Income differences in COVID-19 incidence and severity in Finland among people with foreign and native background: A population-based cohort study of individuals nested within households.

BACKGROUND: Although intrahousehold transmission is a key source of Coronavirus Disease 2019 (COVID-19) infections, studies to date have not analysed socioeconomic risk factors on the household level or household clustering of severe COVID-19. We quantify household income differences and household clustering of COVID-19 incidence and severity. METHODS AND
FINDINGS: We used register-based cohort data with individual-level linkage across various administrative registers for the total Finnish population living in working-age private households (N = 4,315,342). Incident COVID-19 cases (N = 38,467) were identified from the National Infectious Diseases Register from 1 July 2020 to 22 February 2021. Severe cases (N = 625) were defined as having at least 3 consecutive days of inpatient care with a COVID-19 diagnosis and identified from the Care Register for Health Care between 1 July 2020 and 31 December 2020. We used 2-level logistic regression with individuals nested within households to estimate COVID-19 incidence and case severity among those infected. Adjusted for age, sex, and regional characteristics, the incidence of COVID-19 was higher (odds ratio [OR] 1.67, 95% CI 1.58 to 1.77, p < 0.001, 28.4% of infections) among individuals in the lowest household income quintile than among those in the highest quintile (18.9%). The difference attenuated (OR 1.23, 1.16 to 1.30, p < 0.001) when controlling for foreign background but not when controlling for other household-level risk factors. In fact, we found a clear income gradient in incidence only among people with foreign background but none among those with native background. The odds of severe illness among those infected were also higher in the lowest income quintile (OR 1.97, 1.52 to 2.56, p < 0.001, 28.0% versus 21.6% in the highest quintile), but this difference was fully attenuated (OR 1.08, 0.77 to 1.52, p = 0.64) when controlling for other individual-level risk factors-comorbidities, occupational status, and foreign background. Both incidence and severity were strongly clustered within households: Around 77% of the variation in incidence and 20% in severity were attributable to differences between households. The main limitation of our study was that the test uptake for COVID-19 may have differed between population subgroups.
CONCLUSIONS: Low household income appears to be a strong risk factor for both COVID-19 incidence and case severity, but the income differences are largely driven by having foreign background. The strong household clustering of incidence and severity highlights the importance of household context in the prevention and mitigation of COVID-19 outcomes.

Introduction

Following the outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic, evidence has accumulated concerning the unequal distribution of infections, severity, and mortality across socioeconomic groups [1-14]. Various studies have found a higher COVID-19 incidence among people with low education or low income [1-6], but few of them controlled for other established risk factors, such as household size and composition, occupational exposures, ethnicity, or foreign background [15-18]. It thus remains unclear, whether the higher incidence among people with low socioeconomic position is due to occupational exposures, larger households, or other sociodemographic risk factors that are more common among people with lower socioeconomic position. Higher rates of severe COVID-19 resulting in hospitalization or death have also been reported among these groups [7-9,11]. However, as many studies assess these outcomes among the general population as opposed to the infected [7-9], it remains unclear whether socioeconomic position influences the risk of exposure and infection, or case severity, i.e., outcome once infected, or both. The few previous studies that assess case fatality or mortality among the infected have reported inconsistent findings on the impact of socioeconomic factors [10,11,19]. In fact, most studies on the unequal burden of COVID-19 have not been representative of the general population [15,20].

Another key limitation of the current literature on the socioeconomic differences in COVID-19 outcomes is that COVID-19 risk factors have rarely been assessed on the household level [15], and most studies have relied on area-level socioeconomic measures [1-3,5,6,8-13,19]. The lack of household-level data is a major limitation because both socioeconomic risk factors and poor health tend to cluster in households. People who commute or work in high-risk occupations share the risk with their household members, for example, and the probability of secondary transmission depends on household composition. Furthermore, although multiple studies have shown that intrahousehold transmission is a significant source of new COVID-19 infections [21,22], there are no studies on the household clustering of severe COVID-19 cases. Quantifying the significance of the household context for both incidence and severity enhances our understanding of the microlevel dynamics of the COVID-19 pandemic. This is also a crucial public health issue in that the household clustering of severe illness, particularly among socioeconomically deprived or otherwise vulnerable households, could result in the widening of health inequalities [23].

This study aims to address these limitations of the current literature. Using Finnish total population data on individuals nested within households, we investigate how household income is associated with (1) the risk of COVID-19 infections; and (2) the risk of severe illness once infected. We use household income as an indicator for the multifaceted concept of socioeconomic position. We focus on household income as it is reliably measured and available for all individuals irrespective of their age, employment, or immigrant status. We examine whether the socioeconomic gradient in COVID-19 outcomes found in previous studies is independent of other important COVID-19 risk factors, such as work and school exposures of the household members, household size, foreign background, and comorbidities. We also assess income differences in COVID-19 infections and severity across households of different size and for people with native and foreign background. Furthermore, we quantify the household clustering of COVID-19 infections and severe illness.

Methods

This study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 Checklist). We did not have a pre-documented analysis plan. The modelling strategy and analyses were planned in spring 2021 and revised according to reviewer feedback.

Setting and study population

We used individual-level data on the total population of Finland living in private households at the end of 2018, and alive at the end of 2019 (Fig 1), to model the risk of COVID-19 infections from 1 July 2020 to 22 February 2021, and severe illness among those infected from 1 July to 31 December 2020. The different time ranges are due to differences in data availability between sources. Our main analyses are restricted to working age and younger populations, i.e., all members of households with at least 1 person under the age of 65 at the end of 2019. These 1.9 million households comprised 4.3 million people: 76% of them consisted of 1 family or a couple, and most of the rest (17%) were single-person households. The proportion of households with at least 1 member aged 65 or over was 8%.

Fig 1

Data extraction flow chart.

We obtained individual- and household-level data on demographic and socioeconomic characteristics from the population registers maintained by Statistics Finland, and measures of COVID-19 incidence and case severity from registers maintained by the Finnish Institute for Health and Welfare. The national health care registers cover both public and private sector health care providers. The individual-level data were linked using personal identification codes assigned to each resident and household-level data using a unique household identification code. Individuals were nested within households at the end of 2018 because more recent information was unavailable. All the covariates were measured at the most recent time point available in the registers (2017 to 2019).

Outcomes

Information on laboratory-confirmed COVID-19 cases (ICD-10 code U07.1) was obtained from the National Infectious Diseases Register. As an indicator of severe illness, we used inpatient care lasting at least 3 consecutive days with a primary or secondary diagnosis of COVID-19. Data on inpatient care came from the Care Register for Health Care.

Household-level characteristics

Annual household income, including all taxable income of all household members in 2018, was divided by the number of consumption units using the Organisation for Economic Co-operation and Development’s (OECD) modified equivalence scale and categorized into quintiles. Household size was originally categorized as 1, 2, 3, or 4+ based on the number of household members at the end of 2018. We changed the categorization to 1, 2, 3, 4, or 5+ following a peer reviewer’s observation that the 4+ class could be further divided.

People are exposed to COVID-19 infection not only through their own, but also their household members’ social contacts. We included several indicators that reflect such indirect exposures to social contacts at work, school, and daycare. The indicators are based on the household members’ main occupational activity and the presence and age of children in the household. These dummy variables identified households with at least 1 (1) lower nonmanual employee; (2) self-employed person; (3) manual worker; (4) student in secondary or tertiary education; (5) child aged 13 to 15; (6) child aged 7 to 12; and (7) child aged below the age of 7. Information on occupational status was from the year 2017 and student status from the year 2018. The ages of the children in the household were measured at the end of 2019. We based the age categories on educational activity outside the household during the study period: Children in primary education and care (aged under 13) had contact teaching, older children (age 13 to 15) alternated between contact and remote teaching, and most secondary- and higher-level students studied remotely.

The postal code of the permanent place of residence at the end of 2019 was used to define regional characteristics. We categorized urbanicity as: (1) urban area; (2) peri-urban area (including local centres in rural areas); and (3) rural area. We also controlled the analyses for whether the place of residence belonged to the Metropolitan hospital district of Helsinki and Uusimaa (HUS) or to another. HUS is the largest hospital district in Finland and had the highest cumulative number and share of COVID-19 cases compared to the other districts during the whole study period (S1 Table).

