Health Measurement Model-Bringing a Life Course Perspective to Health Measurement: The PRISM Model

Improving child health through Big Data and data science

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.

Designing a Visit Schedule for Longitudinal Studies in Pediatric Research

A challenge for longitudinal studies is combining individual assessments into visits that are scientifically logical, not burdensome for participants, well-choreographed, and operationally feasible. The visits then need to be sequenced and spaced to address the scientific goals and generate a data archive that is sufficiently robust and well-documented to support subsequent analyses. This paper summarizes comprehensive multi-disciplinary activities that were coordinated to design the content, format, and structure of the National Children's Study and concurrently serve as a model and resource for other studies.

Principles of Researching Health Disparities in Longitudinal Cohort Studies Enrolling Children

Health disparities are defined on the basis of specific populations that, when compared to the general population, have a significant disparity on the rate of disease incidence, prevalence, morbidity, mortality, or survival. People that experience health disparities can be defined by multiple criteria. As the diversity of the United States broadens and increases, research on the origins and causes of health disparities becomes more important to support a healthy general population. Children are particularly sensitive to and vulnerable to health disparities due to the potentially life long consequences of events during periods of critical organ, intellectual and social development. The concept of health justice whereby each individual has the opportunity to realize their full health potential can only be realized with proper understanding and relevant data to frame practice, policy and actions. The National Children's Study (NCS) was a longitudinal birth cohort study designed to incorporate the principles of the Federal Collaboration on Health Disparities Research in consultation with subject matter experts, community representatives, and ongoing evaluation to ensure high quality and relevant data on factors that impact health outcomes. The NCS developed and tested a model of enrolling a diverse population, capturing and integrating data using a life course framework, constructing individual profiles, then aggregating individuals into groups based on profiles and outcomes. This approach is applicable to other longitudinal cohort studies.