British Columbia Ministry of Health, Victoria, British Columbia, Canada.
Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Birth Defects Res. 2023 Feb 1;115(3):302-317. doi: 10.1002/bdr2.2112. Epub 2022 Nov 11.
Congenital anomalies (CA) are one of the leading causes of infant mortality and long-term disability. Many jurisdictions rely on health administrative data to monitor these conditions. Case definition algorithms can be used to monitor CA; however, validation of these algorithms is needed to understand the strengths and limitations of the data. This study aimed to validate case definition algorithms used in a CA surveillance system in British Columbia (BC), Canada.
A cohort of births between March 2000 and April 2002 in BC was linked to the Health Status Registry (HSR) and the BC Congenital Anomalies Surveillance System (BCCASS) to identify cases and non-cases of specific anomalies within each surveillance system. Measures of algorithm performance were calculated for each CA using the HSR as the reference standard. Agreement between both databases was calculated using kappa coefficient. The modified Standards for Reporting Diagnostic Accuracy guidelines were used to enhance the quality of the study.
Measures of algorithm performance varied by condition. Positive predictive value (PPV) ranged between approximately 73%-100%. Sensitivity was lower than PPV for most conditions. Internal congenital anomalies or conditions not easily identifiable at birth had the lowest sensitivity. Specificity and negative predictive value exceeded 99% for all algorithms.
Case definition algorithms may be used to monitor CA at the population level. Accuracy of algorithms is higher for conditions that are easily identified at birth. Jurisdictions with similar administrative data may benefit from using validated case definitions for CA surveillance as this facilitates cross-jurisdictional comparison.
先天性异常（CA）是导致婴儿死亡和长期残疾的主要原因之一。许多司法管辖区依靠卫生行政数据来监测这些情况。可以使用病例定义算法来监测 CA；但是，需要验证这些算法，以了解数据的优势和局限性。本研究旨在验证加拿大不列颠哥伦比亚省（BC）CA 监测系统中使用的病例定义算法。
将 2000 年 3 月至 2002 年 4 月期间在 BC 的出生队列与健康状况登记处（HSR）和 BC 先天性异常监测系统（BCCASS）相关联，以确定每个监测系统中特定异常的病例和非病例。使用 HSR 作为参考标准，为每个 CA 计算算法性能的度量。使用 Kappa 系数计算两个数据库之间的一致性。使用诊断准确性标准的修改版报告指南来提高研究质量。
算法性能的衡量标准因情况而异。阳性预测值（PPV）在大约 73％-100％之间。大多数情况下，敏感性低于 PPV。内部先天性异常或出生时不易识别的情况敏感性最低。所有算法的特异性和阴性预测值均超过 99％。
病例定义算法可用于监测人群中的 CA。在出生时易于识别的情况下，算法的准确性更高。具有类似行政数据的司法管辖区可能会受益于使用经过验证的 CA 监测病例定义，因为这便于进行跨司法管辖区比较。