Individual-level characteristics

Sex was measured as binary and age in years at the end of each calendar year.

Foreign background (no/yes) was defined as persons who themselves, both parents, or the only known parent were born outside Finland and whose native language was not Finnish or Swedish.

Comorbidities were identified from information on the right to special reimbursement for medicinal expenses related to specific diagnosed chronic conditions, obtained from the registers of the Social Insurance Institution of Finland. These conditions included cancer, kidney failure, chronic lung disease, diabetes (types 1 and 2), chronic heart disease (heart failure, hypertension, coronary heart disease), and psychotic disorders. Each condition was measured as a separate dummy variable (no/yes), and an individual could have multiple conditions.

Personal occupational status was categorized as: (1) upper nonmanual employee; (2) lower nonmanual employee; (3) self-employed; (4) manual worker; (5) student; (6) pensioner; and (7) other (including unemployed and unknown). This information was measured in 2017, and children aged under 16 were assigned to the same category as the reference person in the household.

Ethics statement

This study is based on secondary data collected for administrative and statistical purposes, and we have obtained permission to access these data for the purpose of this study from Statistics Finland (permission #TK-53-339-13) and Findata Health and Social Data Permit Authority (permission #THL/2180/14.02.00/2020). Access to these data has been granted after consideration by the ethical boards of these statistical authorities. The study complies with the national legal framework for accessing anonymous personal data for scientific research carried out in public interest. The legal basis is stated in the Finnish Personal Data Act (523/1999), Act on Secondary use of Social and Healthcare data (552/2019), Finnish Statistics Act (280/2004), and the EU General Data Protection Regulation (GDPR). The GDPR permits processing this type of data for research without using the GDPR consent (Art. 9 of the GDPR).

Statistical analyses

Analytical strategy

First, we present the incidence rates of COVID-19 infection, and severe illness among the infected for people aged under 65 and over 65, categorized by sex and household income. All further analyses are restricted to the households with at least 1 person aged under 65 years. This is because our main interest lies in household-level risk factors for COVID-19 and the variation in income as well as the distribution of other household-level and individual risk factors is very different for the older population. The interpretation of household income also differs between the working age and retired populations. Finally, only 6% of COVID-19 infections during the study period were diagnosed in households with only over 65 year olds.

We use 2-level logistic regression to model the risk of COVID-19 infection and the risk of severe illness due to COVID-19 among those who had a registered infection. Two-level models with individuals (level 1) nested within households (level 2) are needed to account for the nonindependence of outcomes among members of the same households. If this correlation is not taken into account, the standard errors will be underestimated, leading to biased statistical inference [24].

Our modelling strategy was guided by our interest to examine whether and to what degree any income differences in COVID-19 outcomes are confounded by other sociodemographic risk factors more commonly found among individuals in lower socioeconomic position. We first adjusted our models for age, sex, and regional characteristics as basic demographic confounders to obtain a baseline association between household income and COVID-19 outcomes. Then, we adjusted these baseline models with other established COVID-19 risk factors, namely household size, work and school exposures, and foreign background. These factors were included to the baseline model one by one to explicitly assess the potential confounding role of each covariate. In addition, we tested for a potential interaction between income and household size to assess whether the association between household income and COVID-19 outcomes varied across households of different size. In response to reviewer feedback, we further tested for the interaction between household income and foreign background and a 3-way interaction between household income, household size, and foreign background.

The analyses of COVID-19 incidence and severity included partly different covariates (see below for the exact composition of the models). For the analyses of incidence, we focused on household-level risk factors such as household size and work and school exposures of any household member because they affect the infection risk of all household members through secondary transmission. In contrast, as case severity is likely to be more strongly affected by individual-level vulnerability, in the analyses of severe illness we focused instead on individual-level occupational class and comorbidities.

We used Stata version 16.1 to conduct the analyses, with the procedure “melogit” for the multilevel modelling.

Analyses of incidence

In Model 1, we assessed the risk for COVID-19 infection by household income controlling for age (linear and squared to account for nonlinear effects), sex, and regional characteristics. We also estimated a similar model—including age, sex, and regional characteristics but excluding household income—separately for each of the other COVID-19 risk factors: household size, each household-level indicators of work and school exposure, and foreign background. These models provide crude baseline associations between each risk factor and COVID-19 incidence. We then built on the first model with household income, separately adjusting for household size in Model 2, for all household-level indicators of work and school exposures in Model 3, for foreign background in Model 4, and finally for all the variables simultaneously in Model 5.

We further modelled the interaction effects of household income with household size and foreign background. We adjusted the first interaction model for age, sex, and regional characteristics (as in Model 1 above), and the second model additionally for all other risk factors (as in Model 5 above). We based the interaction models on 1-level logistic regression with household-clustered standard errors.

Analyses of severe illness due to COVID-19

We modelled the risk of severe illness due to COVID-19 among those who had a registered infection. As in the incidence analyses, Model 1 adjusted for age, sex, and regional characteristics and was run separately for household income, household size, each comorbidity, personal occupational status, and foreign background. Model 2 included household income, additionally adjusting for all comorbidities. Subsequent models were built on Model 2, with separate adjustments for household size (Model 3), for personal occupational status (Model 4), for foreign background (Model 5), and finally for all variables simultaneously (Model 6).

As in the incidence analyses, we modelled the interaction effects of household income with household size and foreign background status using 1-level logistic regression with household-clustered standard errors. We adjusted the first interaction model for age, sex, regional characteristics, and comorbidities, and the second model additionally for all other risk factors (as in Model 6 above).

Clustering within households

We calculated intraclass correlations (ICC) for the 2-level regression models. ICC was defined as v/(v + 3.29), where v is the between-household variance [25], and gives is the percentage of total variation in COVID-19 incidence and severity that is attributable to differences between households [25]. It can also be interpreted as the correlation in outcomes between household members [26].

Sensitivity analyses

In response to peer reviewers’ comments, we implemented 3 sensitivity analyses. First, we reestimated case severity models with the outcome defined as hospitalization of any length with COVID-19 diagnosis. This was done because the length of hospital stay may not in itself be a strong criterion for COVID-19 severity. However, in the main analyses, the indicator of 3+ days in hospital was used to ensure that we capture cases severe enough to warrant continuous inpatient care and exclude very short stays, possibly due to other health conditions. Second, we reestimated the analyses of severe COVID-19 including only primary COVID-19 diagnoses in order to exclude those who were in hospital with a COVID-19 diagnosis but not necessarily because of COVID-19. Finally, in order to make our results more comparable with the previous studies assessing hospitalization and mortality in the full population, we reestimated the severity models for the full <65 population.

Results

Of the total 41,022 COVID-19 cases registered from 1 July 2020 to 22 February 2021, 94% were diagnosed in people living in under-65 households (i.e., households with at least 1 member aged less than 65). The incidence among both men and women living in these households was clearly highest in the lowest income quintile (around 1,300 per 100,000 versus around 800 in the other quintiles: Fig 2). The COVID-19 incidence was much lower among men and women living in over-65 households (i.e., households with all members aged 65 or over), possibly due to fewer household-level risk factors, and there was little variation by household income.

Fig 2

COVID-19 incidence by sex and household income quintile.

Incidence per 100,000 from 1 July 2020 to 22 February 2021 among individuals living in households with people aged under 65 and over 65. The whiskers represent 95% confidence intervals.

Fig 3 shows the risk of severe COVID-19 illness, defined as at least 3 consecutive days of inpatient care per 100 infected. The risk was small in the under-65 households (around 3%), and the income differences were modest. Among the over-65 households, on the other hand, the risks multiplied, and the income differences were larger. Men had a higher risk of severe illness than women in both age groups.

Fig 3

Incidence of severe COVID-19 illness among those infected by sex and household income.

Incidence defined as 3+ days in hospital per 100 among those infected (n = 24,138) from 1 July to 31 December 2020 among individuals living in households with people aged under 65 and over 65. The whiskers represent 95% confidence intervals.

