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Related Experiment Video

Updated: Jun 2, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

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

Laurence M Renard¹, Valery Bocquet, Gwenaelle Vidal-Trecan

¹Centre for Health Studies, Public Research Centre for Health, Luxembourg. laurence.renard@crp-sante.lu

BMC Medical Informatics and Decision Making

|April 16, 2011

Summary

A new algorithm, DIABECOLUX, accurately identifies type 2 diabetes patients using health insurance claims. This tool helps estimate diabetes prevalence for public health policy, showing a rising trend in Luxembourg.

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Last Updated: Jun 2, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Public Health
Epidemiology
Health Informatics

Background:

National health authorities require accurate diabetes data for effective public health policy.
Existing methods for tracking diabetes prevalence are limited when diagnosis codes are unavailable.
A novel algorithm was developed to identify treated type 2 diabetes patients using health insurance claims in Luxembourg.

Purpose of the Study:

To develop and validate an algorithm (DIABECOLUX) for identifying patients with treated type 2 diabetes.
To estimate the annual prevalence of type 2 diabetes in Luxembourg using medico-administrative data.
To provide a tool for health authorities to monitor diabetes trends.

Main Methods:

The DIABECOLUX algorithm utilized patient age and reimbursed hypoglycemic agents between 1995-2006.
Algorithm performance was validated against a national study using medical data.
Key performance metrics including sensitivity, specificity, and predictive values were calculated.

Main Results:

The DIABECOLUX algorithm demonstrated high sensitivity (>98.2%).
Between 2000-2006, 95% of 22,178 treated diabetes patients in Luxembourg had type 2 diabetes.
Type 2 diabetes prevalence in Luxembourg reached 3.79% in 2006, showing a consistent increase.

Conclusions:

The DIABECOLUX algorithm is a valid, reproducible, and transferable tool for identifying treated type 2 diabetes patients.
This algorithm offers a cost-effective method for health authorities to estimate diabetes prevalence using medico-administrative databases.
Further research using DIABECOLUX aims to enhance diabetes care quality in Luxembourg.