Identifying pediatric diabetes cases from health administrative data: a population-based validation study in Quebec, Canada
Received 1 June 2019
Accepted for publication 13 August 2019
Published 11 September 2019 Volume 2019:11 Pages 833—843
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Nicola Ludin
Peer reviewer comments 3
Editor who approved publication: Professor Henrik Toft Sørensen
Meranda Nakhla,1,2 Marc Simard,3 Marjolaine Dube,3 Isabelle Larocque,3 Céline Plante,3 Laurent Legault,1,2 Celine Huot,4 Nancy Gagné,5 Julie Gagné,6 Sarah Wafa,2 Eric I Benchimol,7–9 Elham Rahme10
1Department of Pediatrics, Division of Endocrinology, Montreal Children’s Hospital, Montreal, QC, Canada; 2Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; 3Institut National de Santé Publique du Québec, Québec, QC, Canada; 4Department of Pediatrics, Division of Endocrinology, Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada; 5Department of Pediatrics, Division of Endocrinology, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC, Canada; 6Department of Pediatrics, Division of Endocrinology, Centre Hospitalier de l’Université Laval, Quebec City, QC, Canada; 7Children’s Hospital of Eastern Ontario IBD Centre, Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, Ottawa, Canada; 8Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada; 9Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; 10Department of Medicine, Division of Clinical Epidemiology, McGill University, Montreal, QC, Canada
Correspondence: Meranda Nakhla
Center of Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, 5252 de Maisonneuve Blvd, W, 3rd floor, office E3.08, Montreal H4A 3S5, QC, Canada
Background: Type 1 diabetes is one of the most common chronic diseases in childhood with a worldwide incidence that is increasing by 3–5% per year. The incidence of type 2 diabetes, traditionally viewed as an adult disease, is increasing at alarming rates in children, paralleling the rise in childhood obesity. As the rates of diabetes increase in children, accurate population-based assessment of disease burden is important for those implementing strategies for health services delivery. Health administrative data are a powerful tool that can be used to track disease burden, health services use, and health outcomes. Case validation is essential in ensuring accurate disease identification using administrative databases.
Aim: The aim of our study was to define and validate a pediatric diabetes case ascertainment algorithm (including any form of childhood-onset diabetes) using health administrative data.
Research design and methods: We conducted a two-stage method using linked health administrative data and data extracted from charts. In stage 1, we linked chart data from a large urban region to health administrative data and compared the diagnostic accuracy of various algorithms. We selected those that performed the best to be validated in stage 2. In stage 2, the most accurate algorithms were validated with chart data within two other geographic areas in the province of Quebec.
Results: Accurate identification of diabetes in children (ages ≤15 years) required four physician claims or one hospitalization (with International Classification of Disease codes within 1 year (sensitivity 91.2%, 95% confidence interval [CI] 89.2–92.9]; positive predictive value [PPV] 93.5%, 95% CI 91.7–95.0) or using only four physician claims in 2 years (sensitivity 90.4%, 95% CI 88.3–92.2; PPV 93.2%, 95% CI 91.7–95.0). Separating the physician claims by 30 days increased the PPV of all algorithms tested.
Conclusion: Patients with child-onset diabetes can be accurately identified within health administrative databases providing a valid source of information for health care resource planning and evaluation.
Keywords: pediatric, validation, health administrative data, diabetes
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