Incidence models

When we controlled for age, sex, and regional characteristics in a 2-level regression model (Table 1, Model 1), there was an income gradient in COVID-19 incidence, the highest odds being among those in the lowest income quintile (OR 1.67, 95% CI 1.58 to 1.77, p < 0.001, 28.4% of infections) compared to those with the highest (18.9%). Neither a larger household size (Model 2) nor household-level work and school exposures (Model 3) attenuated the higher incidence among those with lower incomes. However, after including foreign background, the association of income and COVID-19 was largely attenuated (Model 4), with only the estimate of the lowest quintile (OR 1.23, 95% CI 1.16 to 1.30, p < 0.001) remaining statistically significant. The incidence of the lowest income quintile remained elevated in the fully adjusted Model 5 (OR 1.30, 95% CI 1.22 to 1.38, p < 0.001).

Table 1

Odds ratios of COVID-19 infection from 1 July 2020 to 22 February 2021 among individuals living in under-65 households.

	Models
	1*	2	3	4	5
1. Household income (ref. 5) (reference)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Quintile 4	1.10 (1.03–1.16)	1.10 (1.04–1.17)	1.03 (0.97–1.10)	1.04 (0.98–1.10)	0.99 (0.93–1.05)
	p = 0.002	p = 0.001	p = 0.28	p = 0.22	p = 0.65
Quintile 3	1.08 (1.01–1.14)	1.07 (1.01–1.14)	0.99 (0.94–1.06)	0.97 (0.91–1.03)	0.91 (0.85–0.97)
	p = 0.02	p = 0.03	p = 0.87	p = 0.35	p = 0.003
Quintile 2	1.24 (1.17–1.32)	1.23 (1.15–1.31)	1.15 (1.08–1.22)	1.05 (0.98–1.11)	0.99 (0.93–1.06)
	p < 0.001	p <0.001	p < 0.001	p = 0.15	p = 0.82
Quintile 1 (lowest)	1.67 (1.58–1.77)	1.79 (1.69–1.91)	1.69 (1.59–1.79)	1.23 (1.16–1.30)	1.30 (1.22–1.38)
	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001
2. Hospital district (ref. other)
Helsinki Metropolitan (HUS)	3.29 (3.17–3.43)	3.43 (3.30–3.57)	3.49 (3.35–3.63)	2.91 (2.80–3.03)	2.98 (2.86–3.10)
	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001
3. Urbanicity (ref. rural)
Urban	2.36 (2.23–2.49)	2.49 (2.35–2.64)	2.42 (2.28–2.57)	2.11 (2.00–2.24)	2.26 (2.13–2.39)
	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001
Peri-urban	1.09 (1.01–1.17)	1.12 (1.05–1.21)	1.12 (1.04–1.20)	1.11 (1.03–1.19)	1.13 (1.05–1.21)
	p = 0.02	p = 0.002	p = 0.002	p = 0.006	p = 0.001
4. Household size (ref. 1)
2	1.03 (0.98–1.09)	1.17 (1.11–1.24)			0.98 (0.93–1.04)
	p = 0.21	p < 0.001			p = 0.48
3	1.16 (1.09–1.23)	1.33 (1.25–1.41)			1.15 (1.08–1.23)
	p < 0.001	p < 0.001			p < 0.001
4	1.12 (1.06–1.19)	1.33 (1.25–1.41)			1.26 (1.16–1.36)
	p < 0.001	p < 0.001			p < 0.001
5+	1.84 (1.72–1.98)	2.02 (1.88–2.18)			1.92 (1.73–2.13)
	p < 0.001	p < 0.001			p < 0.001
5. Household-level work and school exposures
(a) Lower nonmanual	0.95 (0.92–0.99)		1.11 (1.06–1.15)		1.19 (1.14–1.24)
	p = 0.007		p < 0.001		p < 0.001
(b) Self-employed	1.05 (0.99–1.11)		1.15 (1.08–1.22)		1.08 (1.02–1.15)
	p = 0.115		p < 0.001		p = 0.01
(c) Manual worker	1.22 (1.17–1.27)		1.35 (1.29–1.41)		1.22 (1.17–1.28)
	p < 0.001		p < 0.001		p < 0.001
(d) Student	1.55 (1.49–1.61)		1.44 (1.39–1.50)		1.36 (1.30–1.42)
	p < 0.001		p < 0.001		p < 0.001
(e) Child aged 13–15	1.33 (1.26–1.41)		1.30 (1.23–1.38)		1.03 (0.96–1.10)
	p < 0.001		p < 0.001		p = 0.36
(f) Child aged 7–12	1.00 (0.95–1.05)		0.99 (0.94–1.04)		0.77 (0.72–0.82)
	p = 0.93		p = 0.71		p < 0.001
(g) Child aged <7	0.88 (0.84–0.93)		0.92 (0.87–0.96)		0.68 (0.64–0.73)
	p < 0.001		p = 0.001		p < 0.001
6. Foreign background (ref. no)
Yes	4.75 (4.50–5.01)			4.54 (4.30–4.80)	4.59 (4.35–4.86)
	p < 0.001			p < 0.001	p < 0.001
Household ICC	0.770†	0.772	0.771	0.768	0.771
	(0.766–0.774)	(0.768–0.776)	(0.767–0.774)	(0.765–0.772)	(0.767–0.775)

*In Model 1, each variable adjusted separately for age and age squared, sex, and hospital district and urbanicity.

Model 2 adjusted for age and age squared, sex, hospital district and urbanicity, and household size.

Model 3 adjusted for age and age squared, sex, hospital district and urbanicity, household-level work, and school exposures.

Model 4 adjusted for age and age squared, sex, hospital district and urbanicity, and foreign background.

Model 5 adjusted for age and age squared, sex, hospital district and urbanicity, household size, household-level work and school exposures, and foreign background.

†Calculated from a model including age and age squared, sex, and hospital district and urbanicity.

CI, confidence interval; ICC, intraclass correlation; OR, odds ratio; p, p-value; Ref., reference category.

However, interaction between household income and foreign background status (Fig 4A) shows that this income gradient is only present among those with foreign background (p-value for interaction <0.001). Among individuals with a Finnish background, being in the lowest income quintile even appears as a moderate protective factor (OR 0.91, 95% CI 0.87 to 0.96, p < 0.001). People with foreign background in the lowest income households had a particularly high odds of COVID-19 infection (OR 3.81, 95% CI 3.61 to 4.02, p < 0.001 compared to people with a Finnish background in the highest income quintile). Adjustment for household size and household-level work and school exposures did not substantially change the income gradient among those with foreign background (Fig 4B).

Fig 4

Odds ratios of COVID-19 incidence by household income quintile and foreign background status.

In under-65 households (A) adjusted for age and age squared, sex, and regional characteristics; (B) adjusted for age and age squared, sex, regional characteristics, household size, and household-level work and school exposures.

There was also a strong interaction between income and household size (Fig 5A), with low income being a much stronger risk factor in large households (p-value for interaction <0.001). Households in the lowest income quintile with 5 or more members stood out with a particularly high odds of infection (OR 3.72, 95% CI 3.35 to 4.12, p < 0.001 compared with single-person households in the highest income quintile). Following adjustment for household-level work and school exposures and foreign background (Fig 5B), the income gradient was considerably attenuated across all household sizes but the incidence remained high especially in the poorest households with 5 or more members (p-value for interaction <0.001). An additional interaction analysis between household income, household size, and foreign background (S1 Fig) indicated that the excess risk of large poor household was present only among those with foreign background.

Fig 5

Odds ratios of COVID-19 incidence by household income quintile and household size.

In under-65 households (A) adjusted for age and age squared, sex, and regional characteristics; (B) adjusted for age and age squared, sex, regional characteristics, household-level work and school exposures, and foreign background.

The high ICC (about 0.77 in all the models) indicates that if 1 household member was infected, the others were very likely to become infected as well.

Severity models

When we controlled for age, sex, and regional characteristics (Table 2, Model 1), the odds of severe COVID-19 illness was twice as high in the lowest household income quintile (OR 1.97, 95% CI 1.52 to 2.56, p < 0.001, 28.0% of infections) compared to the highest (21.6%).

