Indoor air quality and the risk of lower respiratory tract infections in young Canadian Inuit children
Bibliographic record
Abstract
BACKGROUND: Inuit infants have the highest reported rate of hospital admissions because of lower respiratory tract infections in the world. We evaluated the prevalence of reduced ventilation in houses in Nunavut, Canada, and whether this was associated with an increased risk of these infections among young Inuit children. METHODS: We measured ventilation in 49 homes of Inuit children less than 5 years of age in Qikiqtaaluk (Baffin) Region, Nunavut. We identified the occurrence of lower respiratory tract infections using a standardized questionnaire. Associations between ventilation measures and lower respiratory tract infection were evaluated using multiple logistic regression models. RESULTS: The mean number of occupants per house was 6.1 people. The mean ventilation rate per person was 5.6 L/s (standard deviation [SD] 3.7); 80% (37/46) of the houses had ventilation rates below the recommended rate of 7.5 L/s per person. The mean indoor carbon dioxide (CO2) concentration of 1358 (SD 531) ppm was higher than the recommended target level of 1000 ppm. Smokers were present in 46 homes (94%). Of the 49 children, 27 (55%) had a reported history of lower respiratory tract infection. Reported respiratory infection was significantly associated with mean CO2 levels (odds ratio [OR] 2.85 per 500-ppm increase in mean indoor CO2, 95% confidence interval [CI] 1.23-6.59) and occupancy (OR 1.81 for each additional occupant, 95% CI 1.14-2.86). INTERPRETATION: Reduced ventilation and crowding may contribute to the observed excess of lower respiratory tract infection among young Inuit children. The benefits of measures to reduce indoor smoking and occupancy rates and to increase ventilation should be studied.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".