Data Quality Assessment on Congenital Anomalies in Ontario, Canada

Background: Congenital anomalies (CAs) are a major cause of infant morbidity and mortality in Canada. Reliably identifying CAs is essential for CA surveillance and research. The main objective of this study was to assess the agreement of eight sentinel anomalies including: neural tube defects (NTD), orofacial clefts, limb deficiency defects (LDD), Down syndrome (DS), tetralogy of Fallot (TOF), gastroschisis (GS), hypoplastic left heart syndrome (HLHS) and transposition of great vessels (TGA) captured in the BORN Information System (BIS) database and the Canadian Institute for Health Information (CIHI) Discharge Abstract Database (DAD). Methods: Live birth and stillbirth records between the BIS and CIHI-DAD in the fiscal years of 2012–2013 to 2015–2016 were linked using 10 digit infant Ontario Health Insurance Plan (OHIP) numbers. Percent agreement and Kappa statistics were performed to assess the reliability (agreement) of CAs identified in the linked BIS and CIHI-DAD birth records. Then, further investigations were conducted on those CA cases identified in the CIHI-DAD only. Results: Kappa coefficients of the eight selected CAs between BIS (“Confirmed” or “Suspected” cases) and CIHI-DAD were 0.96 (95% CI: 0.93–0.98) for GS; 0.81 (95% CI: 0.78–0.83) for Orofacial clefts; 0.75 (95% CI: 0.72–0.77) for DS; 0.71 (95% CI: 0.65–0.77) for TOF; 0.62 (95% CI: 0.55–0.68) for TGA; 0.59 (95% CI: 0.49–0.68) for HLHS, 0.53 (95% CI: 0.46–0.60) for NTD-all; and 0.30 (95% CI: 0.23–0.37) for LDD. Conclusions: The degree of agreement varied among sentinel CAs identified between the BIS and CIHI. The potential reasons for discrepancies include incompleteness of capturing CAs using existing picklist values, especially for certain sub-types, incomplete neonatal special care data in the BIS, and differences between clinical diagnosis in the BIS and ICD-10-CA classification in the DAD. A future data abstraction study will be conducted to investigate the potential reasons for discrepancies of CA capture between two databases. This project helps quantify the quality of CA data collection in the BIS, enhances understanding of CA prevalence in Ontario and provides direction for future data quality improvement activities.