Ascertaining and classifying cases of congenital anomalies in the ALSPAC birth cohort

Updated EUROCAT guidelines for classification of cases with congenital anomalies

BACKGROUND

Precise and correct classification of congenital anomalies is important in epidemiological studies, not only to classify according to etiology but also to group similar congenital anomalies together, to create homogeneous subgroups for surveillance and research. This paper presents the updated EUROCAT (European surveillance of congenital anomalies) subgroups of congenital anomalies and the updated multiple congenital anomaly (MCA) algorithm and provides the underlying arguments for the revisions.

METHODS

The EUROCAT methodology is described. In addition, we show how we validated the revised EUROCAT subgroups and MCA algorithm, which are both based on the International Classification of Diseases (ICD10/ICD9) codes.

RESULTS

The updated EUROCAT subgroups and the updated MCA algorithm are described in detail and the updated version is compared to the previous versions.

CONCLUSION

The EUROCAT subgroups and MCA algorithm provide a standardized and clear methodology for congenital anomaly research and epidemiological surveillance of congenital anomalies in order to facilitate the identification of teratogenic exposures and to assess the impact of primary prevention and prenatal screening policies. The EUROCAT subgroups and MCA algorithm are made freely available for other researchers via the EUROCAT Database Management Software.

Ascertaining and classifying cases of congenital anomalies in the ALSPAC birth cohort

Congenital anomalies (CAs) are structural or functional disorders that occur during intrauterine life. Longitudinal cohort studies provide unique opportunities to investigate potential causes and consequences of these disorders. In this data note, we describe how we identified cases of major CAs, with a specific focus on congenital heart diseases (CHDs), in the Avon Longitudinal Study of Parents and Children (ALSPAC). We demonstrate that combining multiple sources of data including data from antenatal, delivery, primary and secondary health records, and parent-reported information can improve case ascertainment. Our approach identified 590 participants with a CA according to the European Surveillance of Congenital Anomalies (EUROCAT) guidelines, 127 of whom had a CHD. We describe the methods that identified these cases and provide statistics on subtypes of anomalies. The data note contains details on the processes required for researchers to access these data.

Ascertaining and classifying cases of congenital anomalies in the ALSPAC birth cohort

Congenital anomalies (CAs) are structural or functional disorders that occur during intrauterine life. Longitudinal cohort studies provide unique opportunities to investigate potential causes and consequences of these disorders. In this data note, we describe how we identified cases of major CAs, with a specific focus on congenital heart diseases (CHDs), in the Avon Longitudinal Study of Parents and Children (ALSPAC). We demonstrate that combining multiple sources of data including data from antenatal, delivery, primary and secondary health records, and parent-reported information can improve case ascertainment. Our approach identified 590 participants with a CA according to the Euro Registers of Congenital Anomalies (EUROCAT) guidelines, 127 of whom had a CHD. We describe the methods that identified these cases and provide statistics on subtypes of anomalies. The data note contains details on the processes required for researchers to access these data.