Validation of classification algorithms for childhood diabetes identified from administrative data.
Vanderloo SE, Johnson JA, Reimer K, McCrea P, Nuernberger K, Krueger H, Aydede SK, Collet J-P, Amed S. Validation of classification algorithms for childhood diabetes identified from administrative data. Objective: Type 1 diabetes is the most common form of diabetes among children; however, the propo...
| Publicado en: | Pediatric Diabetes Vol. 13; no. 3; pp. 229 - 235 |
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| Autores principales: | , , , , , , , , |
| Formato: | algorithm research Journal Article |
| Publicado: |
Wiley-Blackwell
May2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104556806&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104556806 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1399543X FS2 jtl: Pediatric Diabetes issn: 1399543X maglogo: Y pubinfo: dt: May2012 vid: 13 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104556806 74575276 10.1111/j.1399-5448.2011.00795.x NLM21771232 104556806 ppf: 229 ppct: 6 formats: tig: atl: Validation of classification algorithms for childhood diabetes identified from administrative data. aug: au: Vanderloo, Saskia E Johnson, Jeffrey A Reimer, Kim McCrea, Patrick Nuernberger, Kimberly Krueger, Hans Aydede, Sema K Collet, Jean-Paul Amed, Shazhan affil: School of Public Health, University of Alberta, Edmonton, AB, Canada sug: subj: Diabetes Mellitus, Type 1 Diagnosis Classification Algorithms Pediatrics Diabetes Mellitus Physiopathology Diabetes Mellitus, Type 2 Diagnosis Professional Organizations Descriptive Statistics Demography Male Female Canada Hypoglycemia Hospitalization Evaluation Patient Discharge Insulin Administration and Dosage Metformin Administration and Dosage Human Child Child: 6-12 years Male Female ab: Vanderloo SE, Johnson JA, Reimer K, McCrea P, Nuernberger K, Krueger H, Aydede SK, Collet J-P, Amed S. Validation of classification algorithms for childhood diabetes identified from administrative data. Objective: Type 1 diabetes is the most common form of diabetes among children; however, the proportion of cases of childhood type 2 diabetes is increasing. In Canada, the National Diabetes Surveillance System (NDSS) uses administrative health data to describe trends in the epidemiology of diabetes, but does not specify diabetes type. The objective of this study was to validate algorithms to classify diabetes type in children <20 yr identified using the NDSS methodology. Patients and methods: We 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. Results: 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%). Conclusions: 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. pubtype: Academic Journal doctype: algorithm research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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