# Creating a Spatial Vulnerability Index for Environmental Health

Aiden Price  Michael Rigby  Paula Fiévez  Kerrie Mengersen

###### Abstract

Extreme natural hazards are increasing in frequency and intensity. These
natural changes in our environment, combined with man-made pollution, have
substantial economic, social and health impacts globally. The impact of the
environment on human health (environmental health) is becoming well understood
in international research literature. However, there are significant barriers
to understanding key characteristics of this impact, related to substantial
data volumes, data access rights and the time required to compile and compare
data over regions and time. This study aims to reduce these barriers in
Australia by creating an open data repository of national environmental health
data and presenting methodology for the production of health outcome weighted
population vulnerability indices related to extreme heat, extreme cold and air
pollution at various temporal and geographical resolutions.

Current state of the art methods for the calculation of vulnerability indices
include equal weight percentile ranking and the use of principal component
analysis (PCA). The weighted vulnerability index methodology proposed in this
study offers an advantage over others in the literature by considering health
outcomes in the calculation process. The resulting vulnerability percentiles
more clearly align population sensitivity and adaptive capacity with health
risks. The temporal and spatial resolutions of the indices enable national
monitoring on a scale never before seen across Australia. Additionally, we
show that a weekly temporal resolution can be used to identify spikes in
vulnerability due to changes in relative national environmental exposure.

††journal: Environmental Health Perspectives

[first]organization=Centre for Data Science, Faculty of
Science,addressline=Queensland University of Technology, city=Brisbane City,
Brisbane, postcode=4000, state=QLD, country=Australia
[second]organization=Australian Urban Research Infrastructure
Network,addressline=University of Melbourne, city=Parkville, Melbourne,
postcode=3052, state=VIC, country=Australia
[third]organization=FrontierSI,city=Docklands, Melbourne, postcode=3008,
state=VIC, country=Australia

00footnotetext: Address correspondence to Aiden Price, Centre for Data
Science, Faculty of Science, Queensland University of Technology, Brisbane
City, Brisbane, 4000, Queensland, Australia. Email:
<EMAIL_ADDRESS>The authors declare they have no
conflicts of interest to disclose.00footnotetext: No individual subject data
have been acquired for this study. The authors obtained human ethics exemption
for this study (ref. HE-Ex2 2022-4825-7319).00footnotetext: Conclusions and
opinions are those of the individual authors and do not necessarily reflect
the policies or views of EHP Publishing or the National Institute of
Environmental Health Sciences.

##  1 Introduction

In 2016, the World Health Organisation (WHO) stated that 13.7 million deaths a
year, or 24% of all global deaths, are linked to adverse environmental factors
such as poor air quality, unstable climate and the built environment 1. It is
well understood that poor air quality and climate extremes have significant
adverse impacts on human health 2, 3, 4, 5. Even in Australia, where air
quality is generally among the cleanest in the world 6, pockets of poor air
quality are said to be responsible for the deaths of approximately 3000 people
per year 7. Extreme heat is the focus of a large body of research
investigating the relationship between climate and human health, with heatwave
events contributing to excess mortality in Australia more than all other
natural hazards combined since 1844 8, 9. Cold-related mortality is also
significant both internationally 10, 11 and in Australia 12, especially when
considered alongside populations with increased respiratory conditions 13, 14.
These exemplar environmental exposures and their relationship with human
health form the study of environmental health 15.

Environmental health is also affected by the ways in which cities are designed
and built, as these factors can lead to populations being exposed to or
protected from extremes in the natural environment 16. Moreover, population
characteristics may determine areas which are more vulnerable to natural
hazards than others, both socially and economically 17. As the frequency and
intensity of extreme climate events have increased in recent years, greater
emphasis is being placed on adaptation and mitigation strategies which
consider environmental factors as well as population socio-demographics and
the impact of urban infrastructure, both in Australia and globally 18, 19, 20,
21, 22.

Historically, significant efforts have been made to characterise the human and
financial risks related to natural hazards. More recently, these efforts and
the corresponding risks have become focused more sharply on the vulnerability
of exposed human populations rather than the exposure itself 23. The term
vulnerability, in social science research, generally describes a state of
people and populations which varies significantly both geographically and
historically 24. The changing nature of many hazards, coupled with growing and
ageing populations and infrastructure in exposed areas is leading to increased
vulnerability across Australia and internationally 25.

Due to the complex relationships between the environment and humans, no single
vulnerability measure will provide a holistic and comprehensive indicator of
the state of environmental health vulnerability within a population, cohort,
or community of interest 26. Despite this, local government agencies need
information to support vulnerability assessments in order to effectively
develop policy, apply intervention, provide emergency disaster management and
improve their respective community’s ability to adapt to changes in
environment. Effective adaptation and risk management strategies and practices
require an in-depth understanding of population vulnerability, as well as the
various exposures facing populations. In addition, it is important to provide
an evidence-based assessment of changes in these areas 27, as “hazards only
lead to disaster if they intersect with an exposed and vulnerable society and
when the consequences exceed its capacity to cope” 25.

It is especially difficult to assess environmental health risks in Australia,
due to enormous variations in geography, climate and environmental burden of
disease 28, 29, as well as the continent’s extremely heterogeneous population
density and socio-demographics. For instance, Australian regions are located
within large latitude and longitude ranges, between $10^{\circ}$S to
$45^{\circ}$S and $113^{\circ}$E to $153^{\circ}$E, respectively. In addition,
this landscape consists of largely remote regions, with over 68$\%$ of
Australians living in the greater metropolitan area of Australia’s eight
capital cities in 2021 30. Decision making at the local area level is split
between the federated national, state and local government political system,
with policy often informed by national averages, instead of relevant local
characteristics, e.g., area level health, socio-economics and location
characteristics 31.

