Disparities in infant hospitalizations in Indigenous and non-Indigenous populations in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Infant mortality is higher in Indigenous than non-Indigenous populations, but comparable data on infant morbidity are lacking in Canada. We evaluated disparities in infant morbidities experienced by Indigenous populations in Canada. METHODS: We used linked population-based birth and health administrative data from Quebec, Canada, to compare hospitalization rates, an indicator of severe morbidity, in First Nations, Inuit and non-Indigenous singleton infants (< 1 year) born between 1996 and 2010. RESULTS: Our cohort included 19 770 First Nations, 3930 Inuit and 225 380 non-Indigenous infants. Compared with non-Indigenous infants, all-cause hospitalization rates were higher in First Nations infants (unadjusted risk ratio [RR] 2.05, 95% confidence interval [CI] 1.99-2.11; fully adjusted RR 1.43, 95% CI 1.37-1.50) and in Inuit infants (unadjusted RR 1.96, 95% CI 1.87-2.05; fully adjusted RR 1.37, 95% CI 1.24-1.52). Higher risks of hospitalization (accounting for multiple comparisons) were observed for First Nations infants in 12 of 16 disease categories and for Inuit infants in 7 of 16 disease categories. Maternal characteristics (age, education, marital status, parity, rural residence and Northern residence) partly explained the risk elevations, but maternal chronic illnesses and gestational complications had negligible influence overall. Acute bronchiolitis (risk difference v. non-Indigenous infants, First Nations 37.0 per 1000, Inuit 39.6 per 1000) and pneumonia (risk difference v. non-Indigenous infants, First Nations 41.2 per 1000, Inuit 61.3 per 1000) were the 2 leading causes of excess hospitalizations in Indigenous infants. INTERPRETATION: First Nations and Inuit infants had substantially elevated burdens of hospitalizations as a result of diseases of multiple systems. The findings identify substantial unmet needs in disease prevention and medical care for Indigenous infants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".