Head of Tāmaki Innovation Campus seminar: Building an automated prediction tool to identify (at birth) children at risk of being maltreated Event as iCalendar

04 October 2013

3 - 4pm

Venue: Room 731-201, Building 731, Tāmaki Innovation Campus

The White Paper on Vulnerable Children (2012) announced that the Government would explore the possibility of trialing a risk-scoring tool (called a Predictive Risk Model) which automatically identifies children (by age 2) who are at high risk of having a substantiated maltreatment event before five years of age. The policy was based on a feasibility study undertaken by a team at The University of Auckland (Vaithianathan et al., 2012; Vaithianathan, 2013). The study found that the tool could classify 5% of newborns as high risk, and these children would go on to account for 37% of all children who are maltreated by age 5. Three out of four high risk children will be identified at birth. Moreover, the majority of high risk children are identified more than two years before the maltreatment event. The tool therefore holds some promise. The team is currently looking at the ethical issues, and issues for Maori, in implementing such a tool.

Presented by Associate Professor Rhema Vaithianathan

Rhema Vaithianathan is an Associate Professor in Economics at The University of Auckland and a Senior Research Fellow at the Sim Ki Boon Institute at the Singapore Management University. She is also director of the Centre for Applied Research in Economics. She has conducted research on predictive risk modeling and hospital readmission. She is currently implementing predicting risk modeling projects in hospitals across New Zealand and Singapore. She has also undertake research on leadership in health care; safety; health care financing and development economics. She received her PhD in Economics from The University of Auckland in 2000. Prior to entering academia she worked as a policy analyst at the Treasury and health economist at the Health Funding Agency and the Regional Health Authority.


For more information, please contact: Suzanne Mitchell Email: suzanne.mitchell@auckland.ac.nz