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.

The chief determinants of current well being were those reflecting individual level attributes and perceptions, rather than district-level rural characteristics. This has implications for strategies to promote well being within rural communities through enhancing community connectedness and combating social isolation in the face of major adversities such as drought.

The mental health impact of drought is poorly quantified and no previous research has demonstrated a relationship between distress and explicit environmentally based measures of drought. With continuing climate change, it is important to understand what drought is and how it may affect the mental health. We quantified drought in terms of duration and intensity of relative dryness and identified drought characteristics associated with poor mental health to evaluate any vulnerability in rural and urban communities. Our methods involved analysis of 100-year longitudinal records of monthly rainfall linked to one wave (2007-2008) of the Household, Income and Labour Dynamics in Australia Survey. Cluster analysis was used to characterise different patterns of dryness and linear regression analysis was used to examine associations with participant distress, as well as the moderating role of rural locality. The results showed that, during a seven-year period of major and widespread drought, one pattern of relative dryness (extreme cumulative number of months in drought culminating in a recent period of dryness lasting a year or more) was associated with increased distress for rural but not urban dwellers. The increase in distress was estimated to be 6.22%, based on 95% confidence intervals. Thus, we show that it is possible to quantitatively identify an association between patterns of drought and distress.

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