Explaining COVID-19 mortality among immigrants in Sweden from a social determinants of health perspective (COVIS): protocol for a national register-based observational study

Introduction: Adopting a social determinants of health perspective, this project aims to study how disproportionate COVID-19 mortality among immigrants in Sweden is associated with social factors operating through differential exposure to the virus (eg, by being more likely to work in high-exposure occupations) and differential effects of infection arising from socially patterned, pre-existing health conditions, differential healthcare seeking and inequitable healthcare provision. Methods and analysis: This observational study will use health (eg, hospitalisations, deaths) and sociodemographic information (eg, occupation, income, social benefits) from Swedish national registers linked using unique identity numbers. The study population includes all adults registered in Sweden in the year before the start of the pandemic (2019), as well as individuals who immigrated to Sweden or turned 18 years of age after the start of the pandemic (2020). Our analyses will primarily cover the period from 31 January 2020 to 31 December 2022, with updates depending on the progression of the pandemic. We will evaluate COVID-19 mortality differences between foreign-born and Swedish-born individuals by examining each mechanism (differential exposure and effects) separately, while considering potential effect modification by country of birth and socioeconomic factors. Planned statistical modelling techniques include mediation analyses, multilevel models, Poisson regression and event history analyses. Ethics and dissemination: This project has been granted all necessary ethical permissions from the Swedish Ethical Review Authority (Dnr 2022-0048-01) for accessing and analysing deidentified data. The final outputs will primarily be disseminated as scientific articles published in open-access peer-reviewed international journals, as well as press releases and policy briefs

Does Maternal Country of Birth Matter for Understanding Offspring's Birthweight? A Multilevel Analysis of Individual Heterogeneity in Sweden.

Many public health and epidemiological studies have found differences between populations (e.g. maternal countries of birth) in average values of a health indicator (e.g. mean offspring birthweight). However, the approach based solely on population-level averages compromises our understanding of variability in individuals' health around the averages. If this variability is high, the exclusive study of averages may give misleading information. This idea is relevant when investigating country of birth differences in health.To exemplify this concept, we use information from the Swedish Medical Birth Register (2002-2010) and apply multilevel regression analysis of birthweight, with babies (n = 811,329) at the first, mothers (n = 571,876) at the second, and maternal countries of birth (n = 109) at the third level. We disentangle offspring, maternal and maternal country of birth components of the total offspring heterogeneity in birthweight for babies born within the normal timespan (37-42 weeks). We found that of such birthweight variation about 50% was at the baby level, 47% at the maternal level and only 3% at the maternal countries of birth level.In spite of seemingly large differences in average birthweight among maternal countries of birth (range 3290-3677 g), knowledge of the maternal country of birth does not provide accurate information for ascertaining individual offspring birthweight because of the high inter-offspring heterogeneity around country averages. Our study exemplifies the need for a better understanding of individual health diversity for which group averages may provide insufficient and even misleading information. The analytical approach we outline is therefore relevant to investigations of country of birth (and ethnic) differences in health in general

Multilevel linear regression analysis of babies, mothers and maternal countries of birth, modelling birthweight (in grams)¹.

<p>¹ The table presents measures of association (regression coefficients). Model 1 estimates only the overall mean birthweight of countries. Models 2, 3 and 4 include maternal and new-born characteristics, and Model 5 includes also contextual characteristics. Values in brackets are SE.</p><p>Multilevel linear regression analysis of babies, mothers and maternal countries of birth, modelling birthweight (in grams)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129362#t002fn001" target="_blank">¹</a>.</p

Characteristics of the population by maternal country of birth economies according to the World Bank classification based on estimates of gross national income (GNI) per capita.

<p>Characteristics of the population by maternal country of birth economies according to the World Bank classification based on estimates of gross national income (GNI) per capita.</p

Multilevel linear regression analysis of babies, mothers and maternal countries of birth, modelling birthweight (in grams)¹.

<p>¹ The table presents measures of variance. Model 1 contains only random intercepts at each level and informs on the components of variance across levels. Models 2, 3 and 4 include maternal and newborn characteristics, and Model 5 includes also contextual characteristics (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129362#pone.0129362.t002" target="_blank">Table 2</a> for a list of the variables included in each model). Values in parenthesis are SE.</p><p>Multilevel linear regression analysis of babies, mothers and maternal countries of birth, modelling birthweight (in grams)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129362#t003fn001" target="_blank">¹</a>.</p

Unadjusted offspring birthweight distributions.

<p>Box and whisker plots for mothers born in Sweden (n = 647,953), Bangladesh (n = 905), Gambia (n = 713), India (n = 2636), Japan (n = 503), Pakistan (n = 1273), Senegal (n = 100), Sri Lanka (n = 1357), Sudan (n = 324), and in all other countries (n = 153,219). There is an overlap in distributions corresponding to a low ICC.</p

Unadjusted differences in the average birthweight between maternal countries of birth.

<p>The values represent the shrunken residuals and their confidence intervals obtained from the multilevel linear regression analysis.</p

Flow diagram showing the selection of the study population.

<p>Flow diagram showing the selection of the study population.</p