Early life risk factors of motor, cognitive and language development: A pooled analysis of studies from low/middle-income countries

• Main Authors: Ayesha Sania, Christopher R. Sudfeld, Goodarz Danaei, Günther Fink, Dana C. McCoy, Zhaozhong Zhu, Mary C.Smith Fawzi, Mehmet Akman, Shams E. Arifeen, Aluisio J.D. Barros, David Bellinger, Maureen M. Black, Alemtsehay Bogale, Joseph M. Braun, Nynke Van Den Broek, Verena Carrara, Paulita Duazo, Christopher Duggan, Lia C.H. Fernald, Melissa Gladstone, Jena Hamadani, Alexis J. Handal, Siobán Harlow, Melissa Hidrobo, Chris Kuzawa, Ingrid Kvestad, Lindsey Locks, Karim Manji, Honorati Masanja, Alicia Matijasevich, Christine McDonald, Rose McGready, Arjumand Rizvi, Darci Santos, Leticia Santos, Dilsad Save, Roger Shapiro, Barbara Stoecker, Tor A. Strand, Sunita Taneja, Martha Maria Tellez-Rojo, Fahmida Tofail, Aisha K. Yousafzai, Majid Ezzati, Wafaie Fawzi
• 其他作者: NORCE Norwegian Research Centre AS
• 格式: Article
• 出版: 2020
• 主題: Medicine
• 在線閱讀: https://repository.li.mahidol.ac.th/handle/123456789/51408

© 2019 Author(s). Objective To determine the magnitude of relationships of early life factors with child development in low/middle-income countries (LMICs). Design Meta-analyses of standardised mean differences (SMDs) estimated from published and unpublished data. Data sources We searched Medline, bibliographies of key articles and reviews, and grey literature to identify studies from LMICs that collected data on early life exposures and child development. The most recent search was done on 4 November 2014. We then invited the first authors of the publications and investigators of unpublished studies to participate in the study. Eligibility criteria for selecting studies Studies that assessed at least one domain of child development in at least 100 children under 7 years of age and collected at least one early life factor of interest were included in the study. Analyses Linear regression models were used to assess SMDs in child development by parental and child factors within each study. We then produced pooled estimates across studies using random effects meta-analyses. Results We retrieved data from 21 studies including 20 882 children across 13 LMICs, to assess the associations of exposure to 14 major risk factors with child development. Children of mothers with secondary schooling had 0.14 SD (95% CI 0.05 to 0.25) higher cognitive scores compared with children whose mothers had primary education. Preterm birth was associated with 0.14 SD (-0.24 to-0.05) and 0.23 SD (-0.42 to-0.03) reductions in cognitive and motor scores, respectively. Maternal short stature, anaemia in infancy and lack of access to clean water and sanitation had significant negative associations with cognitive and motor development with effects ranging from-0.18 to-0.10 SDs. Conclusions Differential parental, environmental and nutritional factors contribute to disparities in child development across LMICs. Targeting these factors from prepregnancy through childhood may improve health and development of children.