Estimating COVID-19 vaccine uptake and its drivers among migrants, homeless and precariously housed people in France
Bibliographic record
Abstract
BACKGROUND: Migrants, people experiencing homelessness (PEH), or precariously housed (PH) are at high risk for COVID-19 infection, hospitalization, and death from COVID-19. However, while data on COVID-19 vaccine uptake in these populations are available in the USA, Canada, and Denmark, we are lacking, to the best of our knowledge, data from France. METHODS: In late 2021, we carried out a cross-sectional survey to determine COVID-19 vaccine coverage in PEH/PH residing in Ile-de-France and Marseille, France, and to explore its drivers. Participants aged over 18 years were interviewed face-to-face where they slept the previous night, in their preferred language, and then stratified for analysis into three housing groups (Streets, Accommodated, and Precariously Housed). Standardized vaccination rates were computed and compared to the French population. Multilevel univariate and multivariable logistic regression models were built. RESULTS: We find that 76.2% (95% confidence interval [CI] 74.3-78.1) of the 3690 participants received at least one COVID-19 vaccine dose while 91.1% of the French population did so. Vaccine uptake varies by stratum, with the highest uptake (85.6%; reference) in PH, followed by Accommodated (75.4%; adjusted odds-ratio = 0.79; 95% CI 0.51-1.09 vs. PH) and lowest in Streets (42.0%; AOR = 0.38; 95%CI 0.25-0.57 vs. PH). Use for vaccine certificate, age, socioeconomic factors, and vaccine hesitancy is associated with vaccination coverage. CONCLUSIONS: In France, PEH/PH, and especially the most excluded, are less likely than the general population to receive COVID-19 vaccines. While vaccine mandate has proved an effective strategy, targeted outreach, on-site vaccinations, and sensitization activities are strategies enhancing vaccine uptake that can easily be replicated in future campaigns and other settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".