Hot days and heatwaves: an analysis of the temperatures that coincide with increases in emergency department attendances

Thomas Longden

Centre for Health Economics Research and Evaluation – University of Technology Sydney

July 2017

Abstract

The development of heatwave response plans has tended to focus on the prevention of adverse health and mortality associated with a prolonged period of hot weather. Many of these plans do not include contingencies for the severe impacts that a single day of hot weather can have on those vulnerable to thermoregulation dysfunction. This paper reviews whether the burden of risk of increased emergence department (ED) demand coincides with single days of hot weather or heatwaves. The results indicate that extreme heat risk management plans should be revised to include provisions for ED demand during short periods of hot weather.

Keywords: heatwave; Emergency Dept. attendances; threshold regression; Global Climate Models

Running title: Hot days and heatwaves

JEL Classification: I10; C24; C5

Correspondence: thomas.longden@chere.uts.edu.au

Word Count: 5668; No. of Figures: 3; No. of Tables: 6

This research was completed using data that has been sourced from the Emergency Department Data Collection (EDDC) collected by the Western Australian Department of Health. The usual disclaimers apply. The author declares no conflict of interest.

1. INTRODUCTION

Increased use of healthcare services during periods of extreme heat is a concern to health care

professionals and policymakers in Australia and elsewhere. This is evident in the

implementation of heatwave response plans in a range of countries, including Australia,

Canada, Germany, Italy, the Netherlands, Spain, the UK and the US. To develop effective

risk management and response plans, it is important to understand the pressures that extreme

heat places on the entire health system, including emergency departments (EDs). The

development of heatwave response plans has commonly focused on the prevention of adverse

health and mortality associated with a prolonged period of hot weather. However, this means

that many of these plans do not include contingencies for the severe impacts that a single

day/night of hot weather can have on those vulnerable to thermoregulation dysfunction.

While extreme heat preparedness planning has been identified as a priority area for the

adaptation based policy response to climate change (Hess, Heilpern et al. 2009, Navi,

Pisaniello et al. 2017), this paper assesses whether existing risk management and response

plans that solely focus on heatwaves means that cities and regions are ill prepared for the

impact of single days/nights of hot weather. It should be noted that Hess and Ebi (2016)

recently proposed that heatwave response plans and early warning systems be regularly

evaluated and updated. They propose that reassessments occur at least every five years to

ensure continued effectiveness and that this should be based on an improved understanding of

population-level vulnerability (Hess and Ebi 2016).

Whether prescriptions for individual hot days/nights should be built into the risk management

of extreme heat events is the primary focus of this paper. In light of this, this paper will assess

whether the burden of risk of increased ED attendances lies with prolonged heat events or a

1

single day of hot weather. As acclimatization to higher temperatures usually takes up to

several weeks (Vaneckova, Beggs et al. 2010, Nairn and Fawcett 2015, Scalley, Spicer et al.

2015), individual days of hot weather are likely to impact health. The analysis focuses on the

impact of heat on ED attendances within seven hospitals in Perth and surrounding areas

within Western Australia (WA). This is assessed by estimating the temperature thresholds

that correspond with heightened ED attendances. These estimates disentangle the impact that

individual hot days/nights and prolonged periods of heat have on ED demand. To model the

impact of individual hot days/nights, this paper utilises fixed effect threshold regressions to

estimate the thresholds of maximum daily temperature that coincide with greater impacts on

ED attendances. The intention is to prescribe temperature thresholds that can be used in the

re-design of existing response plans (specifically the WA State Hazard Plan) and encourage

the estimation of temperature thresholds for other cities/regions that are developing (or

revising) a risk management plan for extreme heat events. Projections of these different

extreme heat events are sourced from three major Global Climate Models (GCMs) to produce

forecasts of heat-related ED demand for a hypothetical hospital in Perth for the period

between 2017 and 2041.

An understanding of existing heatwave response plans is crucial to appreciating the potential

need to modify existing plans. For example, Lowe, Ebi et al. (2011) reviewed the policy and

government documents of 33 European countries and found that, as of May 2011, 12

countries had Heatwave Early Warning Systems (HEWS). Across these examples, the

number of days of hot weather that trigger a warning differ notably. For example, in

Switzerland extreme heat was assessed on a daily basis, the UK trigger was based on the

forecasted temperatures for two days and one night, a range of HEWS were based on three

days of high temperature (i.e. Belgium, France, Hungary and Italy) and some triggers were

2

based on five days of high temperatures (i.e. Netherlands and Spain). The Heat Action Plan

for the city of Montreal is another response plan that includes a trigger based on one day of

extreme temperature (Price, Perron et al. 2013, Benmarhnia, Bailey et al. 2016).

Within Australia, the regions of New South Wales, Victoria, Queensland, South Australia,

Western Australia and the Australian Capital Territory have some form of heatwave response

plan. Note that there is no national heatwave plan for Australia. The ‘Heat health plan for

Victoria’ is the only current Australian example that makes a distinction between individual

daily cases of extreme heat and heatwaves (which is defined using a measure of three days of

extreme heat). It should be noted that another Australian heatwave response plan did

previously have a prescription for individual days of extreme heat. However, the current State

Hazard Plan for heatwaves in Western Australia (SEMC 2016) no longer contains the

individual hot day component that was part of the original Dept. of Health Operational

Directive (WA Dept of Health 2010). Within the operational directive, which was released in

January 2010, a predicted average daily temperature greater than 32°C for one or more days

led to standby status (WA Dept of Health 2010). This has now changed so that an alert only

occurs when a heatwave is predicted based on three days of average temperatures above 32°C

(SEMC 2016).

The disparity across heatwave response plans raises interesting questions with respect to the

risk management of extreme heat events in Australia and elsewhere. This being whether the

impact of an individual hot day is substantial and whether it is sufficient for response plans to

solely focus on multiple days of extreme heat. A previous study focusing on the region of

New South Wales in Australia found that emergency hospital admissions due to heat related

3

injuries, dehydration and other disorders of fluid, electrolyte and acid-base balance increased

significantly during both individual hot days and prolonged heat events (which coincided

with three days of hot weather) (Khalaj, Lloyd et al. 2010). Renal failure and cancer were the

morbidities for which an extended period of hot weather led to increased admissions, but

individual hot days did not (Khalaj, Lloyd et al. 2010). This suggests that there is a need to

develop more complex extreme heat risk management plans that have targeted responses for

certain morbidities based on the longevity of extreme heat events. This is consistent with

research that indicates that heat-related mortality tends to be related to cardiovascular and

diabetic morbidities, but non-fatal hospital admissions coincide with dehydration, heat stroke,

acute renal failure and respiratory disease (Toloo, FitzGerald et al. 2013).

4

2. DATA AND METHODOLOGY

2.1 Data

The daily ED attendance data used in this study has been sourced from the Emergency

Department Data Collection (EDDC) collected by the Western Australian Department of

Health. This data was collected for eleven hospitals in Perth and surrounding areas. This

paper utilises data for seven of these hospitals to create a balanced panel data set of ED

attendances that begins in January 2014 and ends in September 2016. A snapshot of the most

recent daily ED attendance data can be accessed from the Western Australian Department of

Health website. Table 1 contains summary statistics of the data used in this analysis, which

are broken down by hospital and calendar year. Note that there are notable differences in the

busyness of these hospitals and this is reflected in the maximum number of daily ED

attendances associated with each of the hospitals.

