25 February 2020
PDF version [587 KB]
Michael
Roden
Statistics and Mapping
Executive
summary
- Life expectancy is an intuitive measure of the overall health of
a population and is therefore useful when considering matters such as health
education and policy, access to services, social disadvantage and a range of health
risk factors.
- Life expectancy is a convenient measure for comparing
sub-populations, but as it requires more data and additional modelling for
smaller populations it does not appear to have been published previously for
Australian Commonwealth Electoral Divisions (‘divisions’).
- These divisional life expectancies complement those from the Australian
Bureau of Statistics (ABS) at state and Statistical Area 4 (SA4) levels and for
Aboriginal and Torres Strait Islanders, together with divisional mortality indices
in others’ earlier research.
- This statistical snapshot shows clear spatial differences in life
expectancy across Australia, with major city divisions higher than those in regional
areas and the divisions where years of female life most exceed males. The
implicit impact of socio-economic advantage-disadvantage and Indigenous status
on how long people may expect to live (under current mortality conditions) comes
through strongly in the divisional patterns.
Contents
Executive summary
Introduction
Key findings
Highest and lowest life expectancies
Female-male differences in life
expectancy
Socio-economic and Indigenous status
Conclusion
Further reading
Appendix
Introduction
Demographic indicators such as migration,
population growth, birth and death rates are not always attention-grabbing, yet
life expectancy often provokes a more visceral reaction. After all, this
is how many years we could expect to live in a given region under current
conditions. It is immediately understandable to say that Aboriginal and Torres
Strait Islanders can expect to live 8 years less than non-Indigenous people, or
that life expectancy in Zimbabwe fell by 17 years during the AIDS epidemic, or
that Japanese can expect to live past 85.
While Australia has some of the world’s longest life
expectancies, it is of interest to disaggregate them for various population
sub-groups as this can inform deliberations around public health,
education and access to services. To this end, 2019 Commonwealth Electoral
Divisions (‘divisions’), having large, similar-sized populations are good
subjects for this type of analysis.
Other measures of regional mortality, such as
age-standardised death rates,[1]
require less data to yield satisfactory results; however they are not as
intuitively understandable as life expectancy, and are not as sensitive to
mortality’s age-specific impact on total years of life.
The tool for calculating life expectancies is the life
table, a demographic model which assumes the observed mortality rates by age
(and sex) continue indefinitely in a closed population. While this is not a
realistic picture of how a birth cohort experiences mortality over the years—as
generally mortality rates improve a little each year—it does provide a
convenient snapshot of overall mortality conditions at a point in time. It also
avoids the need to speculate about future mortality. In that respect it is
similar to the total fertility rate, which assumes that age-specific fertility
rates for a given year will be experienced by a woman over her reproductive
years from age 15 to 49.
However, life tables rely on age-specific death rates which
in turn require sufficient deaths to avoid excessive volatility—a problem
especially in younger ages and where populations are small. This is the main
reason why life expectancies tend to be unavailable for smaller areas. Less
populous states and territories often have scant data and thus volatile
age-specific death rates, thus requiring a range of statistical ‘smoothing
techniques’. So if some states have barely adequate numbers of deaths to
produce death rates underpinning their life tables, then a disaggregation into
151 electorates will certainly face this issue.
To manage this, abridged models are built comparing
divisions’ age-specific death rates with their corresponding state rates. This
relationship is then used to calibrate the state age-specific death rates for
the general divisional pattern. This is essentially the approach the ABS takes
when producing life expectancies for SA4 regions.[2]
Such modelling seeks to remove regional age-specific death
rate volatility, while maintaining the overall relationship between the
division and state mortality. Hence divisional life expectancy at birth is
likely reasonable, while more detailed analysis (such as life expectancy at age
65 or the probability of surviving from 30 to 60) would be less reliable and
has not been undertaken here.
In this study, an average of two years’ deaths (June 2016–June
2018) together with June 2017 estimated resident population denominators
provided the life tables’ age-specific death rates.
