Susceptibility to Measles, Mumps, and Rubella in Newly Arrived Adult Immigrants and Refugees
Bibliographic record
Abstract
BACKGROUND: Despite effective vaccination programs for measles, mumps, and rubella in the United States and Canada, outbreaks continue to occur in susceptible subgroups, such as foreign-born persons. OBJECTIVE: To determine the susceptibility of newly arrived immigrants and refugees to measles, mumps, and rubella. DESIGN: Seroprevalence study. SETTING: Two hospitals and three community clinics in Montreal, Quebec, Canada. PATIENTS: 1480 adult immigrants and refugees who were recruited from October 2002 to December 2004. MEASUREMENTS: Sociodemographic and clinical data and serology for measles, mumps, and rubella. RESULTS: Thirty-six percent (range, 22% to 54%) of the study population was nonimmune to at least 1 of the 3 diseases. This proportion varied by age, sex, and region of origin. In multivariate analysis and after adjustment for region of origin, age, and socioeconomic factors, immigrant women had higher odds (odds ratio, 2.1) of being immune to measles (95% CI, 1.2 to 3.8) and an odds ratio of 1.7 of being nonimmune to rubella (CI, 1.2 to 2.6) compared with immigrant men. LIMITATIONS: The results from the community-based convenience sample of immigrants may not be generalizable to all immigrant populations. CONCLUSIONS: Many new immigrants and refugees, particularly women, are susceptible to measles, mumps, or rubella and may benefit from targeted vaccination programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".