Association Between Environmental Health, Ecosystem Vitality, and Early Childhood Caries
Bibliographic record
Abstract
Background: Environmental issues lead to serious health issues in young growing children. This study aims to determine the association between a country’s level of environmental health, ecosystem vitality and prevalence of early childhood caries (ECC). Methods: This was an ecological study. The data for the explanatory variables - country-level environmental performance index (EPI), environmental health and ecosystem vitality - were obtained from the Yale Center for Environmental Law and Policy. The outcome variables were country-level prevalence of ECC in 0-2 and 3-5-year-old children. The country EPI, environmental health and ecosystem vitality were matched with country ECC prevalence for 0-2-year-olds and 3-5-year-olds for the period of 2007 to 2017. Differences in the variables by country income level were determined using ANOVA. Multivariate ANOVA was used to determine the association between ECC prevalence in 0-2-year-olds and 3-5-year-olds, and EPI, environmental health and ecosystem vitality, adjusting for each country’s per capita gross national income. Results: Thirty-seven countries had complete data on ECC in 0-2 and 3-5-year-olds, EPI, environmental health and ecosystem vitality scores. There were significant differences in ECC prevalence of 0-2-year-olds and 3-5-year-olds between countries with different income levels. Also, there were significant differences in EPI (p< 0.0001), environmental health score (p< 0.0001) and ecosystem vitality (p=0.01) score by country income levels. High-income countries had significantly higher EPI scores than did low-income countries (p=0.001), lower-middle-income countries (p<0.0001) and upper-middle-income countries (p<0.0001). There was an inverse non-significant relationship between ECC prevalence and EPI in 0-2-year-olds (B=-0.06; p=0.84) and 3-5-year-olds (B=-0.30; p=0.50), and ecosystem vitality in 0-2-year-olds (B=-0.55, p= 0.08) and 3-5-year-olds (B=-0.96; p=0.02). Environmental health was directly and insignificantly associated with ECC in 0-2-year-olds (B=0.20; p=0.23) and 3-5-year-olds (B=0.22; p=0.32). Conclusions: The inverse, non-significant associations between ECC prevalence, EPI and ecosystem vitality, and direct association between ECC prevalence and ecosystem vitality, which was only significant for 3-5-year-olds indicates a complex relationship between various indicators of environmental performance. There may be higher risk of ECC with greater economic development, industrialization and urbanization, while better ecosystem vitality may offer protection against ECC through the rational use of resources, healthy life choices and preventive health practices.
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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 | 0.000 |
| 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".