Triple Jeopardy in Oral Health: Additive Effects of Immigrant Status, Education, and Neighborhood
Bibliographic record
Abstract
Purpose: To estimate the additive effects of parent’s nativity status/language spoken at country of birth, education, and area-level socioeconomic status (SES) on untreated dental caries among children aged 5 to 9 y in Australia. Methods: Cross-sectional population-based data were obtained from the 2014 National Child Oral Health Study (N = 12,140). Indicators of social position used to explore additive effects on dental caries included nativity status, language, university degree, and neighborhood socioeconomic level. Multiple-way interactions were examined, and departure from additivity resulting from 2- and 3-way interactions were estimated as relative excess risk due to interaction (RERI). Results: Children marginalized across multiple layers of disadvantage had substantially higher frequencies of dental caries compared with children in the most advantaged category. RERI for the 3-way interaction between immigrant status, education, and neighborhood SES was negative (RERI 3 : −0.14; 95% confidence interval [CI]: −1.68, 1.40). When operationalizing language, education, and neighborhood SES, the joint effect of the 3 marginalized positions was additive (RERI 3 : 0.43; 95% CI: −2.08, 2.95). Conclusion: Children marginalized across multiple intersecting axes of disadvantage bear the greatest burden of dental caries, with frequencies surpassing the cumulative effect of each social position alone. Findings emphasize the need to account for intersecting inequities and their oral health effects among children with immigrant backgrounds. Knowledge Transfer Statement: Our analysis underscores the necessity for policies and public health strategies targeting dental caries–related inequities to comprehensively account for various indicators of social disadvantage, particularly encompassing language proficiency, educational attainment, and neighborhood socioeconomic status. Within the intricate interplay of these factors, we identify a vulnerable subgroup comprising children with the highest prevalence of dental decay. Therefore, prioritizing this specific demographic should be the focal point of policies and public health initiatives aimed at fostering equitable oral health outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".