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?