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      Progression to insulin therapy among patients with type 2 diabetes treated with sitagliptin or sulphonylurea plus metformin dual therapy

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          Abstract

          Aim

          To assess time to insulin initiation among patients with type 2 diabetes mellitus ( T2DM) treated with sitagliptin versus sulphonylurea as add‐on to metformin.

          Methods

          This retrospective cohort study used GE Centricity electronic medical records and included patients aged ≥18 years with continuous medical records and an initial prescription of sitagliptin or sulphonylurea (index date) with metformin for ≥90 days during 2006–2013. Sitagliptin and sulphonylurea users were matched 1 : 1 using propensity score matching, and differences in insulin initiation were assessed using Kaplan– Meier curves and Cox regression. We used conditional logistic regression to examine the likelihood of insulin use 1–6 years after the index date for each year.

          Results

          Propensity score matching produced 3864 matched pairs. Kaplan– Meier analysis showed that sitagliptin users had a lower risk of insulin initiation compared with sulphonylurea users (p = 0.003), with 26.6% of sitagliptin users initiating insulin versus 34.1% of sulphonylurea users over 6 years. This finding remained significant after adjusting for baseline characteristics (hazard ratio 0.76, 95% confidence interval 0.65–0.90). Conditional logistic regression analyses confirmed that sitagliptin users were less likely to initiate insulin compared with sulphonylurea users [odds ratios for years 1–6: 0.77, 0.79, 0.81, 0.57, 0.29 and 0.75, respectively (p < 0.05 for years 4 and 5)].

          Conclusions

          In this real‐world matched cohort study, patients with T2DM treated with sitagliptin had a significantly lower risk of insulin initiation compared with patients treated with sulphonylurea, both as add‐on to metformin.

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          Most cited references 28

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          An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

          The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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            Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

            In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the observed covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.
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              Economic Costs of Diabetes in the U.S. in 2012

                (2013)
              OBJECTIVE This study updates previous estimates of the economic burden of diagnosed diabetes and quantifies the increased health resource use and lost productivity associated with diabetes in 2012. RESEARCH DESIGN AND METHODS The study uses a prevalence-based approach that combines the demographics of the U.S. population in 2012 with diabetes prevalence, epidemiological data, health care cost, and economic data into a Cost of Diabetes Model. Health resource use and associated medical costs are analyzed by age, sex, race/ethnicity, insurance coverage, medical condition, and health service category. Data sources include national surveys, Medicare standard analytical files, and one of the largest claims databases for the commercially insured population in the U.S. RESULTS The total estimated cost of diagnosed diabetes in 2012 is $245 billion, including $176 billion in direct medical costs and $69 billion in reduced productivity. The largest components of medical expenditures are hospital inpatient care (43% of the total medical cost), prescription medications to treat the complications of diabetes (18%), antidiabetic agents and diabetes supplies (12%), physician office visits (9%), and nursing/residential facility stays (8%). People with diagnosed diabetes incur average medical expenditures of about $13,700 per year, of which about $7,900 is attributed to diabetes. People with diagnosed diabetes, on average, have medical expenditures approximately 2.3 times higher than what expenditures would be in the absence of diabetes. For the cost categories analyzed, care for people with diagnosed diabetes accounts for more than 1 in 5 health care dollars in the U.S., and more than half of that expenditure is directly attributable to diabetes. Indirect costs include increased absenteeism ($5 billion) and reduced productivity while at work ($20.8 billion) for the employed population, reduced productivity for those not in the labor force ($2.7 billion), inability to work as a result of disease-related disability ($21.6 billion), and lost productive capacity due to early mortality ($18.5 billion). CONCLUSIONS The estimated total economic cost of diagnosed diabetes in 2012 is $245 billion, a 41% increase from our previous estimate of $174 billion (in 2007 dollars). This estimate highlights the substantial burden that diabetes imposes on society. Additional components of societal burden omitted from our study include intangibles from pain and suffering, resources from care provided by nonpaid caregivers, and the burden associated with undiagnosed diabetes.
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                Author and article information

                Journal
                Diabetes Obes Metab
                Diabetes Obes Metab
                10.1111/(ISSN)1463-1326
                DOM
                Diabetes, Obesity & Metabolism
                Blackwell Publishing Ltd (Oxford, UK )
                1462-8902
                1463-1326
                22 June 2015
                October 2015
                : 17
                : 10 ( doiID: 10.1111/dom.2015.17.issue-10 )
                : 956-964
                Affiliations
                [ 1 ] Section of EndocrinologyYale University School of Medicine New Haven CTUSA
                [ 2 ]Merck & Co., Inc. Kenilworth NJUSA
                [ 3 ]Asclepius Analytics LLC New York NYUSA
                [ 4 ]Ochsner Medical Center New Orleans LAUSA
                Author notes
                [* ] Correspondence to: Kaan Tunceli, PhD, MA, Director, Outcomes Research, Center for Observational and Real World Evidence (CORE) Merck Sharp & Dohme Corp., 351 North Sumneytown, North Wales, PA 19454‐2505, USA.

                E‐mail: kaan_tunceli@ 123456merck.com

                Article
                DOM12489
                10.1111/dom.12489
                5033027
                25962401
                © 2015 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                Page count
                Pages: 9
                Product
                Funding
                Funded by: Eli Lilly and Co.
                Funded by: Novo Nordisk
                Funded by: Sanofi US, Inc.
                Funded by: Takeda
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                dom12489
                October 2015
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.4 mode:remove_FC converted:22.09.2016

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