Preventing vaccine drop-outs: Geographic and system-level barriers to full immunization coverage among children in Uttar Pradesh, India
Bibliographic record
Abstract
Objective: Global interventions on routine immunization aim to achieve at least 90 % immunization coverage of all vaccines as per national immunization schedules, aligning with the Immunization Agenda 2030. Despite significant global progress, regions like Uttar Pradesh (UP), India's most populous state, require more efforts to meet this target. Methods: In 2021, a quantitative survey was conducted with 10,591 mothers/caregivers of children aged 0-15 months and 479 linked community health workers (Accredited Social Health Activists, ASHAs) responsible for connecting these families with vaccine services across 444 rural villages in UP. We developed a coverage cascade to assess the coverage of all basic vaccines (1 dose of each BCG and MR, and 3 doses each of DPT/Penta and Polio), immunization dropouts, and their drivers. Findings: While 96.4 % of service platforms had the required vaccines available and 94.7 % of children aged 12-15 months had received the first dose of Pentavalent vaccine, only 67.8 % of children received all basic vaccines, with 53.5 % completing these vaccines in the first year of life. More than half (53 %) of dropouts were concentrated in 30 % of ASHA areas. Among these areas, 13 % had no dropouts, and 29 % had more than 60 % of children aged 12-15 months with incomplete immunization. Areas with high dropout rates had higher rates of home deliveries, lower possession of parent-held vaccination records (MCP cards), and poor community-level factors such as incomplete record keeping by ASHAs, less supportive supervision by their supervisors, and relatively lower work motivation compared to areas with no dropouts. Conclusion: The wide heterogeneity in immunization coverage and dropouts emphasize the need to identify area-specific patterns and reasons for low immunization coverage and to develop interventions to address them. Robust support systems for community health workers and comprehensive record-keeping are pivotal to improve immunization coverage and to reduce the burden of vaccine-preventable diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".