The analysis of health outcome data is approached in two main ways in
environmental health literature. The first approach is a typical statistical
analysis, in which environmental variables are assessed against one or more
health outcomes of interest 32, 33, 34. The second approach is the development
of environmental health indicators or indices, which are based on relevant
literature, rather than being directly assessed against specific health
outcomes 35, 36, 37, 38, 39, 40. Environmental health indicators are
parameters that provide information about the state of the environment and its
link to human health 15. A composite indicator or index, on the other hand, is
a measure that combines multiple parameters or indicators in order to provide
an overall assessment of the environmental health status of a particular area
or population, often summarising underlying information via a single value or
ranking 41. Transforming or combining raw data into indicators and indices
enables vulnerable regions to be more easily identified so that their
underlying characteristics may be further assessed and options for policy
intervention and natural hazard response can be more readily explored. As a
result, indicators and indices have become popular among state governments and
environmental health organisations to communicate environmental health risk in
Australia 18, 19, 20.

This paper utilises a range of environment and health data to provide an
assessment of environmental health risk through the production of
vulnerability indices for two case studies across Australia: extreme climates
(heat and cold) and air quality. These environmental health domains were
prioritised by a large group of relevant government organisations across a
range of domains such as water quality and soil contamination. The
vulnerability indices produced in this work cover various spatial and temporal
resolutions, thereby capturing seasonal and long-term changes in population
vulnerability.

The vulnerability indices produced in this paper have been implemented into
the Australian Environmental Health (AusEnHealth) decision support platform.
AusEnHealth enables the monitoring and analysis of over 100 variables from the
many sectors which contribute to environmental health risk, providing insight
into the impacts of the changing environment on human health in Australia. The
spatial resolutions captured in AusEnHealth include statistical areas (SA2,
SA3 and SA4) and local government areas (LGA). Statistical areas are a
hierarchical construct developed to reflect the location of people and
communities and are updated every five years as part of the Australian
Statistical Geography Standard (ASGS) based on the Australian Census 42. Local
government areas on the other hand, represent boundaries that relate to the
administration of cities, shires, municipalities or towns 43, 44 within
Australian states and territories

This paper is set out as follows. Section 2 introduces the data considered for
use in the creation of vulnerability indices and covers both the existing and
proposed methodology used to create vulnerability indices. This section also
explains data considered by similar works in the literature, as well as
spatio-temporal resolutions and extents. Section 3 presents the main result of
this study and outlines the differences between the existing methodology and
the proposed weighted methodology. In addition, this results section
reiterates the value of the overall data assets curated and created for this
study by demonstrating how vulnerability indices can be pulled apart to more
carefully explore and understand exposed or sensitive populations. In Section
4, the benefits of the proposed methodology are discussed from a health risk
perspective, with particular attention paid to both the granularity of the
indices as well as the ability to observe trends over time. Finally, section 5
summarises the study and suggests potential future direction.

##  2 Methods

###  2.1 Data

The data used in this study have been collected as part of the AusEnHealth
initiative, which involved a national data audit of Australian environmental
health data at both state and national levels. During this process, data
custodians, datasets and their related metadata were compiled and the
available data were assessed for national coverage, relevance, spatial
resolution and accessibility.

This study features data related to extreme climates and air pollution which
were the environmental health domains prioritised by collaborating government
organisations. Ethics for data collection and storage were approved as part of
a human ethics exempt research application at the Queensland University of
Technology (reference HE-Ex2 2022-4825-7319).

The remainder of this section provides an overview of the variables and data
custodians considered to effectively capture risk related to the priority
environmental health domains. Following methods related to the data, we
provide an overview of existing vulnerability index methodology as well as the
modifications made to produce health outcome weighted vulnerability indices.

####  2.1.1 Climate

Climate data relevant to the extreme climates case study comprised minimum and
maximum temperature (Table 1), sourced from the Bureau of Meteorology (BOM)
over a 20-year period 45. These climate data were used to create two
indicators related to the impact of temperature on human health, namely excess
heat factor (EHF) and excess cold factor (ECF) using existing methodology 46,
47. EHF and ECF are metrics used by BOM and commonly in literature 29, 48, 49,
50 to determine whether an area is impacted by a heatwave or coldwave,
respectively, using short- and long-term historical temperature recordings.

####  2.1.2 Air Quality

Relevant air pollutant data were collected from the Copernicus Atmosphere
Monitoring Service (CAMS) 51. Air quality was determined by the measurement of
air pollutants which are introduced into the air via road traffic, industrial
processes and bushfires. The resultant variables comprised five chemical
compounds and three sizes of particulate matter (Table 2).

####  2.1.3 Built Environment

Primary built environment data used in this study are provided in Table 3 and
were acquired from the Australian Bureau of Statistics (ABS) 52, Geoscience
Australia’s Digital Earth Australia (DEA) 53, NASA’s Moderate Resolution
Imaging Spectroradiometer (MODIS) 54 and the Terrestrial Ecosystem Research
Network (TERN) 55. The variables accessed include the number of hospitals,
green space percentages, water surface percentages, the normalised difference
vegetation index (NDVI) and canopy cover percentages.

####  2.1.4 Demographics

Demographics data have been utilised in numerous studies to determine
vulnerable populations 56, 57, 58. Twenty relevant demographic variables were
obtained from the Australian Census data 52. In addition, eleven variables
related to existing health conditions were sourced from the Public Health
Information Development Unit (PHIDU) social health atlas 59, 60. These data
are displayed in Table 4.

####  2.1.5 Mortality

This study utilises publicly available mortality data sourced from the
mortality over regions and time (MORT) books compiled by the Australian
Institute of Health and Welfare (AIHW). This data set contains numbers of
deaths by leading causes of death disaggregated by sex across Australia 61.
Relevant health outcome variables from this data set are given in Table 5.
Leading causes of death have been grouped to to produce mortality data related
to heat, air quality, or all causes, determined by combining or referring to
specific mortality causes listed in Table 9.

