in the treatment equation and a random effect in the outcome equation improved identification between the 2 stages and strengthened computational effi-ciency of the estimation algorithms. In sensitivity analyses, we estimated models with fixed effects in both the first and second stage; results were quali-tatively unchanged but computationally intensive so we chose to present the random effects version.
Patients in the sample were elderly (mean age = 69 years), about 84% white, and overwhelmingly male (Table 1). Over a quarter of the patients had average A1C ≥9% and diabetes complications such as retinopathy or nephropathy during the baseline period. Patients also had high rates of cardiovascular comorbidities, with 42% diag-nosed with obesity and 26% with CHF. Sixteen percent of the sample died and 30% had an ACSC hospitalization in the outcome period.
The first 2 columns of compare means of selected demographics and risk adjustment variables focused on diabetes severity and cardiovascular comor-bidities for patients who started on NPH compared with analogue insulin. Individuals who started on analogue in-sulin were slightly older (aged 70.6 years compared with 69 years) and had more diabetes complications and greater comorbidity burden in the baseline period. Patients on analogue insulin had lower rates of A1C ≥9% at baseline (21% compared with 27%) but were more likely to be missing A1C and BMI measurements during the baseline period. Thirty-two percent of the in-dividuals who started on NPH had an ACSC hospitalization compared with 21% of indi-viduals who started on ana-logue insulin; the figures were 17% and 11%, respectively, for mortality. providers had higher ACSC hospitalization and mortali-ty rates compared with patients assigned to high analogue prescribing providers.
To demonstrate the ef-fectively random nature of the instrumental variable, we divided the patients into those linked to providers with above- or below-median analogue insulin prescribing rates and compared means in the last 2 columns of Table 2. Patients linked to the above-median group received ana-logue insulin on 30% of days in the baseline year compared with 4% for the patients in the below-median group (data not shown). Comparing changes from the first 2 columns to the last 2 columns, the in-dividual-level demographic and comorbidity variables become closely balanced with the instrumental variable of provider analogue prescrib-ing rates. For example, 30% of the individuals who started NPH had a neuropathy com-plication, compared with 34% of patients who started on analogue insulin. When the sample is compared based on provider analogue prescribing rates, 30% of patients assigned to low analogue prescribing providers had a neuropathy complication compared with 31% of patient assigned to high analogue prescribing providers. This consistent pat-tern of narrowing the difference between the first 2 col-umns demonstrates the randomizing effect of the chosen instrument.
eAppendix Table A3
Receipt of analogue insulin was strongly predicted by provider analogue prescribing history in the first-stage equation. The coefficient on provider analogue prescrib-ing history was 2.76 (95% CI, 2.68-2.83) and the F statistic of 5135 easily exceeds the standard threshold for instru-mental variable strength (F >10)16 ().
eAppendix Table A5
There was no significant relationship between the type of insulin initiated and the 2 main outcomes. The esti-mated hazard ratio for the effect of starting analogue in-sulin compared with NPH was 1.05 (95% CI, 0.95-1.16) for ACSC hospitalization (). Older age, A1C values ≥9%, diabetes complications, and cardiovascular comor-bidities significantly increased the likelihood of experi-encing an ACSC hospitalization. Receiving metformin or a thiazolidinedione at baseline significantly decreased this likelihood. The estimated hazard ratio for the effect of starting analogue insulin compared with NPH was 0.97 for mortality (95% CI, 0.85-1.11) (). Older age, cerebrovascular, cardiovascular and peripheral vascular disease complications, and cardiovascular comorbidities such as congestive heart failure significantly increased the likelihood of death (). There was no significant difference in treatment effects in sensitiv-ity analyses that predicted diabetes-specific ACSC hospi-talizations. Individuals who started on analogue insulin were at a significantly increased risk of experiencing a cardiovascular-specific ACSC hospitalization (Table 4).
Short-term studies of analogue insulin and NPH have focused on glycemic control outcomes but have not had sufficient power or length of follow-up to examine long-term outcomes.7,11 Using a national sample of veterans diagnosed with diabetes and up to 10 years of follow-up, we found no significant difference between analogue in-sulin and NPH on the long-term outcomes of mortality and ACSC hospitalization. To our knowledge, no prior studies have examined the long-term differences in effec-tiveness of these 2 insulins.
