Socioeconomic and residence‐based related inequality in childhood vaccination in Sub‐Saharan Africa: Evidence from Benin
Bibliographic record
Abstract
Abstract Background and Aims Childhood vaccination remains a cost‐effective strategy that has expedited the control and elimination of numerous diseases. Although coverage of new vaccines in low‐ and middle‐income countries increased exponentially in the last two decades, progress on expanding routine vaccination services to reach all children remains low, and coverage levels in many countries remains inadequate. This study aimed to examine the pattern of wealth and residence‐based related inequality in vaccination coverage through an equity lens. Methods We used data from the 2017−2018 Benin Demographic and Health Survey. Statistical and econometrics modeling were used to investigate factors associated with childhood vaccination. The Wagstaff decomposition analysis was used to disentangle the concentration index. Results A total of 1993 children were included, with 17% in the wealthiest quintile and 63% were living in rural areas. Findings showed that wealth is positively and significantly associated with vaccination coverage, particularly, for middle‐wealth households. A secondary or higher education level of women and partners increased the odds of vaccination compared to no education (p < 0.05). Women with more antenatal care visits, with multiple births, attending postnatal care and delivery in a health facility had increased vaccination coverage (p < 0.01). Inequalities in vaccination coverage are more prominent in rural areas; and are explained by wealth, education, and antenatal care visits. Conclusion Inequality in child vaccination varies according to socioeconomic and sociodemographic characteristics and is of interest to health policy. To mitigate inequalities in child vaccination coverage, policymakers should strengthen the availability and accessibility of vaccination and implement educational programs dedicated to vulnerable groups in rural areas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".