####  2.1.6 Data Sourcing and Processing

The data were most commonly downloaded manually from the sources using file
transfer protocol (FTP). In some cases, datasets were only available in a
series of files, e.g., yearly, monthly, or daily datasets. Automated downloads
were required for these and relied on Hypertext Transfer Protocol (HTTP)
requests to online data storage servers using the statistical software R 62.
All data used in the creation of the vulnerability indices classify as
geographic or spatial data, i.e., associated with a location relative to
Earth. Where only gridded or raster data were available, these were
transformed using R or GIS into spatially aggregated datasets with consistent
spatial resolutions. Some data sets are stored as XLSX or CSV files with
additional metadata items included in the data entries, where available. In
these instances, variables were extracted and matched with a standard set of
geographical resolutions codes to ensure consistency.

All data types were accessed at their finest temporal resolution and have been
aggregated where needed to produce yearly, monthly and weekly datasets. The
spatial resolutions utilised in this study include ASGS statistical areas
level 2-4 (SA2, SA3, SA4), as well as local government areas (LGA). The number
of SA2, SA3 and SA4 regions in Australia are 2310, 358 and 107, respectively,
with SA2 representing the highest resolution. There are also 566 LGAs in
Australia, with this resolution being included for its relevance to policy
planning and the decision support component of this study and the overall
AusEnHealth initiative. Both the processed data and the associated metadata
were stored in Amazon S3 buckets, maintained on servers at the Queensland
University of Technology.

While linked individual data could add significant value in areas of risk
management, disaster preparedness and policy development, such data could not
be accessed for this work, due to legal, ethical and governance barriers in
accessing socio-demographic and health outcome data from the Australian Bureau
of Statistics and the Australian Institute of Health and Welfare. The release
of individual data must also comply with privacy laws and regulations specific
to each jurisdiction (Australian State or Territory). Hence, aggregated data
were instead used to enable the creation of vulnerability indices at the
national scale. This aligns with many other national and international atlases
63, 64, 65, 66, 67, 68.

###  2.2 Vulnerability Indices

The national vulnerability indices created in this study utilise a consistent
environmental health vulnerability index framework introduced by the
Intergovernmental Panel on Climate Change (IPCC) in 2007 31. This framework
aims to better explain population vulnerability with respect to climate and
consists of three components: exposure, sensitivity and adaptive capacity.
Exposure is a direct measure of household, community, or population exposure
to a certain event (extreme heat, cold, air pollution, etc.). Sensitivity is
defined as the susceptibility of a household, community, or population to the
exposure. Adaptive capacity captures the capabilities of a household,
community, or population to cope with or recover from the impact of the
exposure. In the creation of a vulnerability index, these components form sub-
indices which contain relevant variables and indicators. The sub-indices are
used to capture different aspects of vulnerability and are ultimately combined
into an overall vulnerability index: a single measure of population
vulnerability with respect to a particular exposure event.

####  2.2.1 Vulnerability Index Components and Extent

A number of vulnerability indices exist which focus primarily on population
variables. Prominent $21^{st}$ century examples of these indices include the
social vulnerability index (SVI) 56, the social vulnerability index for the
United States (SoVI) 57 and Australia’s index of relative socio-economic
disadvantage (IRSD) 58. While the composition of each vulnerability index
differs, common variables include employment status, high school education,
English proficiency and income. These social vulnerability indices, as well as
resilience and risk indices, have since become widely known for providing
insight into disaster response, recovery, readiness, adaptability, resilience
and risk assessment 58.

For AusEnHealth, the population vulnerability stemming from demographic
variables was combined with vulnerability associated with exposure to certain
environmental hazards. The variables considered feasible for use in the
creation of AusEnHealth heat, cold and air quality vulnerability indices rely
on existing literature 29, 37, 38, 39, 40 which utilise the accepted exposure,
vulnerability and adaptive capacity framework 31. While vulnerability index
development in recent literature has a heat-health focus, similar data can be
used to produce a cold vulnerability index. There is extensive literature 69,
70, 71 from which to draw information for the development of an air quality
vulnerability index. The components used to create the AusEnHealth
vulnerability indices are listed in Tables 6, 7 and 8 and have been separated
by exposure, sensitivity and adaptive capacity components, respectively.

Of particular note, Table 6 introduces historical temperature percentiles,
which were calculated by comparing each region to its own historical values.
This relative calculation of temperature allows for the resulting indices to
capture spikes in heat vulnerability in regions which are typically cooler or
hotter by accounting for acclimatisation 46.

Interestingly, no two vulnerability index implementations in the literature
use the same set of variables. Reasons for these differences include regional
scope, data availability, application and differences in the groupings of
exposure, sensitivity and adaptive capacity. This also applies to the
AusEnHealth vulnerability indices. Heatwave and coldwave measures were
included in the climate exposure sub-index to incorporate additional
information relating to consecutive high heat or cold days into the overall
vulnerability indices. As noted above, the temperature measures here are given
as historical percentiles to represent out-of-the-ordinary heat on a per-
region basis. As there is no identified acclimatisation to poor air quality in
the literature, only spatial percentiles were considered for the air quality
index. The air quality sub-index only includes air pollutants, following the
same approach as the heat and cold exposure sub-indices. Sensitivity variables
were largely based on those used in published population vulnerability indices
56, 57, 58, with particular influence from the recent heatwave report from BOM
29. Selection of variables for adaptive capacity centred around population
mobility and access to services, such as hospital counts, access to internet
and the number of dwellings with at least one vehicle.

