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Do Biometeorological Indices Improve Modeling Outcomes of Heat-Related Mortality?

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  • 1 School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
  • 2 Environmental Health Branch, Queensland Health, Brisbane, Queensland, Australia
  • 3 Australian Centre for Prehospital Research, Queensland Ambulance Service, Brisbane, Queensland, Australia
  • 4 James Cook University, Townsville, Queensland, Australia
  • 5 School of Public Health and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
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Abstract

Various biometeorological indices and temperature measures have been used to assess heat-related health risks. Composite indices are expected to assess human comfort more accurately than do temperature measures alone. The performances of several common biometeorological indices and temperature measures in evaluating the heat-related mortality in Brisbane, Australia—a city with a subtropical climate—were compared. Daily counts of deaths from organic causes [International Statistical Classification of Diseases and Related Health Problems, 9th Revision, (ICD9) codes 001–799 and ICD, 10th Revision, (ICD10) codes A00–R99] during the period from 1 January 1996 to 30 November 2004 were used. Several composite biometeorological indices were considered, such as apparent temperature, relative strain index, Thom discomfort index, the humidex, and wet-bulb globe temperature. Hot days were defined as those days falling into the 95th percentile of each thermal stress indicator. Case-crossover analysis was applied to estimate the relationship between exposure to heat and mortality. The performances of various biometeorological indices and temperature measures were compared using the jackknife resampling method. The results show that more deaths were likely to occur on hot days than on other (i.e., control) days regardless of the temperature measure or biometeorological index that is considered. The magnitude of the odds ratios varied with temperature indicators, between 1.08 [95% confidence interval (CI): 1.02–1.14] and 1.41 (95% CI: 1.22–1.64) after adjusting for air pollutants (particulate matter with aerodynamic diameter less than 10 μm and ozone). Average temperature performed similarly to the composite indices, but minimum and maximum temperatures performed relatively poorer. Thus, average temperature may be suitable for the development of weather–health warning systems if the findings presented herein are confirmed in different locations.

Corresponding author address: Dr. Pavla Vaneckova, School of Medicine, University of Western Sydney, Locked Bag 1797, Penrith, NSW 2751, Australia. E-mail: apawnr@gmail.com

Abstract

Various biometeorological indices and temperature measures have been used to assess heat-related health risks. Composite indices are expected to assess human comfort more accurately than do temperature measures alone. The performances of several common biometeorological indices and temperature measures in evaluating the heat-related mortality in Brisbane, Australia—a city with a subtropical climate—were compared. Daily counts of deaths from organic causes [International Statistical Classification of Diseases and Related Health Problems, 9th Revision, (ICD9) codes 001–799 and ICD, 10th Revision, (ICD10) codes A00–R99] during the period from 1 January 1996 to 30 November 2004 were used. Several composite biometeorological indices were considered, such as apparent temperature, relative strain index, Thom discomfort index, the humidex, and wet-bulb globe temperature. Hot days were defined as those days falling into the 95th percentile of each thermal stress indicator. Case-crossover analysis was applied to estimate the relationship between exposure to heat and mortality. The performances of various biometeorological indices and temperature measures were compared using the jackknife resampling method. The results show that more deaths were likely to occur on hot days than on other (i.e., control) days regardless of the temperature measure or biometeorological index that is considered. The magnitude of the odds ratios varied with temperature indicators, between 1.08 [95% confidence interval (CI): 1.02–1.14] and 1.41 (95% CI: 1.22–1.64) after adjusting for air pollutants (particulate matter with aerodynamic diameter less than 10 μm and ozone). Average temperature performed similarly to the composite indices, but minimum and maximum temperatures performed relatively poorer. Thus, average temperature may be suitable for the development of weather–health warning systems if the findings presented herein are confirmed in different locations.

Corresponding author address: Dr. Pavla Vaneckova, School of Medicine, University of Western Sydney, Locked Bag 1797, Penrith, NSW 2751, Australia. E-mail: apawnr@gmail.com
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