Daily temperature data has been sourced from the Australian Bureau of Meteorology (BOM)

website for four different weather stations. These are the Perth Metro, Armadale, Medina and

Swanbourne weather stations. The data sourced from the BOM website are the daily

maximum temperature and the daily minimum temperature for the 24 hour period leading up

to 9am1. The temperature data has been matched to the closest hospitals to allow for

variability in temperature across regions. The matching of these stations to the hospitals is

provided in Table 1. Table 1 also contains summary statistics of the daily maximum and

minimum temperatures. Some weather stations have missing observations during the period

1 As the BOM temperature data corresponds to the 24 hours leading up to 9am, the daily maximum temperature data has been adjusted so that it is associated with the day that the ED attendances occurred. A contemporaneous match between the temperature and ED attendance variable occurs with this adjustment as the observations of the daily maximum temperature have been moved backwards by one day. This means that the daily maximum temperature data reported by the BOM for January 1 becomes the observation for December 31. This adjustment has not been made for the daily minimum temperature data as the intention is that this variable captures the temperature of the night preceding an ED attendance.

5

of interest and to create a balanced panel dataset the values from the Perth Metro station were

used in place of these missing cases. Note that this occurred for only 1.6% of the cases in the

sample.

Forecasts of ED demand for a hypothetical Perth hospital are developed using projections of

temperatures for the Perth Airport weather station from three GCMs. An archive of GCM

simulations was created to coincide with the Fifth Assessment Report of the

Intergovernmental Panel on Climate Change (IPCC) and the phase 5 Coupled Model

Intercomparison Project (CMIP5) (Moss, Edmonds et al. 2010, Taylor, Stouffer et al. 2012,

Stocker, Qin et al. 2013). These have been utilised by the Commonwealth Scientific and

Industrial Research Organisation (CSIRO) and the BOM to develop climate change

projections for Australia (CSIRO and Bureau of Meteorology 2015). Projections of maximum

and minimum daily temperatures for the Perth Airport weather station using 8 CMIP5 models

are available online from the ‘Climate Change in Australia’ website. The three GCMs chosen

have been developed by leading World-class meteorology teams. These are the ACCESS1.0

model developed by the CSIRO and BOM (Bi, Dix et al. 2013), the HadGEM2-CC model

developed by the Met Office Hadley Centre for Climate Science and Services (Martin,

Culverwell et al. 2011) and the GFDL-ESM2M model developed by the Geophysical Fluid

Dynamics Laboratory at the National Oceanic and Atmospheric Administration (Dunne, John

et al. 2012). Note that the Representative Concentration Pathway (RCP) 4.5 scenario has been

used as it is a middle of the road climate policy scenario that is consistent with a stabilisation

of radiative forcing by 2100 (Thomson, Calvin et al. 2011).

6

2.2 Methodology

2.2.1 Threshold model of ED attendances

Rather than predetermining the temperature thresholds that coincide with a hot day and hot

night, this paper utilises fixed effects panel threshold regressions to identify the range of

temperatures that have a notable impact on ED attendances. This estimation procedure was

initially proposed in Hansen (1999) and has been implemented in Stata as the ‘xthreg’

command (refer to Wang (2015) for further details of this approach). A simple description of

the approach is that threshold regressions are used to split a variable into classes (or regimes)

that are distinguished from each other by different coefficient estimates that are associated

with a certain dependent variable. The procedure can be estimated for one, two or three

thresholds. The model for one threshold is specified as:

EDAtt¿={( MinT , MaxT ) ( β11 , β12 )'+Z¿α+ HW k δ +ui+e¿ , MaxT<γ(MinT ,MaxT ) ( β21 , β22)'+Z¿ α+HW k δ+u i+e¿ , MaxT ≥ γ

(1)

where Z¿ is a vector of explanatory variables and HW k is a heatwave variable. Note that

section 2.2.2 provides details on the heatwave variables used in the analysis.

While the threshold 𝛾 is specified using the daily maximum temperature (MaxT); in this

model specification, the threshold for MaxT is interacted with both the daily maximum and

minimum temperature variables to obtain estimates of the increase in ED attendances that are

associated with a hot day and a hot night. Accordingly, a one threshold model results in two

regimes and this is reflected in two β estimates for both MaxT and MinT (i.e. β11, β12, β21 and

β22). As the number of thresholds that coincides with the relationship between heat and ED

attendance is unclear, this study will estimate the threshold models using all three

possibilities. The number of thresholds to use in the models will be assessed using the

7

threshold effect F-test produced by the ‘xthreg’ command, as well as, the overall model fit

using the Akaike information criterion (AIC). Accordingly, the model for two thresholds and

three thresholds is specified in equations two and three.

EDAtt¿={ ( MinT , MaxT ) ( β11 , β12 )'+Z¿ α+ HW k δ +ui+e¿ , MaxT<γ1

(MinT ,MaxT ) ( β21 , β22)'+Z¿ α+HW k δ+u i+e¿ , γ1 ≤ MaxT <γ2

( MinT , MaxT ) ( β31 , β32 )'+Z¿ α+HW k δ+u i+e¿ , MaxT ≥ γ2

(2)

EDAtt¿={ ( MinT , MaxT ) ( β11 , β12 )'+Z¿α+ HW k δ +ui+e¿ , MaxT<γ 1

(MinT , MaxT ) ( β21 , β22)'+Z¿α+HW k δ+u i+e¿ , γ1 ≤ MaxT <γ2

(MinT , MaxT ) ( β31 , β32)'+Z¿ α+HW k δ+u i+e¿ , γ2 ≤ MaxT <γ3

( MinT , MaxT ) ( β41 , β42)'+Z¿α+HW k δ+ui+e¿ ,MaxT ≥ γ 3

(3)

Table 2 lists the explanatory variables included in the threshold models and specifies whether

they coincide with a lag, lead and/or contemporaneous variable. The explanatory variables

are grouped into seven types of variables and these include days of the week, public holidays,

months of the year, the calendar year, the hospital’s busyness, temperature and the occurrence

of heatwaves. The formulation and coding of most of these variables are well-explained by

the variable type listed in Table 2. However, the hospital busyness and heatwave variables are

those that need further explanation.

