Effect of vaccination on children’s learning achievements: findings from the India Human Development Survey
Bibliographic record
Abstract
BACKGROUND: Beyond the prevention of illness and death, vaccination may provide additional benefits such as improved educational outcomes. However, there is currently little evidence on this question. Our objective was to estimate the effect of childhood vaccination on learning achievements among primary school children in India. METHODS: We used cohort data from the India Human Development Survey. Vaccination status and confounders were measured among children who were at least 12 months old at baseline in 2004-2005. In 2011-2012, the same children completed basic reading, writing and math tests. We estimated the effect of full vaccination during childhood on learning achievements using inverse probability of treatment-weighted logistic regression models and results reported on the risk difference scale. The propensity score included 33 potential community-, household-, mother- and child-level confounders as well as state fixed effects. RESULTS: Among the 4877 children included in our analysis, 54% were fully vaccinated at baseline, and 54% could read by the age of 8-11 years. The estimated effect of full vaccination on learning achievements ranged from 4 to 6 percentage points, representing relative increases ranging from 6% to 12%. Bias analysis suggested that our observed effects could be explained by unmeasured confounding, but only in the case of strong associations with the treatment and outcome. CONCLUSION: These results support the hypothesis that vaccination has lasting effects on children's learning achievements. Further work is needed to confirm findings and elucidate the potential mechanisms linking vaccines to educational outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".