Injury mortality and socioeconomic influence in Australia 2015–16

Web report

Last updated: 13 Nov 2019

Topic: Injury

AIHW

Australian Institute of Health and Welfare (2019) Injury mortality and socioeconomic influence in Australia 2015–16, AIHW, Australian Government, accessed 20 April 2024.

APA

Australian Institute of Health and Welfare. (2019). Injury mortality and socioeconomic influence in Australia 2015–16. Retrieved from https://www.aihw.gov.au/reports/injury/injury-mortality-and-socioeconomic-influence-in-au

MLA

Injury mortality and socioeconomic influence in Australia 2015–16. Australian Institute of Health and Welfare, 13 November 2019, https://www.aihw.gov.au/reports/injury/injury-mortality-and-socioeconomic-influence-in-au

Vancouver

Australian Institute of Health and Welfare. Injury mortality and socioeconomic influence in Australia 2015–16 [Internet]. Canberra: Australian Institute of Health and Welfare, 2019 [cited 2024 Apr. 20]. Available from: https://www.aihw.gov.au/reports/injury/injury-mortality-and-socioeconomic-influence-in-au

Harvard

Australian Institute of Health and Welfare (AIHW) 2019, Injury mortality and socioeconomic influence in Australia 2015–16, viewed 20 April 2024, https://www.aihw.gov.au/reports/injury/injury-mortality-and-socioeconomic-influence-in-au

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Introduction

According to the World Health Organization (WHO), social inequalities and disadvantage are the main reason for unfair and avoidable differences in health outcomes and life expectancy across groups in society (WHO 2011). Within the Australian population, health and illness vary, generally following a gradient with improvements in overall health linked to improvements in socioeconomic status (SES) (AIHW 2016b). People from poorer social or economic circumstances are known to be at greater risk of poor health; to have higher rates of illness, disability and death; and to live shorter lives than those who are more advantaged (AIHW 2016b).

Injury and socioeconomic influence

Injury is a leading cause of morbidity, disability and premature mortality in Australia (AIHW 2016a; AIHW: Henley & Harrison 2018). Socioeconomic status is an important determinant of injury; however, the relationship between SES and injury has been shown to vary. Very little research on the relationship between injury and SES has been undertaken in Australia and only a small number of international studies and reviews have been published.

The 2009 WHO report Socioeconomic differences in injury risks: a review of findings and a discussion of potential countermeasures reviewing mortality and morbidity studies identified reports published over a 17-year period, predominantly in Europe (Laflamme et al. 2009). Studies were included that addressed the leading causes of injury, both intentional and unintentional, including interpersonal violence, intentional self-harm, traffic, falls, drowning, poisoning and burns.

The review found that, among studies looking at the relationship between SES and injury mortality, the injury mortality of people with low SES tended to be significantly higher than those with high SES. The effect was found for many causes of injury including transport crashes, suicide, interpersonal violence, accidental poisoning and burns. A relationship between injury morbidity and SES was also identified, but less consistently than for mortality studies.

Other reviews and studies have examined the relationship between SES and injury in children and young people. Birken & MacArthur (2004) reviewed studies on the relationship between SES and injury in children throughout Britain and Wales. Evidence was reported demonstrating the link between low SES and higher rates of injury mortality for falls, unintentional poisoning, pedal-cycle crashes, burns and drownings. Similar results were found with respect to the relationship between SES and injury morbidity. Birken & MacArthur concluded that ‘…the inverse relationship between socioeconomic level and injury morbidity and mortality is pervasive, persistent and profound’.

A study of the effect of socioeconomic status (SES) on injury among Victorians found that lower socioeconomic status was associated with increased risk of injury at all levels and with deaths, hospital admissions, and emergency department presentations (Stokes et al. 2001/02). The report also found that persons from areas of low socioeconomic status were more likely to suicide and self-harm or to suffer homicide and other assaults, while persons from areas of high socioeconomic status were more likely to sustain a severe fall.

