Population‐level identification of children and youth with ASD is essential for surveillance and planning for required services. The objective of this study was to develop and validate an algorithm for the identification of children and youth with ASD using administrative health data. In this retrospective validation study, we linked an electronic medical record (EMR)‐based reference standard, consisting 10,000 individuals aged 1–24 years, including 112 confirmed ASD cases to Ontario administrative health data, for the testing of multiple case‐finding algorithms. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and corresponding 95% confidence intervals (CI) were calculated for each algorithm. The optimal algorithm was validated in three external cohorts representing family practice, education, and specialized clinical settings. The optimal algorithm included an ASD diagnostic code for a single hospital discharge or emergency department visit or outpatient surgery, or three ASD physician billing codes in 3 years. This algorithm's sensitivity was 50.0% (95%CI 40.7–88.7%), specificity 99.6% (99.4–99.7), PPV 56.6% (46.8–66.3), and NPV 99.4% (99.3–99.6). The results of this study illustrate limitations and need for cautious interpretation when using administrative health data alone for the identification of children and youth with ASD.

中文翻译：

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评估行政健康数据对识别安大略省儿童和青少年自闭症谱系障碍的有效性

对ASD的儿童和青少年进行人口级识别对于监视和规划所需的服务至关重要。这项研究的目的是开发和验证一种使用行政健康数据识别ASD儿童和青少年的算法。在这项回顾性验证研究中，我们将基于电子病历（EMR）的参考标准（包括112例确诊的ASD病例）与10,000名年龄在1至24岁之间的个体链接到安大略省行政健康数据，以测试多种病例查找算法。为每种算法计算了灵敏度，特异性，阳性预测值（PPV），阴性预测值（NPV）和相应的95％置信区间（CI）。最佳算法在三个外部队列中进行了验证，这些队列分别代表家庭实践，教育，和专业的临床环境。最佳算法包括一次出院或急诊就诊或门诊手术的ASD诊断代码，或3年内的三个ASD医师帐单代码。该算法的灵敏度为50.0％（95％CI 40.7–88.7％），特异性99.6％（99.4–99.7），PPV 56.6％（46.8–66.3）和NPV 99.4％（99.3–99.6）。这项研究的结果说明了仅使用行政管理卫生数据来识别患有ASD的儿童和青少年时的局限性和需要谨慎解释的需求。PPV为56.6％（46.8-66.3），NPV 99.4％（99.3-99.6）。这项研究的结果说明了仅使用行政管理卫生数据来识别患有ASD的儿童和青少年时的局限性和需要谨慎解释的需求。PPV为56.6％（46.8-66.3），NPV 99.4％（99.3-99.6）。这项研究的结果说明了仅使用行政管理卫生数据来识别患有ASD的儿童和青少年时的局限性和需要谨慎解释的需求。