Abstract
Various human heat stress indices have been developed to relate atmospheric measures of extreme heat to human health impacts, but the usefulness of different indices across various health impacts and in different populations is poorly understood. This paper determines which heat stress indices best fit hospital admissions for sets of cardiovascular, respiratory, and renal diseases across five Australian cities. We hypothesized that the best indices would be largely dependent on location. We fit parent models to these counts in the summers (November–March) between 2001 and 2013 using negative binomial regression. We then added 15 heat stress indices to these models, ranking their goodness of fit using the Akaike information criterion. Admissions for each health outcome were nearly always higher in hot or humid conditions. Contrary to our hypothesis that location would determine the best-fitting heat stress index, we found that the best indices were related largely by health outcome of interest, rather than location as hypothesized. In particular, heatwave and temperature indices had the best fit to cardiovascular admissions, humidity indices had the best fit to respiratory admissions, and combined heat-humidity indices had the best fit to renal admissions. With a few exceptions, the results were similar across all five cities. The best-fitting heat stress indices appear to be useful across several Australian cities with differing climates, but they may have varying usefulness depending on the outcome of interest. These findings suggest that future research on heat and health impacts, and in particular hospital demand modeling, could better reflect reality if it avoided “all-cause” health outcomes and used heat stress indices appropriate to specific diseases and disease groups.
Similar content being viewed by others
Change history
01 March 2019
The authors of the article would like to bring the following correction/corrigendum to attention: When recently investigating future changes in heat stress indices, we discovered an error in the use of the heatwave indices we compared in Goldie et al. (2017).
01 March 2019
The authors of the article would like to bring the following correction/corrigendum to attention: When recently investigating future changes in heat stress indices, we discovered an error in the use of the heatwave indices we compared in Goldie et al. (2017).
References
Alexander LV, Arblaster JM (2017) Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5. Weather Clim Extremes 15:34–56. https://doi.org/10.1016/j.wace.2017.02.001
Australian Bureau of Statistics (2014) 3105.0.65.001—Australian Historical Population Statistics, 2014. http://www.abs.gov.au/ausstats/abs@.nsf/mf/3105.0.65.001. Accessed 22 May 2017
Bambrick H, Dear K, Woodruff R et al (2008) The impacts of climate change on three health outcomes: temperature-related mortality and hospitalisations, salmonellosis and other bacterial gastroenteritis, and population at risk from dengue. National Centre for Epidemiology and Population Health (Australian National University) and School of Medicine, University of Western Sydney, Sydney
Barnett AG, Tong S, Clements ACA (2010) What measure of temperature is the best predictor of mortality? Environ Res 110:604–611. https://doi.org/10.1016/j.envres.2010.05.006
Benmarhnia T, Sottile M-F, Plante C et al (2014) Variability in temperature-related mortality projections under climate change. Environ Health Perspect. https://doi.org/10.1289/ehp.1306954
Bull GM, Morton J (1978) Environment, temperature and death rates. Age Ageing 7:210–224. https://doi.org/10.1093/ageing/7.4.210
Bureau of Meteorology (2012) Australian climate averages—climate classifications http://www.bom.gov.au/jsp/ncc/climate_averages/climate-classifications/index.jsp. Accessed 10 Mar 2016
Bureau of Meteorology (2010) Thermal comfort observations http://www.bom.gov.au/info/thermal_stress/. Accessed 4 Aug 2015
Curriero FC, Heiner KS, Samet JM et al (2002) Temperature and mortality in 11 cities of the eastern United States. Am J Epidemiol 155:80–87
