TY - JOUR
T1 - Improving child health through Big Data and data science
AU - Vesoulis, Zachary A.
AU - Husain, Ameena N.
AU - Cole, F. Sessions
N1 - Funding Information:
This work was supported by grants from the National Institutes of Health K23 NS111086 (to Z.A.V.) and the Children’s Discovery Institute (F.S.C.).
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.
PY - 2023/1
Y1 - 2023/1
N2 - Abstract: Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. Impact: Big Data and data science can improve child health.This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies.This review provides recommendations for future pediatric-specific Big Data and data science research.
AB - Abstract: Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. Impact: Big Data and data science can improve child health.This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies.This review provides recommendations for future pediatric-specific Big Data and data science research.
UR - http://www.scopus.com/inward/record.url?scp=85136165743&partnerID=8YFLogxK
U2 - 10.1038/s41390-022-02264-9
DO - 10.1038/s41390-022-02264-9
M3 - Review article
C2 - 35974162
AN - SCOPUS:85136165743
SN - 0031-3998
VL - 93
SP - 342
EP - 349
JO - Pediatric research
JF - Pediatric research
IS - 2
ER -