Turning the Heat up on Admissions A Study of the Impacts of Extreme Heat Events on Tasmanian Hospital Admissions 2003-2010 Judith Singleton 1 , Cunrui Huang 2 , Kaitlyn Porter 1 1 School of Pharmacy, University of Qld; 2 School of Environment, Griffith University
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Turning the Heat up on
Admissions A Study of the Impacts of Extreme Heat Events on
Tasmanian Hospital Admissions 2003-2010
Judith Singleton1, Cunrui Huang2, Kaitlyn Porter1
1 School of Pharmacy, University of Qld; 2 School of Environment, Griffith University
Usual definitions for Australia9: Hot days: max. temp > 350C Very hot days: max. temp >400C
Tasmania selected because of its cooler, temperate
climate
Hobart Climate Data:
o Annual mean temp = 14.30C
o Hottest months: December, January & February
o Mean summer temp for period 2003-2010 = 18.40C
[Compare with Brisbane: 29.80C]
Methods - Data
Patient Data
o Non-identifiable ED data from RHH 2003-2010
o DOA, gender, age, ICD-10
o 324,447 admissions
Climate Data (Hobart) 2003-2013
o Daily max & min temps
o Daily relative humidity data
Methods – Statistical
Analysis
Considerations:
o Lagged effect of extreme temps on morbidity lasting for a period of days after the event 10,11
o Several extreme events close together will mean overlaps in the lagged periods
o Temp-morbidity relationship non-linear 8,12-14
To quantify the main effect of temp, a quassi-Poisson generalised linear regression model combined with a Distributed Lag Non-linear Model (DLNM) as described by Gasparrini et al15 was used to examine both the non-linear & lagged effects of temp simultaneously
Gasparrini et al’s DLNM coding was replicated in ‘R’ for the Hobart data – DLNM package used to fit linear regression model
Max. lag of 14 days used (based on other research)
Confounders such as relative humidity, seasonal trends, public holidays & days of the week were all controlled for
Results
Fig.2: Mean Temperature Trends Hobart 2003-2010
Results
As temps rose above 240C, RR of being admitted
to RHH also rose
Fig.3: Overall Relative Risk of Admission to RHH by temperature at 14 day lag at 95th percentile of temp distribution. The red line shows the mean; grey area shows the 95% confidence intervals.
Results
Lag effects lasted up to 12 days with a spike in
admissions one day after extremely hot day
Fig.4: The Delayed Effects of Temp on Risk of Admission to RHH by lag where temp > 240 C 2003-2010
Discussion For period 2003-2010, when temps in Hobart
exceeded 240C (100C higher than annual mean temp) a significant ↑ in RHH pt admissions was observed
↑ in admissions observed for up to 12 days after extreme heat event with a spike in admissions one day after extremely hot day
Presenting conditions:
o Respiratory, renal, cardiovascular, cerebrovascular and mental health.
Able to replicate Gasparrini et al’s graphs of US data
Results corroborate findings of other studies – both Australian & international
Limitations & Future
Research
Only looked at patient admissions data – not all
ED presentations are admitted so results only
partially demonstrate the real impact on RHH ED
services
Data analysis period did not include 2012-13
‘Angry Summer’
Further analysis using Poisson regression analyses
would be useful to compare admissions for
various morbidities between heat and non-heat periods to identify vulnerable patient groups in
this population
Significance of Research By 2100, increasing atmospheric concentrations of
CO2 will cause a rise in global mean surface temps
by: 2-4.50C (76% probability of occurrence)
> 4.50C (14% probability of occurrence) 16
Fig. 1: ‘Relationship between averages & extremes showing the connection between a shifting average & proportion of extreme events7
Aust. average temps have increased faster
than the global average increase – 0.90C warmer than a century ago
Implications
CC → increase in duration & intensity of heat
events (extremely hot days and heat waves)
Aging population & increasing use of
medications – increased numbers of vulnerable
individuals
Public Health sector needs to build in capacity to adapt to these expected ↑ heat events & increases in patient admissions and be proactive
e.g. Public Health campaigns to forewarn vulnerable patient groups
References 1. Tong S, Wang, XY, Barnet AG. Assessment of heat-related health impacts in Brisbane, Australia:
comparison of different heatwave definitions. PLoS One 2010;5(8):e12155.
2. McGeehin MA, Mirabelli M. The Potential Impacts of Climate Variability and Change on temperature-Related Morbidity and Mortality in the United States. Environmental health Perspectives 2001; 109(Suppl 2):185-9.
3. Nitschke M, Tucker GR, Bi P. Morbidity and mortality during heatwaves in metropolitan Adelaide. The Medical Journal of Australia 2007;187(11-12):662.
4. Nitschke M, Tucker, GR, Hansen, AL, Williams S, Zhang Y, Bi P. Impact of two recent extreme heat episodes on morbidity and mortality in Adelaide, South Australia: a case-series analysis. Environmental Health 2011;10(1);42.
5. Stoffagia M, De Maria M, Michelozzi P, Miglio R, Pandolfi P, Picciotto S, et al. Vulnerability to heat-related mortality: a multicity, population-based, case-crossover analysis. Epidemiology 2006;17(3):315-23.
6. Kovats RS, Hajat S. Heat stress and public heatlh : a critical review. Annu Rev Public Health 2008 29:41-55.
7. Steffen W. The Angry Summer. Canberra: Climate Commission Secretariat, Department of Climate Change and Energy Efficiency, Australian Government; 2013.
8. McMichael AJ. Impediments to comprehensive research on climate change and health. Int J Environ Res Public Health 2013;10(11):6096-105.
References cont.
9. Steffen W, Hughes, L. The Critical Decade 2013: Climate Change Science, Risks and responses, Canberra: Climate Commission Secretariat, Department of Industry, Innovation, Climate Change, Science, Rsearch and tertiary Education, Australian Government; 2013.
10. Braga, ALF, Zanobetti A, Schwartz J. The Time Course of Weather-related Deaths. Epidemiology 2001;12(6):662-7.
11. Huang C, Barnett AG, Wang X, Tong S. effects of extreme temperatures on years of life lost for cardiovascular deaths: a time series study in Brisbane, Australia. Circulation: Cardiovascular Quality and Outcomes 2012;5(5):609-14.
12. Analitis A et al. Effects of Cold Weather on Mortality: Results from 15 European Cities within the PHEWE Project. American journal of Epidemiology 2008;168(12):1397-408.
13. McMichael AJ, Wilkinson P, Kovats R et al. International study of temperature, heat and urban mortality: the ‘ISOTHURM’ Project.International journal of Epidemiology 2008;37(5):1121-31.
14. Huang C, Barnett AG, Xu Z, Chu C, Wang X, Turner LR et al. Managing the heatlh effects of temperature in response to climate change: challenges ahead. Environmental Health Perspectives 2013;121(4):415-9.
15. Gasparrini A, Armstrong B, Kenward MG. Distributed lag non-linear models. Statistics in Medicine 2010;29(21):2224-34
16. Rogelj J, Meinshausen M, Knutti R. Global warming under new and old scenarios using IPCC climate sensitivity range estimates. Nature Clim. Change 2012;2(4):248-53.