On adjusting for life's confounding: Harnessing big data to answer big problems

No Thumbnail Available
File version
Author(s)
Kisely, Steve
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2017
Size
File type(s)
Location
License
Abstract

A wide range of data sources are available to study the epidemiology of mental illness. Community surveys such as Alex Leighton’s Stirling County Study are the gold standard for such data sources, particularly because community surveys cover everyone, not just people seeking treatment. However, as surveys require a lot of resources, other methods are also used, each of which has strengths and weaknesses. For example, medical records contain detailed information, but this can be difficult to extract and the quality may vary. Another source entails administrative data, typically hospital separations, physician billings, ambulatory care visits, and drug databases. While such data require careful analysis with the use of multivariate or propensity score techniques to adjust for potential confounding variables, these data can be invaluable in the study of diseases with multifactorial aetiologies.

Journal Title

The Canadian Journal of Psychiatry

Conference Title
Book Title
Edition
Volume

62

Issue

3

Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Biomedical and clinical sciences

Psychology

Science & Technology

Life Sciences & Biomedicine

Psychiatry

epidemiology

surveillance

Persistent link to this record
Citation

Kisely, S, On adjusting for life's confounding: Harnessing big data to answer big problems, The Canadian Journal of Psychiatry, 2017, 62 (3), pp. 182-185

Collections