Hostname: page-component-7b7567c8c-kwt9d Total loading time: 0 Render date: 2026-11-14T22:09:56.177Z Has data issue: false hasContentIssue false
  • English
  • Français

Multilevel modeling of rural mental health

Published online by Cambridge University Press:  24 June 2014

H Stain,
N Higginbotham,
T Lewin,
B Kelly and
P Lane
H Stain
Affiliation:
Centre for Rural and Remote Mental Health, University of Newcastle
N Higginbotham
Affiliation:
Centre for Clinical Epidemiology and Biostatistics, University of Newcastle
T Lewin
Affiliation:
Hunter New England Mental Health, Newcastle, Australia
B Kelly
Affiliation:
Centre for Rural and Remote Mental Health, University of Newcastle
P Lane
Affiliation:
Centre for Rural and Remote Mental Health, University of Newcastle
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.

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. 271
Copyright
Copyright © 2006 Blackwell Munksgaard

Background:

The lack of consistent findings regarding comparisons of mental health between rural and urban areas has been attributed in part to methodological shortcomings, including poor conceptualization of rurality. The influence of social environment context (community, family and individual factors) on mental health may be addressed through multilevel modeling. A rural mental health database was developed to address the diversity of rural communities and included data on health, lifestyle, social capital, climate patterns, agricultural activity and primary industry.

Aim:

The study sought to investigate the association between mental health, health behaviours and social context in rural communities.

Method:

Items from the NSW Health Survey were used, initially across the 37 Divisions of General Practice in New South Wales. The response variable of the percentage of people who had high or very high psychological distress, as measured by the K10, was regressed against social capital items (such as attending community events), health accessibility item (difficulties in accessing health care) and measures of rurality (remoteness, population density and changes in population structure over time).

Results and Conclusions:

Associations between psychological distress and measures of health service accessibility, social capital, lifestyle and rural population changes will be reported. Analyses will be extended in a multilevel framework to include important agricultural, meteorological and environmental stress indicators, to assess the effects of drought on psychological distress. This analysis will be conducted using the 176 local government areas in New South Wales and will allow more detailed analysis to examine any heterogeneous effects in rural New South Wales.