The majority of vulnerability indices identified in the research literature
combine data from a wide temporal range to form a single index 29, 37. When
changes over time are considered in the creation of vulnerability indices,
this is usually over either a short time period or only a few time points 39.
The AusEnHealth vulnerability indices are presented at weekly, monthly and
yearly scales by incorporating daily climate and air quality data and
considering changes over time to the exposure, sensitivity and adaptive
capacity sections of the created vulnerability indices. As the temporal extent
of collected demographic data covers the years 2011-2019 (see Section 2.1 and
Table 4), the vulnerability indices are limited to this time period.

As demographics data are not available at a weekly temporal resolution, data
from a given year were used at each month and week interval in finer
resolution data sets so that climate and air quality data could be included at
those finer resolutions to show temporal variations in population
vulnerability otherwise obfuscated by longer term averages. In consideration
of variation over time, data were imputed using each region’s temporal data
where available. For example, unemployment rate is available only at yearly
temporal resolution in 2011 and 2016, so it was imputed first annually for the
years 2012 through 2015 and then replicated for each month and week entry in a
given year for a given region. No spatial imputation was considered, due to
large spatial heterogeneity. If missing data from a region could not be
imputed using time-series data, that entry remained missing. The following
section includes details of how this missing data were handled in the creation
of vulnerability indices.

####  2.2.2 Approaches to Vulnerability Index Methodology

There are two prominent approaches to index creation. The first is a
percentile rank-sum approach 56, which transforms variables into percentiles
ranked from highest to lowest inferred vulnerability across all assessed
regions. For a given region, the percentile values corresponding to the set of
variables are then summed arithmetically with equal weights to produce the
vulnerability index for that region.

The conceptual justification behind the use of equal weights in the above
approach is that when trying to cover the broad spectrum of exposure-related
health outcomes, weighting variables by comparing against any single health
outcome is likely to bias the vulnerability index compared to a separate
exposure-related health outcome. However, the disadvantage of the equal
weights approach is that some of the variables may be more important across a
wide range of health outcomes and care must be taken to ensure that all
variables correlate with population vulnerability. Moreover, if health
outcomes are not factored into the creation of the index and equal weights are
applied, the resulting vulnerability index may not be adequately correlated
with any or all health outcomes. This means that a vulnerability index created
in this way in different places in the world or with different variables will
not reliably capture the same population vulnerabilities. Additionally,
substantive dependencies between variables are not taken into account, which
may result in highly correlated variables controlling the overall relationship
of the vulnerability index.

The second approach weights and combines variables using a dimension reduction
approach such as principal component analysis (PCA) or its derivatives such as
factor analysis 57, 58, 72, where the first principal component (or factor) is
taken as the vulnerability index. There are strengths and weaknesses of the
PCA methodology. One strength is that PCA identifies patterns and associations
between the different dimensions of the underlying data and can adjust for the
contribution of multiple correlated variables. On the other hand, PCA does not
compare the variables to a health outcome and so will produce weights that
optimally describes variance in a set of variables that may not be correlated
to any exposure-related health outcome. That is, depending on the quality (and
quantity, perhaps more importantly) of underlying variables, PCA-derived
weights may reduce the overall value of a vulnerability index compared to one
created using equal weights. Moreover, a single principal component (or
factor) may explain a relatively small amount of the total variation in the
input variables.

Ultimately, the approach used to derive a vulnerability index, as well as the
underlying components, must be based on data available within the underlying
region(s). Recent discussions led by NASA 73 conclude that chosen weights,
correlated parameters and categorisation of exposure, sensitivity and adaptive
capacity parameters are largely subjective and will depend on what is being
monitored or evaluated.

A notable characteristic of both of the existing approaches is that neither
are expressly linked to health outcome(s) of interest, other than by the
initial choice of the variables included in the index construction. A strong
link is often difficult to accomplish due to the complex associations and
potential temporal lags between the underlying environment and health
indicators, the complex spatial and temporal variation within these indicators
and the overarching challenge of capturing general vulnerabilities, i.e.,
avoiding the creation of an index that is too prescriptive with respect to a
single health outcome.

This paper presents an alternative to the sum-rank and PCA approaches by
weighting components using their pairwise correlation with a desired health
outcome. The benefit of this approach is that the resulting vulnerability
index is intrinsically but lightly associated at creation with the desired
health outcome.

The following subsections detail both the equal weight and health outcome
weighted methods.

####  2.2.3 Equal Weight Vulnerability Indices

The equal weights method 56 is the baseline approach in the creation of
vulnerability indices. In this approach, a regional sub-index is created by
summing together spatial percentiles of its variables. Let $S_{ikt}$ be a sub-
index of region $i$ at time $t$ containing $N_{k}$ variables, where $k=1,2,3$
corresponds to exposure, sensitivity and adaptive capacity sub-indices,
respectively. With variables $n=1,\dots,N_{k}$, a vulnerability sub-index,
$S_{ikt}$, for a geographical region, $i$, at time, $t$, is calculated as
follows,

$S_{ikt}=\sum_{n=1}^{N_{k}}f_{i}(\underline{x}_{nt}),$ (1)

where $\underline{x}_{nt}$ is a vector containing every region’s observed
value of variable $n$ at time $t$ and the function $f_{i}$ computes the
$i^{th}$ region’s spatial percentile from $\underline{x}_{nt}$. The final
vulnerability index, $VI_{it}$, is calculated in a similar way.

$VI_{it}=\sum_{k=1}^{3}f_{i}(\underline{S}_{kt}),$ (2)

where $\underline{S}_{kt}$ is a vector containing every region’s observed
value of vulnerability sub-index $k$ at time $t$.

