Analytical approach:
The analysis was based on a Markov model, with a lifetime horizon. The authors stated that the perspective of a central health care funder was adopted.
Effectiveness data:
The clinical data appear to have been derived from selected relevant studies. The epidemiological data, patients’ characteristics, and screening uptake were from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), which was a population-representative cohort study. Treatment-related data were from published clinical trials and meta-analyses. The sensitivity and specificity of the screening options were from a published diagnostic study, conducted in several countries. The rate of cardiovascular disease events was the key input.
Monetary benefit and utility valuations:
The utility values were from the AusDiab, which used the Short Form (SF-36) Health Survey, and data from other published reports.
Measure of benefit:
Quality-adjusted life-years (QALYs) were the summary benefit measure and they were discounted at an annual rate of 5%.
Cost data:
The economic analysis included the costs of drugs, consultation visits, diagnostic tests, glycaemic control, hypertension control, protein control, dialysis, and transplants. The resource use data came from various published sources, including Australian reports and the UK Prospective Diabetes Study. The unit costs of tests and most other items were based on data from the Medical Benefits Schedule. All costs were in Australian dollars (AUD) and the price year was 2008. A 5% annual discount rate was applied to future costs.
Analysis of uncertainty:
A probabilistic sensitivity analysis was undertaken to assess the uncertainty underlying all the model inputs, simultaneously, using recommended distributions, and cost-effectiveness acceptability curves were generated. The impact of variations in the starting age for screening and screening participation was tested in a deterministic one-way sensitivity analysis.