Identifying pediatric diabetes cases from health administrative data: a population-based validation study in Quebec, Canada
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Published on: July 5, 2022
Meranda Nakhla1,2, Marc Simard3, Marjolaine Dube3
1Department of Pediatrics, Division of Endocrinology, Montreal Children's Hospital, Montreal, QC, Canada.
Insights
Accurate identification of childhood diabetes is possible using health administrative data. This method is crucial for effective health care planning and resource allocation for pediatric diabetes.
Area of Science:
- Pediatric Endocrinology
- Health Services Research
- Data Science in Healthcare
Background:
- Childhood diabetes incidence, including Type 1 and Type 2, is rising globally.
- Accurate assessment of pediatric diabetes burden is vital for health service delivery strategies.
- Health administrative data offers a powerful tool for tracking disease burden and outcomes, but requires case validation.
Purpose of the Study:
- To develop and validate a pediatric diabetes case ascertainment algorithm using health administrative data.
- To include all forms of childhood-onset diabetes in the algorithm.
- To ensure accurate disease identification in administrative databases.
Main Methods:
- A two-stage method was employed, linking health administrative data with chart data.
- Stage 1 involved comparing algorithm diagnostic accuracy in a large urban region.
- Stage 2 validated the best-performing algorithms in two additional geographic areas within Quebec.
Main Results:
- An algorithm using four physician claims or one hospitalization within a year accurately identified pediatric diabetes (sensitivity 91.2%, PPV 93.5%).
- An alternative algorithm using four physician claims within 2 years also showed high accuracy (sensitivity 90.4%, PPV 93.2%).
- Separating physician claims by 30 days improved the positive predictive value (PPV) of all tested algorithms.
Conclusions:
- Health administrative databases can accurately identify patients with child-onset diabetes.
- This validated approach provides a reliable data source for planning and evaluating healthcare resources.
- The findings support the use of administrative data for understanding and managing pediatric diabetes prevalence.
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.
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