One billion more people benefiting from universal health coverage: where is early childhood caries prevention in the African vision?
Bibliographic record
Abstract
Early childhood caries (ECC) is one of the most common and preventable childhood conditions, yet it remains systematically excluded from Africa’s Universal Health Coverage (UHC) agenda. This study identified the systemic barriers to embedding equitable and cost-effective ECC prevention into the continent’s UHC agenda and proposed an actionable, multi-level framework to overcome these barriers. We conducted a critical review, guided by a conceptual framework integrating the Tanahashi coverage framework, the Socio-Ecological Model, and the UHC principles. The review synthesized evidence from searches of PubMed, Scopus, Web of Science, AJOL, and grey literature. We compared the ECC surveillance capacity of Africa and the World by quantifying data availability. We also developed an implementation framework to facilitate the integration of ECC prevention in UHC. A surveillance gap renders ECC invisible to African health systems, with only 9.3% of countries having any prevalence data for children under 36 months, compared to 33.1% globally. However, feasible integration pathways exist through multi-sectoral collaboration, with platforms like the maternal and child health offering a scalable entry point for task-shifted interventions that can reduce the risk for ECC. Integrating ECC prevention into UHC requires a multi-pronged strategy: generating epidemiologic and local cost-effectiveness evidence, harnessing digital health innovations, embedding prevention within early childhood development programs, and conducting implementation research to secure political commitment for sustainable inclusion in UHC frameworks. This review establishes that integrating ECC prevention into Africa’s UHC is an essential yet overlooked opportunity. To bridge this gap, policymakers must prioritize making ECC visible by embedding indicators into national health surveys and surveillance systems and integrating preventive care into child health-focused platforms. In addition, researchers must build a local evidence base with cost-effectiveness and implementation data.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".