The index detailed in Equation 2 will not produce an accurate result if, for a
given variable n, the vector $\underline{x}_{n}$ contains missing values.
Either the summation will produce a “NaN” result, or the missing value(s) must
be ignored, under-representing the value of the final index. A simplistic
approach is to replace a missing value with the average of the existing data
percentiles. To reduce the impact of highly correlated parameter spatial
rankings and to ensure equal weighting of important aspects of vulnerability,
this approach has also been modified to include sub-index themes, which act as
sub-indices to the existing sub-indices 29. Combined with the above
calculations, this is equivalent to the following changes to the sub-index and
vulnerability index equations.

$S_{ikt}=\frac{1}{T_{k}}\sum_{p=1}^{T_{k}}f_{i}\Bigg{(}\frac{1}{N_{pt}}\sum_{n=1}^{N_{pt}}f_{i}(\underline{x}_{npt})\Bigg{)},$
(3)

where $N_{pt}$ is the number of variables in theme $p$ that contain at least
one non-missing observation at time $t$, $\underline{x}_{npt}$ is a vector
containing every region’s value of variable $n$ within theme $p$ at time $t$
and $T_{k}$ is the number of themes in sub-index $k$. Note that $T_{k}$, like
$N_{pt}$, is free to vary between sub-indices. Hence

$VI_{it}=\frac{1}{3}\sum_{k=1}^{3}f_{i}(\underline{S}_{kt}).$ (4)

An alternative approach to handling missing values is to introduce some
uncertainty in the imputation process. For example, uncertainty can be
introduced through an adaptation of the familiar k-fold multiple imputation
approach 74. The average of the computed indices can then be taken if a single
value of the index is required.

####  2.2.4 Health Outcome Weighted Vulnerability Indices

The second methodology employed in AusEnHealth utilises the pairwise
correlation values between each variable and age-standardised all cause
mortality rates to form a weighted average. Kendall’s Tau has been used over
the typical Pearson correlation, as the linearity and normality assumptions
for Pearson correlations may not be met for all variables. Note that weights
can be positive or negative depending on the relationship between a particular
variable and the health outcomes considered. The calculation of each weighted
sub-index, $WS_{ikt}$, at time $t$ for region $i$ is as follows.

$WS_{ikt}=\frac{1}{T_{k}}\sum_{n=1}^{T_{k}}f_{i}\Bigg{(}\frac{1}{N_{pt}}\sum_{n=1}^{N_{pt}}w_{npk}f_{i}(\underline{x}_{npt})\Bigg{)},$
(5)

where $w_{npk}$ is the Kendall’s Tau correlation between variable $n$ of theme
$p$ and all-cause mortality in sub-index $k$. Note that equal weights are used
for the exposure sub-index, i.e., $w_{np1}=1$ (see Discussion). The
calculation of the overall weighted vulnerability index, $wVI_{it}$, remains
the same, replacing the original sub-indices for the weighted sub-indices:

$wVI_{it}=\frac{1}{3}\sum_{k=1}^{3}f_{i}(\underline{WS}_{kt}).$ (6)

##  3 Results

###  3.1 Weighted Vulnerability Indices

The pairwise correlations (calculated using Kendall’s Tau) between the
weighted vulnerability components and age-standardised all cause mortality
data can be seen in Figure 1 for heat, cold, air quality, their sub-indices
and three mortality categories (heat, air quality and all-cause mortality)
over the 2015-2019 period. The improvement in correlation between the two
vulnerability index methodologies and age-standardised all cause mortality is
shown in Figure 2, where spatially ranked all cause mortality is plotted
directly against both (A) the baseline heat vulnerability index, HVI and (B)
the weighted heat vulnerability indices, wHVI. Choropleth maps additionally
provide spatial insight into this improvement in correlation in Figure 3,
where the difference between spatially ranked all cause mortality (ACM) and
heat vulnerability index methodologies (HVI and wHVI) are shown. The value of
the weighted approach in Figure 3 is clear, showing a substantively smaller
difference between ACM and wHVI than between ACM and HVI, both in urban and
remote regions. Additionally, Figure 3 (C) shows the locations regions whose
vulnerability changes the most between methodologies.

The value of presenting vulnerability indices at a higher temporal resolution
is highlighted in Figure 5 by displaying changes in heat vulnerability at the
yearly, monthly and weekly temporal resolutions for the Derwent Valley LGA
region in Tasmania. Derwent Valley is the 7th most vulnerable region in the
2015-2019 weighted heat vulnerability index and had the 58th highest all cause
mortality over the same period. The weekly heat vulnerability index is shown
over time and compared to the annual index in Figure 6 to reveal how seasons
or events may impact a region’s vulnerability.

###  3.2 Comprehensive Data Asset

Another outcome of this study is the creation of a significant data asset:
almost a decade of time-series climate and air quality vulnerability indices
at multiple geographical resolutions. To enable exploration of the data, an
application has been created using the shiny package in R 75, which offers
flexible visualisations of both the equal and weighted vulnerability indices
for heat, cold and air quality and their underlying components at their
various spatial and geographical resolutions (see
https://qutcds.shinyapps.io/AusEnHealth_Interactive/).

###  3.3 Understanding Vulnerability

One disadvantage of merging variables and indicators in the creation of a
vulnerability index is that it becomes unclear what makes a region vulnerable,
especially when there are many underlying components. While the overall
measure of vulnerability is valuable for relative assessments of regions, the
provision of underlying data allows for particular pockets of vulnerable
populations to be explored. To demonstrate, Figure 4 shows a breakdown of the
weighted air quality vulnerability index, using the Bundaberg local government
area (population density less than 15/km2) to highlight the underlying
vulnerability components in a regional city. It is clear from Figure 4 that
the vulnerability of Bundaberg is heavily influenced by sensitivity, with high
percentiles including percentage with chronic obstructive pulmonary disease
(COPD), percentage with asthma (AST), percentage elderly (ELD), percentage
living in mobile home (MH) and percentage with disability (WD). The underlying
components in the Bundaberg LGA clarify why this particular population remains
vulnerable with respect to air quality, despite a low exposure index. This
breakdown process is just one way in which these indices can be used to better
understand the factors which contribute to a vulnerable population.

