Developing algorithms for identifying major structural birth defects using automated electronic health data.

Area of Science:

• Medical Informatics
• Public Health Surveillance
• Pediatric Epidemiology

Background:

• The transition to International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) in 2015 necessitated updates to diagnostic coding algorithms.
• Accurate identification of birth defects (BDs) is crucial for epidemiological studies, including vaccine safety research.

Purpose of the Study:

• To update, validate, and refine algorithms for identifying selected major structural BDs using ICD-10-CM codes.
• To describe the prevalence of these BDs within the Vaccine Safety Datalink (VSD) population post-transition.

Main Methods:

• Converted existing ICD-9-CM BD codes to ICD-10-CM with manual review.
• Identified, chart-reviewed, and adjudicated infants with potential BD diagnoses in the VSD.
• Calculated positive predictive values (PPVs) and refined algorithms for suboptimal PPVs.
• Applied final algorithms to live births from 10/1/2015 to 9/30/2017 to estimate BD prevalence.

Main Results:

• Of 573 infants reviewed, 69.6% were classified as probable cases of a targeted BD.
• Final BD algorithm PPVs ranged from 0.76 (hypospadias) to 1.0 (gastroschisis).
• An overall prevalence of 1.8% for selected BDs was observed in 212,857 live births over two years.

Conclusions:

• Automated algorithms can identify infants with selected BDs using healthcare data with reasonable accuracy.
• Updated ICD-10-CM algorithms are suitable for observational studies on maternal vaccine safety.
• These refined algorithms can be adapted for use in other public health surveillance systems.