Assessment of the magnitude of geographic variation and socioeconomic contextual effects on children’s dental caries: a multilevel cross-sectional analysis of a population-based sample
Bibliographic record
Abstract
Background: Revealing geographic variation and assessing area contextual influences are important for reducing social inequalities in dental caries. The objective of this study was to investigate area contextual effects on children’s dental caries. Methods: This cross-sectional study included data from Grade 1 and 2 school children attending schools in the Public or Catholic school systems in the urban areas of Calgary and Edmonton in 2013/2014, in Canada. Three sources of data were used: (a) open mouth examinations, (b) parents’ questionnaires, and (c) Pampalon Material Deprivation Index derived from census data. Two dental caries outcomes were considered: (1) presence of dental caries, and (2) caries experience. Data were analyzed using multilevel modelling with two levels: school children (level 1) and dissemination area in which the child’s school was located (level 2). Results: The analytic sample included 5,677 school children attending school in 220 DAs. The study confirmed the existence of geographic variation; levels of dental caries were significantly higher among children attending schools in the most materially-deprived DAs than among those in the least materially-deprived DAs. After controlling for different population compositions in those areas, the DA-level variance represented a small but significant part (5-9%) of total variance in dental caries. Although the highest risk of having dental caries was found in the most materially-deprived DAs, the largest number of children at risk were more thinly spread across all deprivation quintiles. Conclusions: The school DA’s context may have an impact on children’s dental caries, beyond individual- and family-level factors. The study findings are relevant to Alberta Health Services’ practice of basing their selection of targeted areas for dental public health programming on school-DA’s material deprivation level and delivering preventive services to children attending schools in those selected DAs. Specifically, although risk of dental caries is highest in the most deprived quintiles, strategies focusing exclusively on the highest deprivation areas would miss many of the vulnerable children. Multilevel interventions are thus necessary to reduce social inequalities in children’s dental caries.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".