COVID-19 cases by selected demographic and socioeconomic status indicators

This Double map Atlas uses publicly-available data of Total case and Active case counts of COVID-19 as at late January 2022 through to late February 2023 to show patterns of distribution by Local Government Area (LGA) across those States with sufficient cases to map [1].

Table 1: Total and Active cases, January 2022 to February 2023

Capital city	January		March		May		July		February
	Number	Rate per 100	Number	Rate per 100	Number	Rate per 100	Number	Rate per 100	Number	Rate per 100
Total cases
Sydney	557,565	10.8	1,107,474	20.6	1,551,903	28.9	1,892,141	34.9	2,431,376	46.2
Melbourne	444,916	8.9	998,660	20.0&	1,349,962	27.0	1,723,782	34.5	2,189,013	43.8
Adelaide	74,830	5.5	199,929	14.7	404,381	29.6	497,651	36.5	690,263	49.7
Active cases
Sydney	166,787	3.1	175,389	3.3	84,793	1.6	88,884	1.6	3,585	0.1
Melbourne	50,496	1.0	42,743	0.9	55,610	1.1	44,520	0.9	1,939	0.0
Adelaide	n.a.	n.a.	27,934	2.0	33,030	2.4	19,208	1.4	n.a.	n.a.

The following tables show the correlation coefficients for a selection of the indicators available in the Double map Atlas for the capital cities (Greater Capital City areas, as defined by the Australian Bureau of Statistics) with sufficient data to undertake this analysis. The data are shown for the periods from late January 2022 (Table 2) to early February 2023 (Table 6). It is clear that in January, socioeconomic disadvantage was a consistent influence on COVID-19 caseloads, other than in Adelaide, where only the birthplace indicator produced a notable result. In Sydney and Melbourne, cities with larger numbers of humanitarian migrants than Adelaide, correlations with Total cases were strong, and in Melbourne were also strong with Active cases.

However, by March, there was a marked change in the correlations between Active cases and several of the measures shown. In particular, the correlation between Active cases and socioeconomic disadvantage (IRSD) evident in Sydney and Melbourne (in January, r = -0.5 and -0.6 respectively) is reversed, so that, in March, Sydney has a positive correlation of 0.5 and Melbourne of 0.7. These correlations, which were similar in July, suggest that it is the most advantaged populations were then experiencing COVID-19. Correlations with the other variables also swapped markedly, adding to the assertion above as to those most likely to be infected.

By February 2023, with far fewer cases reported, the correlations were no longer evident.

Data for earlier periods were not available; had they been, the correlations would no doubt vary over time between these demographic and socioeconomic groups.

[1]Whereas Brisbane had sufficient Total case numbers to map, there are only seven LGAs, which means that the regression analysis included in the Double map atlas is likely to produce unreliable results.

Table 2: Correlation at Local Government Area level between Total cases or Active cases and selected demographic and socioeconomic status indicators, January 2022

Capital city	Birthplace (NES)¹	Humanitarian migration	Social housing	Unemployment benefit²	Blue collar workers³	Summary measure of socioeconomic disadvantage⁴
	Total cases, late January 2022
Sydney	0.4	0.6	0.6	0.5	0.4	-0.6
Melbourne	0.5	0.6	0.3	0.6	0.3	-0.6
Adelaide	0.7	0.1	0.4	0.1	-0.2	-0.1
	Active cases, late January 2022
Sydney	0.3	0.3	0.5	0.4	0.5	-0.5
Melbourne	0.3	0.6	-0.1	0.7	0.5	-0.6

Table 3: Correlation at Local Government Area level between Total cases or Active cases and selected demographic and socioeconomic status indicators, March 2022

Capital city	Birthplace (NES)¹	Humanitarian migration	Social housing	Unemployment benefit²	Blue collar workers³	Summary measure of socioeconomic disadvantage⁴
	Total cases, late March 2022
Sydney	0.1	0.3	0.4	0.2	0.2	-0.2
Melbourne	0.3	0.6	0.2	0.6	0.1	-0.6
Adelaide	0.6	0.1	0.4	0.1	-0.2	-0.1
	Active cases, late March 2022
Sydney	-0.3	-0.4	-0.2	-0.3	-0.4	0.5
Melbourne	-0.5	-0.7	0.3	-0.6	-0.6	0.7

Table 4: Correlation at Local Government Area level between Total cases or Active cases and selected demographic and socioeconomic status indicators, May 2022

Capital city	Birthplace (NES)¹	Humanitarian migration	Social housing	Unemployment benefit²	Blue collar workers³	Summary measure of socioeconomic disadvantage⁴
	Total cases, mid May 2022
Sydney	-0.1	0.1	0.2	0.1	0.1	0.0
Melbourne	0.1	0.4	0.2	0.5	0.3	-0.4
Adelaide	0.5	0.0	0.3	0.0	-0.2	0.0
	Active cases, mid May 2022
Sydney	-0.3	-0.3	-0.1	-0.3	-0.4	0.4
Melbourne	-0.5	-0.4	0.2	-0.3	-0.1	0.5

Table 5: Correlation at Local Government Area level between Total cases or Active cases and selected demographic and socioeconomic status indicators, July 2022

Capital city	Birthplace (NES)¹	Humanitarian migration	Social housing	Unemployment benefit²	Blue collar workers³	Summary measure of socioeconomic disadvantage⁴
	Total cases, mid July 2022
Sydney	-0.1	0.0	0.1	-0.1	-0.1	0.1
Melbourne	-0.1	0.2	0.2	0.3	0.2	-0.2
Adelaide	-0.3	-0.4	-0.3	-0.5	-0.3	0.5
	Active cases, mid July 2022
Sydney	-0.3	-0.4	-0.3	-0.5	-0.5	0.6
Melbourne	-0.2	-0.5	-0.1	-0.5	-0.2	0.5

Table 6: Correlation at Local Government Area level between Total cases or Active cases and selected demographic and socioeconomic status indicators, February 2023

Capital city	Birthplace (NES)¹	Humanitarian migration	Social housing	Unemployment benefit²	Blue collar workers³	Summary measure of socioeconomic disadvantage⁴
	Total cases, early February 2022
Sydney	-0.2	-0.1	0.1	-0.1	-0.1	0.1
Melbourne	-0.2	0.0	0.1	0.2	0.1	0.0
Adelaide	0.3	-0.1	0.2	0.0	-0.2	0.0
	Active cases, early February 2023
Sydney	0.0	0.0	0.1	-0.1	-0.2	0.1
Melbourne	0.1	-0.1	0.3	0.1	-0.1	-0.1

¹Birthplace – people born in predominantly non-English-speaking countries, as a proportion of the total population of the LGA, 2016 Census of Population and Housing, Australian Bureau of Statistics

²Unemployment benefit – people receiving the JobSeeker Payment or Youth Allowance (other) at June 2021

³Blue collar workers – includes people with occupations of technicians and trade workers, Machinery operators and drivers and Labourers, 2016 Census of Population and Housing, Australian Bureau of Statistics

⁴Summary measure of socioeconomic disadvantage – the Index of Relative Socio-economic Disadvantage (IRSD), compiled by the Australian Bureau of Statistics, with data from the 2016 Census of Population and Housing (note that an inverse (negative) correlation with the IRSD indicates a positive association with disadvantage)

Notes: Correlations in bold typeface are strong, data only shown for capital cities with sufficient cases and LGAs to undertake a correlation analysis.

Source: Data for Humanitarian migrants and Social housing are from the 2016 Census of Population and Housing, Australian Bureau of Statistics.

Publicly-available data of Total and Active cases on the Internet.