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Supplements Improving Treatment Success Rates for Type 2 Diabetes: Balancing Safety, Cost, and Outcome [CME/CPE]

Decision Support Tools to Optimize Economic Outcomes for Type 2 Diabetes

Fadia T. Shaya, PhD, MPH; and Viktor V. Chirikov, MS

As the costs of type 2 diabetes mellitus (T2DM) care and related clinical trials continue to rise, economically viable methods are being sought to effectively predict the relative utility of various treatment options. The high price of clinical trials has led to the development of alternative methods to collect and consolidate data. Comparative effectiveness research (CER) synthesizes existing evidence to address knowledge gaps and drive patient-focused clinical decisions and outcomes. CER methods compare the health outcomes and costs associated with interventions to determine the option with the maximum patient benefit at optimal cost. In addition to traditional CER approaches such as systematic reviews, meta-analyses, and retrospective claims analyses, Markov modeling and Bayesian analysis can be applied to predict patient outcomes in scenarios where clinical trials are not feasible. Additionally, cost-benefit, cost-effectiveness, and cost-utility analyses comprise “cost-effectiveness analyses.” Cost-benefit analysis looks solely at monetary value, while cost-effectiveness and cost-utility analyses include gains in health and quality of life, providing a ratio of cost to benefit. This paper will discuss a range of approaches to CER including Markov modeling, mixed treatment comparisons, the Archimedes model, and Bayesian statistics, and provide guidance in interpreting data from these studies in a managed care context, with a particular focus on evaluating treatments for T2DM. It will also provide guidance on common indices of comorbidity used in health economics research. Data from these models can be used to reduce treatment costs and improve the overall quality of population-level health.

(Am J Manag Care. 2011;17:S377-S383)

Healthcare delivery is extremely expensive, and costs continue to rise as demand for care increases and newer treatments are developed.1 By 2020, US healthcare expenses are expected to increase to nearly 20% of the total gross domestic product.2 Healthcare delivery for type 2 diabetes mellitus (T2DM) is particularly costly; currently, 1 out of 5 US healthcare dollars is used to treat patients with diabetes.3 Likewise, the costs of clinical trial programs required for drug development have continued to rise sharply.4 This has led to an increased focus on alternate approaches to effectively predict the relative utility of various treatment options.

Considered broadly, these new approaches can be labeled as comparative effectiveness research (CER). The US Federal Coordinating Council describes CER as “The conduct and synthesis of systematic research comparing different interventions and strategies to prevent, diagnose, treat and monitor health conditions.”5 CER enables stakeholders to work toward a common goal of improving access to effective treatments, while keeping costs in mind. CER can identify ways to lower costs through improved outcomes, such as fewer emergency department visits and hospitalizations, and can be used to stratify treatment options based on efficacy, cost, and other factors.6

CER can also provide data related to drug effectiveness, making it highly relevant to managed care decision making. Until recently, clinical trials applying active comparators were considered to be the most reliable type of CER. Such trials, however, are not only expensive to conduct, but also only predict outcomes over a short period of time.

Emerging CER approaches have started to address these issues by consolidating data from sources such as existing placebo-controlled clinical trials or patient data sets using computer-assisted analytic techniques.6,7 Some of these alternate approaches to evidence acquisition have achieved a high level of evidentiary acceptance, and many are conducted using fairly straightforward research methodologies; for example, meta-analyses and retrospective claims analyses.6,7 Other, more sophisticated approaches, such as Markov modeling or Bayesian analysis of mixed treatment comparisons (MTCs), are not as well understood by healthcare professionals.7

This paper will summarize our current understanding of T2DM health economics research, discuss the role and benefits of CER in managed care decision making for T2DM, and provide an overview of Markov and Bayesian modeling techniques, as well as guidance on how to interpret data obtained from such studies in a managed care context.

Health Economic Analyses of Antidiabetic Medications

Economic evaluations of T2DM treatment and care compare the health outcomes and costs of different interventions to determine which option, when utilized, achieves maximum patient benefit at optimal cost.1

There are 3 primary analytic methods for evaluating the economic value of an intervention that fall under the umbrella term “cost-effectiveness analysis.” These are cost-benefit, cost-effectiveness, and cost-utility analyses. Cost-benefit analysis typically measures and reports results in the form of the overall monetary value, whether costs or savings, associated with use of the treatment. Cost-effectiveness and cost-utility analyses also project monetary outcomes, but go further to measure gains in health and quality of life using a ratio of overall cost (in dollars) divided by health effect (eg, number of heart attacks prevented), or assessed as quality-adjusted life-years (QALYs).1

In a systematic review, Klonoff et al evaluated 17 cost-benefit analyses performed to evaluate common diabetes interventions; the authors used this prior research to develop a scale estimating the economic impact of various diabetes interventions.8 The Table summarizes the key findings of this systematic review, including the ratings for each type of intervention. This provides a stark demonstration of the lack of cost-effectiveness of many interventions, as well as the high utility of some underappreciated concerns in diabetes, such as preconception care. However, it is important to keep in mind that a lack of identified cost-effectiveness may simply indicate a need to modify an intervention, rather than discontinue its use.

