Socioeconomic inequalities in child vaccination in low/middle-income countries: what accounts for the differences?
Bibliographic record
Abstract
BACKGROUND: Socioeconomic inequalities in child vaccination continue to be a global public health concern. This study aimed to measure and identify factors associated with socioeconomic inequalities in full immunisation coverage against the four core vaccine-preventable diseases (ie, bacille Calmette-Guérin, diphtheria-tetanus-pertussis (three doses), polio (three doses) and measles vaccines) in 46 low/middle-income countries. METHODS: The most recent nationally representative samples of children (aged 10-59 months, n=372 499) collected through the Demographic Health Surveys were used to measure vaccination rates. The concentration index (C) was used to quantify socioeconomic inequalities in vaccination coverage. Furthermore, meta-regression analyses were used to determine factors affecting socioeconomic inequalities in vaccination coverage across countries. RESULTS: Results suggested that immunisation coverage was pro-rich in most countries (median C=0.161, IQR 0.131). Gambia (C=-0.146, 95% CI -0.223 to -0.069), Namibia (C=-0.093, 95% CI -0.145 to -0.041) and Kyrgyz Republic (C=-0.227, 95% CI -0.304 to -0.15) were the only countries where children who belong to higher socioeconomic status group were less likely to receive all the four core vaccines than their lower socioeconomic status counterparts. Meta-regression analyses suggested that, across countries, the concentration of antenatal care visits among wealthier mothers was positively associated with the concentration of vaccination coverage among wealthier children (coefficient=0.606, 95% CI 0.301 to 0.911). CONCLUSIONS: Pro-rich distribution of child vaccination in most low/middle-income countries remains an important public health policy concern. Policies aimed to improve antenatal care visits among mothers in lower socioeconomic groups may mitigate socioeconomic inequalities in vaccination coverage in low/middle-income countries.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".