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Moving Beyond Program to Population Impact: Toward a Universal Early Childhood System of Care.

ABSTRACT: Families have clearly benefited from increased availability of evidence-based intervention, including home-visiting models and increased federal funding for programs benefiting parents and children. The goal of population-level impact on the health and well-being of infants and young children across entire communities, however, remains elusive. New approaches are needed to move beyond scaling of individual programs toward an integrated system of care in early childhood. To advance this goal, the current article provides a framework for developing an early childhood system of care that pairs a top-down goal for the alignment of services with a bottom-up goal of identifying and addressing needs of all families throughout early childhood. Further, we describe how universal newborn home visiting can be utilized to both support alignment of and family entry into an early childhood system of care with broad reach, high quality, and evidence of population impact for families and children.

SUBMITTER: Goodman WB

PROVIDER: S-EPMC6433400 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Moving Beyond Program to Population Impact: Toward a Universal Early Childhood System of Care.

Goodman W Benjamin WB O'Donnell Karen K Murphy Robert A RA Dodge Kenneth A KA

Journal of family theory & review 20181115 1

Families have clearly benefited from increased availability of evidence-based intervention, including home-visiting models and increased federal funding for programs benefiting parents and children. The goal of population-level impact on the health and well-being of infants and young children across entire communities, however, remains elusive. New approaches are needed to move beyond scaling of individual programs toward an integrated system of care in early childhood. To advance this goal, the ...[more]

PMID: 30923572

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Project description:BackgroundDecisions regarding whether to administer intensive care to extremely premature infants are often based on gestational age alone. However, other factors also affect the prognosis for these patients.MethodsWe prospectively studied a cohort of 4446 infants born at 22 to 25 weeks' gestation (determined on the basis of the best obstetrical estimate) in the Neonatal Research Network of the National Institute of Child Health and Human Development to relate risk factors assessable at or before birth to the likelihood of survival, survival without profound neurodevelopmental impairment, and survival without neurodevelopmental impairment at a corrected age of 18 to 22 months.ResultsAmong study infants, 3702 (83%) received intensive care in the form of mechanical ventilation. Among the 4192 study infants (94%) for whom outcomes were determined at 18 to 22 months, 49% died, 61% died or had profound impairment, and 73% died or had impairment. In multivariable analyses of infants who received intensive care, exposure to antenatal corticosteroids, female sex, singleton birth, and higher birth weight (per each 100-g increment) were each associated with reductions in the risk of death and the risk of death or profound or any neurodevelopmental impairment; these reductions were similar to those associated with a 1-week increase in gestational age. At the same estimated likelihood of a favorable outcome, girls were less likely than boys to receive intensive care. The outcomes for infants who underwent ventilation were better predicted with the use of the above factors than with use of gestational age alone.ConclusionsThe likelihood of a favorable outcome with intensive care can be better estimated by consideration of four factors in addition to gestational age: sex, exposure or nonexposure to antenatal corticosteroids, whether single or multiple birth, and birth weight. (ClinicalTrials.gov numbers, NCT00063063 [ClinicalTrials.gov] and NCT00009633 [ClinicalTrials.gov].).

Project description:Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone.

Dataset Information

Moving Beyond Program to Population Impact: Toward a Universal Early Childhood System of Care.

Publications

Moving Beyond Program to Population Impact: Toward a Universal Early Childhood System of Care.

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