Prevention and awareness of birth defects across the lifespan using examples from congenital heart defects and spina bifida

Farr, Sherry L.; Riley, Catharine; Zutphen, Alissa R. Van; Brei, Timothy J.; Leedom, Vinita; Kirby, Russell S.; Pabst, Laura J.

doi:10.1002/bdr2.1972

Cited by 1 publication

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References 55 publications

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“…A typical case definition algorithm might include/exclude specific diagnostic codes, health encounters, and/or include a specific number of data sources in which diagnoses appear (refer to Table 1 ). Although this approach has proven to be an effective way to monitor disease status (J. Salemi, Rutkowski, Tanner, Matas, & Kirby, 2018 ; Kharbanda et al, 2017 ; Shiff, Oen, Rabbani, & Lix, 2017 ; Nasr, Sullivan, Chan, Wong, & Benchimol, 2017 ), it is important to note that the development of standardized case definition algorithms can be challenging because of jurisdictional differences, differences in disease classification nomenclature, revisions in disease classification coding systems over time (Johnson & Nelson, 2013 ; Shiff et al, 2017 ) and concerns about the validity of diagnostic codes (Farr et al, 2021 ). Also, differences in presentation and severity of CA result in diagnostic variability, which influences ascertainment (Langlois, Sheu, & Scheuerle, 2010 ).…”

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confidence: 99%

Validation of case definition algorithms for the ascertainment of congenital anomalies

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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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