Key findings
The 2016–18 life expectancy at birth [’e(0)’] across the 151
divisions was a median[3]
of 83.1 years, being 81.0 for males and 85.2 for females. Of all divisions, 121
(80%) had e(0) in the range 81 to 85 years. The gap between the highest
division (Bradfield) and lowest (Lingiari) was 10.8 years.
Highest and
lowest life expectancies
The 20 divisions with the highest and lowest e(0) are shown
in Figure 1. All of the longest-living divisions were in capital cities, and
all but two were in Sydney or Melbourne—peaking at over 86 years on Sydney’s
North Shore. The lowest 20 divisions were spread across most states and were
all regional apart from a handful of outer-urban divisions.
Figure 1: life expectancy at birth, highest and lowest
20 Commonwealth Electoral Divisions, 2016-18
The spatial distribution of life expectancy is more clearly
evident when mapped (Figure 2). While these patterns are already largely known,
as the ABS publishes e(0) for states and SA4s, as divisions are on average 70
per cent more granular than SA4s, regional e(0) differences can be examined
more closely.
Of the 7 divisions with lowest life expectancy (mapped dark
red), northern-remote Australia stands out with Lingiari, Kennedy, Leichhardt
and Durack, accompanied by Parkes. Bass in north-east Tasmania and Spence in
northern Adelaide were the 5th and 6th lowest respectively. Nearby Grey (SA)
and Lyons (Tas.) also have low e(0), confirming that poorer life expectancy is
not only a northern phenomenon in Australia.
Divisions around Sydney’s north and north-west, Melbourne’s
east, Adelaide’s Hills and Perth waterside tend to have the highest life
expectancies.
Figure 2: life expectancy at birth by Commonwealth Electoral Divisions, 2016-18
Female-male
differences in life expectancy
Nationally, females can expect to live 4.2 years longer than
males. Figure 3 gives the 20 divisions with the smallest gap in female-male
life expectancy and the 20 with the largest gap. Such differences range from
2.1 additional years for females in Clark to 5.9 years in Farrer. With the
notable exception of Lingiari, divisions where the female-male gap is smallest
are in the capital cities, while female life expectancy exceeds that for males
most strongly in regional/remote NSW and Qld, peri-urban Perth, Sydney’s
inner-west and also Spence and Grey in SA.
Figure 3: gap between female and male life expectancy at
birth, highest and lowest 20 Commonwealth Electoral Divisions, 2016-18
The reasons for e(0) differentials are many, varied, complex
and widely canvassed, such as access to medical care, smoking, accidents,
nutrition/obesity/diabetes and underlying social determinants of health.[4]
Though specific causes are not examined here, it is apparent from divisions’
remoteness classification (column colours) in Figures 1 and 2 that life
expectancy is generally higher in major cities than in regional areas,
particularly for males.
Socio-economic
and Indigenous status
The remoteness of an area does not of itself determine life
expectancy, but rather is indicative of relationships with a range of direct and
indirect health risk factors such as those previously mentioned. Nevertheless
the findings point to two factors long associated with health outcomes:
socio-economic status (SES) and Indigenous status. The ABS[5]
reports that life expectancy is on average 8.2 years lower for Aboriginal and
Torres Strait Islanders than the non-Indigenous population, while the NSW
Government[6]
recently cited a 4.8 year e(0) gap between the highest and lowest SES quintile
areas in that state.
Figure 4 shows the association between SES and life
expectancy across the 151 divisions (r2=0.64, p<0.0000).[7]
The gradient indicates that for every 50 points (i.e. more advantage) on the
2016 Census Index of Relative Socio-economic Advantage and Disadvantage (SEIFA)
an extra year of life expectancy is gained. Divisions are coloured according to
state, in some cases reflecting the overall state mortality situation, for
example in Tasmania, and in some jurisdictions indicating wide intra-state e(0)
disparities.
The median life expectancy in the most advantaged quintile
of 85.3 is 3.7 years higher than the median in the least advantaged quintile
(81.6). Such results are consistent with earlier studies examining the effect
that relative disadvantage and/or geographic remoteness has on mortality across
Australia.[8]
[9]
By adding divisional population proportions of Aboriginal
and Torres Strait Islanders to the regression model, the predictive power
increases to an adjusted r2 of 0.84 (p<0.0000). Thus 84 per cent
of the variation in divisional life expectancy can be explained by SES and Indigenous
status.[10]
These factors do not inherently determine life expectancy, but do point towards
many of the known causes of better and poorer health outcomes.
