THE IMPACT OF RESIDENTIAL SOURCES OF LEAD ON BLOOD LEAD LEVELS OF YOUNG CHILDREN IN MONTRÉAL, QUÉBEC (CANADA)
Bibliographic record
Abstract
Background and Aims: Lead sources in the general environment have reduced dramatically but the importance of domestic sources in Canada remains unknown. This study evaluated the contribution of residential sources of lead to blood lead levels (BLLs) of young children. Methods: We conducted a cross-sectional survey from September 2009 to March 2010. A total of 306 children aged 1-5 yrs, selected at random in old sectors of Montréal, participated in the study. Only participants who drank tapwater and had lived at least one year at the same residence were included. During home visits, the following environmental samples were collected: 5 kitchen tap water samples (1 liter after 5 minutes flushing and 4 liters after 30 minutes stagnation), 3 house dust floor and one windowsill samples. Paint content for lead was also evaluated by XRF. Parents answered questionnaires regarding dietary and general habits. A venous blood sample was drawn from the child. All laboratory analyses were done using established or slightly modified USEPA methods and included internal and external quality control. Multiple linear regression analyses of the log transformed BLL were used for statistical analysis. Results: The geometric mean (GM) of BLLs was 0.065 µmol/dl (95% Confidence Intervals: 0.061-0.069). There was a positive relationship between BLLs and the arithmetic mean (AM) of lead concentrations in tapwater treated in tertiles or as a continous variable (p <0.05). There was also a positive relationship with window dust with significant interaction with season and daycare at home (p< 0.05). The predictive model explained 22.2% (R2) of the variation of BLLs. Both regression models were adjusted for co-variates and other residential exposures. Conclusions: Despite low BLLs in young children living in old sectors of Montréal, tapwater and house dust are still responsible of some increase of BLL during fall and winter.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".