Peer-reviewed article
Health databases and early identification of autism spectrum disorder: a scoping review.
Luiz Jupiter Carneiro de Souza, Mariana Del Grossi Moura, Luis Phillipe Nagem Lopes, Katia Miyuki Sasaki Zeredo, José Fernando Salvador Carrillo et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This scoping review examined how health databases and digital tools are being used to identify autism spectrum disorder early in children. The review found 37 studies, mostly from wealthy countries, using electronic health records and machine learning to spot early signs of autism. Common early indicators included being male, maternal age, language delays, and frequent medical visits. However, the review identified significant barriers including limited access to databases, standardisation challenges, and diagnostic delays particularly affecting low-income and underrepresented groups.
Key points
- 37 studies included, predominantly from high-income countries with children and adolescents
- Methods used: machine learning (14 studies), natural language processing (7), predictive modelling (6), and diagnostic algorithms (7)
- Early predictors identified: male sex, advanced maternal age, immigrant background, low socioeconomic status, perinatal complications, language or motor delays
- Diagnostic delays were more pronounced among low-income and underrepresented groups despite early parental concerns
- Only one health database was publicly accessible, limiting clinical application
Limitations
The review does not evaluate the effectiveness of any intervention for improving outcomes in autistic children, nor does it assess whether early identification through these methods leads to better health or developmental outcomes. The review maps evidence on detection methods but cannot establish causal relationships or the clinical impact of early identification.
Abstract
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.