For each day in the data, the percent of ED attendances that each hospital accounts for is

calculated with respect to the total number of attendances across all seven hospitals. Three

dummy variables are created using this indicator so as to measure the distribution of ED

demand during the period in question based on unobserved characteristics known to be

associated with the number of ED attendances, such as ambulance diversions, health needs

and the size of population catchment (He, Hou et al. 2011). Endogeneity is not a concern as

8

this classification should not be notably influenced by weather as the regions are all from one

greater city area and these variables are calculated using the total number of attendances on

each given day. The primary reason for including these variables is to capture the differences

in demand between the hospitals based on size, population catchment and diversions due to

capacity constraints. Note that a dummy variable of the size of the ED would be dropped

from the equation due to the fixed effects model specification. Hospitals are classified as

being ‘very busy’ on that day when the percent of ED attendances is greater than 20%. This

occurs once in the data and corresponds to Joondalup Health Campus. Hospitals are classified

as ‘busy’ when the percent of ED attendances is greater than 15% and less than 20%. This

captures Joondalup Health Campus for the majority of the time. Hospitals are classified as

‘moderately busy’ when the percent of ED attendances is greater than 10% and less than

15%. Note that these variables do not conflict with the fixed effects estimation as only one

hospital remains in one category all of the time. King Edward Memorial Hospital remains in

the lowest category of busyness for the entire sample period.

2.2.2 Specifying the incidence of a heatwave

Definitions of a heatwave differ widely and there is a body of literature that focuses upon

measuring extreme heat using region specific data, for examples, refer to Barnett, Mercer et

al. (2012) and Leary, Young et al. (2015). The measures used as part of existing heatwave

response plans within Australia include the daily average temperature (Victoria), the three

daily maximum temperature (NSW and QLD) and the three daily average temperature (WA

and SA) (Scalley, Spicer et al. 2015). The BOM defines a heatwave as “three days or more of

high maximum and minimum temperatures that is unusual for that location” (BOM 2016). To

accompany this definition, the BOM has developed a metric to measure the Excess Heat

Factor (EHF), which is defined in Nairn, Fawcett et al. (2013) and further described in Nairn

9

and Fawcett (2015). This measure uses the conditional sum of two indices that account for

the significance of the event (EHI_S) and the level of acclimatisation to warmer temperatures

(EHI_A).

Motivated by the invention of this metric, Scalley, Spicer et al. (2015) focused on ED

attendances and heat-related inpatient admissions to compare the predictive performance of

the EHF in comparison to alternate heatwave measures using data from Perth. They

concluded that the EHF is superior to the metrics currently used by most of the Australian

states in their heatwave response plans. Based on this study, this paper will also assess

whether the EHF is associated with ED attendances, but will do so in comparison to a model

that solely focuses on individual daily events of extreme heat. The importance of the

acclimatisation component of the EHF metric (i.e. EHI_A) is also investigated so as to

capture the impact of three daily average temperatures that are notably different to the thirty

day average. Table 3 provides the computation of these measures and an accompanying

description.

Amongst the heatwave measures used in this paper are the three heatwave measures that were

outlined in Scalley, Spicer et al. (2015) that require cut-off values to determine whether a

heatwave occurs. Table 4 contains a breakdown of the incidence of heatwaves using these

measures and selected cut-offs for each year. There is great variation in the number of days

that correspond to a heatwave event depending upon the year, the weather station, the

heatwave measure and the percentile cut-off used. For both the three daily maximum

temperature (3DMT) and three daily average temperature (3DAT) measures the cut-off

values are set using the 90th and 95th percentile daily maximum/average temperatures

10

recorded at the Perth Metro weather station. These percentiles are calculated for the period

between the beginning of January 1994 and the beginning of November 2016. The EHF cut-

offs used are those associated with severe heatwaves that appear in PwC (2011) and Nairn,

Fawcett et al. (2013). As shown in Table 4, these cut-offs are titled PwC and BOM to match

their sources. The 3DAT measure identifies heatwaves more often than the other measures.

Accordingly, the cut-off used in the WA State Hazard Plan for heatwaves (32°C) has been

added to capture the incidence of heatwaves that are consistent with triggering a status of

‘standby’. This is listed as SHP in Table 4 and coincides with only four heatwave events that

impacted the area around the Armadale weather station.

The crucial distinguishing feature of the EHF measure from the 3DAT measure is the

inclusion of the acclimatisation excess heat index (EHI_A) component and the use of

thresholds to classify events into severe and extreme categories. To capture the impact of a

lack of acclimatisation, the analysis will use the EHI_A as a separate heatwave measure to

capture the impact of a period of warmer weather that is notable in comparison to the

previous 30 days. This will be implemented as two variables that capture the magnitude of

the three daily average temperature with respect to the thirty daily average temperature when

it is positive or negative. While this will capture a range of prolonged heat events (both mild

and extreme), it is still referred to as a heatwave measure as it will associate extremes with

higher demand estimates. Figure 1A in the appendix is a histogram of the percent of days

with certain EHI_A levels across the entire sample. Approximately 53.6% of the days had a

negative EHI_A with a three daily average temperature lower than the thirty daily average

temperature. Approximately 18.5% of the days had a positive EHI_A with a three daily

average temperature greater than the thirty daily average temperature by 2°C or more. More

than 62% of days had a three daily average temperature that was between 2°C and -2°C of the

11

thirty daily average temperature. Accordingly, extreme values of this measure are likely to

correspond with heatwaves and coldwaves.

2.2.3 Using GCMs to develop forecasts of ED attendances

As noted in section 2.1, projected maximum and minimum temperatures for the period

between 2017 and 2041 will be used to develop forecasts of ED attendances for a

hypothetical hospital in Perth using the threshold model results. These forecasts are

formulated using the Perth Airport weather station GCM projections. Equation four specifies

how the heat-related ED demand forecasts (i.e. HR_EDAtt) are derived using the projected

temperature measures (i.e. MinT , MaxT and HW ) that are set using the daily temperature

variables from three GCMs, g.

HREDAtt¿={ ( MinT , MaxT ) ( β̂11 , β̂12 )'+HW k δ̂ , MaxT< γ̂1

(MinT , MaxT ) ( β̂21 , β̂22)'+HW k δ̂ , γ̂1 ≤ MaxT < γ̂2

(MinT , MaxT ) ( β̂31 , β̂32)'+HW k δ̂ , γ̂2 ≤ MaxT < γ̂3

( MinT , MaxT ) ( β̂41 , β̂42)'+ HW k δ̂ ,MaxT ≥ γ̂ 3

(4)

12

3. RESULTS

3.1 Estimated threshold models of emergency department attendances

In this first section of section three, the threshold regression estimations are the focus of the

discussion and Table 5 presents the model performance of the estimated threshold models.

These include models that do and do not include heatwave variables. These heatwave

variables include dummy variables for the incidence of a heatwave (DV) and dummy

variables for day one to day three (or more) of a heatwave (Days 1-3). While the threshold

models that include heatwave variables tend to have improved model performance; the

parameters associated with the heatwave variables tend to be negative and none of these

regressions perform notably better when compared to the three threshold (3T) model with no

heatwave variable. As all of the models include the individual daily temperature variables,

the heatwave variables capture the additional impact that prolonged periods of hot weather

has on ED demand. The DV specification of the 3DAT measure with a 95 th percentile cut-off

and the EHI_A model are those that perform the best based on the AIC measure. Note that

the three models that perform best within three categories of heatwave specifications (i.e.

without a heatwave, with one of the first three heatwave measures and with the EHI_A

measure) are highlighted in Table 5 using black shading. These are the threshold model

estimation results that will be focused upon in the paper and are presented in Table 6.

