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Modeling risk from conception to disease: issues in the design of a population health database for psychiatric research

Published online by Cambridge University Press:  24 June 2014

G Valuri,
M Croft,
V Morgan and
A Jablensky
G Valuri
Affiliation:
School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Australia
M Croft
Affiliation:
School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Australia
V Morgan
Affiliation:
School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Australia
A Jablensky
Affiliation:
School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Australia
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Abstract

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Information

Type
Abstracts from ‘Brainwaves’— The Australasian Society for Psychiatric Research Annual Meeting 2006, 6–8 December, Sydney, Australia
Information
Acta Neuropsychiatrica , Volume 18 , Issue 6 , December 2006, pp. 273
Copyright
Copyright © 2006 Blackwell Munksgaard

Background:

In Western Australia, mental health register data are linked to other state-wide health registers. A population-based study of children born in Western Australia, 1980–2001, to mothers with schizophrenia or affective psychosis and mothers with no psychiatric illness aimed to examine genetic and environmental risk factors for schizophrenia. Longitudinal linked data were used to analyze health patterns and associations of these children.

Methods:

The linked data were of such diverse types, it was difficult to retrieve data for analysis based on the relationships between individuals. A data model was required to manage this complexity. We describe the database design and issues in its construction and the adaptation of a validated system, which uses case note reviews for scoring obstetric complications as risk factors for psychiatric morbidity, for use with electronic registers.

Results:

The database includes 472 733 births to 249 119 women, with paternal data available for most children. The data model design views all records as related to a person with one or potentially many ‘events’ recorded across health registers over the study period, and linked by a unique identifier. It allows for consideration of families from an intergenerational perspective. We can easily extract information such as the number of siblings in the database, family sizes, intervals between siblings' births, illnesses within families and changes of residences between siblings' births.

Conclusion:

A data model has been implemented that permits intergenerational gene-environment analysis and expansion to include new data and validation of event records across datasets.