Example 1: Demographic characteristics
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This section demonstrates how the COVID‑19 Register can be used to explore demographic patterns in a cohort of COVID‑19 cases. Using linked data allows for missing data in one data set (such as age) to be supplemented by complete fields in other linked data sets and makes this a much richer analysis. This analysis also demonstrates how different definitions of location (state of notification versus state of usual residence) can tell a different story. The results shown in this report do not inform about the demographic characteristics of COVID‑19 cases in Australia, since it relates to a specific, non-representative sample. The aim is to give an indication of the richness of the demographic details that can be used in analyses as the data becomes more representative.
Age and sex
In this example, a hierarchical approach was used to select the variables for age and sex, depending on the completeness of the variables across the data sets once they were joined to the cohort. While the variables used to derive age have no missing values in the Medicare Consumer Directory, individuals in the cohort may not have a record in these datasets if they have never been enrolled in Medicare at the time of linkage.
For example, we found that once linked, around 5% of the analysis cohort did not have a value for age in the Medicare Consumer Directory. To address this, where the age was missing in one dataset, it was supplemented with an age variable from another dataset if present, using a hierarchical approach. This initial exploration of the completeness of variables in a study population once linked to administrative records is something for future researchers to consider and build into their own analysis projects.
Once missing age data had been supplemented so that every record had an age, in the analysis cohort, the median age was 29 years (interquartile range 15–44). Around 52% of the cohort were male and there were more males than females for all age groups between 25–94 (Figure 2). The highest proportion of people diagnosed with COVID‑19 was people aged 15–19 years (963 persons per 100,000 population), and the lowest was people aged 70–74 (205 per 100,000 population). These results were consistent with previously reported surveillance reports. The COVID‑19 Weekly Surveillance in NSW report for the week ending 4 December 2021 reported a median age of 28 years for cases, interquartile range 15–44 (NSW Health, 2021).
It is important to note that COVID‑19 testing rates were high during the analysis cohort time period as the Delta wave was occurring and this may influence the age and sex patterns outlined in this report.
The interactive data visualisation (Figure 2) can be customised by using the 2 drop-down menus to explore data by measure and sex. Access the Data table by selecting the tab. There is one drop-down menu in the Data table where the data can be filtered by sex.
Figure 2: Linked analysis cohort by age group and sex
The vertical bar chart shows COVID‑19 proportions where the proportions are generally higher among the younger age groups.
- Age group is based on age as at 31 December 2021.
- For details on how COVID‑19 proportions (people diagnosed with COVID‑19 per 100,000 population) were calculated and for how age and sex was derived, refer to Technical notes.
Source: AIHW analysis of COVID‑19 Register (version 1).
State/territory of usual residence
To explore geographical information in this report, the geographical region in which a person usually resides has been used, as represented by a ‘state of usual residence’. Linked data allows researchers to look at cross border effects and identify people who may have been diagnosed in a jurisdiction that is not where they usually live, which can be useful for health service planning purposes.
Defining state of usual residence
State of usual residence was derived from Statistical Area level 2 (SA2), a small area unit within the ABS’ 2016 Australian Statistical Geography Standard (ASGS). State of usual residence is not always the place in which a person had their COVID‑19 diagnosis, for example a person may have tested positive for COVID‑19 in New South Wales, but usually lives in the Victoria. More information on geography can be found in the Technical notes.
Around 91% of the analysis cohort using version 1 of the COVID‑19 Register had a usual residence within NSW (Table 1). This can be explained by the varying dates in which data has been supplied for this project, as detailed in Table A1 (see Technical notes). While Victoria, Queensland and WA were not included in the first version of the data used for this report, there are some people (1,410) included in the cohort who usually reside in these areas and who had a positive COVID‑19 test notified in one of the other participating jurisdictions. This demonstrates the utility of linking COVID‑19 cases on a national level, given that people move locations over time and may seek health care in a jurisdiction that differs from where they have a diagnostic test. Around 4% of people had a missing SA2, future reports will explore supplementing this missing field with alternative sources of geography, for example postcode from the notifications data.
|State/territory of usual residence|
Number (%) of people in analysis cohort
|Other state or territorya||1,410 (1.9%)|
- People who usually reside in WA, Qld, Vic, and Other Territories (Jervis Bay, Cocos (Keeling) Islands, Christmas Island and Norfolk Island) who had a positive COVID‑19 test notified in one of the other participating jurisdictions (NSW, SA, Tas, ACT, NT).
This section demonstrates how information from the Medicare Consumer Directory in the COVID‑19 Register could be used to explore remoteness areas of the residence of COVID‑19 cases. About 28% of the Australian population live in regional and remote areas (ABS 2022). Data show that people living in rural and remote areas have higher rates of hospitalisations, deaths, injury and also have poorer access to, and use of, primary health care services, compared with people living in Major cities (AIHW 2022b).
This analysis used the Australian Statistical Geography Standard Remoteness Structure, 2016 (ABS 2021) which defines remoteness areas in 5 classes of relative remoteness:
- Major cities
- Inner regional
- Outer regional
- Very remote.
