Health databases and early identification of autism spectrum disorder: a scoping review

Souza L.J.C.; Moura M.D.G.; Lopes L.P.N.; Sasaki Zeredo K.M.; Carrillo J.F.S.; Júnior J.L.; Figueiredo Modesto A.C.; de Oliveira A.M.; Motter F.R.; Ito M.; Barbosa D.F.; Neto J.A.S.F.; Lopes L.C.; Rodrigues W. · BMJ open (2026) · DOI: 10.1136/bmjopen-2025-112845

Resumen

OBJECTIVES: To map the available evidence on the use of health databases for the early identification of autism spectrum disorder (ASD) across diverse populations and settings. DESIGN: Scoping review conducted in accordance with the Joanna Briggs Institute framework. DATA SOURCES: Searches were conducted in MEDLINE, Embase, Scopus, PsycINFO, Web of Science and Latin American and Caribbean Health Sciences Literature, with grey literature searched through ProQuest Dissertations and Theses, up to December 2024, and updated on March 2026. ELIGIBILITY CRITERIA: Studies including individuals with diagnosed or suspected ASD were considered without restrictions on age, country or healthcare setting. Early identification was defined as the detection of ASD-related features prior to formal diagnosis. Health databases included digital sources such as electronic health records and surveillance systems, excluding those based solely on biological or genetic data. DATA EXTRACTION AND SYNTHESIS: Study selection and data extraction were performed independently by two reviewers. Extracted data were synthesised descriptively according to database type, analytical methods and reported outcomes. RESULTS: Thirty-seven studies were included, mostly from high-income countries and involving children or adolescents. Data sources included electronic medical records (n=25), automated healthcare databases (n=6) and national surveys or data sets (n=6); only one database was publicly accessible. Analytical methods comprised machine learning (ML) (n=14), predictive modelling (n=6), natural language processing (NLP) (n=7) and diagnostic code algorithms (n=7). Common early predictors reported across studies include male sex, advanced maternal age, immigrant background, low SES, perinatal complications, and language or motor delays. Reported comorbidities included epilepsy, attention-deficit/hyperactivity disorder, mood/anxiety disorders and sensory issues. Children later diagnosed with ASD had more frequent medical visits, hospitalisations and emergency care. Diagnostic delays were more pronounced among low-income and underrepresented groups, despite early parental concerns. CONCLUSION: Health databases have been increasingly used to support early ASD detection through methods like ML and NLP; however, their clinical application is limited by challenges related to standardisation, ethical considerations, feasibility and data access. Addressing these barriers is essential to support inclusive, evidence-based strategies. TRIAL REGISTRATION NUMBER: Open Science Framework. DOI: 10.17605/OSF.IO/RMVWE. © Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.

Instituciones: Universidad Privada San Juan Bautista