Seroprevalence of measles antibodies and factors associated with susceptibility: a national survey in Mexico using a plaque reduction neutralization test
Bibliographic record
Abstract
Measles continues to be one of the leading causes of child mortality worldwide, even though a highly effective vaccine has existed for more than 40 years. We aimed to describe the seroprevalence of measles antibodies in Mexico in 2012 and the risk factors associated with susceptibility. A total of 7,785 serum samples were analyzed from the National Health and Nutrition Survey in Mexico. This national survey is representative of the general population, including noninstitutionalized adult, adolescent, and child populations. Antibody titers were classified into protective (> 120 mIU/mL) or susceptible (≤ 120 mIU/mL) levels. The weighted seroprevalence and susceptibility of the overall population were 99.37% (95% CI 99.07-99.58) and 0.63% (95% CI 0.42-0.93), respectively. Among 1-to-4-year-old children, 2.18% (95% CI 1.36-3.48) were susceptible to measles. Among adolescents and young adults, the prevalence of susceptibility was as follows: those 15-19 years of age had a prevalence of 0.22% (95% CI 0.09-0.57), and those 30-39 years of age had a prevalence of 1.17% (95% CI 0.47-2.85). Susceptibility was associated with young age, living in Mexico City, living in crowded households and unknown or nonvaccinated status among 1- to 5-year-old children. Although the overall sample population seroprevalence for measles is above 95%, increased susceptibility among younger children signals the importance of the timely administration of the first vaccine dose at 12 months of age. Furthermore, increased susceptibility among specific subgroups indicates the need to reinforce current vaccination policies, including the immunization of unvaccinated or incompletely vaccinated individuals from 10 to 39 years of age.
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.
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 | 0.000 | 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".