title: Cost-effectiveness analysis of different screening procedures for type 2 diabetes: the KORA Survey 2000.
abstract: OBJECTIVE
  To compare the cost-effectiveness of different type 2 diabetes screening strategies using population-based data (KORA Survey; Augsburg, Germany; subjects aged 55-74 years), including participation data.
  
  
  METHODS
  The decision analytic model, which had a time horizon of 1 year, used the following screening strategies: fasting glucose testing, the oral glucose tolerance test (OGTT) following fasting glucose testing in impaired fasting glucose (IFG) (fasting glucose + OGTT), OGTT only, and OGTT if HbA(1c) was >5.6% (HbA(1c) + OGTT), all with or without first-step preselection (p). The main outcome measures were costs (in Euros), true-positive type 2 diabetic cases, incremental cost-effectiveness ratios (ICERs), third-party payers, and societal perspectives.
  
  
  RESULTS
  After dominated strategies were excluded, the OGTT and HbA(1c) + OGTT from the perspective of the statutory health insurance remained, as did fasting glucose + OGTT and HbA(1c) + OGTT from the societal perspective. OGTTs (4.90 per patient) yielded the lowest costs from the perspective of the statutory health insurance and fasting glucose + OGTT (10.85) from the societal perspective. HbA(1c) + OGTT was the most expensive (21.44 and 31.77) but also the most effective (54% detected cases). ICERs, compared with the next less effective strategies, were 771 from the statutory health insurance and 831 from the societal perspective. In the Monte Carlo analysis, dominance relations remained unchanged in 100 and 68% (statutory health insurance and societal perspective, respectively) of simulated populations.
  
  
  CONCLUSIONS
  The most effective screening strategy was HbA(1c) combined with OGTT because of high participation. However, costs were lower when screening with fasting glucose tests combined with OGTT or OGTT alone. The decision regarding which is the most favorable strategy depends on whether the goal is to identify a high number of cases or to incur lower costs at reasonable effectiveness.
authors.affiliation: German Diabetes Research Institute, Department of Biometrics and Epidemiology, Heinrich Heine University, Auf'm Hennekamp 65, 40225 Düsseldorf, Germany. icks@ddfi.uni-duesseldorf.de
authors.forename: Andrea
authors.initials: A
authors.lastname: Icks
authors.forename: Burkhard
authors.initials: B
authors.lastname: Haastert
authors.forename: Afschin
authors.initials: A
authors.lastname: Gandjour
authors.forename: Jürgen
authors.initials: J
authors.lastname: John
authors.forename: Hannelore
authors.initials: H
authors.lastname: Löwel
authors.forename: Rolf
authors.initials: R
authors.lastname: Holle
authors.forename: Guido
authors.initials: G
authors.lastname: Giani
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Rathmann
mesh_terms: D000368:Aged; D003362:Cost-Benefit Analysis; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology*; D005260:Female; D005858:Germany / Q000453:epidemiology; D018149:Glucose Intolerance / Q000175:diagnosis / Q000453:epidemiology; D005951:Glucose Tolerance Test; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000191:economics*; D008875:Middle Aged; D015995:Prevalence; D012680:Sensitivity and Specificity