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An algorithm to identify patients with treated type 2 diabetes using medico-administrative data

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 availab...

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Autores principales: Renard, Laurence M, Bocquet, Valery, Vidal-Trecan, Gwenaelle, Lair, Marie-Lise, Couffignal, Sophie, Blum-Boisgard, Claudine
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3090314/
https://www.ncbi.nlm.nih.gov/pubmed/21492480
http://dx.doi.org/10.1186/1472-6947-11-23
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author Renard, Laurence M
Bocquet, Valery
Vidal-Trecan, Gwenaelle
Lair, Marie-Lise
Couffignal, Sophie
Blum-Boisgard, Claudine
author_facet Renard, Laurence M
Bocquet, Valery
Vidal-Trecan, Gwenaelle
Lair, Marie-Lise
Couffignal, Sophie
Blum-Boisgard, Claudine
author_sort Renard, Laurence M
collection PubMed
description 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.
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spelling pubmed-30903142011-05-10 An algorithm to identify patients with treated type 2 diabetes using medico-administrative data Renard, Laurence M Bocquet, Valery Vidal-Trecan, Gwenaelle Lair, Marie-Lise Couffignal, Sophie Blum-Boisgard, Claudine BMC Med Inform Decis Mak Research Article 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. BioMed Central 2011-04-14 /pmc/articles/PMC3090314/ /pubmed/21492480 http://dx.doi.org/10.1186/1472-6947-11-23 Text en Copyright ©2011 Renard et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Renard, Laurence M
Bocquet, Valery
Vidal-Trecan, Gwenaelle
Lair, Marie-Lise
Couffignal, Sophie
Blum-Boisgard, Claudine
An algorithm to identify patients with treated type 2 diabetes using medico-administrative data
title An algorithm to identify patients with treated type 2 diabetes using medico-administrative data
title_full An algorithm to identify patients with treated type 2 diabetes using medico-administrative data
title_fullStr An algorithm to identify patients with treated type 2 diabetes using medico-administrative data
title_full_unstemmed An algorithm to identify patients with treated type 2 diabetes using medico-administrative data
title_short An algorithm to identify patients with treated type 2 diabetes using medico-administrative data
title_sort algorithm to identify patients with treated type 2 diabetes using medico-administrative data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3090314/
https://www.ncbi.nlm.nih.gov/pubmed/21492480
http://dx.doi.org/10.1186/1472-6947-11-23
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