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Exploration of the spatial patterns and determinants of asthma prevalence and health services use in Ontario using a Bayesian approach

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  • Alexandra M Ouédraogo
  • Eric J Crighton
  • Michael Sawada
  • Teresa To
  • Kevin Brand
  • Eric Lavigne

Abstract

The purpose of this study was to examine the spatial variability of asthma outcomes in Ontario, Canada and broad environmental factors that contribute to this variability. Age-/sex-standardized asthma prevalence and health services use rates (2003–2013) were obtained from a provincial cohort of asthma patients. Employing an ecological-level study design, descriptive and Bayesian spatial regression analyses were used to examine patterns of asthma outcomes and their relationship to physical environment, socioeconomic environment and healthcare factors. Significant spatial variation in asthma outcomes was found between southern urban/suburban areas and northern/rural areas. Rurality was found to have a substantial effect on all asthma outcomes, except hospitalizations. For example, the most rural areas were associated with lower asthma prevalence and physician visits [RR = 0.708, 95% credible interval (CI): 0.636–0.795 and RR = 0.630, 95% CI: 0.504–0.758, respectively], and with higher ED visits (RR = 1.818, 95% CI: 1.194–2.858), when compared to urban areas. Strong associations were also found between material deprivation and ED visits (RR = 1.559, 95% CI: 1.358–1.737) and hospitalizations (RR = 1.259, 95% CI: 1.143–1.374). Associations between asthma outcomes and environmental variables such as air pollution and temperature were also found. Findings can be expected to inform the development of improved public health strategies, which take into account local environmental, socioeconomic and healthcare characteristics.

Suggested Citation

  • Alexandra M Ouédraogo & Eric J Crighton & Michael Sawada & Teresa To & Kevin Brand & Eric Lavigne, 2018. "Exploration of the spatial patterns and determinants of asthma prevalence and health services use in Ontario using a Bayesian approach," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0208205
    DOI: 10.1371/journal.pone.0208205
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    References listed on IDEAS

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