The aim of this report is to examine the effects of socioeconomic status on injury mortality in Australia, using national sources of data.

How is SES measured?

One of the complicating factors identified by researchers examining the relationship between SES and injury is in the measurement of SES itself. SES is a complex concept that can be measured at multiple levels (for example, at an individual level or area level). A variety of indicators such as education, occupation and income can be used individually or in combination to define a person’s socioeconomic position. The potential for differences in measures of SES to influence the outcomes reported above have been highlighted in the reviews described above. In Australia, much of the research examining SES is based on the Australian Bureau of Statistics (ABS) area-based Index of Relative Socio-economic Disadvantage (IRSD) (ABS 2013).

The IRSD is a ranking based on geographic areas and is used to stratify the population by SES. The index is compiled from information collected in the Census of Population and Housing and represents the socioeconomic conditions of Australian geographic areas by measuring aspects of disadvantage. The IRSD scores each area by summarising attributes of their populations, such as low income, low educational attainment, high unemployment and jobs in relatively unskilled occupations. Areas can then be ranked by their IRSD score and are classified into groups based on their rank.

Commonly, 5 categories are used, meaning the IRSD commonly describes the population living in the 20% of areas with the greatest overall level of disadvantage as ‘living in the lowest socioeconomic areas’ or the ‘lowest socioeconomic group’. The 20% at the other end of the scale—the top fifth—is described as the ‘living in the highest socioeconomic areas’ or the ‘highest socioeconomic group’.

It is important to understand that the IRSD reflects the overall or average socioeconomic position of the population of an area; it does not show how individuals living in the same area might differ from each other in their socioeconomic position.

Methods

Data for this report are extracted from nationally held databases. Data on fatal cases are from the AIHW National Mortality Database (NMD). The NMD comprises cause of death unit record file (CODURF) data, which are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Information Coronial System (NCIS) and coded by the ABS.

Which injury deaths were included?

Injury deaths that met all of the following criteria were included in this report:

deaths that occurred between 1 July 2009 to 30 June 2016 and had been registered by 31 December 2016; and
- the underlying cause of death (UCoD) was an external cause in the range V01–Y36; or
- at least 1 multiple cause of death (MCoD) was an external cause code in the range V01–Y36 and at least 1 other MCoD was a code for Injury (S00–T75, T79).

In some tables, rates are accompanied by a rate ratio. The rate ratio is equal to the rate for people living in low SES areas, divided by the rate for people from all SES areas combined. If the rate ratio is greater than 1, then the rate for people living in low SES areas was higher than the rate for people from all SES areas combined.

In tables and charts, unless stated otherwise:

age is calculated based on the date of death
in tables by age group and sex, separations for which age and sex were not reported were included in totals
rates were age-standardised (see ‘Appendix A: Data issues’)
trends were analysed using negative binomial regression, as described in Berry & Harrison (2006). (See ‘Appendix A: Data issues’ for more information).

Structure of this report

The broad topics in this report are:

SES and its relationship to injury mortality in Australia
trends in injury mortality by SES (the number and rate of separations and estimated cases over time, by age and by sex).

SES and injury mortality presents information on the relationship between SES and injury mortality. It explores SES by age and by sex, as well as by a number of external causes of injury.

Trends over time looks at changes over time in the rate of injury deaths for the most disadvantaged and least disadvantaged socioeconomic groups. In addition, it also looks at changes over time by 6 age groups and by selected external causes of injury.

Appendix A: Data issues provides summary information on the data used in the report; notes on the presentation of data; the population estimates used to calculate population rates; and analysis methods.

Box 1.1: Key terms and concepts

An external cause is the environmental event, circumstance or condition that was the cause of injury or poisoning. Multiple causes of death (MCoD) are defined as all causes listed on the death certificate. This includes the underlying cause of death and all associated causes of death. This information is useful in describing the role of all diseases/conditions involved in deaths. The underlying cause of death (UCoD) is a code representing the external cause of the injury which initiated the train of morbid events leading directly to a person’s death, according to information available to the coder.

