Inequalities in oral health: Estimating the longitudinal economic burden of dental caries by deprivation status in six countries
Bibliographic record
Abstract
Abstract Background The recent World Health Organization (WHO) resolution on oral health urges pivoting to a preventive approach and integration of oral health into the non-communicable diseases agenda. This study aimed to: 1) explore the healthcare costs of managing dental caries between the ages of 12 and 65 years across socioeconomic groups in six countries (Brazil, France, Germany, Indonesia, Italy, UK), and 2) estimate the potential reduction in direct costs from non-targeted and targeted oral health-promoting interventions. Methods A cohort simulation model was developed to estimate direct costs of over time for different socioeconomic groups. National-level DMFT (dentine threshold) data, the relative likelihood of receiving an intervention (such as a restorative procedure, tooth extraction and replacement), and clinically-guided assumptions were used to populate the model. A hypothetical group of upstream and downstream preventive interventions were applied either uniformly across all deprivation groups to reduce caries progression rates by 30% or in a levelled-up fashion with the greatest gains seen in the most deprived group. Results The population level direct costs of caries from 12 to 65 years of age varied between US10.2bn in Italy to US$36.2bn in Brazil. The highest per-person costs were in the UK at US$22,910 and the lowest in Indonesia at US$7,414. The per-person direct costs were highest in the most deprived group across Brazil, France, Italy and the UK. With the uniform application of preventive measures across all deprivation groups, the greatest reduction in per-person costs for caries management was seen in the most deprived group across all countries except Indonesia. With a levelling-up approach, cost reductions in the most deprived group ranged from US$3,948 in Indonesia to US$17,728 in the UK. Conclusion Our exploratory analysis shows the disproportionate economic burden of caries in the most deprived groups and highlights the significant opportunity to reduce direct costs via levelling-up preventive measures. The healthcare burden stems from a higher baseline caries experience and greater annual progression rates in the most deprived. Therefore, preventive measures should be primarily aimed at reducing early childhood caries, but also applied across all ages.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".