Before discussing the estimation results in Table 6, the fixed effects three threshold model

needs to be confirmed as the preferred model specification. Both the Hausman test and the

Breusch-Pagan Lagrange multiplier test indicated that the fixed effects model is appropriate.

Sensitivity testing of the model specification indicated that the unobserved differences in the

ED attendances are correlated with the ED busyness variables. Based on the AIC statistics

and the threshold effect F-test produced by the ‘xthreg’ command (shown in Table 5), the

13

three threshold model is the preferred specification. The first threshold (γ1) is a daily

maximum temperature of 18.7°C, with the second (γ2) and third (γ3) thresholds being daily

maximum temperatures of 28.1°C and 34.9°C, respectively.

As previously noted, Table 6 contains the threshold model estimation results for three

selected regressions. Within all of the categories of variables there are significant

coefficients. For days of the week, it is shown that Sunday and Monday tend to coincide with

higher ED demand. Note that the estimates shown in Table 6 should be interpreted with

regard to a Wednesday in May 2016. This is the case as selected dummy variables have been

dropped from the regression (i.e. the Wednesday, May and 2016 variables) and absorbed by

the intercept to prevent the dummy variable trap. In the case of the public holiday variables,

the greatest increase in ED attendances tended to occur on the day after Australia Day and on

New Year’s Day. It is highly likely that these are ED attendances related to accidents and

harm that coincide with excessive alcohol consumption. This is also likely to be the case for

the Christmas period as while Christmas Eve and Christmas Day are associated with negative

parameter estimates and more toned-down celebrations, Boxing Day and the day after that

have statistically significant increases in ED attendances. The busyness variables are also

significant using a 1% confidence interval across all of the threshold models shown in Table

6.

The threshold models have been specified to interact the endogenously determined thresholds

with four temperature variables to capture the impact of high temperatures during the day of

the ED attendance (i.e. MaxT), the night preceding the ED attendance (i.e. MinT) and the day

and night before that (i.e. the one day lag of MaxT and MinT). Figure 1 contains the impact

14

of daily temperature on ED attendances using values of MaxT and MinT to capture the

immediate impact (i.e. the contemporaneous 24 hour period), the lagged effect (i.e. the

previous 24 hour period) and the full 48 hour impact of hot weather that coincide with the

parameter estimates of all four temperature variables. This allows for a comparison of the

estimation results shown in Table 6 using temperatures between 0°C and 45°C. Note that

upon producing Figure 1, the MinT level is assumed to be half the MaxT used so that only

one temperature axis needs to be shown. For example, a 24 hour period that consists of a

maximum temperature of 34.8°C and a minimum temperature of 17.4°C is estimated to

coincide with an additional 30 ED attendances per hospital (on average) using the three

threshold (3T) model parameter estimates. This is composed of 25 ED attendances as an

immediate impact and 5 ED attendances as a lagged impact. In relation to the median max

temperature of 23.8°C (and a minimum temperature of 11.9°C, which is half the maximum

temperature and thus consistent with the formulation used in the previous example), these

temperatures coincide with an additional 7 ED attendances per hospital (on average), which is

an increase of approximately 32% with respect to the median daily ED attendance.

The fluctuations of the immediate and lagged impact estimates across temperatures indicate

that the four temperature variables should be interpreted together as this provides a

consistently positive relationship between temperature and ED attendances over a 48 hour

period (as shown in Figure 1a). Nevertheless, the threshold model estimation has identified a

range of temperatures (i.e. between 28.1°C and 34.9°C) where the immediate impact of heat

on ED attendances is pronounced. Above a temperature of 34.9°, people tend to adapt their

behaviour as the temperature becomes extreme and certain activities prove to be difficult or

may get cancelled. However, between 28.1°C and 34.9°C certain people do not adapt and

continue to expose themselves to the heat. While the 48 hour impact is greater than that for

15

higher temperatures, there is a burden on ED departments from people who are caught out in

less extreme temperatures (as captured by the contemporaneous temperature variables in

Figure 1b).

There are other studies that have estimated temperature thresholds for Perth. Loughnan,

Tapper et al. (2012) estimated a maximum daily temperature threshold for Perth of 43°C and

this coincided with increased ambulance call-outs of 14% in comparison to the median. A

maximum daily temperature threshold of 44°C was associated with a 30% increase in

mortality (Loughnan, Tapper et al. 2012). Williams, Nitschke et al. (2012) used data on

mortality to estimate heat thresholds of between 34°C and 36°C for the daily maximum

temperature and 20°C for the daily minimum temperature. These last set of thresholds are

similar to those estimated in this study. And while differences between these studies are

likely to be explained by the method of analysis, the dependant variable and the use of

control variables; the results of all of these studies do indicate that an individual hot day

impacts health service demand. Accordingly, there is evidence that extreme heat risk

management should consider individual daily and prolonged heat events. However, the

impact of heatwaves should be accounted for before confirming this conclusion.

While the model estimates for all of the three heatwave measures are very similar and closely

conform with those shown in Figure 1 for 3T and 3DAT, the model performance notably

improves when the EHI_A variables are included in the model. This implies that a lack of

acclimatisation to hot weather is important and that further focus on the EHI_A variable is

warranted. As noted, the 3DAT model (with the DV specification) does not notably change

the estimates of the four temperature variables in comparison to the 3T model. The EHI_A

16

model leads to lower ED attendances associated with the 48 hour period of interest (as

captured by the estimates associated with the MaxT and MinT variables). The immediate

impact of hot weather remains high irrespective of whether the focus is on the results of

model 3T, 3DAT or EHI_A (as shown in Figure 1b). For all three models, a maximum

temperature of 34.8°C and minimum temperature of 17.4°C is associated with between 23.70

and 24.84 heat-related ED attendances per hospital (on average) during a 24 hour period.

The estimates from the EHI_A model that are associated with the MaxT and MinT variables

need to be reviewed with the estimates associated with the EHI_A variable kept in mind.

These are shown in Figure 2. These variables capture the additional impact of three days of

warmer or cooler weather with respect to the thirty day average. A three daily average

temperature that is 1°C above the 30 day average is associated with an increase of 1.23 ED

attendances per hospital (on average) above that associated with the maximum and minimum

temperature variables. For a three daily average temperature that is 2°C above the 30 day

average, there is an increase of 2.46 ED attendances per hospital (on average). Note that

based on the histogram of the EHI_A measure that is shown in Figure 1A in the appendix,

temperatures that correspond to these levels (i.e. EHI_A of between 1°C and 2°C) occur 11%

of the time. The ED demand estimates increase to 6.15 for a three daily average temperature

that is 5°C above the 30 day average and 7.38 for a three daily average temperature that is

6°C above the 30 day average. Based on the histogram of the EHI_A measure, this

corresponds to approximately 1.76% of the time (i.e. EHI_A of between 5°C and 6°C).

