How well can poor child development be predicted from early life characteristics? A whole-of-population data linkage study

The Australian Early Development Census (AEDC) is a holistic measure of children's health and development. Local communities and service providers can use AEDC results to develop support for children and their families. A core concept in supporting child development is,to provide services in a progressive universal framework. A challenge for progressive universal services is identifying, as early as possible, the children who are most at risk of later poor health and development. This study used de-identified, linked perinatal and AEDC data for 13,827 children to explore whether characteristics routinely collected in the perinatal period can predict which children will be vulnerable on two or more AEDC domains in their first year at school, A model containing 22 perinatal predictors demonstrated similar discrimination to a model of six predictors (maternal age, smoking during pregnancy, parity, marital status, and both parents' occupation, area under the receiver operating characteristic curve = 0.682 males, 0.724 females). If these six characteristics were used for targeting intensive support services, and the program targeted families with at least three of the six perinatal risk factors, approximately 10% of families in the population would be identified as needing an intensive intervention soon after birth. Sensitivity of the risk prediction model showed that such a targeted program would have the potential to prevent one-quarter of the cases of being vulnerable on two or more AEDC domains at age five. When assessing whether such prediction models could be turned into useful screening tools for determining eligibility for family support services, service providers need to consider the trade-off between sensitivity and the proportion of the population that would require services. (C) 2015 Elsevier Inc. All rights reserved.