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Abstract
<p class="first" id="d3049263e75">Loneliness is a significant negative predictor of
ageing well and a contra-indicator
for resilience against requiring long-term residential care. Health geographers can
contribute to the loneliness and ageing literature through examining how exposures
in the physical and social landscape can affect positive and negative health outcomes.
As well as improving individual experiences of ageing, spatial analysis may help to
contribute to better understandings of loneliness and help reduce the $1.7 billion
per annum New Zealand currently spends on publicly-funded aged residential care. Using
New Zealand Home Care International Residential Assessment Instrument data from 2012
to 2016, the spatial distribution of the interRAI-HC cohort was examined. Urban and
rural distribution, socioeconomic status, and ethnicity within the interRAI-HC cohort
was compared against total population and population aged 65 plus. Relative to the
socioeconomic status of the 65 plus cohort, those being interRAI-HC assessed were
more likely to live in socially deprived areas. Socioeconomic deprivation also positively
correlated with loneliness (OR = 1.002). Carer stress was negatively correlated with
socioeconomic status (OR = 0.99). Those in rural areas were predicted to be less lonely
than urban dwellers (OR = 0.98), and this observation remained similar and significant
when socioeconomic status, the experience of negative social interactions or carer
stress, and whether they lived alone were included. Living in rural areas had a protective
effect against loneliness for all ethnic groups apart from Pasifika. 'Hot' and 'cold'
clusters of loneliness were identified, with the distribution of interRAI-HC assessments
in hot clusters less likely to be rural areas (OR = 0.71). Our findings did not diverge
greatly from prior research on older people and loneliness in rural areas. Observations
of regional differences regarding rurality and socioeconomic status did not show large
differences, and this research will benefit in future from analysis at finer geographic
scale.
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