Using Modeling to Estimate the Value of T2DM Screening Strategies

The Archimedes model is a tool that assesses the potential long-term health and economic impact of new treatments on the healthcare system from the perspective of changes in medical guidelines, processes, and practice patterns. The model has been validated by more than 50 clinical trials.9,10

A recent analysis by Kahn et al used the Archimedes model to simulate and compare 8 T2DM screening strategies, compared with a control group receiving no screening. Screening strategies included routine screening once patients reached a certain age, or at hypertension diagnosis, as well as repeated screening at various time intervals. All simulated patients were followed for 50 years, or until death, with outcomes recorded annually.9

The model found that the T2DM screening strategy associated with the highest benefit, in terms of QALYs gained, was to initiate T2DM screening for all patients at 30 years of age, with repeat screening every 6 months. Investigators found that the use of this strategy resulted in earlier diagnosis (mean, 7.8 years) compared with the control group. Additionally, the simulation showed the effects of each screening strategy on myocardial infarction, stroke, microvascular outcomes, and death over a 50-year period.9

The ability to compare the outcomes of various screening strategies, plus the benefits of early T2DM diagnosis, against the costs of frequent screening provides useful information for informed managed care decision making.

Comparative Effectiveness Research on Treatments for T2DM

One of the main purposes of CER is to synthesize existing evidence in order to address knowledge gaps and drive patient-focused clinical decisions and outcomes.11 Thus, CER can identify the most beneficial treatment based on patient characteristics and inform real-world practice.6

Two recent large, systematic T2DM reviews conducted for the Agency for Healthcare Research and Quality (AHRQ) summarize existing evidence for the use of oral agents and insulin.12,13 The AHRQ review of oral medications included outcomes from 140 randomized controlled trials (RCTs) and 26 observational trials; the results support the use of metformin as first-line monotherapy.12 In contrast, comparisons of dual drug combinations (metformin thiazolidinedione, sulfonylurea, meglitinide, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 agonists, or basal or premixed insulin) identified no greater benefit with any specific treatment combination. Similarly, the insulin CER review included results from 50 studies and found, for example, that premixed insulin analogs were more effective in lowering fasting glucose compared with long-acting insulin analogs and noninsulin antidiabetic agents.13 As with the oral agents review, however, this paper identified research gaps. Specifically, certain comparator designs were not available (eg, basal-bolus vs premixed insulin), and a need was observed for more real-world effectiveness data on insulin use. In some cases, these limitations precluded the ability to draw definitive conclusions.

Observational studies have also been instrumental in CER when evidence gaps from RCT findings exist, despite their methodological ability to establish a reliable relationship between cause and effect.14-16 Observational studies use real-world data to assist in decision making. Sources of “realworld data” include administrative claims, patient registries, large simple trials, resource use information collected as part of clinical trials, supplements to clinical registration studies, health surveys, and electronic medical records.16,17 However, due to the lack of standardized instruments available to appraise quality of evidence, real-world data are not yet widely used for decision-making purposes.17 This highlights the need for innovative, sophisticated techniques such as Markov modeling and Bayesian MTCs to overcome these barriers. A study by Zhou et al illustrates how, in the absence of high-quality real-world data, an analytical computer model can have significant predictive power. Zhou and colleagues built a Markov-like model of diabetes progression, based on the baseline characteristics of patients from a populationbased study in southern Wisconsin. When the model was followed over time, predicted values were consistent with actual observed outcomes such as mortality, quality of life, and cost.18

Common Indices of Comorbidity Used in Health Economics Research

A number of comorbidity indices have been developed and validated for use in health economics research. These assessments evaluate patient risk and enhance study designs by accounting for patient comorbidity assessment, mortality risk, and comedication indicators. Some key indices used in CER evaluations include:

Charlson Comorbidity Index (CCI)

The CCI predicts the 10-year mortality risk of patients with certain illnesses, based on medical record review. Patients are given scores of 0 to 5, with 0 representing the absence of comorbidities and 5 indicating a moribund state. Conditions that contribute to the CCI score are weighted by illness severity. This measure can be used to predict mortality and healthcare utilization costs.19,20

Index of Coexistent Disease (ICED)

The ICED assesses the burden of coexistent diseases by combining 2 scales that reflect: (1) burden of coexistent disease, based on disease severity; and (2) overall physical impairment, based on the level of disability caused by illness. The burden of coexistent diseases can affect multiple aspects of care, such as recovery from surgery, and affect patient outcomes and resource use.21

Chronic Disease Index (CDI)

The CDI uses data from a medication database to estimate a patient’s chronic disease burden. Although this model can miss chronic diseases that are not treated with medication, it can be used to risk-adjust a large patient population by predicting outcomes such as morbidity and mortality, costs of medical care, and healthcare utilization.22

RxRisk Model

The RxRisk model is an algorithm that uses prescription medication data to predict the cost of chronic diseases, making it useful for medical risk analyses. The use of specific prescription medications classifies a patient into a specific risk category, which, combined with the patient’s age and sex, provides an estimate of future healthcare costs.23

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