Figure 4: socio-economic status by life expectancy, Commonwealth Electoral
Divisions, 2016-18
Conclusion
As ‘health’ is a complex and multi-dimensional realm, the
formulation of an overarching metric is both problematic and inevitably
incomplete. While imperfect, life expectancy is arguably the best yardstick for
measuring the general health of a population, making comparisons and highlighting
inequities. Chronic morbidity is not accounted for except to the extent that it
shortens life, which it often does. Disability-free life expectancy, if the
requisite data is available, can also be illuminating.
So, while life expectancy cannot inform us about diabetes,
melanoma, strokes or depression, one may stand back and look at the overall
health of an electoral division through the prism of how many years a newborn
could presume to live in such a location.
This statistical snapshot shows that the number of years of
life is generally higher in capital city divisions, especially in the more
advantaged areas, and lower Aboriginal and Torres Strait Islander life
expectancy is also clearly evident at the divisional level.
Further
reading
A Lopez and T Adair, Slower increase in life expectancy
in Australia than in other high income countries: the contributions of age and
cause of death, Med J Aust, 210(9), 403-409, 2019
Australian Institute of Health and Welfare, Mortality
inequalities in Australia 2009–2011, Bulletin 124, 2014.
A Stephens et al, Socioeconomic, remoteness and sex
differences in life expectancy in New South Wales, Australia, 2001–2012: a
population-based study, BMJ Open, 7(1), 2016
P Clarke and A Leigh, Death, dollars, and degrees:
Socioeconomic status and longevity in Australia, Economic Papers, 30(3),
348–355, 2011.
V Raleigh, What is happening to life expectancy in the
UK?, The King’s Fund, 2019
R Layte and J Banks, Socioeconomic differentials in
mortality by cause of death in the Republic of Ireland, 1984–2008, European
Journal of Public Health, 26(3), 451–458, 2016
National Research Council (US) Panel on Understanding
Divergent Trends in Longevity in High-Income Countries, editors E Crimmins et
al, Chapter 2: Causes of Death, Health Indicators, and Divergence in Life
Expectancy, National Academies Press (US), 2011
Health Agenda Magazine, Life expectancy, how long can you
live?, HCF, 2019
Appendix
Years
of life expectancy at birth, 2019 Commonwealth Electoral Divisions, 2016-18
|
State
|
Division
|
Males
|
Females
|
Persons
|
|
State
|
Division
|
Males
|
Females
|
Persons
|
|
