Rubella Immune Status in Pregnant Women in a Northern Mexican City
Bibliographic record
Abstract
BACKGROUND: The seroepidemiology of rubella virus infection in pregnant women in northern Mexico is largely unknown. We sought to determine the seroprevalence of rubella virus infection in pregnant women in the northern Mexican city of Durango, Mexico. Seroprevalence association with the socio-demographic, clinical and behavioral characteristics of the pregnant women was also investigated. METHODS: Through a cross-sectional study, we determined the seroprevalence of IgG and IgM anti-rubella virus in 279 pregnant women (mean age 29.17 ± 5.96 years; range 15 - 43 years) attending in a clinic of family medicine using enzyme-linked fluorescent assays. A questionnaire was used to obtain the socio-demographic, clinical and behavioral characteristics of the pregnant women. The association of rubella seropositivity and characteristics of the women was assessed by bivariate and multivariate analyses. RESULTS: Anti-rubella IgG antibodies (≥ 15 IU/mL) were found in 271 (97.1%) of the 279 pregnant women examined. None of the 279 pregnant women were positive for anti-rubella IgM antibodies. Multivariate analysis of socio-demographic, clinical and behavioral variables showed that seroreactivity to rubella virus was positively associated with national trips (OR = 7.39; 95% CI: 1.41 - 38.78; P = 0.01), and negatively associated with age (OR = 0.26; 95% CI: 0.06 - 0.99; P = 0.04). CONCLUSIONS: Rate of rubella immunity in pregnant women in the northern Mexican city of Durango is high. However, nearly 3% of pregnant women are susceptible to rubella in our setting. Risk factors associated with rubella seropositivity found in this study may be useful for optimal design of preventive measures against rubella and its sequelae.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".