Implementation of the BC Congenital Anomalies Surveillance System (BCCASS)

Escalante, Yonabeth Nava de; Abayomi, Aanu; Erickson, Anders C.; Ye, Xibiao; Armour, Rosemary; Arbour, Laura; Langlois, Sylvie; Henry, Bonnie

doi:10.17269/s41997-021-00607-3

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…In 2015, a BC Ministry of Health environment scan concluded that “the HSR data were no longer actively monitored for completeness and accuracy, the number of data sources contributing to the registry had diminished, and the quality of data had declined such that it is no longer considered complete enough for surveillance purposes” (de Escalante et al, 2022 ). Given the existence of multiple administrative databases and the capacity to link them, a decision was made to implement a new system that would leverage these administrative databases while utilizing less resources than the HSR.…”

Section: Methodsmentioning

confidence: 99%

“…Cases coded as gastroschisis and omphalocele, and those coded as cleft lip and holoprosencephaly are flagged and further explore with the Provincial Advisory Committee prior to their inclusion in the BCCASS. Further information related to the BCCASS program planning, methodology and case validation has been previously published (de Escalante et al, 2022 ). The final BCCASS cohort is a dataset of unduplicated birth and stillbirth records with documented baby or mother personal health number.…”

Section: Methodsmentioning

confidence: 99%

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Validation of case definition algorithms for the ascertainment of congenital anomalies

Escalante

Abayomi

Langlois

et al. 2022

Birth Defects Research

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Background 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. Methods 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. Results 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. Conclusion 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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%