NSW
|
Banks
|
82.8
|
86.3
|
84.5
|
|
Vic
|
Aston
|
82.4
|
85.5
|
83.9
|
|
NSW
|
Barton
|
81.4
|
86.3
|
83.8
|
|
Vic
|
Ballarat
|
80.5
|
84.2
|
82.3
|
|
NSW
|
Bennelong
|
83.9
|
86.8
|
85.3
|
|
Vic
|
Bendigo
|
80.5
|
84.0
|
82.2
|
|
NSW
|
Berowra
|
83.8
|
87.3
|
85.5
|
|
Vic
|
Bruce
|
81.4
|
85.4
|
83.4
|
|
NSW
|
Blaxland
|
81.0
|
85.9
|
83.4
|
|
Vic
|
Calwell
|
81.5
|
84.9
|
83.2
|
|
NSW
|
Bradfield
|
85.3
|
87.4
|
86.3
|
|
Vic
|
Casey
|
82.4
|
86.0
|
84.1
|
|
NSW
|
Calare
|
78.9
|
83.1
|
81.0
|
|
Vic
|
Chisholm
|
84.4
|
87.2
|
85.8
|
|
NSW
|
Chifley
|
79.4
|
82.9
|
81.1
|
|
Vic
|
Cooper
|
82.1
|
85.2
|
83.6
|
|
NSW
|
Cook
|
82.9
|
86.9
|
84.8
|
|
Vic
|
Corangamite
|
83.0
|
86.1
|
84.5
|
|
NSW
|
Cowper
|
79.6
|
84.6
|
82.0
|
|
Vic
|
Corio
|
80.7
|
84.3
|
82.4
|
|
NSW
|
Cunningham
|
80.5
|
85.2
|
82.8
|
|
Vic
|
Deakin
|
83.3
|
86.4
|
84.8
|
|
NSW
|
Dobell
|
78.7
|
83.3
|
80.9
|
|
Vic
|
Dunkley
|
81.6
|
84.8
|
83.2
|
|
NSW
|
Eden-Monaro
|
80.1
|
84.5
|
82.2
|
|
Vic
|
Flinders
|
81.8
|
85.9
|
83.8
|
|
NSW
|
Farrer
|
78.8
|
84.7
|
81.7
|
|
Vic
|
Fraser
|
80.8
|
85.2
|
83.0
|
|
NSW
|
Fowler
|
80.9
|
85.8
|
83.3
|
|
Vic
|
Gellibrand
|
81.7
|
85.9
|
83.7
|
|
NSW
|
Gilmore
|
80.1
|
84.7
|
82.3
|
|
Vic
|
Gippsland
|
79.3
|
84.0
|
81.6
|
|
NSW
|
Grayndler
|
81.6
|
86.5
|
84.0
|
|
Vic
|
Goldstein
|
83.3
|
87.3
|
85.3
|
|
NSW
|
Greenway
|
82.0
|
84.9
|
83.4
|
|
Vic
|
Gorton
|
81.6
|
85.0
|
83.3
|
|
NSW
|
Hughes
|
82.2
|
85.8
|
84.0
|
|
Vic
|
Higgins
|
83.5
|
87.2
|
85.3
|
|
NSW
|
Hume
|
80.7
|
84.6
|
82.6
|
|
Vic
|
Holt
|
82.7
|
85.5
|
84.1
|
|
NSW
|
Hunter
|
79.0
|
83.4
|
81.1
|
|
Vic
|
Hotham
|
82.6
|
87.0
|
84.7
|
|
NSW
|
Kingsford Smith
|
81.5
|
85.9
|
83.7
|
|
Vic
|
Indi
|
80.5
|
84.5
|
82.5
|
|
NSW
|
Lindsay
|
79.0
|
83.8
|
81.3
|
|
Vic
|
Isaacs
|
82.8
|
86.4
|
84.6
|
|
NSW
|
Lyne
|
79.7
|
85.0
|
82.2
|
|
Vic
|
Jagajaga
|
83.7
|
86.6
|
85.1
|
|
NSW
|
Macarthur
|
80.4
|
84.5
|
82.4
|
|
Vic
|
Kooyong
|
84.9
|
87.4
|
86.1
|
|
NSW
|
Mackellar
|
83.7
|
86.7
|
85.2
|
|
Vic
|
La Trobe
|
82.9
|
85.8
|
84.3
|
|
NSW
|
Macquarie
|
80.9
|
85.2
|
83.0
|
|
Vic
|
Lalor
|
81.0
|
84.7
|
82.8
|
|
NSW
|
McMahon
|
81.4
|
85.3
|
83.3
|
|
Vic
|
Macnamara
|
82.9
|
86.5
|
84.7
|
|
NSW
|
Mitchell
|
84.6
|
87.6
|
86.1
|
|
Vic
|
Mallee
|
79.4
|
84.1
|
81.7
|
|
NSW
|
New England
|
79.2
|
84.0
|
81.5
|
|
Vic
|
Maribyrnong
|
82.4
|
86.7
|
84.5
|
|
NSW
|
Newcastle
|
79.5
|
83.5
|
81.4
|
|
Vic
|
McEwen
|
82.3
|
85.2
|
83.7
|
|
NSW
|
North Sydney
|
85.0
|
87.5
|
86.2
|
|
Vic
|
Melbourne
|
82.2
|
86.3
|
84.2
|
|
NSW
|
Page
