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