Identifying pediatric diabetes cases from health administrative data: a population-based validation study in Quebec, 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.

Informations de copyright

© 2019 Nakhla et al.

Déclaration de conflit d'intérêts

Laurent Legault has served on advisory boards for Medtronic and Lilly; has received grants for unrelated research from Merck, Sanofi and AstraZeneca; and holds a share of intellectual property not related to this work. No competing interests were declared by any other authors in this work.