|
79.4
|
84.5
|
81.9
|
|
Vic
|
Menzies
|
84.5
|
87.1
|
85.8
|
|
NSW
|
Parkes
|
76.7
|
82.3
|
79.4
|
|
Vic
|
Monash
|
81.5
|
84.6
|
83.0
|
|
NSW
|
Parramatta
|
81.5
|
85.3
|
83.4
|
|
Vic
|
Nicholls
|
80.5
|
84.2
|
82.3
|
|
NSW
|
Paterson
|
79.9
|
84.1
|
81.9
|
|
Vic
|
Scullin
|
82.5
|
85.7
|
84.1
|
|
NSW
|
Reid
|
83.5
|
87.9
|
85.7
|
|
Vic
|
Wannon
|
80.9
|
84.5
|
82.7
|
|
NSW
|
Richmond
|
80.2
|
84.9
|
82.5
|
|
Vic
|
Wills
|
81.9
|
85.9
|
83.8
|
|
NSW
|
Riverina
|
79.0
|
84.1
|
81.5
|
|
SA
|
Adelaide
|
80.4
|
84.5
|
82.4
|
|
NSW
|
Robertson
|
80.6
|
85.0
|
82.7
|
|
SA
|
Barker
|
79.9
|
84.1
|
81.9
|
|
NSW
|
Shortland
|
80.6
|
85.0
|
82.8
|
|
SA
|
Boothby
|
82.1
|
86.7
|
84.3
|
|
NSW
|
Sydney
|
81.7
|
86.3
|
83.9
|
|
SA
|
Grey
|
78.1
|
83.5
|
80.7
|
|
NSW
|
Warringah
|
84.8
|
87.6
|
86.2
|
|
SA
|
Hindmarsh
|
80.7
|
85.2
|
82.9
|
|
NSW
|
Watson
|
81.0
|
85.9
|
83.4
|
|
SA
|
Kingston
|
80.3
|
84.3
|
82.3
|
|
NSW
|
Wentworth
|
84.2
|
87.6
|
85.9
|
|
SA
|
Makin
|
81.7
|
84.8
|
83.2
|
|
NSW
|
Werriwa
|
80.9
|
84.6
|
82.7
|
|
SA
|
Mayo
|
82.9
|
86.3
|
84.5
|
|
NSW
|
Whitlam
|
80.4
|
84.4
|
82.4
|
|
SA
|
Spence
|
77.9
|
83.1
|
80.5
|
|
ACT
|
Bean
|
81.7
|
85.1
|
83.3
|
|
SA
|
Sturt
|
82.7
|
86.1
|
84.4
|
|
ACT
|
Canberra
|
81.5
|
85.3
|
83.4
|
|
NT
|
Lingiari
|
74.5
|
76.5
|
75.5
|
|
ACT
|
Fenner
|
83.0
|
85.6
|
84.3
|
|
NT
|
Solomon
|
79.2
|
83.4
|
81.2
|
| State |
Division |
Males |
Females |
Persons |
| Qld |
Blair |
79.3 |
83.5 |
81.4 |
| Qld |
Bonner |
82.4 |
86.0 |
84.2 |
| Qld |
Bowman |
81.9 |
85.6 |
83.7 |
| Qld |
Brisbane |
82.3 |
85.2 |
83.7 |
| Qld |
Capricornia |
79.1 |
84.4 |
81.7 |
| Qld |
Dawson |
79.1 |
84.5 |
81.7 |
| Qld |
Dickson |
82.8 |
85.5 |
84.1 |
| Qld |
Fadden |
81.3 |
85.2 |
83.2 |
| Qld |
Fairfax |
82.6 |
85.9 |
84.2 |
| Qld |
Fisher |
82.0 |
86.2 |
84.1 |
| Qld |
Flynn |
79.2 |
84.6 |
81.9 |
| Qld |
Forde |
79.6 |
84.2 |
81.9 |
| Qld |
Griffith |
81.3 |
85.8 |
83.5 |
| Qld |
Groom |
81.0 |
84.7 |
82.8 |
| Qld |
Herbert |
78.2 |
84.1 |
81.1 |
| Qld |
Hinkler |
78.9 |
84.1 |
81.4 |
| Qld |
Kennedy |
77.5 |
83.2 |
80.3 |
| Qld |
Leichhardt |
77.9 |
83.3 |
80.5 |
| Qld |
Lilley |
80.4 |
85.2 |
82.7 |
| Qld |
Longman |
80.1 |
84.1 |
82.0 |
| Qld |
Maranoa |
79.2 |
83.2 |
81.1 |
| Qld |
McPherson |
81.9 |
86.2 |
84.0 |
| Qld |
Moncrieff |
81.7 |
85.4 |
83.5 |
| Qld |
Moreton |
81.4 |
85.4 |
83.4 |
| Qld |
Oxley |
80.4 |
84.6 |
82.4 |
| Qld |
Petrie |
80.7 |
84.4 |
82.5 |
| Qld |
Rankin |
80.2 |
83.4 |
81.8 |
| Qld |
Ryan |
84.0 |
86.7 |
85.3 |
| Qld |
Wide Bay |
80.1 |