These remoteness areas are centred on the Accessibility/ Remoteness Index of Australia, which is based on the road distances people have to travel for services (ABS 2021).
The majority (90%) of people in the analysis cohort usually resided in Major cities (Figure 3). This pattern was similar when looking at the COVID‑19 proportions (people diagnosed per 100,000 population) in the Australian population for this cohort. After adjusting for age, the highest number of people diagnosed per 100,000 population was in Major cities (841 per 100,000 population). There were slightly more males than females affected per 100,000 population in Major cities (847 versus 778 per 100,000 population, respectively).
The interactive data visualisation (Figure 3) can be customised by using the 2 drop-down menus to explore data by measure and sex. Access the Data table using the tab. There is one drop-down menu where the data can be filtered by sex.
Figure 3: Linked analysis cohort by remoteness area and sex
Chart shows the age-standardised COVID‑19 proportions are highest among those residing in major cities.
- Remoteness area is based on usual place of residence.
- Excludes records with missing information on remoteness area.
- For more details on how the COVID‑19 proportions (people diagnosed with COVID‑19 per 100,000 population) were computed and how sex was derived, refer to Technical notes.
Source: AIHW analysis of COVID‑19 Register (version 1)
This section demonstrates how information from the Medicare Consumer Directory in the COVID‑19 Register can be used to explore socioeconomic groups, as having access to material and social resources (such as secure housing and a stable income) are key determinants of health. There is little known about the impacts of COVID‑19 in different population groups or by factors such as education level or income, and this example demonstrates the rich source of information researchers can use in the COVID‑19 Register to explore these questions. A composite measure of socioeconomic position known as the Index of Relative Socio-economic Disadvantage (IRSD) (ABS 2018) was used based on the usual area of residence.
Definition of Index of Relative Socio-economic Disadvantage (IRSD)
The IRSD classifies individuals according to the socioeconomic characteristics of the area in which they live. It scores each area by summarising attributes of the population, such as low income, low educational attainment, high unemployment and jobs in relatively unskilled occupations. Areas can then be ranked according to their score. The population living in the 20% of areas with the greatest overall level of disadvantage is described as the ‘lowest socioeconomic group’. The 20% at the other end of the scale – the top fifth – is described as the ‘highest socioeconomic group’.
However, it should be noted that the IRSD reflects the overall or average level of disadvantage of the population of an area; it does not show how individuals living in the same area differ from each other in their socioeconomic position. Inequality estimates based on area-level measures of socioeconomic position underestimate inequalities because of the variation in socioeconomic position within areas (Mather et al. 2014).
Nearly half (45%) of all people in the analysis cohort were in the lowest socioeconomic group as shown in Figure 4. This pattern was consistent after adjusting for age and population size in each group, with the highest proportion occurring in the lowest socioeconomic group (1,463 per 100,000 population). There was a slightly higher proportion among males than females in all socioeconomic groups.
The higher proportion in the lowest socioeconomic group may be explained by more risk factors for exposure to the virus in this group, for instance they may be more likely to be casual frontline workers unable to work from home (Roder et al 2020).
The interactive data visualisation (Figure 4) can be customised by using the 2 drop-down menus to explore data by measure and sex. Access the Data table by selecting the tab.
Figure 4: Linked analysis cohort by socioeconomic group and sex
Charts shows the age-standardised COVID‑19 proportions, where the proportion is highest among the lowest socio-economic group. A drop-down menu allows the option to look at the percentage of people or number of people with the same socioeconomic group and sex breakdown.
- Socioeconomic groups are classified according to population-based quintiles using the Index of Relative Socio-economic Disadvantage (IRSD) based on Statistical Area level 2 (SA2) of current residence.
- Excludes records with missing information on IRSD ranking.
- For more details on how COVID‑19 proportions (people diagnosed with COVID‑19 per 100,000 population) were computed and how sex was derived, refer to Technical notes.
Source: AIHW analysis of COVID Register (version 1)
ABS (Australian Bureau of Statistics) (2018) Census of population and housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016, ABS, Australian Government, accessed 14 April 2023.
ABS (2021) Remoteness Structure, ABS website, accessed 14 April 2023.
ABS (2022) Regional population, ABS website, accessed 14 April 2023.
AIHW (2022b) Rural and remote health, AIHW, Australian Government, accessed 14 April 2023.
Mather T, Banks E, Joshy G, Bauman A, Phongsavan P and Korda RJ (2014) ‘Variation in health inequalities according to measures of socioeconomic status and age’ Australian and New Zealand Journal of Public Health, 38(5):436–440, doi:10.1111/1753-6405.12239.
NSW Health (2021) COVID-19 Weekly Surveillance in NSW – Epidemiological Week 48, ending 4 December 2021, NSW Health website, accessed 15 June 2023.
Roder C, Maggs C, McNamara BJ, et al. Area‐level social and economic factors and the local incidence of SARS‐CoV‐2 infections in Victoria during 2020. Med J Aust 2022; 216: 349‐356.