Clinical outcomes and risk factors for COVID-19 among migrant populations in high-income countries: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Migrants, including refugees, asylum seekers, labour migrants, and undocumented migrants, now constitute a considerable proportion of most high-income countries’ populations, including their skilled and unskilled workforces. Migrants may be at increased risk of COVID-19 due to their health and social circumstances, yet the extent to which they are being affected and their predisposing risk factors are not clearly understood. We did a systematic review to assess clinical outcomes of COVID-19 in migrant populations (cases, hospitalisations, deaths), indirect health and social impacts, and to determine key risk factors. Methods We did a systematic review following PRISMA guidelines, registered with PROSPERO (CRD42020222135). We searched databases including PubMed, Global Health, Scopus, CINAHL, and pre-print databases (medRxiv) via the WHO Global Research on COVID-19 database to Nov 18, 2020 for peer-reviewed and grey literature pertaining to migrants (defined as foreign born) and COVID-19 in 82 high-income countries. We used our international networks to source national datasets and grey literature. Data were extracted on our primary outcomes (cases, hospitalisations, deaths) and we evaluated secondary outcomes on indirect health and social impacts, and risk factors, using narrative synthesis. Results 3016 data sources were screened with 158 from 15 countries included in the analysis (35 data sources for primary outcomes: cases [21], hospitalisations [4]; deaths [15]; 123 for secondary outcomes). We found that migrants are at increased risk of infection and are disproportionately represented among COVID-19 cases. Available datasets suggest a similarly disproportionate representation of migrants in reported COVID-19 deaths, as well as increased all-cause mortality in migrants in some countries in 2020. Undocumented migrants, migrant health and care workers, and migrants housed in camps and labour compounds may have been especially affected. In general, migrants have higher levels of many risk factors and vulnerabilities relevant to COVID-19, including increased exposure to SARS-CoV-2 due to high-risk occupations and overcrowded accommodation, and barriers to health care including inadequate information, language barriers, and reduced entitlement to healthcare coverage related to their immigration status. Conclusions Migrants in high-income countries are at high risk of exposure to, and infection with, COVID-19. These data are of immediate relevance to national public health responses to the pandemic and should inform policymaking on strategies for reducing transmission of COVID-19 in this population. Robust data on testing uptake and clinical outcomes in migrants, and barriers and facilitators to COVID-19 vaccination, are urgently needed, alongside strengthening engagement with diverse migrant groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it