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This paper, presented at the 2010 rural health researchers' National Scientific Symposium on Rural and Remote Health, provides an overview of large Australian population mental health cohort studies which have a focus on climate‐related and environmental adversity, social factors and mental health. These studies highlight the value of exploiting multiple exceptional datasets to better understand the drivers of rural health, including how to use population‐level research to improve health resources in non‐metropolitan areas. We show how the key characteristics of rural and remote mental health might be explored by exploiting the following cohort studies: Household, Income and Labour Dynamics in Australia Survey; Australian Rural Mental Health Study; Hunter Community Study; and Extending Treatments, Education and Networks in Depression study. Existing cohort studies that focus on significant rural and regional characteristics can be creatively analysed to better understand geographic variation in mental health. They have the potential to move understanding beyond simple prevalence to building knowledge about the trajectories of psychological distress and determinants of mental disorders and outcomes over time. [ABSTRACT FROM AUTHOR]

Objective: This paper outlines the methods and baseline data from a multisite cohort study of the determinants and outcomes of mental health and well-being within rural and remote communities. Methods: A stratified random sample of adults was drawn in non-metropolitan New South Wales using the Australian Electoral Roll, with the aim of recruiting all adult members of each household. Surveys assessed psychological symptoms, physical health and mental disorders, along with individual-, family/household- and community-level characteristics. A stratified subsample completed a telephone-administered World Mental Health-Composite International Diagnostic Interview (World Mental Health-3.0). Proxy measures of child health and well-being were obtained. Follow up of this sample will be undertaken at one, three and five years. Results: A total of 2639 individuals were recruited (1879 households), with 28% from remote/very remote regions. A significant relationship was found between recent distress (Kessler-10 scores), age and remoteness, with a linear reduction of Kessler-10 scores with age and the lowest mean scores in remote regions. Conclusions: Existing rurality categories cannot address the diverse socio-cultural, economic and environmental characteristics of non-metropolitan regions. While it has limitations, the dataset will enable a fine-grained examination of geographic, household and community factors and provide a unique longitudinal dataset over a five-year period. [ABSTRACT FROM AUTHOR]