The threshold model results show that a period of hot weather that does not coincide with a

period of acclimatisation leads to notable increases in ED attendances, but so do individual

17

hot days and nights. The heatwave variables capture the additional impact of prolonged

periods of weather and the two sets of variables need to be interpreted together. To help

emphasise this point, the next section focuses on forecasts constructed on this basis as they

are separated into two types of weather event.

3.2 Forecasts of ED demand

Having discussed the threshold model estimates, this section focuses on the forecasts

developed using projections of temperature sourced from three major GCMs. Figure 3

provides the forecasts of heat-related ED attendances for the period between 2017 and 2041.

These forecasts are separated into two components to highlight the impact that individual hot

days and an extended period of heat have on ED demand for a hypothetical hospital within

the city of Perth. For the majority of years between 2017 and 2041, the impact of individual

hot days that are extreme (i.e. greater than or equal to 34.9ºC) is greater than that of

heatwaves (as captured in positive values of the EHI_A measure above 3.67, which is the 95th

percentile value of the EHI_A measure in this sample). However, it should be noted that

these results need to be assessed together as there will be overlaps during some of these

events. For the five years between 2017 and 2021, the impact of extreme heat on ED

attendances at one Perth hospital is forecast to be between 803 and 954 and this increases by

between 601 and 641 attendances when extended periods of hot weather are taken into

account.

18

4. CONCLUSION

Within this paper, fixed effect threshold regressions have been applied to determine the

threshold temperatures that coincide with greater impacts of heat on emergency department

attendances. Notable ED attendances are associated with individual hot days/nights

regardless of adjustments made for the impact of heatwaves, the day of the week and the

month of the year. The importance of accounting for multiple types of extreme heat events is

also captured in the forecasts of ED demand that have been built using projected temperatures

sourced from GCMs. The forecasts of ED demand related to extreme heat events confirm that

the burden of risk associated with increased ED attendances for Perth between 2017 and 2041

does coincide with both individual and prolonged heat events. In most years, the forecasted

number of ED attendances associated with individual heat events is greater than those related

with prolonged heat events.

These results indicate that extreme heat risk management plans that do not account for the

impact of individual days of extreme heat should be reconsidered. As there are cases of

heatwave response plans that include daily triggers, such as the plans in place for Montreal in

Canada, Victoria in Australia and the national plan of Switzerland, it is hoped that these

results inspire the inclusion of contingencies for individual days of extreme heat into new and

existing response plans. Similar prescriptions have been made before, for example, Nicholls,

Skinner et al. (2008) proposed a simple heat alert system for Melbourne, in the Australian

state of Victoria, that was based on daily average temperatures above 30°C (or an alternative

version that was based on predicted minimum temperatures of above 24°C). These

prescriptions were based on findings that temperatures above these thresholds coincided with

19

an increased likelihood of excess mortality amongst the Melbournian population over 64

years of age (Nicholls, Skinner et al. 2008).

There also needs to be more research on the evaluation of existing plans. For example, Toloo,

FitzGerald et al. (2013) found fifteen articles on this issue during a systematic review of the

literature conducted for the period up until January 2013. While they noted that research on

the evaluation of the effectiveness of heatwave response plans was limited, Toloo, FitzGerald

et al. (2013) did find that there were some evaluations that assessed whether the

implementation of heatwave response plans coincided with decreased mortality and reduced

ambulance use. However, at that time there was no research found that assessed the impact of

these response plans on ED attendances (Toloo, FitzGerald et al. 2013). Indeed, most of the

research on heatwaves has focused on mortality and the focus on ED attendances is relatively

recent. Whether heat-related ED demand can effectively be reduced using extreme heat

response plans and whether this can occur across different types of heat events will be an

important contribution of future research. The level of heat-related ED attendances and how

much impact risk management and response plans can have will depend on how people adapt

to the heat.

20

ReferencesBarnett, K., S. W. Mercer, M. Norbury, G. Watt, S. Wyke and B. Guthrie (2012). "Epidemiology of

multimorbidity and implications for health care, research, and medical education: a cross-sectional study." The Lancet 380(9836): 37-43.

Benmarhnia, T., Z. Bailey, D. Kaiser, N. Auger, N. King and J. S. Kaufman (2016). "A Difference-in-Differences Approach to Assess the Effect of a Heat Action Plan on Heat-Related Mortality, and Differences in Effectiveness According to Sex, Age, and Socioeconomic Status (Montreal, Quebec)." Environmental Health Perspectives 124(11): 1694-1699.

Bi, D., M. Dix, S. J. Marsland, S. O’Farrell, H. Rashid, P. Uotila, A. Hirst, E. Kowalczyk, M. Golebiewski and A. Sullivan (2013). "The ACCESS coupled model: description, control climate and evaluation." Aust. Meteorol. Oceanogr. J 63(1): 41-64.

BOM. (2016). "About the Heatwave Service for Australia."CSIRO and Bureau of Meteorology (2015). "Climate Change in Australia Information for Australia’s

Natural Resource Management Regions: Technical Report." CSIRO and Bureau of Meteorology, Australia.

Dunne, J. P., J. G. John, A. J. Adcroft, S. M. Griffies, R. W. Hallberg, E. Shevliakova, R. J. Stouffer, W. Cooke, K. A. Dunne, M. J. Harrison, J. P. Krasting, S. L. Malyshev, P. C. D. Milly, P. J. Phillipps, L. T. Sentman, B. L. Samuels, M. J. Spelman, M. Winton, A. T. Wittenberg and N. Zadeh (2012). "GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics." Journal of Climate 25(19): 6646-6665.

Hansen, B. E. (1999). "Threshold effects in non-dynamic panels: Estimation, testing, and inference." Journal of econometrics 93(2): 345-368.

He, J., X.-y. Hou, S. Toloo, J. R. Patrick and G. Fitz Gerald (2011). "Demand for hospital emergency departments: a conceptual understanding." World Journal of Emergency Medicine 2(4): 253-261.

Hess, J. J. and K. L. Ebi (2016). "Iterative management of heat early warning systems in a changing climate." Annals of the New York Academy of Sciences 1382(1): 21-30.

Hess, J. J., K. L. Heilpern, T. E. Davis and H. Frumkin (2009). "Climate change and emergency medicine: impacts and opportunities." Academic Emergency Medicine 16(8): 782-794.

Khalaj, B., G. Lloyd, V. Sheppeard and K. Dear (2010). "The health impacts of heat waves in five regions of New South Wales, Australia: a case-only analysis." International archives of occupational and environmental health 83(7): 833-842.

Leary, E., L. J. Young, C. DuClos and M. M. Jordan (2015). "Identifying Heat Waves in Florida: Considerations of Missing Weather Data." PloS one 10(11): e0143471.

Loughnan, M., N. Tapper, K. Lynch, J. McInnes and T. Phan (2012). A spatial vulnerability analysis of urban populations during extreme heat events in Australian capital cities, National Climate Change Adaptation Research Facility.

Lowe, D., K. L. Ebi and B. Forsberg (2011). "Heatwave early warning systems and adaptation advice to reduce human health consequences of heatwaves." International journal of environmental research and public health 8(12): 4623-4648.

