title: An algorithm to identify patients with treated type 2 diabetes using medico-administrative data.
abstract: BACKGROUND
  National authorities have to follow the evolution of diabetes to implement public health policies. An algorithm was developed to identify patients with treated type 2 diabetes and estimate its annual prevalence in Luxembourg using health insurance claims when no diagnosis code is available.
  
  
  METHODS
  The DIABECOLUX algorithm was based on patients' age as well as type and number of hypoglycemic agents reimbursed between 1995 and 2006. Algorithm validation was performed using the results of a national study based on medical data. Sensitivity, specificity and predictive values were estimated.
  
  
  RESULTS
  The sensitivity of the DIABECOLUX algorithm was found superior to 98.2%. Between 2000 and 2006, 22,178 patients were treated for diabetes in Luxembourg, among whom 21,068 for type 2 diabetes (95%). The prevalence was estimated at 3.79% in 2006 and followed an increasing linear trend during the period. In 2005, the prevalence was low for young age classes and increased rapidly from 40 to 70 for male and 80 for female, reaching a peak of, respectively 17.0% and 14.3% before decreasing.
  
  
  CONCLUSIONS
  The DIABECOLUX algorithm is relevant to identify treated type 2 diabetes patients. It is reproducible and should be transferable to every country using medico-administrative databases not including diagnosis codes. Although undiagnosed patients and others with lifestyle recommendations only were not considered in this study, this algorithm is a cheap and easy-to-use tool to inform health authorities. Further studies will use this tool with the aim of improving the quality of health care dedicated to diabetic patients in Luxembourg.
authors.affiliation: Centre for Health Studies, Public Research Centre for Health, Luxembourg. laurence.renard@crp-sante.lu
authors.forename: Laurence M
authors.initials: LM
authors.lastname: Renard
authors.forename: Valery
authors.initials: V
authors.lastname: Bocquet
authors.forename: Gwenaelle
authors.initials: G
authors.lastname: Vidal-Trecan
authors.forename: Marie-Lise
authors.initials: ML
authors.lastname: Lair
authors.forename: Sophie
authors.initials: S
authors.lastname: Couffignal
authors.forename: Claudine
authors.initials: C
authors.lastname: Blum-Boisgard
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016208:Databases, Factual; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology* / Q000628:therapy; D005260:Female; D006801:Humans; D007349:Insurance, Health, Reimbursement; D008189:Luxembourg; D008297:Male; D008875:Middle Aged