Quantifying Fluoridation Exposure Over Time in Alberta, Canada: Challenges and Implications for Dental Public Health Surveillance.
Bibliographic record
Abstract
BACKGROUND: Community water fluoridation is one component of a multifactorial approach to preventing dental caries. Yet, fluoridation monitoring in Canada has historically been fragmented, and recent national estimates give little indication of trends at the provincial or municipal levels. We aimed to quantify fluoridation exposure trends in Alberta from 1950 to 2018 at both the population and municipal levels. Insights have implications for dental public health surveillance. METHODS: Drawing from various public sources, we compiled a list of all Alberta municipalities, noting type of municipality and annual population count from 1950 to 2018. We recorded fluoridation status (excluding naturally occurring fluoride) by year for each municipality, based on the start and end (if ever) dates. We calculated annual fluoridation exposure at the population level (% of Alberta population exposed) and the municipality level (number of municipalities exposed) to visually assess trends over time. RESULTS: Population exposure to fluoridation in Alberta generally increased from 1950 to 2010. A sharp drop occurred in 2011, after which exposure fluctuated at around 43-45%. Municipality exposure generally increased from 1958 to 2006 and from 2012 to 2018, except for small declines during 2007-2008 and 2010-2011. Challenges concerning data completeness were considerable. CONCLUSION: Our findings illuminate the substantial variation in fluoridation exposure of Albertans over time, and they elucidate the complexities of estimating such exposure. They speak to the value of centralized fluoridation monitoring mechanisms as a key part of dental public health surveillance infrastructure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".