##  4 Discussion

###  4.1 New Approach to Vulnerability Index Creation

Vulnerability indices are well known in the literature for their utility in
emergency response 56 and are emerging as a way to communicate the need for
changes to policy, adaptation plans and intervention strategies by
highlighting critical population vulnerabilities using a single value 29, 73.
Despite the reputation of vulnerability indices in the literature, the
strength of the relationship between most existing vulnerability indices and
adverse health outcomes is not strong 76. The proposed inclusion of weights
that are directly tied to targetted health outcomes (e.g., mortality data)
improve the strength of this relationship and hence inherently increase the
value of the indices for use in relevant risk assessment purposes in practice.

It is of particular importance to raise the temporal resolution of existing
vulnerability indices for the purpose of communicating need. While it has been
common historically and recently to combine multiple years of data in the
creation of vulnerability indices 29, 72, it is clear that fine temporal-scale
extreme events are lost in such averages (see correlations between exposure
sub-indices and mortality in Figure 1). It is for this reason that no weight
has been applied to the exposure sub-index in the vulnerability index
methodology.

A range of data collected for the AusEnHealth initiative has enabled the
presentation of a finer spatial and temporal scale vulnerability index,
allowing for variation in region-specific vulnerability to be observed at the
monthly and weekly temporal resolution (see Figures 5 and 6). Figure 5
demonstrates how population vulnerability differs when more granular temporal
data are considered and compared. For example, the monthly and weekly wHVI
values in 2019 both reach the 100th percentile, while the year is represented
by a wHVI of $0.74$. In fact, Figure 6 shows that weekly vulnerability index
calculations are often significantly higher than the 2019 annual wHVI for the
Terwent Valley LGA. Given the temporal resolution of the underlying data,
these intra-seasonal changes are a direct result of fluctuations in exposure,
which when combined with the underlying demographics and built environment
characteristics over a larger time period, represent reactive population
vulnerability at a resolution never seen before across Australia.

It is important to also comment on the utility of the proposed vulnerability
index methodology over the general use of all cause mortality for population
vulnerability assessments. First, health outcome data can be difficult to
attain, especially at fine temporal and geographical resolutions. Health
outcome data requests can be overcome by using the more granular underlying
characteristics to create fine temporal and geographical vulnerability indices
at a national scale, using the proposed correlation-based weights approach to
appropriately connect mortality to population vulnerability. Second, the
underlying components comprising these vulnerability indices can be explored
to identify which components are outliers, thereby contributing significantly
to a region’s high vulnerability percentile. Finally, the methodology
showcased via AusEnHealth is generally applicable globally where relevant
spatial data are accessible. Vulnerability indices created using this weighted
average approach represent empirical associations with health outcomes used to
generate weights and are not necessarily causal. Case-specific epidemiological
modelling should be employed to pursue such relationships.

##  5 Conclusion

Environmental health vulnerability indices offer a way to assess population
vulnerability despite variations in population density and environmental
burden of disease. However, common index creation methodology may not
accurately reflect timely population health risks. The pairwise correlation
approach to vulnerability index methodology allows for a health outcome of
interest to guide population vulnerability calculations, while maintaining the
critical exposure, sensitivity and adaptive capacity framework used in recent
vulnerability index methods. For decision makers, these improved indices offer
a data-driven foundation for guiding informed long-term strategic planning,
helping to mitigate risks before they escalate into full-blown crises.

The volume of data collected in this study also allows for high temporal and
geographical resolutions, allowing users of the data to observe fine changes
in environmental exposure and hence determine sharp and sudden changes in
population vulnerability due to an emerging natural hazard.

The categories of mortality, as well as the exposure, sensitivity and adaptive
capacity variables used in this study reflect the data available at the time
of writing. Data included can be greatly expanded given future data
availability, including health service availability data, urban environment
variables such as road and building density data and upcoming 2021 Australian
Census data. There are also opportunities to explore the vulnerability index
methodology, including a more rigorous assessment of underlying variables and
related health outcomes, as well as addressing variable inter-dependencies.

## Acknowledgements

We are grateful for the support of the AusEnHealth Steering Committee: Mike
Lindsay, Andrea Hinwood, Martine Dennekamp, Phil Delaney, Stuart Barr, Beryl
Morris and Claire Sparke. We would also like to acknowledge the guidance and
advice provided by AusEnHealth Advisory Board. This group comprises of experts
in multidisciplinary fields and are a significant source of knowledge to the
project team.

## Funding

The AusEnHealth initiative has been funded through a collaborative research
agreement between FrontierSI, Western Australia Department of Health,
Environmental Protection Agency Victoria, Queensland University of Technology
(QUT), NGIS, Australian Urban Research Infrastructure Network (AURIN) and the
NCRIS Terrestrial Ecosystem Research Network (TERN). We also acknowledge the
Healthy Environments and Lives (HEAL) Network, whose ongoing funding has
supported the development of this manuscript.

