Inequalities in ownership and availability of home-based vaccination records in 82 low- and middle-income countries
Bibliographic record
Abstract
Abstract Introduction Home-based records (HBR) are widely used for recording health information including child immunizations. We studied levels and inequalities in HBR ownership in low- and middle-income countries (LMICs) using data from national surveys conducted since 2010. Methods 465,060 children aged 6-35 months from 82 LMICs were classified into four categories: HBR seen by the interviewer; never had an HBR; had an HBR that was lost; and reportedly have an HBR that was not seen by the interviewer. Inequalities according to age, sex, household wealth, maternal education, antenatal care and institutional delivery were studied, as were associations between HBR ownership and vaccine coverage. Pooled analyses were carried out using country weights based on child populations. Results An HBR was seen for 67.8% of the children, 9.2% no longer had an HBR, 12.8% reportedly had an HBR that was not seen and 10.2% had never received one. The lowest percentages of HBRs seen were in Kiribati (22.1%), the Democratic Republic of Congo (24.5%), Central African Republic (24.7%), Chad (27.9%), and Mauritania (35.5%). The proportions of HBRs seen declined with age and were inversely associated with household wealth and maternal schooling. Antenatal care and institutional delivery were positively associated with ownership. There were no differences between boys and girls. When an HBR was seen, higher immunization coverage and lower vaccine coverage rates were observed, but the direction of this association remains unclear. Interpretation HBR coverage levels were remarkably low in many LMICs, particularly among children from the poorest families and those whose mothers had low schooling. Contact with antenatal and delivery care was associated with higher HBR coverage. Interventions are urgently needed to ensure that all children are issued HBRs, and to promote proper storage of such cards by families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".