The advantages and disadvantages of analogue insulin compared with NPH in the care of patients with diabetes have been subject to debate. Some hypothesized that the decreased nocturnal hypoglycemia of analogue insulin would encourage long-term patient adherence and inten-sification of insulin when needed,1 resulting in fewer long-term complications. Others concluded that any benefits of analogue insulin were insufficient to offset the substan-tially higher cost.12 Our study’s finding of no benefit in long-term outcomes for patients prescribed analogue in-sulin instead of NPH suggests that long-acting analogue insulin may not be cost-effective as a routine treatment for diabetes.
The lack of improvement in outcomes is striking when considering the cost implications of prescribing analogue insulin. Gellad et al (2013) found that Medicare Part D beneficiaries were 3 times more likely to be prescribed analogue insulin than a comparable VA population. The researchers estimated that Medicare Part D could have saved $189 million in 2008 if prescribing patterns of long-acting insulin in Part D matched VA prescribing patterns.8 These specific cost-effectiveness calculations may change if biosimilars (eg, generic versions of analogue insulin) become available as the patents for analogue insulin ex-pire.31 To successfully enter the market, manufacturers of biosimilars must prove they are just as safe and effective as the reference product. This is a more cumbersome task for protein-based drugs such as insulin compared with small-molecule generics, since small changes in protocol can lead to significant differences in the final product.32 Consequently, biosimilars are estimated to be only 20% to 40% cheaper than current analogue insulin products,32 and NPH may continue to be the most cost-effective alternative.
This study helps to establish innovative compara-tive effectiveness methods featuring provider prescribing patterns as instrumental variables.33-36 These methods are increasingly applicable with the growing adoption of electronic medical records. The use of instrumental variables minimizes the risk of confounding due to un-measured differences in patient health. Applying these quasi-experimental methods to retrospective data allows providers and policy makers to make decisions about the most effective and cost-efficient treatments in a timely manner, improving outcomes and lowering costs.
The main limitation of this study is that data were not available to examine hypoglycemia and subsequent quality-of-life outcomes. Our study data indicate that most patients did not switch from NPH to analogue in-sulin during the study period, suggesting that any quality-of-life differences were likely small. However, accurately measuring the occurrence and frequency of hypoglycemia is difficult.37 Surveys relying on self-report by patients find that severe and nocturnal hypoglycemia have a nega-tive impact on health-related quality of life and work productivity,38,39 and future research should continue to systematically study the frequency and impact of these quality-of-life outcomes through self-monitoring (includ-ing continuous glucose monitoring) and patient self- report. Another limitation is that the study population was nearly all male and composed entirely of elderly vet-erans. Future research should compare the long-term out-comes of NPH and analogue insulin in populations not well represented in our sample.
Our study found no benefit in long-term health out-comes for patients who were prescribed analogue insulin compared with NPH. Given the higher cost of analogue insulin, these findings raise questions about the cost-effectiveness of this prescribing practice. The growing prevalence of diabetes along with the proliferation of new treatments emphasizes the need for such observational comparative effectiveness studies to help identify the most efficient uses of healthcare resources.
Author Affiliations: VA Boston Healthcare System (JCP, PRC, SDP), Boston, MA; Boston University School of Medicine (JCP), Boston, MA; Harvard Medical School (PRC), Boston, MA; VA Pittsburgh Medical Center (WFG), Pittsburgh, PA; University of Pittsburgh (WFG), Pitts-burgh, PA; VA Durham Medical Center (DE), Durham, NC; Duke Uni-versity School of Medicine (DE), Durham, NC; University of Illinois at Chicago (TAL), Chicago, IL; Northeastern University (SDP), Boston,MA.
Source of Funding: Health Services Research and Development Ser-vice of the US Department of Veterans Affairs (Grant No. IIR 10-136), AHRQ R01 HS019708, and NIH K24 DK63214.
Author Disclosures: The 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 (JCP, TAL, WFG, SDP, DE, PRC); acquisition of data (JCP); analysis and interpretation of data (JCP, TAL, WFG, SDP, DE, PRC); drafting of the manuscript (JCP, PRC); critical revision of the manuscript for important intellectual content (JCP, WFG, SDP, DE, PRC); statistical analysis (JCP, TAL, SDP, DE); obtaining funding (SDP); administrative, technical, or logistic support (DE, WFG, TAL, JCP); and supervision (JCP, SDP).
Address correspondence to: Julia C. Prentice, PhD, Health Care Fi-nancing & Economics, VA Boston Healthcare System, 150 So Hunting-ton Ave (152H), Boston, MA 02130. E-mail: firstname.lastname@example.org.
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