COVID-19 cases, vaccination rates and selected demographic and socioeconomic status indicators

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

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 two periods, late January (Table 1) and late March (Table 2). It is clear that in Janurary, 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 are strong, and in Melbourne are 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 (both r = -0.6) is reversed, so that Sydney has a positive correlation of 0.5 and Melbourne of 0.7. These correlations suggest that it is the most advantaged populations now experiencing COVID-19. Correlations with the other variables also swapped markedly, adding to the assertion above as to those most likely to be infected.

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

Capital cityBirthplace (NES)1Humanitarian migrationSocial housingUnemployment benefit2Blue collar workers3Summary 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 2: Correlation at Local Government Area level between Total cases or Active cases and demographic and socioeconomic status indicators, March 2022

Capital cityBirthplace (NES)1Humanitarian migrationSocial housingUnemployment benefit2Blue collar workers3Summary 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 -0.4 -0.4 -0.2 -0.4 -0.4 0.5
 Melbourne -0.6 -0.7 0.3 -0.6 -0.6 0.7

 

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.