Small-area variation in child under-vaccination in India: a multilevel analysis of cross-sectional data from 36 states and Union Territories, 707 districts, and 22,349 small-area clusters
Bibliographic record
Abstract
Background: India has made exceptional advances in child immunisation, but subnational inequities in vaccination coverage impede attainment of key programmatic goals. Our study provides an up-to-date national portrait of local variations in child vaccination using a comprehensive set of indicators relevant to routine immunisation. Methods: Indicators representing unvaccinated (zero-dose) children, incomplete basic immunisation, and vulnerability to measles and polio, were constructed from India's 2019-2021 National Family Health Survey. We used four-level random effects logistic regression models to partition the total outcome variation over state, district and cluster levels, and produce precision-weighted estimates of prevalence across clusters. District-level prevalence and within-district variation using standard deviation measures were derived for each outcome. Boxplots graphically summarised the distribution of precision-weighted mean cluster prevalence by state. Findings: The analysis included 87,622 children aged 12-36 months. Clusters accounted for 67.6% (var: 1.36; SE: 0.127) of the variation among zero-dose children, and more than 50% for all indicators. Districts with a higher prevalence of under-vaccination tended to have higher within-district heterogeneity, interpretable as greater within-district child vaccination inequities. For vaccines administered in the first year of life, the northeastern states and Uttar Pradesh had the highest median under-vaccination. Despite India's high aggregate vaccine coverage, the distribution of small-area (cluster) mean prevalence highlighted pockets of low coverage in most states, suggesting ongoing vulnerability to measles and polio. Interpretation: Achieving India's vaccination goals requires a strategic shift towards identification and targeting of low-immunity clusters at the sub-district level. Funding: Canadian Institutes of Health Research.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".