Validation of classification algorithms for childhood diabetes identified from administrative data
Pediatric Diabetes, 04/25/2012
Vanderloo SE et al. – Demographic characteristics in combination with drug–utilization patterns can be used to differentiate diabetes type among cases of pediatric diabetes identified within administrative health databases. Validation of similar algorithms in other regions is warranted.
The authors applied the NDSS case definition to children living in British Columbia between 1 April 1996 and 31 March 2007.
Through an iterative process, four potential classification algorithms were developed based on demographic characteristics and drug-utilization patterns.
Each algorithm was then validated against a gold standard clinical database.
Algorithms based primarily on an age rule (i.e., age <10 at diagnosis categorized type 1 diabetes) were most sensitive in the identification of type 1 diabetes; algorithms with restrictions on drug utilization (i.e., no prescriptions for insulin ± glucose monitoring strips categorized type 2 diabetes) were most sensitive for identifying type 2 diabetes.
One algorithm was identified as having the optimal balance of sensitivity (Sn) and specificity (Sp) for the identification of both type 1 (Sn: 98.6%; Sp: 78.2%; PPV: 97.8%) and type 2 diabetes (Sn: 83.2%; Sp: 97.5%; PPV: 73.7%).
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