Table 2

Odds ratios of severe COVID-19 illness among those infected from 1 July to 31 December 2020, individuals living in under-65 households.

	Models
	1*	2	3	4	5	6
1. Household income (ref. 5) (reference)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Quintile 4	1.05 (0.79–1.38)	1.03 (0.78–1.36)	1.03 (0.78–1.37)	0.96 (0.72–1.28)	0.99 (0.75–1.31)	0.94 (0.71–1.26)
	p = 0.74	p = 0.86	p = 0.82	p = 0.77	p = 0.94	p = 0.69
Quintile 3	1.25 (0.94–1.65)	1.19 (0.90–1.59)	1.20 (0.90–1.60)	1.07 (0.79–1.44)	1.13 (0.84–1.50)	1.03 (0.76–1.39)
	p = 0.12	p = 0.23	p = 0.22	p = 0.67	p = 0.42	p = 0.87
Quintile 2	1.23 (0.92–1.65)	1.14 (0.84–1.53)	1.14 (0.84–1.53)	0.96 (0.70–1.31)	1.02 (0.75–1.38)	0.88 (0.64–1.21)
	p = 0.16	p = 0.40	p = 0.41	p = 0.80	p = 0.91	p = 0.43
Quintile 1 (lowest)	1.97 (1.52–2.56)	1.84 (1.41–2.40)	1.80 (1.37–2.36)	1.37 (1.01–1.86)	1.45 (1.08–1.94)	1.08 (0.77–1.52)
	p < 0.001	p < 0.001	p < 0.001	p = 0.04	p = 0.01	p = 0.64
2. Hospital district (ref. other)
Helsinki Metropolitan (HUS)	0.76 (0.63–0.92)	0.77 (0.64–0.93)	0.77 (0.64–0.94)	0.78 (0.64–0.94)	0.74 (0.61–0.89)	0.75 (0.61–0.91)
	p = 0.004	p = 0.007	p = 0.009	p = 0.01	p = 0.002	p = 0.003
3. Urbanicity (ref. rural)
Urban	1.01 (0.76–1.33)	0.93 (0.70–1.24)	0.93 (0.70–1.23)	0.94 (0.70–1.25)	0.88 (0.66–1.17)	0.87 (0.65–1.17)
	p = 0.97	p = 0.62	p = 0.61	p = 0.66	p = 0.38	p = 0.37
Peri-urban	0.73 (0.50–1.05)	0.73 (0.50–1.07)	0.73 (0.50–1.07)	0.74 (0.50–1.08)	0.73 (0.50–1.07)	0.74 (0.51–1.09)
	p = 0.09	p = 0.10	p = 0.11	p = 0.12	p = 0.11	p = 0.13
4. Comorbidities
(a) Cancer	1.65 (0.93–2.94)	1.48 (0.81–2.71)	1.49 (0.81–2.72)	1.49 (0.81–2.75)	1.52 (0.83–2.77)	1.51 (0.82–2.79)
	p = 0.09	p = 0.20	p = 0.20	p = 0.20	p = 0.18	p = 0.18
(b) Kidney failure	19.46 (6.04–62.70)	10.58 (3.09–36.14)	10.33 (3.02–35.33)	9.09 (2.63–31.46)	10.56 (3.09–36.11)	8.78 (2.53–30.41)
	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p = 0.001
(c) Chronic lung disease	2.37 (1.74–3.22)	2.35 (1.71–3.22)	2.34 (1.70–3.21)	2.30 (1.67–3.17)	2.46 (1.79–3.38)	2.39 (1.73–3.30)
	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001
(d) Diabetes	2.29 (1.73–3.03)	1.86 (1.39–2.49)	1.87 (1.39–2.50)	1.82 (1.35–2.44)	1.83 (1.36–2.45)	1.80 (1.34–2.41)
	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001	p < 0.001
(e) Chronic heart disease	1.82 (1.33–2.49)	1.35 (0.96–1.88)	1.35 (0.96–1.88)	1.31 (0.93–1.85)	1.41 (1.01–1.97)	1.37 (0.97–1.93)
	p < 0.001	p = 0.08	p = 0.08	p = 0.12	p = 0.05	p = 0.07
(f) Psychotic disorders	2.08 (1.07–4.02)	1.58 (0.80–3.13)	1.56 (0.78–3.09)	1.14 (0.56–2.30)	1.77 (0.89–3.51)	1.21 (0.60–2.46)
	p = 0.03	p = 0.19	p = 0.21	p = 0.72	p = 0.10	p = 0.59
5. Household size (ref. 1)
2	0.87 (0.68–1.12)		0.97 (0.75–1.26)			0.88 (0.68–1.15)
	p = 0.28		p = 0.84			p = 0.35
3	0.76 (0.57–1.02)		0.85 (0.63–1.14)			0.77 (0.57–1.04)
	p = 0.07		p = 0.28			p = 0.08
4	0.72 (0.53–0.98)		0.80 (0.59–1.09)			0.73 (0.53–1.00)
	p = 0.04		p = 0.16			p = 0.05
5+	1.04 (0.77–1.40)		1.01 (0.74–1.37)			0.87 (0.63–1.19)
	p = 0.80		p = 0.96			p = 0.37
6. Occupation (ref. upper nonmanual)
Lower nonmanual	1.13 (0.84–1.54)			1.07 (0.78–1.46)		1.05 (0.76–1.44)
	p = 0.42			p = 0.68		p = 0.77
Self-employed	1.22 (0.82–1.82)			1.15 (0.76–1.73)		1.12 (0.74–1.69)
	p = 0.33			p = 0.51		p = 0.60
Manual worker	1.26 (0.92–1.72)			1.19 (0.86–1.66)		1.08 (0.77–1.51)
	p = 0.15			p = 0.29		p = 0.65
Student	1.22 (0.76–1.98)			1.03 (0.62–1.71)		0.94 (0.56–1.56)
	p = 0.41			p = 0.91		p = 0.81
Pensioner	3.67 (2.48–5.43)			2.77 (1.81–4.25)		2.70 (1.76–4.15)
	p < 0.001			p < 0.001		p < 0.001
Other/unknown	2.04 (1.50–2.77)			1.62 (1.14–2.30)		1.44 (1.01–2.06)
	p < 0.001			p = 0.007		p = 0.05
7. Foreign background (ref. no)
Yes	1.74 (1.43–2.12)				1.55 (1.23–1.96)	1.62 (1.26–2.06)
	p < 0.001				p < 0.001	p < 0.001
Household ICC	0.185†	0.202	0.204	0.216	0.202	0.214
	(0.075–0.392)	(0.088–0.399)	(0.090–0.398)	(0.101–0.403)	(0.088–0.400)	(0.099–0.401)

*In Model 1, each variable adjusted separately for age and age squared, sex, and hospital district and urbanicity.

Model 2 adjusted for age and age squared, sex, hospital district and urbanicity, and comorbidities.

Model 3 adjusted for age and age squared, sex, hospital district and urbanicity, comorbidities, and household size.

Model 4 adjusted for age and age squared, sex, hospital district and urbanicity, comorbidities, and personal occupation.

Model 5 adjusted for age and age squared, sex, hospital district and urbanicity, comorbidities, and foreign background.

Model 6 adjusted for age and age squared, sex, hospital district and urbanicity, comorbidities, household size, personal occupation, and foreign background.

†Calculated from a model including age and age squared, sex, and hospital district and urbanicity.

CI, confidence interval; ICC, intraclass correlation; OR, odds ratio; p, p-value; Ref., reference category.

These income differences were not attributable to comorbidities (Model 2) or household size (Model 3). They were, however, attributable in part to individual-level occupational status (Model 4) and foreign background (Model 5). In the fully adjusted Model 6, the other risk factors attenuated all the income differences in the risk of severe illness.

There were too few observations to draw reliable conclusions about the interaction of household income with foreign background (Fig 6) or household size (Fig 7). However, these effects appear weak and inconsistent. The ICC (about 0.20) indicated strong household clustering of severe COVID-19, even when we controlled for the individual-level and household-level risk factors.

