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Bayesian spatial methods for small-area injury analysis: a study of geographical variation of falls in older people in the Wellington–Dufferin–Guelph health region of Ontario, Canada
  1. Wing C Chan1,
  2. Jane Law1,2,
  3. Patrick Seliske1,3
  1. 1School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada
  2. 2School of Planning, University of Waterloo, Waterloo, Ontario, Canada
  3. 3Wellington–Dufferin–Guelph Public Health, Guelph, Ontario, Canada
  1. Correspondence to Wing C Chan, School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada; wing.chan{at}uwaterloo.ca

Abstract

Objectives To examine falls in older people in the Wellington–Dufferin–Guelph (WDG) health region of Ontario, Canada, and to identify areas with excess RR and associated risk factors, particularly those related to private dwellings.

Methods Cases of hospitalisation following falls among older people in the WDG health region between 2002 and 2006 were geocoded to the dissemination area level and used in the spatial analysis. The falls data and covariates from the 2006 Canadian census were analysed using Poisson log-linear models with (spatial and non-spatial) random effects at the dissemination area level. A Bayesian approach with Markov chain Monte Carlo simulation allowed the spatial random effects models to be fitted. Map decomposition was used to visualise the results.

Results The percentage of occupied private dwellings requiring repairs and median income were significantly associated with falls in older people in the WDG health region. Twenty-six dissemination areas with high RR of falls in older people in the WDG health region were identified. Map decomposition revealed that RR were also driven by unknown factors that have spatial patterns.

Conclusions This research identified an association between falls in older people and housing conditions; the higher the percentage of dwellings requiring repairs in an area, the higher its risk of falls in older people. Bayesian spatial modelling accounts for measurement errors and unobserved or unknown risk factors that have spatial patterns. The findings have the potential to contribute to future research in reducing falls in older people and generate more interest in using Bayesian spatial modelling approaches in injury and public health research.

  • Accidental falls
  • aged
  • ecological study
  • environmental modification
  • epidemiology
  • fall
  • older people
  • planning
  • public health
  • risk factor research
  • risk factors
  • small-area analysis

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Footnotes

  • Funding This study was supported by the Natural Sciences and Engineering Research Council of Canada, grant no RGPIN-371625-2009.

  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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