PDB13: OPTIMIZATION OF DIABETES MANAGEMENT IN GERMANY USING A COMPUTER BASED OUTCOME PROGNOSES MODEL

Abstract

OBJECTIVES: Optimization of type 2 diabetes intervention strategies in Germany based on stepwise prognoses of expected medical and economic outcomes in population subgroups. METHODS: A published, editable diabetes model was used to assess the outcomes of different degrees of secondary prevention measures for different diabetes type 2 patient sub-groups in Germany. Clinical data were derived from German diabetes quality of care circles. Incremental cost-effectiveness ratios (ICERs) were calculated as the differences of average lifetime cost divided by the difference of average life expectancy. Optimization was approximated by calculating ICERs for stepwise modified prevention strategies, including screening and complication treatments for variable population risk characteristics. RESULTS: Compared to the prognosis of overall life expectancy and cost consequences the more refined stepwise approach generates a series of results for all combinations of intervention strategy and population subgroup. At certain risk levels the ICER based treatment recommendation may change if subgroup prognosis is applied. But for all age groups of diabetes patients secondary prevention of complication is the dominant variant. Medical outcomes and incremental cost-effectiveness are improved by additional secondary prevention measures except for patients with non-reversible risks. The potential savings from improved prevention amount to 10% of total expenditures for diabetes care in Germany, i.e., DEM 3000 million. CONCLUSIONS: With the stepwise assessment of subgroup outcomes a treatment optimization and optimal allocation of diabetes management to patient subgroups is feasible. Using average data to calculate overall ICER for the total diabetes population may ignore the best treatment strategy in different population subgroups. Subgroup analysis represents a helpful tool in the health economic evaluation of diabetes treatment strategies when variable population risk characteristics and baseline complications affect the clinical and economic outcome.