Martin, G. M., Bellouin, N., Collins, W.J., , I. D. Culverwell, Halloran, P R., Hardiman, S.C., Hinton, T.J., Jones, , M. C.D., R.E., McLaren, A.J., O’Connor, F.M., Roberts, M.J., , J. M. Rodriguez, Woodward, S., Best, M.J., Brooks, M.E., Brown, A.R., , N. Butchart, Dearden, C., Derbyshire, S.H., Dharssi, I., Doutriaux-, M. Boucher, Edwards, J. M., Falloon, P.D., Gedney, N., Gray, L.J., , H. T. Hewitt, Hobson, M., Huddleston, M.R., Hughes, J., Ineson, S., , W. J. Ingram, James, P.M., Johns, T.C., Johnson, C.E., Jones, A., Jones, , J. C.P., M.M., Keen, A.B., Liddicoat, S., Lock, A.P., Maidens, A.V., , J. C. Manners, Milton, S.F., Rae, J. G.L., Ridley, J.K., Sellar, A., Senior, and T. C.A., I.J., Verhoef, A., Vidale, P.L. and Wiltshire, A. (2011). "The HadGEM2 family of met office unified model climate configurations." Geoscientific Model Development 4(3): 723-757.

Moss, R. H., J. A. Edmonds, K. A. Hibbard, M. R. Manning, S. K. Rose, D. P. Van Vuuren, T. R. Carter, S. Emori, M. Kainuma and T. Kram (2010). "The next generation of scenarios for climate change research and assessment." Nature 463(7282): 747-756.

21

Nairn, J. and R. Fawcett (2015). "The Excess Heat Factor: A Metric for Heatwave Intensity and Its Use in Classifying Heatwave Severity." International Journal of Environmental Research and Public Health 12(1): 227.

Nairn, J. R., R. G. Fawcett and K. A. Day (2013). Defining heatwaves: heatwave defined as a heat-impact event servicing all community and business sectors in Australia, Centre for Australian Weather and Climate Research.

Navi, M., D. Pisaniello, A. Hansen and M. Nitschke (2017). "Potential Health Outcome and Vulnerability Indicators of Climate Change for Australia: Evidence for Policy Development." Australian Journal of Public Administration 76(2): 160-175.

Nicholls, N., C. Skinner, M. Loughnan and N. Tapper (2008). "A simple heat alert system for Melbourne, Australia." International Journal of Biometeorology 52(5): 375-384.

Price, K., S. Perron and N. King (2013). "Implementation of the Montreal heat response plan during the 2010 heat wave." Can J Public Health 104(2): e96-e100.

PwC (2011). "Protecting human health and safety during severe and extreme heat events: A national framework." Commonwealth Government Report, Australia.

Scalley, B. D., T. Spicer, L. Jian, J. Xiao, J. Nairn, A. Robertson and T. Weeramanthri (2015). "Responding to heatwave intensity: Excess Heat Factor is a superior predictor of health service utilisation and a trigger for heatwave plans." Australian and New Zealand journal of public health 39(6): 582-587.

SEMC (2016). "State Hazard Plan for Heatwave (WESTPLAN - Heatwave)."Stocker, T., D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, B. Bex and B.

Midgley (2013). "IPCC, 2013: climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change."

Taylor, K. E., R. J. Stouffer and G. A. Meehl (2012). "An overview of CMIP5 and the experiment design." Bulletin of the American Meteorological Society 93(4): 485.

Thomson, A. M., K. V. Calvin, S. J. Smith, G. P. Kyle, A. Volke, P. Patel, S. Delgado-Arias, B. Bond-Lamberty, M. A. Wise and L. E. Clarke (2011). "RCP4.5: a pathway for stabilization of radiative forcing by 2100." Climatic change 109(1-2): 77.

Toloo, G., G. FitzGerald, P. Aitken, K. Verrall and S. Tong (2013). "Evaluating the effectiveness of heat warning systems: systematic review of epidemiological evidence." International journal of public health 58(5): 667-681.

Vaneckova, P., P. J. Beggs and C. R. Jacobson (2010). "Spatial analysis of heat-related mortality among the elderly between 1993 and 2004 in Sydney, Australia." Social Science & Medicine 70(2): 293-304.

WA Dept of Health (2010). "Operational Directive - Heatwave Policy."Wang, Q. (2015). "Fixed-effect panel threshold model using Stata." Stata Journal 15(1): 121-134.Williams, S., M. Nitschke, P. Weinstein, D. L. Pisaniello, K. A. Parton and P. Bi (2012). "The impact

of summer temperatures and heatwaves on mortality and morbidity in Perth, Australia 1994–2008." Environment international 40: 33-38.

22

Table 1 – Hospital and weather data summary statistics

Hospital/Weather Station Statistic

VariableED Attendances

(no.)Maximum Temperature

(°C)Minimum Temperature

(°C)2014 2015 2016 2014 2015 2016 2014 2015 2016

Armadale- Kelmscott District/Armadale

Max 224 221 209 44.0 43.8 42.8 29.7 27.5 28.4Median 169 163 162 24.8 25.5 21.6 14.3 14.0 11.8Min 124 118 119 15.5 14.4 14.2 3.0 2.5 1.6

Joondalup Health Campus/Perth Metro

Max 339 326 320 43.3 44.4 42.5 29.7 23.2 26.0Median 262 268 269 24.0 24.9 21.2 13.8 13.9 12.0Min 201 226 224 15.6 15.0 14.2 1.9 0.8 0.6

King Edward Memorial/Perth Metro

Max 63 60 58 43.3 44.4 42.5 29.7 23.2 26.0Median 39 36 33 24.0 24.9 21.2 13.8 13.9 12.0Min 13 15 14 15.6 15.0 14.2 1.9 0.8 0.6

Princess Margaret Hosp. for Children/Perth Metro

Max 280 241 244 43.3 44.4 42.5 29.7 23.2 26.0Median 195 178 172 24.0 24.9 21.2 13.8 13.9 12.0Min 132 119 126 15.6 15.0 14.2 1.9 0.8 0.6

Rockingham General/Medina

Max 187 186 182 44.4 44.1 44.0 29.7 24.9 24.5Median 144 146 149 23.9 24.8 21.5 13.2 13.0 11.8Min 113 108 104 14.6 15.1 14.3 1.0 0.8 1.0

Royal Perth Hospital/Perth Metro

Max 270 307 260 43.3 44.4 42.5 29.7 23.2 26.0Median 228 200 192 24.0 24.9 21.2 13.8 13.9 12.0Min 183 158 152 15.6 15.0 14.2 1.9 0.8 0.6

Sir Charles Gairdner/Swanbourne

Max 237 274 241 44.3 43.2 42.8 26.8 25.8 26.8Median 185 193 189 23.7 24.1 21.0 14.5 14.5 12.9Min 140 157 148 15.8 15.1 13.8 4.8 3.7 3.4

