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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.

BACKGROUND:The linkage of routine data collections are valuable for population-based evaluation of smoking cessation pharmacotherapy in pregnancy where little is known about the utilisation or safety of these pharmacotherapies antenatally. The use of routine data collections to study smoking cessation pharmacotherapy is limited by disparities among data sources. This study developed an algorithm to resolve disparity between the evidence of pharmacotherapy utilisation for smoking cessation and the recording of smoking in pregnancy, examined its face validity and assessed the implications on estimates of smoking cessation pharmacotherapy utilisation. METHODS:Perinatal records (n = 1,098,203) of women who gave birth in the Australian States of Western Australia and New South Wales (2004-2012) were linked to hospital admissions and pharmaceutical dispensing data. An algorithm, based on dispensing information about the type of smoking therapy, timing and quantity of supply reclassified certain groups of women as smoking during pregnancy. Face validity of the algorithm was tested by examining the distribution of factors associated with inaccurate recording of smoking status among women that the algorithm classified as misreporting smoking in pregnancy. Rate of utilisation among smokers, according to original and reclassified smoking status, was measured, to demonstrate the utility of the algorithm. RESULTS:Smoking cessation pharmacotherapy were dispensed to 2184 women during pregnancy, of those 1013 women were originally recorded as non-smoking as per perinatal and hospital data. Application of the algorithm reclassified 730 women as smoking during pregnancy. The algorithm satisfied the test of face validity-the expected demographic factors of marriage, private hospital delivery and higher socioeconomic status, were more common in women whom the algorithm identified as misreporting their smoking status. Application of the algorithm resulted in smoking cessation pharmacotherapy utilisation estimates ranging from 2.3-3.6% of all pregnancies. CONCLUSION:Researchers can use the algorithm presented herein to improve the identification of smoking among women who use cessation pharmacotherapies during pregnancy. Improved identification can improve the validity of safety analyses of smoking cessation pharmacotherapy-providing clinicians with valuable evidence to use when counselling women on the role of pharmacotherapy for smoking cessation during pregnancy.