The economic analyses of the UK prospective diabetes study

Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention

BACKGROUND

Diabetes mellitus brings an increased risk for cardiovascular complications and patients profit from prevention. This prevention also suits the general population. The question arises what is a better strategy: target the general population or diabetes patients.

METHODS

A mathematical programming model was developed to calculate optimal allocations for the Dutch population of the following interventions: smoking cessation support, diet and exercise to reduce overweight, statins, and medication to reduce blood pressure. Outcomes were total lifetime health care costs and QALYs. Budget sizes were varied and the division of resources between the general population and diabetes patients was assessed.

RESULTS

Full implementation of all interventions resulted in a gain of 560,000 QALY at a cost of €640 per capita, about €12,900 per QALY on average. The large majority of these QALY gains could be obtained at incremental costs below €20,000 per QALY. Low or high budgets (below €9 or above €100 per capita) were predominantly spent in the general population. Moderate budgets were mostly spent in diabetes patients.

CONCLUSIONS

Major health gains can be realized efficiently by offering prevention to both the general and the diabetic population. However, a priori setting a specific distribution of resources is suboptimal. Resource allocation models allow accounting for capacity constraints and program size in addition to efficiency.

Cost implications to health care payers of improving glucose management among adults with type 2 diabetes

Objective. To assess the cost implications to payers of improving glucose management among adults with type 2 diabetes. Data Source/Study Setting. Medical-record data from the Community Quality Index (CQI) study (1996-2002), pharmaceutical claims from four Massachusetts health plans (2004-2006), Medicare Fee Schedule (2009), published literature. Study Design. Probability tree depicting glucose management over 1 year. Data Collection/Extraction Methods. We determined how frequently CQI study subjects received recommended care processes and attained Health Care Effectiveness Data and Information Set (HEDIS) treatment goals, estimated utilization of visits and medications associated with recommended care, assigned costs based on utilization, and then modeled how hospitalization rates, costs, and goal attainment would change if all recommended care was provided. Principal Findings. Relative to current care, improved glucose management would cost U.S.$327 (U.S.$192-711 in sensitivity analyses) more per person with diabetes annually, largely due to antihyperglycemic medications. Cost-effectiveness to payers, defined as incremental annual cost per patient newly attaining any one of three HEDIS goals, would be U.S.$1,128; including glycemic crises reduces this to U.S.$555-1,021. Conclusions. The cost of improving glucose management appears modest relative to diabetes-related health care expenditures. The incremental cost per patient newly attaining HEDIS goals enables payers to consider costs as well as outcomes that are linked to future profitability.

Economic models in type 2 diabetes

OBJECTIVE

To identify and critically appraise cost-effectiveness models developed to evaluate type 2 diabetes (T2D) treatments and to assess which types of treatment effects they capture.

RESEARCH DESIGN AND METHODS

A systematic search was performed in MEDLINE, EMBASE, Centre for Reviews and Dissemination databases at the University of York, and Health Economic Evaluation Database for the period to September 2008. The websites of Health Technology Assessment (HTA) bodies in different countries were also screened for relevant models. For each of the identified original models, details of the structure, data in- and outputs were extracted and the overall quality of the model in terms of the combination of structure, assumptions and data inputs were appraised using published criteria.

RESULTS

Seventy-eight articles and 41 HTAs reporting relevant economic evaluations were identified. There were ten models with multiple publications, and a further ten models with one associated publication. The critical review demonstrated that most had the same fundamental structure, used similar micro-simulation techniques and were based on the same key data sources. However, the process for identification of relevant data and their synthesis, and the selection of outcomes lacked transparency. The models differed according to the extent and type of interventions they evaluated and which diabetes complications and treatment-related adverse events were captured. For example, just one model incorporated changes in patient weight, despite the fact that weight gain can be a side-effect of some treatments, and weight loss a potential benefit of others.

CONCLUSIONS

Whilst many economic models exist in T2D, most share common features such as the model type. Identified shortcomings are lack of transparency in data identification and evidence synthesis as well as the selection of the modelled outcomes. Future models should aim to include all relevant treatment outcomes, whether these relate to effects on underlying diabetes and its complications or to short- or long-term side effects of treatment.

Conducting cost-effectiveness analyses of type 2 diabetes in low- and middle-income countries: can locally generated observational study data overcome methodological limitations?

In low- and middle-income countries, the high personal and economic burden of type 2 diabetes is further compounded by inadequate resources for diabetes care when compared with high-income countries. Health technology assessments (HTAs) aim to inform policy decision makers in their efforts to achieve more effective allocation of resources by providing evidence-based input on new technologies. Within the hierarchy of evidence, randomized controlled trials (RCTs) remain the 'gold standard' used to inform HTAs, but are limited by poor external validity (ie, generalizability to real-world populations). Unlike RCTs, observational studies are able to enrol broader patient populations, but their design renders such studies vulnerable to confounding factors and selection bias. However, it is increasingly recognized that observational studies can complement RCTs by supporting and extending efficacy findings from RCTs to real-world clinical practice, particularly across geographical populations. They can also provide locally relevant baseline and disease natural history data to populate health economic models. Thus, observational data are likely to be of considerable informative value to policy makers in developing countries reaching decisions on diabetes care within an environment of scarce resources.