Fig 6

Odds ratios of severe COVID-19 illness among those infected by household income quintile and foreign background status.

In under-65 households (A) adjusted for age and age squared, sex, regional characteristics, and comorbidities; (B) adjusted for age and age squared, sex, regional characteristics, comorbidities, household size, and household-level work and school exposures.

Fig 7

Odds ratios of severe COVID-19 illness among those infected by household income quintile and household size.

In under-65 households (A) adjusted for age and age squared, sex, regional characteristics, and comorbidities; (B) adjusted for age and age squared, sex, regional characteristics, comorbidities, household-level work and school exposures, and foreign background.

Sensitivity analyses

In case severity models, defining the outcome as any inpatient care instead of a care episode of at least 3 days increased the number of severe cases by about 10%, but had little impact on the results (S2 Table). Results from the models where the outcome was defined to include only primary COVID-19 diagnoses were also highly similar to our main analyses including both primary and secondary diagnoses (S3 Table).

Results from severity models conducted among the full population instead of those infected reflect income differences in both incidence and case severity (S4 Table), and the estimates lied in between the corresponding estimates from our main results on incidence and case severity.

Discussion

We used total population data covering over 4 million individuals nested within households to assess the associations of household income with COVID-19 incidence and severity, and to quantify the clustering of COVID-19 in working-age households. In line with prior evidence, we found that individuals living in low-income households had higher risk of both COVID-19 incidence and severity—however, this was largely driven by the foreign background of household members. In fact, a separate analysis revealed a strong income gradient among individuals with foreign background only, and no income association at all among those with native background. The odds of severe illness among the infected were likewise highest among those with lowest income, but this association was also strongly driven by other risk factors: comorbidities, personal occupational status, and foreign background. Both incidence and severity were strongly clustered in households: Around 77% of the variation in incidence and 20% of the variation in severity were attributable to differences between households.

Comparison with other studies

To our knowledge, this is the first study to assess income and other sociodemographic risk factors for COVID-19 incidence and severity using both household and individual-level data. Incorporating the household level is a major contribution, because, as we show, not only infections, but also severe illness is strongly clustered within households.

Our general finding of a higher COVID-19 incidence in low-income households is in accordance with previous results associating incidence with area-level measures of low education, deprivation [1,6], and low mean income by district [2,3]. Our study differed from many others in that we were able to control for a set of other important individual- and household-level risk factors, and indeed we found that having foreign background was a major driver of the income differences, while other risk factors for higher COVID-19 incidence made no difference for the income association. Furthermore, we showed that the income gradient was only present among people with foreign background and nonexistent among those with a Finnish background. New to the existing literature, this finding has no direct point of reference from previous studies. While foreign background and ethnicity have been linked to higher COVID-19 incidence and severity in many previous studies [18], to our knowledge, only 1 prior study has assessed the role of foreign background or ethnicity in the association between income and COVID-19 outcomes [1]. Their results contrast with ours: In this study based on UK Biobank data, controlling for ethnicity and country of birth explained little of the higher incidence in socioeconomically deprived areas [1]. The differing results may relate to the selective sample and area-based measurement of socioeconomic exposures in the UK Biobank data, compared with our total population data with household-based measures.

Our results suggest that, overall, there is no independent association between household income and COVID-19 incidence. Low income was related to higher COVID-19 incidence only among people with foreign background, and this association was independent of household size and the work and school exposures that we measured. The combination of foreign background and low income is likely to capture vulnerabilities related to race, ethnic minority, and refugee background which may influence infection risk through material, social, and behavioural mechanisms. Prior studies from the United States indicate that people with low income have fewer material and social resources to protect themselves from COVID-19 infection [27]. Social mechanisms may relate to lower health literacy, language barriers, racism, and structural discrimination faced by people with foreign background and low socioeconomic position [28-30]. Higher susceptibility to COVID-19 may also relate to a lowered immune response due to higher stress levels [14,31]. Finally, behavioural factors such as possible broader social interactions among people with foreign background [29] may further add to the clustering of risk factors among those with foreign background and low income. Due to the epidemic nature of COVID-19, the extent of social interactions could be of particular importance if outbreaks are concentrated in schools and neighborhoods having more people with low socioeconomic position and foreign background. The lack of neighborhood-level controls is a limitation of our study.

A major contribution of our study is that we were able to assess the risk factors for case severity in the total infected population. Previous studies on severe COVID-19 have either limited the study population to those already hospitalized or seeking health care [10,11,19], or analysed the general population irrespective of infection status [7-9]. The former target a group that is already selected on case severity whereas the latter conflate the risk factors of incidence and severity. A study on the general population of Sweden, for example, found that low income and educational level predicted increased COVID-19 mortality [7], but these estimates may reflect socioeconomic differences in either incidence or case fatality, or both. Moreover, few previous studies have been able to control for important confounders. A Swiss study reported higher case severity and fatality rates among the infected in neighbourhoods with a low socioeconomic index based on 2,000 census data, but it did not adjust for individual risk factors other than age and sex [12]. In contrast, our adjusted results indicate that household income is not independently associated with case severity. In fact, the higher risk of severe COVID-19 in low-income households was strongly driven by personal occupational status and foreign background. Personal occupational status is likely to capture health-related confounding by controlling for being on early-age pension or having an unknown personal occupational status. Foreign background, in turn, may capture ethnic differences in the risk for severe COVID-19, which could relate to a complex set of factors such as racism and structural discrimination, or barriers in access to care that we were not able to measure [18,32]. Another reason for our results may relate to selection bias caused by differential testing [33]. If people with foreign background tend to test less for mild COVID-19 symptoms, this will lead to a disproportional share of severe cases among those identified as infected in this group. Further studies should investigate the mechanisms producing social and ethnic differences in case severity, preferably in samples where infections are identified by screening rather than by self-selective testing.

We found very high infection clustering within households, corroborating previous evidence that the risk of COVID-19 infection is higher within households than in other social contexts [21,22]. Likewise, our finding that large low-income households had the highest incidence implies an accumulation of risk factors in specific types of household, and supports the earlier observation on the disadvantages of household crowding [5]. Our results further suggest that this accumulation mainly occurs among the population with foreign background. Our study is the first to assess the household clustering of case severity. The correlation in the likelihood of severe illness between household members was around 20%, which the measured individual-level risk factors failed to explain. However, unmeasured risk factors such as general health, obesity, smoking, and other health behaviours may cluster in households and offer some explanation of why several members of some households tend to have severe COVID-19. Shared genetic vulnerability is unlikely to explain much of the household clustering of severity because the vast majority of multigenerational households in our data consist of parents and their underage children, and severe COVID-19 is rare among the young.

The strengths and weaknesses of the study

The unique strength of our study lies in the use of total population register data comprising individuals nested within households. This enabled us to assess household-level risk factors for COVID-19 while properly taking into account the household clustering of outcomes [24]. Furthermore, we were able to quantify the household clustering for both incidence and case severity. The use of up-to-date administrative data provided us with a more accurate measurement of the socioeconomic and other risk factors at both individual and household level. This is an important contribution as the existing evidence is mostly based on area-level socioeconomic measures [1-3,5,6,8-13,19], or rely on measures from more than a decade ago [34,12]. Of the common indicators of socioeconomic position (education, income, deprivation), we chose to focus on household income. It is reliably measured and available for all individuals irrespective of their age, employment or immigrant status, and well-suited for household-level analyses. We also had access to data on annual household income from a time point close to the pandemic, 2018. However, a limitation of our study is that household income captures only one aspect of the multidimensional concept of socioeconomic position. While our study also included information on occupational status, future research incorporating multiple dimensions of socioeconomic position is needed for a more comprehensive picture of the social inequalities in COVID-19 outcomes.

