Progress in reaching unvaccinated (zero-dose) children in India, 1992–2016: a multilevel, geospatial analysis of repeated cross-sectional surveys
Bibliographic record
Abstract
BACKGROUND: Reaching zero-dose children (infants who receive no routine vaccinations) is a global strategic priority. We studied zero-dose children in India over 24 years to clarify aggregate trends and the contribution of large-scale social, economic, and geographical inequalities to these. METHODS: We did a multilevel, geospatial analysis of repeated cross-sectional surveys of all four rounds (1992-2016) of India's National Family Health Survey to study the prevalence, distribution, and drivers of zero-dose (no first dose of diphtheria, tetanus, and pertussis) vaccination status. We included all children born to participating women who were aged 12-23 months at the time of the survey, as this is the standard age at which immunisation data are assessed. Children who died before the survey and those missing data on key outcomes or correlates were excluded. The outcome was child zero-dose vaccination status. We also compared the prevalence of nutritional deficits among zero-dose versus vaccinated children. For the most recent survey, we produced geospatial estimates identifying the prevalence of zero-dose children across states and districts and used these to project head count. FINDINGS: We examined 393 167 children for eligibility. 72 848 children were included in the final analytic data set. The proportion of zero-dose children in India declined from 33·4% (95% CI 32·5-34·2) in 1992 to 10·1% (9·8-10·4) in 2016. Progress notwithstanding, in 2016, zero-dose children remained concentrated among disadvantaged groups (prevalence in the bottom wealth quintile 15·3%, 95% CI 14·6-16·0; prevalence among mothers with no education 16·8%, 16·1-17·4). Compared with vaccinated children, zero-dose children were more likely to suffer from malnutrition in all survey rounds (prevalence of severe stunting in 1992: zero dose 41·3%, 95% CI 39·2-43·8 vs vaccinated 28·5%, 27·2-29·7; 2016: zero dose 24·9%, 23·6-26·2 vs vaccinated 18·7%, 18·3-19·1). In 2016, there were an estimated 2·88 (95% CI 2·86-2·89) million zero-dose children in India, concentrated in less developed states and districts and several urban areas. INTERPRETATION: Over a 24-year period in India, child zero-dose status was shaped by large-scale social inequalities and remained a consistent marker of generalised vulnerability. Interventions that address this cycle of intergenerational inequities should be prioritised. FUNDING: None. TRANSLATIONS: For the French, Spanish, and Hindi translations of the abstract see Supplementary Materials section.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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".