Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Centre for Child Development and Education, Menzies School of Health Research, Charles Darwin University, Darwin, Australia.
PLoS One. 2023 Jan 19;18(1):e0280648. doi: 10.1371/journal.pone.0280648. eCollection 2023.
Early identification of vulnerable children to protect them from harm and support them in achieving their long-term potential is a community priority. This is particularly important in the Northern Territory (NT) of Australia, where Aboriginal children are about 40% of all children, and for whom the trauma and disadvantage experienced by Aboriginal Australians has ongoing intergenerational impacts. Given that shared social determinants influence child outcomes across the domains of health, education and welfare, there is growing interest in collaborative interventions that simultaneously respond to outcomes in all domains. There is increasing recognition that many children receive services from multiple NT government agencies, however there is limited understanding of the pattern and scale of overlap of these services. In this paper, NT health, education, child protection and perinatal datasets have been linked for the first time. The records of 8,267 children born in the NT in 2006-2009 were analysed using a person-centred analytic approach. Unsupervised machine learning techniques were used to discover clusters of NT children who experience different patterns of risk. Modelling revealed four or five distinct clusters including a cluster of children who are predominantly ill and experience some neglect, a cluster who predominantly experience abuse and a cluster who predominantly experience neglect. These three, high risk clusters all have low school attendance and together comprise 10-15% of the population. There is a large group of thriving children, with low health needs, high school attendance and low CPS contact. Finally, an unexpected cluster is a modestly sized group of non-attendees, mostly Aboriginal children, who have low school attendance but are otherwise thriving. The high risk groups experience vulnerability in all three domains of health, education and child protection, supporting the need for a flexible, rather than strictly differentiated response. Interagency cooperation would be valuable to provide a suitably collective and coordinated response for the most vulnerable children.
早期识别弱势儿童,保护他们免受伤害,并支持他们发挥长期潜力,这是社区的优先事项。这在澳大利亚北部地区(NT)尤为重要,那里的原住民儿童约占所有儿童的 40%,而原住民澳大利亚人所经历的创伤和劣势对他们的后代仍有持续影响。鉴于共同的社会决定因素会影响健康、教育和福利等领域的儿童的结果,人们越来越关注同时应对所有领域结果的协作干预措施。越来越多的人认识到,许多儿童从多个 NT 政府机构获得服务,但对这些服务的重叠程度和规模了解有限。在本文中,首次将 NT 的卫生、教育、儿童保护和围产期数据集联系起来。使用以人为中心的分析方法对 2006-2009 年在 NT 出生的 8267 名儿童的记录进行了分析。使用无监督机器学习技术发现了经历不同风险模式的 NT 儿童群集。模型显示了四个或五个不同的群集,包括一个主要患病且遭受一些忽视的儿童群集、一个主要遭受虐待的儿童群集和一个主要遭受忽视的儿童群集。这三个高风险群集的儿童入学率都很低,加起来占总人口的 10-15%。有一大群茁壮成长的儿童,健康需求低、入学率高、CPS 接触率低。最后,一个出乎意料的群集是一群规模适度的非参与者,他们主要是原住民儿童,入学率低,但其他方面都表现出色。高风险群体在健康、教育和儿童保护这三个领域都面临脆弱性,这支持了灵活而非严格区分的应对措施的必要性。机构间合作对于为最弱势的儿童提供适当的集体和协调的应对措施将是有价值的。