Temperature and humidity effects on hospital admissions in Darwin, AustraliaJames Goldie
With Steven Sherwood, Lisa Alexander & Donna Green
17 July 2015
This talk
!Background
Heat stress andepidemiology
?Our studyDarwin hospital
admissions
#Results &
DiscussionOvernight humidity
How we overheat
https://www.flickr.com/photos/hddod/203141621
☼When it’s hot,
we sweatWhen it’s
humid, our sweat drips
(and doesn’t cool us)
☼ ○When it’s hot and humid,
we’re in trouble!
Epidemiology: statistical analysis of public health
Epi models can make inferences about health relationships and predict future health responses
&Health
response
☼Climate
predictor(s)
TemperatureHumidityPollution
Hospital admissionsMortalities
Ambulance callouts
~
Where’s the humidity?
Google Earth: Data SIO, NOAA, US Navy, NGA, GEBCO, Image Landsat
Melbourne: ~ 15–20 hPa
Sydney: ~ 25 hPaAdelaide: ~ 15–20 hPa
Perth: ~ 20 hPa
Brisbane: ~ 25–30 hPa
Cairns: ~ 30–35 hPa
Darwin: ~ 30–35 hPa
There aren’t enough epi studies looking at humidity in the tropics!
#*
#* Darwin Airport HadISD Station
Included Darwin Airport SLAs
Other 2006 SLAs
0 30 60 90 12015Kilometers
Darwin Airport CohortDarwin residents (~ 18k admissions)
ResponseDaily hospital admission count
PredictorsDaily temperature and relative humidity
Tmax, Tmin, Tmean, RHmax, RHmin, Rhmean
Our study in Darwin
Darwin Airport weather station
Cohort areas
Non-cohort areas
&
☼
'
Linear effects studies with GLMs
admission count ~logged population offset +predictor
Non-linear effects studied with subset
Daily series split into five equal binsAdmission rates of bins compared w/
Two analyses
Each analysis performed for one predictor, then two
https://upload.wikimedia.org/wikipedia/commons/c/cc/Darwin_Australia_aerial_photo_1984.JPEG
Linear analysis of one predictor
Predictor Tmax Tmin Tmean RHmax RHmin RHmean
Effect Size 1.74% -0.19% 0.13% 3.73% 0.02% 1.21%P-value 0.049 0.800 0.863 < 0.001 0.968 0.049
Max. relative humidity is extremely significantMax. temp, mean relative humidity are also significant
% change per 2 °C change % change per 10 p.p. change
Non-linear analysis of one predictor
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Tmax Tmin Tmean RHmax RHmin RHmean
Daily predictor
Mea
n da
ily a
dmis
sion
rate
(per
100
k re
side
nts)
with
95%
con
fiden
ce in
terv
al
Predictor bin�
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P0−20
P20−40
P40−60
P60−80
P80−100
Estimates (points) and 95% confidence intervals (lines) for each bin
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3.5
4.0
P0−20 P20−40 P40−60 P60−80 P80−100
Tmax bin
Mea
n da
ily a
dmis
sion
rate
(per
100
k re
side
nts)
with
95%
con
fiden
ce in
terv
al
RHmean bin�
●
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P0−20
P20−40
P40−60
P60−80
P80−100
Non-linear analysis of two predictors: RHmean within TmaxEstimates (points) and 95% confidence intervals (lines) for each bin
What do the results mean?
Overnight humidity is important!Other studies found no humidity effects—but they weren’t in the tropics!
Humidity and temperature act at different times of dayHeat policies assume equal contributions throughout the day
Humid heat affects sleepMore wakefulness, less deep sleep on humid/hot days
(
https://www.flickr.com/photos/mindfulness/21264368/
)
Summary
Epidemiology: stats meets healthNot enough epi studies looking at humidity in the tropics
Hospital admissions of Darwin residentsDaily count of selected hospital admissions
Daytime temp, overnight humidity affect admissionsHeat policies assume equal contributions throughout the day#
&
○
Thanks!Questions?
J. Goldie, S. C. Sherwood, D. Green & L. Alexander (Accepted). Temperature and humidity effects on hospital morbidity in Darwin, Australia. Annals of Global Health.+
References
Tong, S., Wang, X. Y., Yu, W., Chen, D., & Wang, X. (2014). The impact of heatwaves on mortality in Australia: a multicity study. BMJ Open, 4(2), e003579. doi:10.1136/bmjopen-2013-003579
Vaneckova, P., Neville, G., Tippett, V., Aitken, P., FitzGerald, G., & Tong, S. (2011). Do Biometeorological Indices Improve Modeling Outcomes of Heat-Related Mortality? Journal of Applied Meteorology and Climatology, 50(6), 1165–1176. doi:10.1175/2011JAMC2632.