Environmental Determinants of Early Childhood Caries: A Narrative Synthesis of Observational Evidence and Implications for Global Policy
Bibliographic record
Abstract
Early childhood caries (ECC) remains a significant global health challenge, disproportionately affecting marginalized populations. While traditional research emphasizes behavioral and biological risk factors, emerging evidence highlights the critical role of environmental determinants. This narrative synthesis aims to highlight the role of environmental determinants as a risk factor for ECC pathogenesis. Environmental toxins (e.g., lead, perfluoroalkyl acids, tobacco smoke, air pollution) disrupt enamel development, impair salivary function, and compromise immune responses, directly increasing caries susceptibility. Environmental degradation, including air pollution, reduces ultraviolet B radiation exposure, limiting endogenous vitamin D synthesis that is vital for enamel mineralization and immune regulation. These risks are compounded in low- and middle-income countries, where structural inequities, inadequate sanitation, and climate disruptions exacerbate ECC burdens. We introduce ecovitality-the resilience of ecosystems supporting human health-as a novel framework linking ecological vitality to oral health. Degraded environments limit access to fluoridated water and nutrient-dense foods while promoting sugary diets and endocrine disruptors. A One Health approach is advocated to address interconnected environmental, social, and biological determinants of the risk for ECC. Despite global reductions in tobacco use and lead exposure, the Global Burden of Disease 2021 analysis reports stagnation in ECC prevalence. This underscores the critical need for longitudinal and mechanistic studies to establish causality, quantify the contributions of environmental controls, and explore how mitigating these risks can reduce the global ECC burden. Such evidence may promote interdisciplinary action to align oral health promotion for children with the Sustainable Development Goals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".