Davis RE, McGregor GR, Enfield KB (2016) Humidity: a review and primer on atmospheric moisture and human health. Environ Res 144:106–116. https://doi.org/10.1016/j.envres.2015.10.014
de Freitas CR, Grigorieva EA (2015) A comprehensive catalogue and classification of human thermal climate indices. Int J Biometeorol 59:109–120. https://doi.org/10.1007/s00484-014-0819-3
Donat MG, Alexander LV, Yang H et al (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. J Geophys Res Atmos 118:2098–2118. https://doi.org/10.1002/jgrd.50150
Dunn RJH, Willett KM, Thorne PW et al (2012) HadISD: a quality-controlled global synoptic report database for selected variables at long-term stations from 1973–2011. Clim Past 8:1649–1679. https://doi.org/10.5194/cp-8-1649-2012
Fearon JD (2003) Ethnic and cultural diversity by country. J Econ Growth 8:195–222
García-Trabanino R, Jarquín E, Wesseling C et al (2015) Heat stress, dehydration, and kidney function in sugarcane cutters in El Salvador—a cross-shift study of workers at risk of Mesoamerican nephropathy. Environ Res 142:746–755. https://doi.org/10.1016/j.envres.2015.07.007
Gasparrini A, Guo Y, Hashizume M et al (2015) Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet 386:369–375. https://doi.org/10.1016/S0140-6736(14)62114-0
Goldie J, Alexander L, Lewis S, Sherwood SC (2017) Comparative evaluation of human heat stress indices on selected hospital admissions in Sydney, Australia. Aust N Z J Public Health. https://doi.org/10.1111/1753-6405.12692
Goldie J, Sherwood SC, Green D, Alexander L (2015) Temperature and humidity effects on hospital morbidity in Darwin, Australia. Ann Glob Health 81:333–341. https://doi.org/10.1016/j.aogh.2015.07.003
Gosling SN, Lowe JA, McGregor GR et al (2008) Associations between elevated atmospheric temperature and human mortality: a critical review of the literature. Clim Chang 92:299–341. https://doi.org/10.1007/s10584-008-9441-x
Hansen A, Bi P, Nitschke M et al (2008a) The effect of heat waves on mental health in a temperate Australian city. Environ Health Perspect 116:1369–1375. https://doi.org/10.1289/ehp.11339
Hansen AL, Bi P, Ryan P et al (2008b) The effect of heat waves on hospital admissions for renal disease in a temperate city of Australia. Int J Epidemiol 37:1359–1365. https://doi.org/10.1093/ije/dyn165
Kenefick RW, Cheuvront SN (2016) Physiological adjustments to hypohydration: impact on thermoregulation. Auton Neurosci 196:47–51. https://doi.org/10.1016/j.autneu.2016.02.003
King AD, Karoly DJ, Henley BJ (2017) Australian climate extremes at 1.5 °C and 2 °C of global warming. Nat Clim Change. https://doi.org/10.1038/nclimate3296
Kingsley SL, Eliot MN, Gold J et al (2016) Current and projected heat-related morbidity and mortality in Rhode Island. Environ Health Perspect 124:460–467. https://doi.org/10.1289/ehp.1408826
Knowlton K, Lynn B, Goldberg RA et al (2007) Projecting heat-related mortality impacts under a changing climate in the New York City region. Am J Public Health 97:2028–2034. https://doi.org/10.2105/AJPH.2006.102947
Kosaka M, Yamane M, Ogai R et al (2004) Human body temperature regulation in extremely stressful environment: epidemiology and pathophysiology of heat stroke. J Therm Biol 29:495–501. https://doi.org/10.1016/j.jtherbio.2004.08.019
Li T, Horton RM, Bader DA et al (2016) Aging will amplify the heat-related mortality risk under a changing climate climate: projection for the elderly in Beijing, China. Sci Rep. https://doi.org/10.1038/srep28161
Li T, Horton RM, Kinney PL (2013) Projections of seasonal patterns in temperature-related deaths for Manhattan, New York. Nat Clim Chang 3:717–721. https://doi.org/10.1038/nclimate1902
Lin S, Luo M, Walker RJ et al (2009) Extreme high temperatures and hospital admissions for respiratory and cardiovascular diseases. Epidemiology 20:738–746
Nairn J, Fawcett R (2013) Defining heatwaves: heatwave defined as a heat-impact event servicing all community and business sectors in Australia. The Centre for Australian Weather and Climate Research, Melbourne