Consistent information on laboratory-confirmed infections and hospital care records allowed us to study both incidence and case severity. Finland’s testing strategy during the study period was to include even the mildly symptomatic. The tests were free of charge and widely available, although waiting times still varied during the late summer of 2020 [35]. It has been estimated that laboratory-confirmed COVID-19 cases in early 2021 represented at least half of the total infections in Finland [36]. Such an underestimation of cases could lead to bias if there were differences in the testing threshold according to our key variables of interest. We have no direct way of assessing the magnitude of this bias. However, evidence from Switzerland [12] indicates that test uptake may be lower among more disadvantaged population subgroups and may thus lead to the underestimation of income differentials in incidence. Furthermore, if people with a higher income were tested for milder symptoms, this may lead to the overestimation of income differentials in illness severity among the infected [37]. It would be valuable in future research to obtain direct evidence of testing frequency and the true infection prevalence in specific subpopulations.

Our data cover a period before vaccinations were available to the under 65. Although the social dynamics of COVID-19 infections and outcomes will most likely change as the rates of vaccination increase and new variants of the virus emerge, the significance of the household context of individuals is unlikely to diminish. Moreover, the clustering of severe illness could have long-lasting effects in the households as the severe illness increases the risk for long COVID symptoms [38]. The potential impact of socioeconomic differentials in vaccination take up on COVID-19 incidence and severity will also be a relevant topic for future studies.

Conclusions

We showed that people with a low household income are at higher risk of COVID-19 incidence and case severity. However, these income differences in incidence were only present among the population with foreign background, while there was no association between income and COVID-19 incidence among those with native background. The income differences found in case severity were also strongly driven by other individual and household-level risk factors, foreign background in particular. Socioeconomic position as reflected by household income may thus not be an independent risk factor for COVID-19 outcomes among people with a native background. However, people with foreign background living in low-income households emerged as a particularly vulnerable group to consider when planning preventive measures. Both incidence and case severity are strongly clustered within households. This highlights the importance of the household context in understanding the microlevel dynamics of the COVID-19 pandemic.

STROBE checklist.

(DOCX)

Click here for additional data file.

The distributions of the population at risk and cases in the incidence and severity analyses by risk factors.

(DOCX)

Click here for additional data file.

Odds ratios of hospitalization with COVID-19 diagnosis (N = 696) among those infected from 1 July to 31 December 2020 (N = 24,138), individuals living in under-65 households.

Hospitalization is defined as any admission to the hospital with a COVID-19 diagnosis. Results are from 2-level logistic regressions, with individuals at level 1 nested in households at level 2. All models are adjusted for age and age squared and sex.

(DOCX)

Click here for additional data file.

Odds ratios of severe illness with COVID-19 as the primary diagnosis (N = 387) among those infected from 1 July to 31 December 2020 (N = 24,138), individuals living in under-65 households.

Severe illness is defined as having at least 3 consecutive days of inpatient care with a COVID-19 diagnosis. Results are from 2-level logistic regressions, with individuals at level 1 nested in households at level 2. All models are adjusted for age and age squared and sex.

(DOCX)

Click here for additional data file.

Odds ratios of severe COVID-19 illness (N = 636) from 1 July to 31 December 2020, among all individuals living in under-65 households (N = 4,315,342).

Severe illness is defined as having at least 3 consecutive days of inpatient care with a COVID-19 diagnosis. Results are from 1-level logistic regressions with household-clustered standard errors. All models are adjusted for age and age squared and sex.

(DOCX)

Click here for additional data file.

Odds ratios of COVID-19 incidence by household income quintile, household size, and foreign background status in under-65 households.

Adjusted for age and age squared, sex, regional characteristics, and household-level work and school exposures.

(TIF)

Click here for additional data file.

8 Oct 2021

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17 Feb 2022

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Comments from the reviewers:

Reviewer #1: "Income differences in COVID-19 incidence and severity: a nationwide register study of over four million individuals nested in households" analyzes Finnish national data, primarily towards identifying the effect of income differences on COVID-19 incidence and severity. The main conclusions were that low household income was a strong risk factor for both COVID-19 incidence and case severity, which is further highly attributable to a foreign background (Line 252). Particular strengths of the study include the availability of total population register data at the household level, and fairly complete data on common risk factors. The findings generally collaborate previous work on the impact of low household income on COVID-19 infection risk. Nevertheless, a number of points might be clarified:

1. In Line 96, it is stated that the main analyses are on COVID-19 infections (amongst the full population), and risk of severe illness (only amongst the infected). It might be clarified further as to whether the risk of severe illness amongst the full population would be a relevant analysis, and if so, it might be considered to be undertaken.

2. In Line 128, it is stated that severe illness was defined as inpatient care lasting at least three consecutive days with a primary or secondary diagnosis of COVID-19. Sensitivity analysis (with accompanying demographics presented) for primary vs. secondary diagnosis might be considered, if possible.

3. In Line 135, it is stated that household size was categorized as 1, 2, 3 or 4+. From Supplementary Table 1, 4+ household size accounts for 36.8% of the population at risk. Sensitivity analysis at higher granularities (i.e. 5+, 8+, etc.) might be considered if the data is available.

4. In Line 181, it is stated that two-level logistic regression models (individuals nested in households) was used. A brief explanation of the construction/implementation of such two-level models might be provided, possibly in supplementary material.

5. In Line 186, it is stated that for incidence, Models 2 to 5 were built on Model 1 (Table 1). However, Table 1 appears to show missing values for entire groups for Models 2 to 4 in particular (suggesting that they may not be entirely "built on" Model 1?), and it is not immediately clear as to exactly what was included/adjusted for, in each of these models (as also relating to the previous point). As such, the full definition of the various models in Tables 1 & 2 - including any interaction variables - might be provided in supplementary material (and perhaps briefly clarified in the main text), if possible.

6. The choice of which categories/groups adjusted for in each model (as in Tables 1 & 2) might be explained further, if possible. For example, while foreign background was adjusted for and found to account for much of the effect from income differences, it appears possible that other risk factors such as urbanicity (urban with consistently significantly higher risk, may also be expected to correlate highly with foreign background) might have similar effects.

7. In Line 317, the limitation of laboratory-confirmed COVID-19 cases likely not representing the full incidence, and potential bias in the distribution of laboratory-confirmed cases, was acknowledged. Related to this, the expected coverage of the national register used for incidence/severe illness might be briefly described. In particular, does the register cover all relevant medical facilities, or does it possibly exclude some categories, e.g. private clinics?

8. For Tables 1 & 2, the commonly-recognized major risk factors of age and sex appear not to have their odds ratios included. This might be addressed if possible (with accompanying demographics included in Supplementary Table 1)

9. For Tables 1 & 2, the top row might be captioned as being the Model(s), for greater clarity.

10. From Figure 2, COVID-19 incidence is significantly lower in 65+ households, as compared to <65 households. This observation might be briefly commented on, if possible.

Reviewer #2: This is an interesting and important study.

I do have the following question and comment:

Why the time range of the two cohorts is different?

The length of hospital stay is not a criterion for COVID-19 severity. There are other clinical criteria the most important of which is saturation level. With regard to this I think it is better to analyze incidence of hospitalization rather than incidence of severe illness.

Reviewer #3: Overall comments:

This study examines a national data in Finland to investigate the association between household income and risk of COVID-19 infection and severe illness. A major strength of this manuscript is the database that they are using, which includes data on all individuals in Finland. In general, most of the manuscript is written appropriately. My main critique is that I think the analyses and writing need to be framed and much more rooted in modern causal epidemiologic terms. This involves thinking carefully about how adjustment is done and what the interpretation of each analysis. Many of the analyses seem to be focused on trying to understand mechanisms so formal mediation analyses may actually be the most appropriate. The strategy forward for doing this I think is conceptualizing the hypothesizing causal mechanisms using perhaps DAGs. This will help sharpen the exact questions being answered, how the analyses should be designed, and the appropriate interpretation of the results. Also, in general, I think the association with being foreign born came out very strongly but this relationship was not thoroughly explored.

Specific comments:

Intro

* The manuscript starts with the fact many studies have identified associations with low income and education and COVID-19, but they weren't adjusted for other covariates. I think an important question to ask is: Should they be adjusted? There are significant association between occupation, ethnicity, household size, and income. Other covariates are not necessarily confounders but may in fact be important mediators of effect. A simple example. Being foreign born leads to only able to acquire low income jobs leading to the need to live in multi-generational households, which leads to increased risk of COVID-19 due crowding. This manuscript (and others) by Camara Jones provides a lot insights in how to conceptualize design choices when considering disparities (https://academic.oup.com/aje/article/154/4/299/61900). It mostly applies to racism in the United States by a lot of the principles are applicable. In the end, these covariates are not independent biologic-like factors, but representative of a complex societal network and system that we all live in.

