Registries and surveillance models used to screen neonates for congenital anomalies in LMIC settings: a scoping review.

Objectives: To map and characterise congenital anomalies (CAs) registries and surveillance systems implemented in low- and middle-income countries (LMICs), and to examine their reported effectiveness, challenges and potential for replication. Design: Scoping review conducted in accordance with Joanna Briggs Institute methodology and reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Data sources: PubMed, Epistemonikos, Global Index Medicus, EMBASE, PakMediNet, CINAHL (EBSCO), and Google Scholar were searched from database inception to April 2025. Eligibility criteria: Studies describing registries, surveillance systems or screening programmes for externally visible CAs in neonates or stillbirths implemented in LMIC settings were included. Data extraction and synthesis: Data were extracted on publication characteristics, registry design, surveillance methods, screening personnel, diagnostic approaches and anomaly types. Findings were synthesised descriptively and mapped across WHO regions. Results: Of 12 328 records identified, 39 studies met the inclusion criteria. Most studies were hospital-based (79%) and regional in scope (74%). Prospective surveillance was used in 79% of studies, with physical examination as the primary screening method. Doctors conducted screening in 41% of studies, while 13% employed mixed cadres including nurses, midwives and health workers. Nine sustained national or subnational registries and two multinational surveillance networks were identified. Neurological, musculoskeletal and cardiovascular anomalies were most frequently reported. Conclusions: CAs surveillance in LMICs is predominantly hospital-based, with limited community-level coverage. Sustainable registries demonstrate feasibility when integrated into existing health systems, supported by trained frontline workers and focused on pragmatic datasets. Future efforts should prioritise scalability, community integration and linkage to care pathways.