Association Between Dietary Fluoride and Calcium Intake of School-Age Children With Symptoms of Dental and Skeletal Fluorosis in Halaba, Southern Ethiopia
Bibliographic record
Abstract
Background: In the Ethiopian Rift Valley, ways to reduce the fluoride (F) burden from drinking water have been unsuccessful. Calcium (Ca) intake may mitigate fluorosis by binding with F ions and preventing absorption. The purpose of this study was to examine the association between Ca intake and proportion of fluorosis symptoms in school-age children in an area where F levels are known to be higher than WHO limit of 1.5 mg F/L water. Methods: A cross-sectional survey in the Halaba zone involved 135 eligible children aged 6-13 year who were recruited to have dental fluorosis assessed by a dentist and skeletal fluorosis assessed by a physiotherapist. Dietary Ca intake was determined by 24-h recall. Food items and samples from ground wells, taps and spring water were collected for F concentration. Associations were measured using bivariate logistic regression, adjusted for known confounders. Results: Water F averaged 5.09 mg/L. Total F intake was high, 10.57 mg/day, and Ca intake was low, 520 mg/day. Prevalence of dental fluorosis (from very mild to severe symptoms) was 73.1% for younger children (6-8 years) and 68.3 % for older children (9-13 years). The prevalence of children having symptoms of skeletal fluorosis ranged between 55.1 and 72.4%, with no apparent age difference. Dietary F intake of children was significantly positively associated with presence of dental fluorosis. Dietary Ca intake of children was significantly negatively associated with dental fluorosis. Higher than average dietary F intake significantly increased the odds of developing skeletal fluorosis symptoms when measured as inability to stretch and fold arms to touch back of head. Higher than average Ca intake was significantly associated with decreased odds of developing skeletal fluorosis measured as inability to bend body to touch the toes or floor. Conclusions: High dietary F, as expected, was associated with fluorosis in children. In the presence of higher Ca intake (>520 mg/day) some fluorosis symptoms were mitigated. There is a need to improve Ca intakes as all were below recommended levels, and this nutritional strategy may also reduce burden of excess F.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".