84.7 |
82.3 |
| Qld |
Wright |
80.9 |
85.1 |
83.0 |
| WA |
Brand |
80.4 |
84.8 |
82.6 |
| WA |
Burt |
80.4 |
85.4 |
82.8 |
| WA |
Canning |
80.3 |
85.5 |
82.8 |
| WA |
Cowan |
82.2 |
85.2 |
83.6 |
| WA |
Curtin |
82.8 |
86.8 |
84.7 |
| WA |
Durack |
78.2 |
81.9 |
80.0 |
| WA |
Forrest |
81.1 |
85.0 |
83.0 |
| WA |
Fremantle |
81.7 |
85.9 |
83.8 |
| WA |
Hasluck |
80.3 |
85.4 |
82.8 |
| WA |
Moore |
83.1 |
87.6 |
85.3 |
| WA |
O'Connor |
78.8 |
83.5 |
81.1 |
| WA |
Pearce |
82.1 |
85.8 |
83.9 |
| WA |
Perth |
80.8 |
85.6 |
83.1 |
| WA |
Stirling |
81.6 |
85.9 |
83.7 |
| WA |
Swan |
80.6 |
85.1 |
82.8 |
| WA |
Tangney |
84.1 |
87.4 |
85.7 |
| Tas |
Bass |
78.3 |
82.7 |
80.4 |
| Tas |
Braddon |
79.0 |
83.1 |
81.0 |
| Tas |
Clark |
80.6 |
82.7 |
81.6 |
| Tas |
Franklin |
81.0 |
84.4 |
82.6 |
| Tas |
Lyons |
78.8 |
83.2 |
80.9 |
[1]
Death rates which have been adjusted for a population’s age structure, thus
facilitating mortality comparisons in (say) a region with a relatively young
population versus a more elderly region.
[2]
Australian Bureau of Statistics (ABS), Life
Tables, States, Territories and Australia, 2016-2018, cat. no. 3302.0.55.001
[3]
That is, the age at which half of the 151 divisions have a lower life
expectancy, and half a higher.
[4]
For example, WHO
strategic meeting on Social determinants of health (2019), World Health
Organization, Geneva
[5]
ABS, Life
Tables for Aboriginal and Torres Strait Islander Australians, 2015-2017,
cat. no. 3302.0.55.003
[6]
NSW Government, Life
expectancy (2017), HealthStats NSW
[7]
The r2 indicates that
64% of the variability in life expectancy is explained by SES. The very low p-value signifies
a high probability that SES is related to life expectancy.
[8]
P Clarke et al, Mortality by Commonwealth Electoral Divisions in Australia,
Public Health Information Development Unit, Torrens University Australia and
Centre for Health Policy, University of Melbourne, 2016.
[9]
G Draper et al, Health Inequalities in Australia: Mortality, Health
Inequalities Monitoring Series No. 1. AIHW cat. no. PHE 55, Canberra, 2004.
[10]
Interestingly, square-root transformation of the divisional proportions of
Aboriginal & Torres Strait Islander population gives a multiple regression
model with adjusted r2 of 0.88. That is, there is a better linear
relationship between e(0) and the square-root of the Indigenous percentage than
simply the Indigenous percentage, arguably due to the moderation of outlying
values.
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