All Hospitals/All Weather Stations

Max 339 326 320 44.4 44.4 44.0 29.7 27.5 28.4Median 183 178 173 24.0 24.7 21.4 13.9 13.9 12.0Min 13 15 14 14.6 14.4 13.8 1.0 0.8 0.6N 2555 2555 1918 2555 2555 1918 2555 2555 1918

23

Table 2 – Specification of the explanatory variables in the threshold models Type of Variable Variable description Abbreviati

on Variable Type Lag variable

Contemporaneous variable

Lead variable

Days of the week

Monday Mon. DV N Y NTuesday Tues. DV N Y NThursday Thurs. DV N Y NFriday Fri. DV N Y NSaturday Sat. DV N Y NSunday Sun. DV N Y N

Public holidays

New Year’s Day NYD DV Y Y YAustralia Day AUSD DV Y Y YLabour Day LBD DV Y Y YGood Friday GFriD DV Y Y YEaster Monday EMonD DV Y Y YANZAC Day ANZD DV Y Y YWestern Australia Day WAD DV Y Y YQueen’s Birthday QBD DV Y Y YChristmas Day CHD DV Y Y NBoxing Day BXD DV N Y Y

Months

January Jan. DV N Y NFebruary Feb. DV N Y NMarch Mar. DV N Y NApril Apr. DV N Y NJune Jun. DV N Y NJuly Jul. DV N Y NAugust Aug. DV N Y NSeptember Sep. DV N Y NOctober Oct. DV N Y NNovember Nov. DV N Y NDecember Dec. DV N Y N

Years Year 2014 Yr2014 DV N Y NYear 2015 Yr2015 DV N Y N

Hospital busyness

Moderately busy M. Bus. DV N Y NBusy Bus. DV N Y NVery busy V. Bus. DV N Y N

Temperature

Maximum temperature MaxT °C Y Y NMinimum temperature MinT °C Y Y NExcess Heat Index - Acclimatisation

EHI_A °C (wrt 30 day average) Y Y N

Heatwave

Three daily maximum temperature

3DMT DV Y Y N

Three daily average temperature 3DAT DV Y Y NExcess Heat Factor EHF DV/Level Y Y N

24

Table 3 – Heatwave measuresHeatwave measure/ temperature variable

Description Formulation

Three daily maximum temperature (3DMT)

This is compared to a climate reference value, 3 DMT ¿ ,k . 3 DMT ¿=min ( MaxT ¿ , MaxT ¿−1 , MaxT ¿−2 )

Daily average temperature (DAT)

This calculation is used in the following measures (listed below). DAT ¿=( MaxT ¿+MinT ¿)/2

Three daily average temperature (3DAT)

This is compared to a climate reference value, MaxT ¿ , k. 3 DAT ¿=( DAT ¿+ DAT ¿−1+DAT ¿−2 )/3

Significant excess heat index (EHI_S)

This measure captures the excess heat that coincides with a high daytime temperature that is not dissipated overnight due to an unusually high overnight temperature. This includes the climate reference value in the index’s formulation, MaxT ¿ , k.

EHI S¿=3 DAT ¿−MaxT ¿ ,k

Acclimatisation excess heat index (EHI_A)

This measure captures the heat stress that is related to a period of warmer weather that is notable in comparison to the previous 30 days. It captures a short-term (acclimatisation) temperature anomaly.

EHI A¿=3 DAT ¿−( DAT ¿−1+…+DAT ¿−30) /30

Excess Heat Factor (EHF) This measure combines two measures to simultaneously capture the effect of Excess Heat (EHI_S) and Heat Stress (EHI_A). Heatwave conditions exist when the EHF is positive.

EHF¿=EHI S¿× max(1 , EHI A¿)

Note: this table is based on Table 1 from (Scalley, Spicer et al. (2015)).

25

Table 4 – Incidence of heatwaves using three measures and different cut-off valuesHospital 3DMT 3DAT EHF

P90 P95 P90 P95 SHP PwC BOM2014

Armadale/Kelmscott District 17 7 56 22 1 0 0Joondalup Health Campus 12 5 39 13 0 0 0King Edward Memorial 12 5 39 13 0 0 0Princess Margaret Hosp. for Children 12 5 39 13 0 0 0Rockingham General 13 4 34 12 0 0 0Royal Perth Hospital 12 5 39 13 0 0 0Sir Charles Gairdner 13 5 38 15 0 0 0

2015Armadale/Kelmscott District 17 3 59 24 0 10 2Joondalup Health Campus 11 1 42 12 0 5 0King Edward Memorial 11 1 42 12 0 5 0Princess Margaret Hosp. for Children 11 1 42 12 0 5 0Rockingham General 7 1 29 9 0 4 0Royal Perth Hospital 11 1 42 12 0 5 0Sir Charles Gairdner 3 0 31 8 0 1 0

2016Armadale/Kelmscott District 14 3 44 22 3 9 5Joondalup Health Campus 9 2 36 11 0 8 2King Edward Memorial 9 2 36 11 0 8 2Princess Margaret Hosp. for Children 9 2 36 11 0 8 2Rockingham General 13 3 37 13 0 5 0Royal Perth Hospital 9 2 36 11 0 8 2Sir Charles Gairdner 3 1 28 9 0 6 1

Cut-off values3DMT 3DAT EHF

P90 P95 P90 P95 SHP PwC BOM33.7°C 36.1°C 25.8°C 27.8°C 32.0°C 15.4 17.9

26

27

Table 5 – Model performance with and without heatwavesHeatwave scenario

and cut-off specification

Variable Type

Number of temperature thresholds

AIC First threshold

Second threshold

Three threshold

Threshold effect F-test

No HW

None (FE) 57180.88 N/A

1 57106.35 18.7 71.16***

2 57069.67 18.7 28.1 38.42**3 57058.19 18.7 28.1 34.9 16.72

HW_p90/ PwC

3DAT DV

3

56938.00

18.7 28.1 34.9 N/A

Days 1-3 57057.39

3DMT DV 57056.78Days 1-3 57057.08

EHFDV 56943.48Days 1-3 57060.41LVL 56972.54

HW_p95/ BOM

3DAT DV 56935.40Days 1-3 57056.47

3DMT DV 57056.07Days 1-3 57052.15

EHFDV 56943.33Days 1-3 57061.79LVL 56972.54

HW_SHP 3DAT DV 56941.96Days 1-3 57063.96

EHI_A +/- 55264.42Note: selected threshold models are highlighted with black shading and white font to identify those models that performed best based on the AIC statistics shown. The Threshold effect F-test is run sequentially for each threshold. The null hypothesis is whether a lesser number of thresholds is appropriate. For the three threshold model, the null is whether a model with less than three thresholds is appropriate versus the alternative hypothesis of a three threshold model being appropriate. Statistical Significance is indicated as: *** for p<0.01, ** for p<0.05, * for p<0.1.

