Prevalence and Trends of Not Receiving a Dose of DPT-Containing Vaccine Among Children 12–35 Months: An Analysis of 81 Low- And Middle-Income Countries
Bibliographic record
Abstract
Not receiving a DPT-containing vaccine in early childhood indicates an absence of routine immunization, which puts children at an elevated risk of mortality, morbidity, and worse human development over the life course. We estimated the percentage of children 12-35 months who did not receive a dose of DPT-containing vaccine (termed zero-dose children) using household surveys from 81 low- and middle-income countries conducted between 2014 and 2023. For 68 countries with more than one survey (with the earlier survey conducted 2000-2013), we estimated the average annual percentage point change in prevalence of zero-dose children between the earliest and latest surveys. We also explored the association of zero-dose prevalence with postneonatal and child mortality, health expenditure, and Gavi-eligibility. Overall, 16% of children in our pooled sample had not received a dose of DPT-containing vaccine. There was a 0.8% point decline in zero-dose prevalence per year on average across the period studied. A single percentage point average annual decline in zero-dose prevalence was associated with an average annual decrease of 1.4 deaths in the postneonatal and childhood period per 1000 live births. Gavi-eligible countries had a much faster decline in zero-dose prevalence than other countries. Large gains have been made in reducing the percentage of children who did not receive a DPT-containing vaccine. Efforts to reduce the number of zero-dose children should focus on countries with high prevalence to achieve the Immunization Agenda 2030. Healthcare spending could be prioritized so that the prevalence of zero-dose children is reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".