Trend and inequity in infant vaccination coverage: Analysis from three recent Demographic Health Surveys in Nepal
Bibliographic record
Abstract
Abstract Background Despite policy intention to reach disadvantaged populations, inequities in child health care use and health outcomes persist in Nepal. The current study aimed to investigate the trend of full vaccination coverage among infants and its equity gaps between 2002 and 2016.Methods Using data from demographic health surveys conducted in 2006, 2011 and 2016, we investigated the trend of coverage of six antigens: Bacille Calmette Guerin (BCG), Diptheria, Pertussis, Tetanus (DPT), Polio, and Measles) between 2002 to 2016. Rich-poor difference, Rich: Poor ratio and concentration index were calculated to measure income inequity. Lorentz curve was drawn to show the change in income-related inequity over time. Bivariate and multivariate logistic regression analyses were conducted to investigate socio-demographic correlates of full vaccination coverage.Results Full immunization coverage was slightly increased from an average of 83% during 2002-2006 to 87% during 2007-2011, but it decreased to 78% during 2012-2016. There was a significant increase in full vaccination coverage among infants from the poorest income quintile and a simultaneous decrease among infants from richer income quintiles. Province 2 saw the largest drop, from 79.2% (95%CI 64.8-88.8) during 2002-2006 to 65.2% (95%CI 56.4-73.0) during 2012-2016. In Province 2, maternal education was the independent predictor of full vaccination coverage; the mother with secondary education was over three times more likely to fully immunize their children compared to mothers with no formal education (AOR 3.2; 95% CI:1.5-6.7).Conclusion Full vaccination coverage in Nepal saw significant decrement away from the national target after 2011. A sharp decrease in coverage of full vaccination among infants from wealthier income quintiles and an increase in coverage among infants from the poorest income quintile between 2002 and 2016 created a pro-poor equity gain. While a national effort to improve full vaccination coverage is overdue, children from province 2, specifically those born to mothers with no or primary education need particular programmatic focus. Further research is needed to understand the reasons behind decrement in full vaccination coverage, particularly among rich income quintiles.
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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 | 0.004 |
| 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 | 0.001 |
| 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".