The diseases or conditions recorded on the death certificate consist of:

the cause that led directly to the death (the UCoD)
the cause that gave rise to the underlying cause of death
the causes of death that contributed to the death but were not related to the disease or condition causing it.

Coding is according to the 10th revision of the International Classification of Diseases (ICD-10), which includes a chapter for injury and another for external causes of injuries and other conditions. Rules that form part of the ICD determine which cause should be coded as the UCoD.

Box 1.2: Socioeconomic status

Socioeconomic factors both influence and reflect health, and many recent AIHW reports on injury include measures of socioeconomic status (SES). The information is usually based on the Socio-Economic Indexes for Areas (SEIFA) developed by the ABS. SEIFA indexes rank areas in Australia according to relative socioeconomic advantage and disadvantage, estimated using information collected every 5 years in the national Census of Population and Housing (ABS 2018b). Each of the 4 SEIFA indexes is based on a set of Census variables, such as the percentage of people who lived in a low (or high) income household, or who had particular educational attainment or employment status; and the percentage of dwellings with characteristics such as high rent or mortgage repayments, or spare rooms available.

SEIFA indexes can be applied to various types of areas into which Australia has been divided, such as ABS Statistical Areas, postcode areas and remoteness zones. The socioeconomic comparisons in AIHW injury mortality reports have been based on SEIFA values that were calculated for ABS Statistical Area 2 (SA2) areas, which are quite large, having an average population of about 10 thousand people. SA2-based SEIFAs were used because place of residence (before death) of the people who are the subject of the reports is only specified to SA2 level in the AIHW National Mortality Database (NMD). SA2 areas are ranked (that is, sorted) according to a SEIFA index (usually the Index of Relative Socio-Economic Disadvantage, IRSD) and then divided into groups, each including about one-fifth (‘quintiles’) or one-tenth (‘deciles’) of the total population of Australia. The deaths included in a report can each be assigned to 1 of the groups, according to the person’s SA2 of residence before death. If suitable population data are available, then mortality rates can be calculated for each of the quintiles or deciles. Injury mortality rates are generally highest for the quintile comprising SA2s ranked lowest in terms of IRSD, and lowest for the quintile comprising the SA2s ranked highest in terms of IRSD.

Several interrelated problems arose when considering use of that method for this report, which led to a decision not to include SEIFA-based results by Indigenous status.

First, suitable population files (that is, estimated numbers by SEIFA quintile or decile, age group, sex and year) were not available for Indigenous Australians. Files of that type are available for the total population, and Indigenous population tables are available for the related topic of remoteness area, by age group, by sex and by year (ABS 2019).

Second, there is considerable variation of socioeconomic status within SA2 areas and the SEIFA-based decile of an SA2 often differs from the deciles of the smaller SA1 areas from which it is composed. Use of SEIFA measures based on the smaller SA1 areas of residence would be better, but the NMD does not currently provide for that.

Third, there are statistical and conceptual reasons to doubt whether SEIFA indexes are a good basis for measuring the relative socioeconomic status of Indigenous Australians. A statistical reason is the high over-representation of Indigenous people at the lower levels of SEIFA-based categories, which reduces analytical power and might obscure relationships of interest (Shepherd et al. 2012). Conceptual reasons include the sometimes large differences between the Indigenous population and other Australians in characteristics such as place of residence (for example, with Indigenous Australians comprising a large proportion of the population in remote regions) and household composition (for example, in the 2016 Census, Indigenous one-family households were 2.5 times more likely to be one-parent families than were other one-family households) (ABS 2018a). It has been suggested that factors other than those included in classifications such as SEIFA might have a strong effect on Indigenous Australians: for example, kinship networks and connection to Country (Shepherd et al. 2012). Investigations and deliberations by the Commonwealth Grants Commission (CGC) have made a case for developing a new measure of Indigenous relative disadvantage (CGC 2012).