Multi-agency data linkage- How to and lessons learnt through the Western Australian Developmental Pathways Program IJPDS (2017) Issue 1, Vol 1:199, Proceedings of the IPDLN Conference (August 2016)

Main Article Content

Rebecca Glauert

Abstract

ABSTRACT

Objectives
This presentation will showcase the Western Australia Developmental Pathways Program, the largest cross- agency data linkage program in Australia.

Approach
The Developmental Pathways Program links data from 9 State government agencies to create a world class data resource. This program has paved the way for cross- agency data linkage in Australia. The linked data allow government and researchers to investigate trends, risk and protective factors and outcomes, across a number of areas, including child maltreatment, juvenile offending, physical health, mental health, education, housing and disability. The Program also links researchers with government, allowing the research to be translated into policy and practice.

Results
Through engagement, communication and good governance, this program has expanded the West Australian Data Linkage System by including a large number of non-health datasets to be linked for Epidemiological research. In this presentation we will demonstrate not only our research capability, but also the type of governance and relationships that are needed in order to create a successful cross agency data linkage capability, and some of the lessons learnt along the way. We will also be presenting some of the new initiatives which are helping it to progress forward into a more useful and meaningful resource.

Conclusion
This Program is the largest data linkage program in Australia, and is continually expanding with new agencies coming on board every year. Here we will provide a useful overview of the Program, along with key lessons learnt.

Article Details

How to Cite
Glauert, R. (2017) “Multi-agency data linkage- How to and lessons learnt through the Western Australian Developmental Pathways Program: IJPDS (2017) Issue 1, Vol 1:199, Proceedings of the IPDLN Conference (August 2016)”, International Journal of Population Data Science, 1(1). doi: 10.23889/ijpds.v1i1.219.

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