COVID-19 Hit Unequal Intersections of Swedish Society Hardest, Landmark Study Finds

When Sweden declared the acute phase of the COVID-19 pandemic over in March 2022, nearly 100,000 of its citizens had been hospitalised and roughly 17,000 had died in a country of just over ten million people. Behind those national totals, however, lay a story of profound and shifting inequality that single-number summaries could never reveal. A new nationwide study, published in SSM – Population Health, has now mapped that hidden landscape in unprecedented detail, showing that the risk of severe COVID-19 in Sweden was shaped not by any single social characteristic but by the way age, gender, income, education, and birthplace intersected — and how those intersections shifted across all four pandemic waves between 2020 and 2022.

The research team, led by Jesper Löve of the University of Gothenburg together with colleagues including Gunilla Priebe, Bo Burström, Ailiana Santosa, and Nawi Ng, drew on the SCIFI-PEARL database, a nationwide register platform that links national and regional records on notifiable diseases, hospitalisations, causes of death, and socioeconomic circumstances. Their dynamic cohort covered the entire adult population of Sweden, some eight million people aged 18 and older, followed from March 2020 to March 2022. Over that period the registers recorded 67,997 COVID-19 hospitalisations and 15,895 deaths, corresponding to 84.3 hospitalisations and 19.7 deaths per 10,000 population.

What distinguishes the study is its method. Rather than treating each social factor separately, the researchers used an approach known as Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy, or MAIHDA, which is grounded in intersectionality theory. The framework, first articulated by legal scholar Kimberlé Crenshaw in 1989, holds that social categories such as gender, class, and migrant status do not operate in isolation but mutually shape one another within systems of privilege and oppression. The team combined five dimensions — age group, sex, income relative to the at-risk-of-poverty threshold, educational attainment, and birthplace classified by the World Bank income level of the country of origin — into 80 distinct intersectional strata, and modelled individuals nested within those strata.

The statistical logic is elegant. A first, unadjusted model estimates how much of the total variation in outcomes is attributable to the intersectional strata, expressed as the intraclass correlation coefficient. A second model adds all five defining dimensions as fixed main effects; if no interactions existed between the dimensions, all between-stratum variance would be explained and the random effects would vanish. Whatever variance remains points to intersectional interactions beyond the simple sum of each characteristic’s effect. This structure also partially pools information across strata, stabilising estimates for small or sparsely populated combinations — a decisive advantage over saturated interaction models that become unstable when many dimensions are crossed at once.

The results were striking. Intersectional strata explained a substantial share of the variance in outcomes, particularly for mortality among adults under 70, where intraclass correlations reached 25 percent in the first wave, 15 percent in the second, 17 percent in the third, and 22 percent in the fourth. For hospitalisation, the corresponding figures ranged between 7 and 14 percent depending on age group. Among people aged 70 and older, by contrast, the strata explained less of the variation — typically at or below 10 percent — suggesting that biological susceptibility and frailty dominated severe outcomes in old age, whereas in younger adults the patterns more clearly reflected socially structured differences in exposure, living conditions, and access to protective resources.

The most severe risks clustered in specific and sometimes unexpected combinations. During the first wave, hospitalisation among older migrants born in low-income countries was roughly four times higher than among their counterparts born in high-income countries. Older men born in low-income countries with low income and low education recorded hospitalisation rates as high as 3,550 per 10,000 in wave one. Yet the study repeatedly found that broad labels concealed enormous heterogeneity. Among older men born in low-income countries, first-wave mortality ranged nearly fourfold, from 346 to 1,377 per 10,000, depending on income and education. In some waves, migrants in advantaged strata fared better than certain Swedish-born men with low income and low education, who themselves appeared among the ten highest-risk strata in three of the four waves.

Equally important was the finding that vulnerability was temporally fluid. No single stratum occupied the highest-risk position across all four waves. In the first two waves, the ten highest hospitalisation strata among younger adults consisted exclusively of migrant men, but by wave three upper-middle-income-country-born women with low income and low education had joined them, and by wave four migrant women filled five of the ten highest positions. Mortality showed even greater fluctuation: among low-income, low-educated men born in low-income countries, rates fell from 44 per 10,000 in the first wave to 6, 18, and 7 per 10,000 in subsequent waves, a trajectory the authors link to shifting viral variants, the vaccination rollout, and evolving policy conditions.

One particularly revealing interaction emerged among younger men born in low-income countries who had high educational attainment but low income. The usually protective effect of education reversed in these strata — a pattern the authors attribute to the well-documented occupation–education mismatch experienced by many migrants in Sweden, whereby highly educated immigrants are over-represented in essential, high-exposure occupations. Overall, however, most inequalities followed an additive pattern, best described as ‘double jeopardy’, in which each marginalised position adds to disadvantage, rather than the compounded ‘multiple jeopardy’ of effects beyond additivity.

The authors are careful to stress that intersectional strata are analytical constructs, not inherent risk groups. As they and other scholars note, identifying categories as stand-alone risk factors risks diverting attention from the structural processes — crowded housing, high-risk work, inadequate health information, institutional distrust — that actually produce vulnerability. The pandemic, they argue, was a syndemic in which viral exposure interacted with pre-existing medical and social inequalities, and Sweden’s early reliance on voluntary recommendations and individual responsibility may have interacted differently with the social and occupational conditions of different groups.

The study’s implications reach well beyond Sweden. Because vulnerability shifted across pandemic phases and social positions, the authors conclude that pandemic preparedness cannot rely on static assumptions about ‘high-risk’ groups. Surveillance and interventions, they argue, should be responsive to changing patterns of inequality across intersecting social positions, and reducing the inequitable consequences of future pandemics will require not only intersectionality-informed monitoring but political commitment to addressing the structural conditions — living circumstances, social protection, and pre-existing health burdens — that make some people far more exposed than others when a new pathogen arrives.

Subject of Research: Intersectional inequalities in COVID-19 hospitalisation and mortality across four pandemic waves in Sweden, analysed with multilevel intersectional methods.

Article Title: Intersectional Inequalities in COVID-19 Hospitalisation and Mortality: A Nationwide MAIHDA Study of Swedish Adults Across Four Pandemic Waves (2020–2022)

Article References: Löve, J., Priebe, G., Burström, B., Santosa, A., & Ng, N. (2026). Intersectional Inequalities in COVID-19 Hospitalisation and Mortality: A Nationwide MAIHDA Study of Swedish Adults Across Four Pandemic Waves (2020–2022). SSM – Population Health, Article 101968. https://doi.org/10.1016/j.ssmph.2026.101968

Image Credits: AI Generated

DOI: 10.1016/j.ssmph.2026.101968

Keywords: COVID-19, intersectionality, MAIHDA, health inequalities, Sweden, hospitalisation, mortality, pandemic waves, migration background, socioeconomic status, public health, pandemic preparedness