Peng RD, Bobb JF, Tabaldi C et al (2011) Toward a quantitative estimate of future heat wave mortality under global climate change. Environ Health Perspect 119:701–706. https://doi.org/10.1289/ehp.1002430
Posada D, Buckley TR (2004) Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Syst Biol 53:793–808. https://doi.org/10.1080/10635150490522304
Rodopoulou S, Samoli E, Analitis A et al (2015) Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities. Int J Biometeorol. https://doi.org/10.1007/s00484-015-0965-2
Scalley BD, Spicer T, Jian L et al (2015) Responding to heatwave intensity: Excess Heat Factor is a superior predictor of health service utilisation and a trigger for heatwave plans. Aust N Z J Public Health. https://doi.org/10.1111/1753-6405.12421
Schnaper HW (2014) Remnant nephron physiology and the progression of chronic kidney disease. Pediatr Nephrol Berl Ger. https://doi.org/10.1007/s00467-013-2494-8
Semenza JC, McCullough JE, Flanders WD et al (1999) Excess hospital admissions during the July 1995 heat wave in Chicago. Am J Prev Med 16:269–277. https://doi.org/10.1016/S0749-3797(99)00025-2
Sherwood SC, Huber M (2010) An adaptability limit to climate change due to heat stress. Proc Natl Acad Sci U S A 107:9552–9555. https://doi.org/10.1073/pnas.0913352107
Tong S, Wang XY, Barnett AG (2010) Assessment of heat-related health impacts in Brisbane, Australia: comparison of different heatwave definitions. PLoS One 5:e12155–e12155. https://doi.org/10.1371/journal.pone.0012155
Vaneckova P, Bambrick H (2013) Cause-specific hospital admissions on hot days in Sydney, Australia. PLoS One. https://doi.org/10.1371/journal.pone.0055459
Webb L, Bambrick H, Tait P et al (2014) Effect of ambient temperature on Australian northern territory public hospital admissions for cardiovascular disease among indigenous and non-indigenous populations. Int J Environ Res Public Health 11:1942–1959. https://doi.org/10.3390/ijerph110201942
Woodhead M (2016) Hospitals overwhelmed with patients after “thunderstorm asthma” hits Melbourne. Br Med J. doi. https://doi.org/10.1136/bmj.i6391
Ye X, Wolff R, Yu W et al (2012) Ambient temperature and morbidity: a review of epidemiological evidence. Environ Health Perspect 120:19–28. https://doi.org/10.1289/ehp.1003198
(1910) Holidays Act 1910 (SA)
(1912) Banks and Bank Holidays Act 1912 (NSW)
(1972) Public and Bank Holidays Act 1972 (WA)
(1983) Holidays Act 1983 (Qld)
(2010) Public Holidays Act 2010 (NSW)
Acknowledgements
The authors would like to thank Marissa Parry of the Climate Change Research Centre for her assistance defining public holidays across Australia, Peter Tait of the Australian National University Medical School for his assistance identifying relevant diseases, and Donna Mary Salopek of the UNSW Statistical Consulting service for her guidance in optimizing the fit of the regression models.
Funding information
S.C.L. is funded through the Australian Research Council (DE160100092). J.G., L.A., S.C.L., and S.C.S. are funded by an Australian Research Council grant (CE110001028).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
This research was approved by the UNSW Human Research Ethics Advisory (HREA) (HC15125), the Australian National University Human Research Ethics Committee (HREC) (2015/239), the Royal Brisbane & Women’s HREC (HREC/15/QRBW/202), the SA Health HREC (HREC/15/SAH/41), and the Department of Health WA HREC (2015/27).
Conflict of interest
The authors declare that they have no competing interests.
Electronic supplementary material
ESM 1
(DOCX 143 kb)
Rights and permissions
About this article
Cite this article
Goldie, J., Alexander, L., Lewis, S.C. et al. Changes in relative fit of human heat stress indices to cardiovascular, respiratory, and renal hospitalizations across five Australian urban populations. Int J Biometeorol 62, 423–432 (2018). https://doi.org/10.1007/s00484-017-1451-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00484-017-1451-9