The first 1,000 days of life and early childhood caries: closing the global data gap
Bibliographic record
Abstract
The first 1,000 days of life represent a critical window for preventing Early Childhood Caries (ECC). However, a significant global data gap obscures the true scale of ECC within this critical period. This review aims to systematically examine the global availability of ECC data for children under 36 months, discuss age-specific prevalence trends, and synthesize evidence to highlight the implications of missing data. A comprehensive analysis of a global dataset reporting ECC prevalence across 193 United Nations member states (2007-2017) was conducted. Analysis of the data was organized by the World Health Organization Region. The analysis revealed a profound data gap: 73.6% of countries had no data for children under 36 months, and only 19.7% had current data. Where data existed, rates approach or exceed 50% in some countries (e.g., Egypt: 69.6%, Mongolia: 47.5%), indicating that ECC is often well-established in the first 1,000 days of life. Significant regional disparities were identified, with the highest burden in the European Region, the Eastern Mediterranean Region, and the Western Pacific Region. Even within regions, there are extreme disparities in prevalence between countries (e.g., Kuwait at 3.0% vs. Egypt at 69.6% in the Middle East; Finland at 0.3% vs. Kazakhstan at 45.0% in Europe). The scarcity of data and high prevalence rates highlight a public oral health problem in infancy. Closing this global data gap is an essential first step to mobilize resources and implement targeted, effective prevention strategies where we can have the greatest impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".