Material and method
On 3 December 2020, sociodemographic data were retrieved from Oslo City Government's statistics database, whose data is sourced from Statistics Norway (4). Data on the cumulative number of registered cases of SARS-CoV-2 confirmed in a polymerase chain reaction (PCR) test as of 3 December 2020 were obtained from Oslo City Government's Agency of Health (5).
In the analyses, we applied the same definitions as those used in the statistics database (4). Immigrants were defined as people born abroad to foreign parents (formerly called first-generation immigrants) or Norwegian-born with two parents born abroad (formerly called second-generation immigrants). Cramped living conditions were defined as households with more than one person per room or less than 20 square metres per person. Multi-family households were defined as households where the occupiers are made up of two or more families. Low level of education referred to 21–29-year-olds who had not completed upper secondary school. People aged 30–59 with no attachment to the labour market were defined as unemployed. Mean income was calculated on the basis of gross income per person over 16 years of age.
We created two composite variables. The formative variable socioeconomic status was created by converting the variables 'education', 'income' and 'employment' to the same scale (0 to 1) and taking an equally weighted average of these (6). Similarly, we converted the proportions in cramped living conditions and multi-family households to a variable that we called household density.
We first examined bivariate correlations for all variables in three of the statistics database's sub-categories: population, living conditions and living conditions indicators, with registered cases of SARS-CoV-2 infection. In the further analysis, we selected variables that had a significant correlation with infection, with p <0.01 (alone or as part of a composite variable). We then calculated Pearson's correlation coefficient between these variables and infection rates. Next, we performed a linear regression analysis using infection rates in the districts as a dependent variable and the two composite variables and immigrant ratio as independent variables, both separately and simultaneously in a multi-adjusted analysis. This analysis was weighted for the number of people in the districts.
The assumption of normal distribution was checked using a histogram of the residual plot. The analyses were performed in SPSS version 27.
The data are publicly available and are not personally identifiable. There was therefore no need to apply for permission to use the data.