RESEARCH PAPER SERIES, 2019–20              25 FEBRUARY 2020

Life expectancy in Australia’s Commonwealth Electoral Divisions, 2016–2018

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.

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

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[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.