## Abbreviations

Abbreviations are written as they appear. World Health Organisation (WHO),
Australian Environmental Health (AusEnHealth), Bureau of Meteorology (BOM),
excess heat factor (EHF), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO2),
Sulphur Dioxide (SO2), Carbon Monoxide (CO), Ozone (O3), Particulate Matter
$<10\mu m$ (PM10), Particulate Matter $<2.5\mu m$ (PM2.5), Particulate Matter
$<1\mu m$ (PM1), Copernicus Atmosphere Monitoring Service (CAMS), Normalised
Difference Vegetation Index (NDVI), Australian Bureau of Statistics (ABS),
Digital Earth Australia (DEA), Moderate Resolution Imaging Spectroradiometer
(MODIS), Terrestrial Ecosystem Network (TERN), Public Health Information
Development Unit (PHIDU), mortality over regions and time (MORT), Australian
Institute of Health and Welfare, file transfer protocol (FTP), Hypertext
Transfer Protocol (HTTP), Australian Statistical Geography Standards (ASGS),
statistical area level 2 (SA2), local government area (LGA), Queensland
Univerity of Technology (QUT), Intergovernmental Panel on Climate Change
(IPCC), social vulnerability index (SVI) social vulnerability index for the
United States (SoVI), index of relative socio-economic disadvantage (IRSD),
excess cold factor (ECF), principal component analysis (PCA), baseline
vulnerability index (VI), weighted vulnerability index (wVI), baseline heat
vulnerability index (HVI), weighted heat vulnerability index (wHVI).

## Availability of data and materials

The data sets associated with this work are provided at an aggregated level
and do not contain individual exposures, risk factors or health outcomes. The
data are publicly available at https://github.com/QUTCDS/AusEnHealth/Data and
are provided from 2011 through to 2019 at SA2, SA3, SA4 and LGA geographical
resolutions and at weekly, monthly and yearly temporal resolutions.

## Authors’ contributions

Data acquisition and processing: Aiden Price and Michael Rigby. Project and
stakeholder communication: Paula Fiévez and Aiden Price. Technical
methodology: Aiden Price and Kerrie Mengersen. Manuscript contributions: Aiden
Price, Kerrie Mengersen. All authors contributed to the review of the
manuscript.

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## Figures and Tables

Figure 1: Pairwise correlation plot comparing health outcome weighted
vulnerability index components against and health outcomes (heat, air quality
and all-cause mortality). E, S and A correspond to exposure, sensitivity and
adaptive capacity sub-indices, respectively. Figure 2: Scatter plot of age-
standardised all cause mortality (ACM) against (A) the baseline heat
vulnerability index (HVI) showing low correlation and (B) the weighted heat
vulnerability index (wHVI) showing improved correlation (B). Figure 3:
Choropleth map displaying the absolute difference between (A) age-standardised
all cause mortality (ACM) and the baseline equally weighted heat vulnerability
index (HVI), (B) ACM and the weighted heat vulnerability index (wHVI) and (C)
HVI and wHVI. Figure 4: Breakdown of 2017 weighted air quality vulnerability
index (wAQVI). Column A displays exposure, sensitivity, adaptive capacity and
overall wAQVI indices. Column B displays ordered bar charts of the relevant
index, with the Bundaberg LGA region marked white to indicate relative
vulnerability among each index. Column C displays the spatial percentiles of
all underlying parameters available for the Bundaberg LGA region in 2017, used
to create each index. For Columns B and C, dark blue is used to represent
values below the 50th percentile, with dark green representing values at or
above the 50th percentile. Abbreviations used in this figure can be identified
from the corresponding data tables 6, 7 and 8 for exposure, sensitivity and
adaptive capacity, respectively. Figure 5: Representations of weighted heat
vulnerability index (wHVI) calculations at different temporal resolutions.
These are presented as (A) a time series plot of annual wHVI calculations, (B)
yearly box plots of monthly wHVI calculations and (C) yearly box plots of
weekly wHVI calculations. Figure 6: Connected line plot of weekly weighted
heat vulnerability index (wHVI) showing fine-scale changes in population
vulnerability in 2019 in the Terwent Valley local government area, Tasmania.
The annual 2019 vulnerability index is also displayed as a dashed line to
visualise the difference in these relative measures of population
vulnerability. Recall that this line is the calculation of the vulnerability
index using annual averages and is not equivalent to the average of the
monthly index values.

