Socioeconomic and geographic inequities in vaccination among children 12 to 59 months in Mexico, 2012 to 2021
Bibliographic record
Abstract
<sec> <title>Objective.</title> To document the evolution of socioeconomic and geographical inequalities in childhood vaccination in Mexico from 2012 to 2021. </sec> <sec> <title>Methods.</title> Repeated cross-sectional analysis using three rounds of National Health and Nutrition Surveys (2012, 2018, and 2021). Dichotomous variables were created to identify the proportion of children who received no dose of each vaccine included in the national immunization schedule (BCG; diphtheria, pertussis, and tetanus-containing; rotavirus; pneumococcal conjugate; and measles, mumps, and rubella [MMR]), and the proportion completely unvaccinated. The distribution of unvaccinated children was analyzed by state, and by socioeconomic status using the concentration index. </sec> <sec> <title>Results.</title> The prevalence of completely unvaccinated children in Mexico was low, with 0.3% children in 2012 and 0.8% children in 2021 receiving no vaccines (<italic>p</italic> = 0.070). Notwithstanding, for each vaccine, an important proportion of children missed receiving any dose. Notably, the prevalence of MMR unvaccinated children was 10.2% (95% CI 9.2–11.1) in 2012, 22.3% (95% CI 20.9–23.8) in 2018, and 29.1% (95% CI 26.3–31.8) in 2021 (<italic>p</italic> < 0.001 for the difference between 2012 and 2021). The concentration index indicated pro-rich inequalities in non-vaccination for 2 of 5 vaccines in 2012, 3 of 5 vaccines in 2018, and 4 of 5 vaccines in 2021. There were marked subnational variations. The percentage of MMR unvaccinated children ranged from 3.3% to 17.9% in 2012, 5.5% to 36.5% in 2018, and 13.1% to 72.5% in 2021 across the 32 states of Mexico. </sec> <sec> <title>Conclusions.</title> Equitable access to basic childhood vaccines in Mexico has deteriorated over the past decade. Vigilant equity monitoring coupled with tailored strategies to reach those left out is urgently required. </sec>
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".