Table 6 – Threshold regression model results with and without heatwave variablesVariables 3T model 3DAT model EHI_A model

Coeff. s.e. Coeff. s.e. Coeff. s.e.Intercept 119.912*** 2.44 118.596*** 2.48 131.689*** 3.39

Days of the week

Monday 16.603*** 0.65 16.654*** 0.65 16.647*** 0.66Tuesday 2.852*** 0.65 2.880*** 0.65 2.931*** 0.66Thursday -1.111* 0.64 -1.119* 0.64 -1.056 0.64Friday 2.495*** 0.64 2.352*** 0.64 2.325*** 0.65Saturday 4.409*** 0.64 4.415*** 0.64 4.368*** 0.65Sunday 14.046*** 0.65 14.080*** 0.65 14.139*** 0.66

Public holidays

NYD (Day before) -3.523 3.89 -4.348 3.90 -4.507 3.89NYD 30.064*** 3.84 29.597*** 3.84 25.462*** 3.86NYD (Day after) 14.383*** 3.16 16.327*** 3.87 12.815*** 3.89AUSD (Day before) 1.190 3.15 0.633 3.17 1.030 3.84AUSD 10.292*** 3.19 9.650*** 3.20 4.653 3.86AUSD (Day after) 30.387*** 3.17 30.271*** 3.19 36.191*** 3.90LBD (Day before) 4.571 3.19 4.286 3.19 4.650 3.18LBD 6.327** 3.18 5.669* 3.19 6.257** 3.17LBD (Day after) 13.420*** 3.18 12.700*** 3.18 13.620*** 3.17GFriD (Day before) 4.965 3.15 4.876 3.15 5.358* 3.14GFriD 2.303 3.15 2.418 3.15 3.321 3.14GFriD (Day after) -3.875 3.14 -3.812 3.14 -3.921 3.12EMonD (Day before) -6.433** 3.14 -6.377** 3.14 -5.904* 3.14EMonD 4.455 3.15 4.574 3.15 5.452* 3.14EMonD (Day after) 12.352*** 3.15 12.511*** 3.15 12.631*** 3.13ANZD (Day before) 2.246 2.81 2.413 2.81 1.248 2.81ANZD 0.476 2.75 0.619 2.75 -0.942 2.75ANZD (Day after) 5.496* 2.81 5.561** 2.81 4.700* 2.80WAD (Day before) -0.742 3.14 -0.769 3.14 -0.906 3.12WAD -0.204 3.17 -0.215 3.17 -0.018 3.15WAD (Day after) 9.493*** 3.17 9.469*** 3.17 10.240*** 3.16QBD (Day before) 3.847 3.18 3.673 3.18 4.316 3.16QBD 0.982 3.17 0.804 3.17 1.436 3.16QBD (Day after) 7.751** 3.19 7.673** 3.18 7.420** 3.17CHD (Day before) -9.073** 3.86 -9.354** 3.86 -11.555*** 3.87CHD -11.189*** 3.90 -11.161*** 3.90 -13.446*** 3.92BXD 10.806*** 3.23 11.270*** 3.23 10.347*** 3.23BXD (Day after) 14.484*** 3.23 14.138*** 3.23 13.978*** 3.23

Months

Jan -11.766*** 1.16 -12.307*** 1.17 -6.654*** 1.55Feb -6.759*** 1.15 -7.445*** 1.17 -2.024 1.57Mar -4.917*** 1.05 -5.538*** 1.07 -0.420 1.39Apr -5.354*** 0.93 -5.707*** 0.94 -2.564** 1.08Jun 6.460*** 0.83 6.548*** 0.83 4.430*** 0.89Jul 4.133*** 0.85 4.280*** 0.85 0.666 1.04Aug 12.213*** 0.82 12.297*** 0.82 8.543*** 1.01Sep 8.146*** 0.83 8.207*** 0.83 5.081*** 0.95Oct -0.048 0.94 -0.196 0.94 -1.684* 0.96Nov 1.389 1.00 1.128 1.00 1.388 1.02Dec -3.394 1.08 -3.788 1.08 -2.241** 1.14

Years 2014 0.514 0.48 0.409 0.48 1.384*** 0.512015 -1.055** 0.46 -1.145** 0.46 -0.617 0.47

BusynessModerately busy 23.641*** 0.67 23.638*** 0.67 23.606*** 0.68Busy 61.051*** 1.26 61.056*** 1.26 60.579*** 1.30Very busy 107.844*** 14.16 107.674*** 14.15 106.911*** 14.07

Max Temp (Day of attendance)

Below (or no) threshold -0.480** 0.24 -0.433* 0.24 -0.445* 0.24First threshold (18.7°C) 0.247** 0.12 0.269** 0.12 0.232* 0.13Second threshold (28.1°C) 0.432*** 0.12 0.381*** 0.12 0.576*** 0.13Third threshold (34.9°C) 0.272 0.18 0.093 0.19 0.246 0.21

Min Temp (Night preceding attendance)

Below (or no) threshold -0.436*** 0.15 -0.435*** 0.15 -0.689*** 0.15First threshold (18.7°C) 0.331*** 0.09 0.337*** 0.09 0.086 0.10Second threshold (28.1°C) 0.574*** 0.20 0.669*** 0.20 0.211 0.21

Third threshold (34.9°C) 0.108 0.32 0.365 0.34 -0.122 0.36

Max Temp (Day before attendance)

Below (or no) threshold 1.250*** 0.22 1.273*** 0.22 1.011*** 0.22First threshold (18.7°C) 0.545*** 0.08 0.571*** 0.08 0.328*** 0.10Second threshold (28.1°C) -0.088 0.13 -0.025 0.13 -0.351** 0.14

Third threshold (34.9°C) 0.095 0.22 0.218 0.23 -0.036 0.24

Min Temp (Night before last)

Below (or no) threshold 0.367*** 0.13 0.372*** 0.13 0.103 0.14First threshold (18.7°C) -0.009 0.09 0.017 0.09 -0.225** 0.10Second threshold (28.1°C) 0.458*** 0.17 0.473*** 0.17 0.129 0.18Third threshold (34.9°C) 0.696** 0.29 0.747** 0.29 0.357 0.32

Heatwave variables

3DAT (lag) -0.238 1.18

3DAT -3.438** 1.34

Positive EHI_A (lag) 1.230*** 0.29

Positive EHI_A -0.318 0.33

Negative EHI_A (lag) 0.571* 0.30

Negative EHI_A 0.670** 0.31

F-statistic 78.66*** 76.64*** 73.45***

AIC 57058.19 56935.40 55264.42

R-squared 0.6558 0.6554 0.6522

Within 0.4279 0.4294 0.4332

Between 0.7797 0.7793 0.7782

N 7014 7000 6804Note: Statistical Significance is indicated as: *** for p<0.01, ** for p<0.05, * for p<0.1.

30

Figure 1 – Model estimates of heat-related ED attendances associated with the maximum and minimum temperature variables

48 h

our

impa

ct (m

ax a

nd m

in)

Imm

edia

te im

pact

(max

and

min

)L

agge

d ef

fect

(max

and

min

)

Figure 2 – Model estimates of heat-related ED attendances associated with the EHI_A measure

Figure 3 – Forecasts of extreme heat-related ED attendances by GCM and type of heat event

32

Appendix

33

Figure 1A – Histogram of the percent of days with certain EHI_A levels (whole sample)

34