Table 1: Climate variables collected for use in the AusEnHealth vulnerability indices. Variable | Temporal Properties | Geographical Properties | Data Source
---|---|---|---
Maximum Temperature (∘C) | 2000-2020 (daily) |  Australia (0.05∘ raster) | BOM
Minimum Temperature (∘C) | 2000-2020 (daily) |  Australia (0.05∘ raster) | BOM
Table 2: Air quality variables collected for use in the AusEnHealth vulnerability indices. Variable | Temporal Properties | Geographical Properties | Data Source
---|---|---|---
Nitrogen Monoxide, NO (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Nitrogen Dioxide, NO2 (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Sulphur Dioxide, SO2 (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Carbon Monoxide, CO (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Ozone, O3 (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Particulate Matter $<10\mu m$, PM10 (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Particulate Matter $<2.5\mu m$, PM2.5 (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Particulate Matter $<1\mu m$, PM1 (kg kg-1) | 2003-2020 (3-hourly) |  Australia (0.75∘ raster) | Copernicus
Table 3: Built environment variables collected for use in the AusEnHealth vulnerability indices. Variable | Temporal Properties | Geographical Properties | Data Source
---|---|---|---
Hospital Counts (per population) | 2021 (yearly) | Australia (SA2, LGA) | ABS
Green Space (% parklands) | 2016 (yearly) | Australia (SA2, LGA) | ABS
Water Bodies (%) | 2019 (yearly) | Australia (shapefiles) | DEA
Normalised Difference Vegetation Index | 2000-2019 (monthly) |  Australia (0.05∘ raster) | MODIS
Canopy Cover (%) | 2000-2021 (seasonal) | Australia (25m grid) | TERN
Table 4: Demographic variables collected for use in the AusEnHealth vulnerability indices. Variable | Temporal Properties | Geographical Properties | Data Source
---|---|---|---
Population Density | 2014-2019 (yearly) | Australia (SA2, LGA) | ABS
Median Income | 2014-2017 (yearly) | Australia (SA2, LGA) | ABS
% Low Income | 2016 (yearly) | Australia (SA2, LGA) | ABS
% Not Finished High School | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
Unemployment Rate | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Elderly ($>$65 years old | 2014-2019 (yearly) | Australia (SA2, LGA) | ABS
% Infant ($<$5 years old) | 2014-2019 (yearly) | Australia (SA2, LGA) | ABS
% Single Parents | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Providing Unpaid Childcare | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Persons Requiring Core Assistance | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Persons with a Disability | 2015, 2018 (yearly) | Australia (SA2, LGA) | ABS
% Living Alone | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Speaking English as Second Language | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Indigenous | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Mobile Homes | 2014-2019 (yearly) | Australia (SA2, LGA) | ABS
% Homes with Vehicle(s) | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Crowded Dwellings | 2016 (yearly) | Australia (SA2, LGA) | ABS
% Renters | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Mortgage Payers | 2011, 2016 (yearly) | Australia (SA2, LGA) | ABS
% Internet | 2016 (yearly) | Australia (SA2, LGA) | ABS
Condition: Respiratory Diseases | 2011, 2014 (yearly) | Australia (LGA) | PHIDU
Condition: Asthma | 2011, 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Condition: Chronic Obstructive Pulmonary Disease | 2011, 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Condition: Circulatory System Diseases | 2011, 2014 (yearly) | Australia (LGA) | PHIDU
Condition: High Blood Pressure | 2011, 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Condition: High Blood Cholesterol | 2011, 2014 (yearly) | Australia (LGA) | PHIDU
Condition: Cardiovascular Disease | 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Condition: Overweight | 2011, 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Condition: Obese | 2011, 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Condition: Smoker | 2011, 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Condition: High Alcohol Consumption | 2011, 2014, 2017 (yearly) | Australia (LGA) | PHIDU
Table 5: Health outcome variables collected for use in the AusEnHealth vulnerability indices. Mortality Category | Temporal Properties | Geographical Properties | Data Source
---|---|---|---
Coronary Heart Disease | 2014-2019 (5-yearly) | Australia (SA3) | AIHW
Cerebrovascular Disease | 2014-2019 (5-yearly) | Australia (SA3) | AIHW
Heart Failure | 2014-2019 (5-yearly) | Australia (SA3) | AIHW
Cardiac Arrhythmia | 2014-2019 (5-yearly) | Australia (SA3) | AIHW
Chronic Obstructive Pulmonary Disease | 2014-2019 (5-yearly) | Australia (SA3) | AIHW
All Cause | 2014-2019 (5-yearly) | Australia (SA3) | AIHW
Table 6: Exposure sub-index components used in AusEnHealth to create heat, cold and air quality vulnerability indices. Theme | Variable | Heat | Cold | Air Quality
---|---|---|---|---
Heat Exposure | Historical Temperature Percentiles (Heat) | x |  |
Excess Heat Factor (EHF) | x |  |
Cold Exposure | Historical Temperature Percentiles (Cold) |  | x |
Excess Cold Factor (ECF) |  | x |
Air Pollutant Exposure | Nitrogen Monoxide (NO) |  |  | x
Nitrogen Dioxide (NO2) |  |  | x
Ozone (O3) |  |  | x
Particulate Matter (PM2.5) |  |  | x
Table 7: Sensitivity sub-index components used in AusEnHealth to create heat, cold and air quality vulnerability indices. Theme | Variable | Heat | Cold | Air Quality
---|---|---|---|---
Socio-economic Status | Population Density (PD) | x | x | x
Median Income (MI) | x | x | x
Low Income (LI) | x | x | x
Education (Not Completed High School) (HS) | x | x | x
Unemployment Rate (UR) | x | x | x
Household Composition |  Elderly ($>65$) (ELD) | x | x | x
Infants ($<5$) (INF) | x | x | x
Single Parents (of Dependants) (SP) | x | x | x
Unpaid Childcare (UC) | x | x | x
Persons Requiring Significant Assistance (ASS) | x | x | x
Persons with a Disability (WD) | x | x | x
Living Alone (LA) | x | x | x
Language and Culture | English as a Second Language (SL) | x | x | x
Indigenous (IND) | x | x | x
Housing Conditions | Mobile Homes (MH) | x | x | x
Crowded Dwellings (CD) | x | x | x
Renters (RD) | x | x | x
Mortgage Payers (MP) | x | x | x
Health Status | Condition: Respiratory Diseases (RESP) |  |  | x
Condition: Asthma (AST) |  |  | x
Condition: Chronic Obstructive Pulmonary Disease (COPD) |  |  | x
Condition: Circulatory System Diseases (CSD) | x | x |
Condition: High Blood Pressure (BP) | x | x |
Condition: High Blood Cholesterol (BC) | x | x |
Condition: Cardiovascular Disease (CD) | x | x |
Health Risk Factors | Condition: Overweight (OW) | x | x | x
Condition: Obese (OB) | x | x | x
Condition: Smoker (SMO) | x | x | x
Condition: High Alcohol Consumption (ALC) | x | x | x
Table 8: Adaptive capacity sub-index components used in AusEnHealth to create heat, cold and air quality vulnerability indices. Theme | Variable | Heat | Cold | Air Quality
---|---|---|---|---
Health Services | Hospitals (HOSP) | x | x | x
Cool Places | Greenspace (Parklands) (GS) | x | x | x
Water Bodies (WB) | x | x |
Vegetation Index (NDVI) | x | x | x
Social Connectedness | Dwellings with a Vehicle (NV) | x | x | x
Access to Internet (INT) | x | x | x
Table 9: Health categories created from the AIHW MORT Books data set. Health Category | Mortality Cause
---|---
Heat | Coronary Heart Disease
Cerebrovascular Disease
Heart Failure
Cardiac Arrhythmia
Air Quality | Chronic Obstructive Pulmonary
All Cause | All Cause