Leveraging automated approaches to categorize birth defects from abstracted birth hospitalization data.

BACKGROUND: The Surveillance for Emerging Threats to Pregnant People and Infants Network (SET-NET) collects data abstracted from medical records and birth defects registries on pregnant people and their infants to understand outcomes associated with prenatal exposures. We developed an automated process to categorize possible birth defects for prenatal COVID-19, hepatitis C, and syphilis surveillance. By employing keyword searches, fuzzy matching, natural language processing (NLP), and machine learning (ML), we aimed to decrease the number of cases needing manual clinician review. METHODS: SET-NET captures International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes and free text describing birth defects. For unstructured data, we used keyword searches, and then conducted fuzzy matching with a cut-off match score of ≥90%. Finally, we employed NLP and ML by testing three predictive models to categorize birth defect data. RESULTS: As of June 2023, 8326 observations containing data on possible birth defects were submitted to SET-NET. The majority (n = 6758 [81%]) were matched to an ICD-10-CM code and 1568 (19%) were unable to be matched. Through keyword searches and fuzzy matching, we categorized 1387/1568 possible birth defects. Of the remaining 181 unmatched observations, we correctly categorized 144 (80%) using a predictive model. CONCLUSIONS: Using automated approaches allowed for categorization of 99.6% of reported possible birth defects, which helps detect possible patterns requiring further investigation. Without employing these analytic approaches, manual review would have been needed for 1568 observations. These methods can be employed to quickly and accurately sift through data to inform public health responses.

Evaluation of Serial Testing After Exposure to COVID-19 in Early Care and Education Facilities, Illinois, March-May 2022.

OBJECTIVE: To understand SARS-CoV-2 transmission in early care and education (ECE) settings, we implemented a Test to Stay (TTS) strategy, which allowed children and staff who were close contacts to COVID-19 to remain in person if they agreed to test twice after exposure. We describe SARS-CoV-2 transmission, testing preferences, and the number of in-person days saved among participating ECE facilities. METHODS: From March 21 through May 27, 2022, 32 ECE facilities in Illinois implemented TTS. Unvaccinated children and staff who were not up to date with COVID-19 vaccination could participate if exposed to COVID-19. Participants received 2 tests within 7 days after exposure and were given the option to test at home or at the ECE facility. RESULTS: During the study period, 331 TTS participants were exposed to index cases (defined as people attending the ECE facility with a positive SARS-CoV-2 test result during the infectious period); 14 participants tested positive, resulting in a secondary attack rate of 4.2%. No tertiary cases (defined as a person with a positive SARS-CoV-2 test result within 10 days after exposure to a secondary case) occurred in the ECE facilities. Most participants (366 of 383; 95.6%) chose to test at home. Remaining in-person after an exposure to COVID-19 saved approximately 1915 in-person days among children and staff and approximately 1870 parent workdays. CONCLUSION: SARS-CoV-2 transmission rates were low in ECE facilities during the study period. Serial testing after COVID-19 exposure among children and staff at ECE facilities is a valuable strategy to allow children to remain in person and parents to avoid missing workdays.