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BMC Emerg Med. 2015 Aug 6;15:17. doi: 10.1186/s12873-015-0043-4.

A spatial analysis of heat stress related emergency room visits in rural Southern Ontario during heat waves.

Author information

1
Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada. kbishop@uoguelph.ca.
2
Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada. oberke@uoguelph.ca.
3
Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada. dpearl@uoguelph.ca.
4
Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada. dkelton@uoguelph.ca.

Abstract

BACKGROUND:

In Southern Ontario, climate change may have given rise to an increasing occurrence of heat waves since the year 2000, which can cause heat stress to the general public, and potentially have detrimental health consequences. Heat waves are defined as three consecutive days with temperatures of 32 °C and above. Heat stress is the level of discomfort. A variety of heat stress indices have been proposed to measure heat stress (e.g., the heat stress index (HSI)), and has been shown to predict increases in morbidity and/or mortality rates in humans and other species. Maps visualizing the distribution of heat stress can provide information about related health risks and insight for control strategies. Information to inform heat wave preparedness models in Ontario was previously only available for major metropolitan areas.

METHODS:

Hospitals in communities of fewer than 100,000 individuals were recruited for a pilot study by telephone. The number of people visiting the emergency room or 24-hour urgent care service was collected for a total of 27 days, covering three heat waves and six 3-day control periods from 2010-2012. The heat stress index was spatially predicted using data from 37 weather stations across Southern Ontario by geostatistical kriging. Poisson regression modeling was applied to determine the rate of increased number of emergency room visits in rural hospitals with respect to the HSI.

RESULTS:

During a heat wave, the average rate of emergency room visits was 1.11 times higher than during a control period (IRR = 1.11, CI95% (IRR) = (1.07,1.15), p ≤ 0.001). In a univariable model, HSI was not a significant predictor of emergency room visits, but when accounting for the confounding effect of a spatial trend polynomial in the hospital location coordinates, a one unit increase in HSI predicted an increase in daily emergency rooms visits by 0.4% (IRR = 1.004, CI95%(IRR) = (1.0005,1.007), p = 0.024) across the region. One high-risk cluster and no low risk clusters were identified in the southwestern portion of the study area by the spatial scan statistic during heat waves. The high-risk cluster is located in a region with high levels of heat stress during heat waves.

CONCLUSIONS:

This finding will aid hospitals and rural public health units in preventing and preparing for emergencies of foreseeable heat waves. Future research is needed to assess the relation between heat stress and individual characteristics and demographics of rural communities in Ontario.

PMID:
26245846
PMCID:
PMC4527124
DOI:
10.1186/s12873-015-0043-4
[Indexed for MEDLINE]
Free PMC Article

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