The economic value of Quebec’s water fluoridation program
Bibliographic record
Abstract
AIM: Dental caries is a major public health problem worldwide, with very significant deleterious consequences for many people. The available data are alarming in Canada and the province of Quebec. The water fluoridation program has been shown to be the most effective means of preventing caries and reducing oral health inequalities. This article analyzes the cost-effectiveness of Quebec's water fluoridation program to provide decision-makers with economic information for assessing its usefulness. METHODS: An approach adapted from economic evaluation was used to: (1) build a logic model for Quebec's water fluoridation program; (2) determine its implementation cost; and (3) analyze its cost-effectiveness. Documentary analysis was used to build the logic model. Program cost was calculated using data from 13 municipalities that adopted fluoridation between 2002 and 2010 and two that received only infrastructure grants. Other sources were used to collect demographic data and calculate costs for caries treatment including costs associated with travel and lost productivity. RESULTS: The analyses showed the water fluoridation program was cost-effective even with a conservatively estimated 1 % reduction in dental caries. The benefit-cost ratio indicated that, at an expected average effectiveness of 30 % caries reduction, one dollar invested in the program saved $71.05-$82.83 per Quebec's inhabitant in dental costs (in 2010) or more than $560 million for the State and taxpayers. CONCLUSION: The results showed that the drinking-water fluoridation program produced substantial savings. Public health decision-makers could develop economic arguments to support wide deployment of this population-based intervention whose efficacy and safety have been demonstrated and acknowledged.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".