Turning the Heat up on Admissions - QUT 2014 .pdf · morbidities (particularly cardiovascular,...
Transcript of Turning the Heat up on Admissions - QUT 2014 .pdf · morbidities (particularly cardiovascular,...
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
Extreme Heat Events
Extreme Heat event: mean daily temp exceeding
the mean daily temp for the time of year in that
locality
Heat Wave:
o No universal definition in published research1
o Usually taken to be 3 or more consecutive days
where daily max. temp exceeds mean daily max.
temp for locality for that time of year
Effects of Heat Events Numerous studies have demonstrated a link between
extreme heat events & increased risk of morbidity & mortality
Once core body temp reaches 39.50C most bodily processes start to break down
Direct Effects: heat stroke, heat exhaustion, heat syncope, heat cramps & heat exhaustion
Indirect Effects: Exacerbation of pre-existing co-morbidities (particularly cardiovascular, renal & respiratory conditions, diabetes & mental illness)2-6
Cause more deaths in Australia than any other extreme weather event7
Individuals’ responses to heat events also altered by medications & acclimatisation
Association Between Temp.
& Morbidity/Mortality
Fig.1: The Relationship between Risk of Adverse Health Outcomes and Exposure Outside Individual’s Comfort Zone8
Aim
To identify if extreme heat events in Hobart were
associated with any changes in emergency
department admissions to Royal Hobart Hospital
for the period 2003-2010
Why admissions?
Previous research looked at mortality data – need to look at patient admissions data to really
understand impact on hospital EDs
Why Tasmania?
Most research in this area has looked at hotter climates e.g:
Europe: Mediterranean Australia: Adelaide & Brisbane
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.
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Questions?