Housing conditions and respiratory morbidity in Indigenous children in remote communities in Northwestern Ontario, Canada
Bibliographic record
Abstract
Background: Rates of lower respiratory tract infection (LRTI) among First Nations (FN) children living in Canada are elevated. We aimed to quantify indoor environmental quality (IEQ) in the homes of FN children in isolated communities and evaluate any associations with respiratory morbidity. Methods: We performed a cross-sectional evaluation of 98 FN children (81 with complete data) aged 3 years or younger, living in 4 FN communities in the Sioux Lookout region of Northern Ontario. We performed medical chart reviews and administered questionnaires. We performed a housing inspection, including quantifying the interior surface area of mould (SAM). We monitored air quality for 5 days in each home and quantified the contaminant loading of settled floor dust, including endotoxin. We analyzed associations between IEQ variables and respiratory conditions using univariable and multivariable analyses. Results: Participants had a mean age of 1.6 years and 21% had been admitted to hospital for respiratory infections before age 2 years. Houses were generally crowded (mean occupancy 6.6 [standard deviation 2.6, range 3–17] people per house). Serious housing concerns were frequent, including a lack of functioning controlled ventilation. The mean SAM in the occupied space was 0.2 m2. In multivariable modelling, there was evidence of an association of LRTI with log endotoxin (p = 0.07) and age (p = 0.02), and for upper respiratory tract infections, with SAM (p = 0.07) and age (p = 0.03). Wheeze with colds was associated with log endotoxin (p = 0.03) and age (p = 0.04). Interpretation: We observed poor housing conditions and an association between endotoxin and wheezing in young FN children living in Northern Ontario.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".