* The introduction is written okay, but I feel like it bounces around a lot. I get the sence that the questions of interest relate to household income and context in understanding COVID-19 transmission, but I do not come away understanding the specific questions or actual hypotheses. I think the focus of the first two paragraphs needs to be sharpened. The third paragraph is clear.

Methods:

* A major major strength of this paper is the data that is being used. It contain information on all individuals and households in Finland.

* I somewhat understand the rationale for only including households with working-age individuals, but in general the older population is the most concern for COVID-19. There still is something to be said about COVID risk in older households too. They are not likely isolated and inclusion could perhaps yield some insights about COVID acquisition through occupational hazards vs. non-occupational.

* I think more clarity on the rationale for selecting covariates and designing analyses are needed. In general, I would strongly advocate for drawing a DAG (directed acyclic graph) to formalize the hypothesized causal mechanisms at play. This also helps formalize how analyze should be design and how to handle adjustments.

* For example, when invoking mechanism I would think more about thinking about mediation analyses and trying to understand direct and indirect effects. This approach handles covariates differently then if one thinks they are confounders. I think the authors in general should consider carefully whether some of the analyses should be changed to formal mediation analyses (either causual mediation or standard mediation approaches would be appropriate).

* I am not sure I understand the rationale of the different models that add different covariates. Again, I would try to incorporate some more causal framing to designing the analyses. What question gets answered in each model? What questions get answered by comparing Model 2 to 3, for example. Is an unadjusted vs. adjusted analysis all that is needed? Again the question of whether covariates are confounders or mediators in a particular analysis is also not answered, and I think a major question here (or at least for interpretation).

* I would avoid the using "predict". These aren't really prediction models (which would be cross-validated to assess prediction performance) but rather identifying associations. I think also the word "effect" is used inappropriately throughout the results when most things are associations.

* Use of multi-level models is appropriate. It very much like the use of intraclass correlations for households.

Results

* I see in paragraph 2 of the results how the different models were interpreted. It is not very clear what it means to say that being foreign born "explained" the higher incidence. Again, I think the language needs to be framed in modern causal epidemiological terms and concepts like mediation. For example, the fact that association with income was attenuated with foreign background I think might suggest that income is a mediator of the impact of being foreign born (because the causal relationship can't go the other way) that one is mediating. If only interesting at looking at the impact of income, it is appropriate to adjust foreign born as a confounder too though. But this clarity on what the results mean does not come through.

* Rather than an interaction between income and household size…I would be most interested in income and foreign born interaction. I think this should be seriously considered.

* The thing that stands out the most to me in Figure 4 is how the relationships change once adjusting for foreign background. This to me suggests important aspects of the relationship between foreign background, income, and household size. Again assessing the interaction with being foreign born seems like it would be quite interesting.

* It would be interesting to understand the impact of neighborhood socioeconomic impacts as will. There are some basic descriptors of neighborhood (e.g., urban). Neighborhoods are often segregative into areas with great heterogeneity in terms of social vulnerability, but I don't these variables capture that.

* I would more clearly label the lowest and highest income quintiles throughout.

* What about clustering that happens at the neighborhood level and important neighborhood level covariates?

Discussion

* I think in general more contextualization is needed in terms of what the findings mean beyond the different analytic strategies (e.g., this analysis was adjusted this one was not). What is larger of importance of what the results mean because of these analytic differences.

* For example, I think more contextualization is needed in terms of this finding "In fact, the higher risk of severe COVID-19 in low-income households seems to be strongly driven by personal occupational status and a foreign background." Why would this be the case? What are the mechanisms

Any attachments provided with reviews can be seen via the following link:

[LINK]

23 Mar 2022

Submitted filename: Response_to_reviewers.docx

Click here for additional data file.

5 Apr 2022

Dear Dr. Saarinen,

Thank you very much for submitting your manuscript "Income differences in COVID-19 incidence and severity in Finland: a population-based cohort study of individuals nested in households" (PMEDICINE-D-21-04207R2) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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Comments from the reviewers:

Reviewer #1: We thank the authors for addressing our previous concerns, as well as the point raised by Reviewer #3 on adjustment for other covariates. The manuscript appears clearer on the whole. However, in Table 2, The second "Model 5" might be "Model 6".

Reviewer #3: Overall comments:

This study examines a national data in Finland to investigate the association between household income and risk of COVID-19 infection and severe illness. The authors made several changes which I think have strengthened the paper. In particular, examining the interaction between being foreign-born and income gradient. My main critique is that there still seems to be a bit of a disconnect between what the authors think is important to analyze with regards to socioeconomic drivers of COVID-19. In the intro and discussion, they talk about broader socioeconomic drivers, many of which could be the root causes of the disparities seen in COVID-19 outcomes (and also the reason there is an association with income and COVID-19). They also seem to discuss in essence income as a mediator of being foreign-born. However, the analysis focuses on the independent association of income with COVID-19, yet there isn't as much discussion about the important mechanisms of income that are independent of the other confounders. Thus, the focus on income when the results seem to point to the fact that there actually other more important drivers at play (being foreign-born in particular…and income as a mediator of that) is a bit confusing and should be sharpened and clarified. The reason to focus on income (and only income) in this setting needs to clear and compelling (currently I generally find the confounders more compelling and there is also more discussion of them as well).

Specific comments:

Intro

* "thus it remains unclear whether the worse outcomes reflect socioeconomic differences in incidence, case severity, or both" - I think this could be more specific by saying whether socioeconomic status influences risk of exposure and infection, outcomes once some is infected, or both.

* Also, I think socioeconomic status often encompasses a broader array of factors including income, education, occupation, social class (which is also tied with race/ethnicity) (per APA definition). If this analysis is focused on income, I would use that specifically (which is also different than wealth).

* When reading the introduction (particularly second paragraph), I still get the sense that this analysis is focused on the broader milieu of socioeconomic factors as opposed to just income. The discussion of household clustering, occupation exposure seems to suggest that understanding their role is also important to this analysis (and then I would also add foreign-born status to that list). However, if the goal is only assess how income-level may drive infections and severity, I think the discussion should be focused there. Household size, occupation, race/ethnicity are then just confounders. How does income drive COVID things independent of household size, occupation, race/ethnicity? Is it related to underlying health access (which could lead to more comorbidities and then also worse COVID outcomes)? Access to information? In general (and I imagine particularly in Finland where there is a strong public healthcare system), the financial component is less the issue, but income may be an important proxy for several other risk factors (as the authors note).

Methods:

* The authors state they want to focus in on household level factors, but neighborhood-level factors are also a likely important confounder to consider when considering the relationship between income and COVID outcomes. If available, I would consider how it could be incorporated (assessments of clustering at the neighborhood level are also still interesting). If unavailable, that is fine, but I wasn't convinced by the rationale for not including them. Ultimately, they likely represent important confounders, and if unavailable, should be included as a limitation.

* I don't think necessary to say "in response to peer review comments". I think can just say "We conducted three sensitivity analyses."

*

* For under vs. over 65 year old households, the definitions are not clear. Were only households with everyone under 65 included? Where were households where the main earner was less than 65, but some over 65 years olds also lived (e.g., mixed households with grandparents)? I understand excluding households where everyone is over 65 years old, but this latter group seems like they should still be included (and perhaps they are). I think the definitions need to be clarified and also needs clarity of where these mixed households fit in. This composition is even an interesting covariate.

Results

* Incidence models - I would create a separate sentence highlighting that when foreign background was included as a covariate, the association of income and COVID-19 was largely attenuated. As presented, seems like a secondary point.

