Understanding the excess COVID-19 burden among immigrants in Norway
Bibliographic record
Abstract
BACKGROUND: We aim to use intermarriage as a measure to disentangle the role of exposure to virus, susceptibility and care in differences in burden of COVID-19, by comparing rates of COVID-19 infections between immigrants married to a native and to another immigrant. METHODS: Using data from the Norwegian emergency preparedness, register participants (N=2 312 836) were linked with their registered partner and categorized based on own and partner's country of birth. From logistic regressions, odds ratios (OR) of COVID-19 infection (15 June 2020-01 June 2021) and related hospitalization were calculated adjusted for age, sex, municipality, medical risk, occupation, household income, education and crowded housing. RESULTS: Immigrants were at increased risk of COVID-19 and related hospitalization regardless of their partners being immigrant or not, but immigrants married to a Norwegian-born had lower risk than other immigrants. Compared with intramarried Norwegian-born, odds of COVID-19 infection was higher among persons in couples with one Norwegian-born and one immigrant from Europe/USA/Canada/Oceania (OR 1.42-1.46) or Africa/Asia/Latin-America (OR 1.91-2.01). Odds of infection among intramarried immigrants from Africa/Asia/Latin-America was 4.92. For hospitalization, the corresponding odds were slightly higher. CONCLUSION: Our study suggests that the excess burden of COVID-19 among immigrants is explained by differences in exposure and care rather than susceptibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".