Nakhla Meranda, Simard Marc, Dube Marjolaine, Larocque Isabelle, Plante Céline, Legault Laurent, Huot Celine, Gagné Nancy, Gagné Julie, Wafa Sarah, Benchimol Eric I, Rahme Elham
Department of Pediatrics, Division of Endocrinology, Montreal Children's Hospital, Montreal, QC, Canada.
Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada.
Clin Epidemiol. 2019 Sep 11;11:833-843. doi: 10.2147/CLEP.S217969. eCollection 2019.
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.
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.
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.
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.
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.
1型糖尿病是儿童期最常见的慢性病之一，全球发病率每年以3%至5%的速度增长。2型糖尿病传统上被视为成人疾病，但其在儿童中的发病率正以惊人的速度上升，与儿童肥胖率的上升同步。随着儿童糖尿病发病率的增加，基于人群的准确疾病负担评估对于实施卫生服务提供策略的人员而言至关重要。卫生行政数据是一种强大的工具，可用于跟踪疾病负担、卫生服务利用情况和健康结果。病例验证对于确保使用行政数据库准确识别疾病至关重要。
我们研究的目的是使用卫生行政数据定义并验证一种儿科糖尿病病例确定算法（包括任何形式的儿童期发病糖尿病）。
我们采用了两阶段方法，使用了关联的卫生行政数据和从病历中提取的数据。在第一阶段，我们将来自一个大城市地区的病历数据与卫生行政数据相链接，并比较了各种算法的诊断准确性。我们选择表现最佳的算法在第二阶段进行验证。在第二阶段，最准确的算法在魁北克省的另外两个地理区域内用病历数据进行了验证。
准确识别儿童（年龄≤15岁）糖尿病需要4次医生诊断记录或1次住院记录（疾病国际分类编码在1年内）（敏感性91.2%，95%置信区间[CI]89.2 - 92.9]；阳性预测值[PPV]93.5%，95%CI 91.7 - 95.0），或者仅在2年内有4次医生诊断记录（敏感性90.4%，95%CI 88.3 - 92.2；PPV 93.2%，95%CI 91.7 - 95.0）。将医生诊断记录间隔30天分开可提高所有测试算法的PPV。
在卫生行政数据库中可以准确识别儿童期发病的糖尿病患者，为医疗资源规划和评估提供了有效的信息来源。