* Is it possible to present a threeway interaction between income, foreign-born, and household size. When I see the interaction between household size and income, the first question that I have…are foreign born individuals more likely to have larger household sizes. And then the household size interaction ends up still being related to foreign born status too. This may or may not be true but I think a question that should be answered in someway. If space is an issue, could consider only included the adjusted model in the figures and also figure 6 and 7 could potentially be supplementary.

* Even without a mediation analysis, it is clear that income is an important mediator of being foreign born it seems (agree that there aren't measured mediators of the effect of income on COVID). But it does not mediate the full effect as even among the highest income, there are differences between native Finnish and foreign-born. This piece gets lost for me.

Discussion

* I think leading with this sentence "Individuals living in low-income households appeared to have higher odds of both COVID-19 incidence and severity" ends up being somewhat misleading (even though it is clarified later). I think needs to also include that is specific for foreign born individuals only and not among native Finnish. The point is there is an income gradient among foreign born and there is not among native Finnish.

*

* I also don't know that I would say that this relationship is largely confounded by foreign born. Though technically true, that implies there was a false association (which I don't think is the authors' point). I think I would try to clarify what the results indicate, which to me is that being foreign-born is a major driver of differences in outcomes, and the income mediates some but not all of it (and also that there is no income gradient among native Finnish).

* The response to reviewers and analysis seems to emphasize that the point of this analysis is to assess the association of income with COVID-19 outcomes that is independent of other socioeconomic factors such as household size, occupation, foreign-born, etc. However, the intro nor the discussion really dives into the important independent mechanisms of income. Most of the discussion actually seems to discuss income as a mediator for these other more proximal variables. This is the basis for my previous comments that it might be prudent to also have some focus on income as mediator for these more proximal variables (as opposed to mediators of income itself). But if the focus is on the independent effects of income, there needs to be discussion of those specifically.

* To be fair, my thinking generally is in line with the authors framing of the broader socioeconomic milieu being very important drivers, with income as one of the mediators of them. But the stated focus of the analysis is somewhat contrary to that, so that discussion needs to be there.

*

* For example, "Taken together, our results suggest that income is an independent risk factor for COVID-19 incidence only among people with foreign background. The disadvantage among those with foreign background and low household income may relate to the work and school exposures, household size, and comorbidities that we were able to measure." These sentences combine income as mediator of foreign background (along with household composistion, work, school) and the independent mechanisms of income alone. The discussion of how income may independently relate to COVID-19 outcomes is lacking.

* "Foreign background, in turn, may capture ethnic differences in the risk for severe COVID-19, the reasons for which are likely to be multifaceted and currently not very well understood". I am not sure I agree with this sentence. First, it does seem to imply a biologic reason for differences (which I am not sure the authors intended). Second, I do think we know a lot about why being foreign-born person relates to COVID-19, many of which the authors do discuss throughout. But it relates to structural racism, issues with access (which could be driven by language, education, access to information), socioeconomic factors, neighborhood deprivation, etc. There has been a lot of literature about drivers of health disparities (even prior to COVID), and many probably are also relevant to Finland. This discussion could be more robust and perhaps combined with the discussion in the preceding paragraph.

Any attachments provided with reviews can be seen via the following link:

[LINK]

27 Apr 2022

Submitted filename: Response_to_reviewers.docx

Click here for additional data file.

10 May 2022

Dear Dr. Saarinen,

Thank you very much for re-submitting your manuscript "Income differences in COVID-19 incidence and severity in Finland: a population-based cohort study of individuals nested in households" (PMEDICINE-D-21-04207R3) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by one reviewer. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please carefully consider the reviewers outstanding concerns and take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

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We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by May 17 2022 11:59PM.

Sincerely,

Beryne Odeny,

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1) Please revise your title to include “foreign background,” considering the association between living in low-income households and COVID-19 incidence and severity is specific for foreign born individuals only and not among native Finnish.

2) Please revise your subtitle to “…nested within households” as opposed to “…nested in households.”

3) Abstract and Results- Please include the actual amounts or percentages of relevant outcomes in addition to the ORs

4) Discussion, Line 457 - Please avoid assertions of primacy (“Our study is the first to..."). Instead, state “To our knowledge, …” or similar

5) Please include definitions for all abbreviations in the tables, e.g., ICC

Comments from Reviewers:

Reviewer #3: Overall comments:

I thank the authors' for their efforts in addressing my comments, and I think they have provided additional clarity on several issues that were raised. There are still a few points that I think would strengthen the paper (which I list below), though I think there is likely a conceptual disagreement on how I and the authors' think they should be approached (which is okay). I only raise them again as I think causal and conceptual clarity is particularly important in discussions of disparities—in large part because of the history how of they have been inadequately discussed—but would defer to the editors and authors' on how best to handle them at this stage.

Specific comments:

* I think there is too much interchangeability between the use of socioeconomic factors and income in the introduction. As mentioned previously, I think at least some discussion that clearly differentiates the mechanisms between the two is important somewhere. They do so in discussion of the methods, though some readers may interpret a false equivalence between the two after reading the intro. I do not think the authors' intend this, but do think it is easy to miss the distinction, which is key to this paper, because they have too often been conflated for each other in the past.

* I think the threeway interaction between income, foreign-born, and household size could still be interesting and important. It is difficult to assess just from the p-values since it is not unexpected that p-values would be high in a threeway interaction (p=0.29 for a threeway is not too high). When point estimates and CIs are graphed, are there any insights from the trends…is everything still driven by being foreign born? That would be the data that would inform my assessment of whether it is relevant or not. It would be great if authors' could share these results at least and could consider as supplemental figure. Again, the authors' may already done this type of full assessment, but from the reponse it seems solely based on the p-value.

*

* It took me a few reads to understand the point being made in the discussion by "in the total population, individuals living in low-income households had higher risk of both COVID-19 incidence and severity…" My first two reads interpreted this has trying to make a claim about some level of generalizability of the findings (and not in just an artefactual mathematical sense) even though the following statements did clarify the issue. Though I think the authors' are trying to say that they found a similar result to previous studies, but they also find that it is in fact not generazable. Could consider saying something like "Prior evidence has shown associations with income in the total population, but our deeper dive indicated that these trends were in fact driven primarily by a specific population and not generalizable to the whole population" or something of that nature.

Any attachments provided with reviews can be seen via the following link:

[LINK]

16 May 2022

Submitted filename: Response_to_reviewers.docx

Click here for additional data file.

23 May 2022

Dear Dr. Saarinen,

Thank you very much for re-submitting your manuscript "Income differences in COVID-19 incidence and severity in Finland among people with foreign and native background: a population-based cohort study of individuals nested in households" (PMEDICINE-D-21-04207R4) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by ome reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by May 30 2022 11:59PM.

Sincerely,

Beryne Odeny,

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Comments from Reviewers:

Reviewer #3: Thank you to the authors' for their revisions and their inclusion of a figure of the threeway interaction. I do think it really helps to emphasize the studies main findings. I appreciate the authors' clarification that they are using income as a proxy for the multidimensional construct for socioeconomic status. I think clarifications are needed in two places: in the intro and strengths and limitations.

When the authors say that household income is a "reliable" measure, do they mean reliable because it was available for everyone or reliable because it is an adequate proxy for this complicated multidimensional construct. It reads like the latter, but I think the results themselves directly speak against it being the latter. I would change the wording of this in the intro.

I also think one or two sentences need to be added to the strengths and limitations to be clear that this is a limitation (currently the wording seems to describe this as a strength). Specifically, that they used income as a proxy for a complicated multidimensional construct, which is not ideal because socioeconomic status also includes considerations related to race, community, etc., but was done because that data was what was reliably available. The completeness of the income data is a strength but using at is a proxy for complex construct is certainly an important limitation.

Any attachments provided with reviews can be seen via the following link:

[LINK]

26 May 2022

Submitted filename: Response_to_reviewer.docx

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31 May 2022

Dear Dr Saarinen,

On behalf of my colleagues and the Academic Editor, Dr. Aaloke Mody, I am pleased to inform you that we have agreed to publish your manuscript "Income differences in COVID-19 incidence and severity in Finland among people with foreign and native background: a population-based cohort study of individuals nested within households" (PMEDICINE-D-21-04207R5) in PLOS Medicine.

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