Incidence and prevalence of neurodevelopmental disorders and disabilities among métis children in Alberta, Canada: A retrospective birth cohort study
Bibliographic record
Abstract
Limited research has examined the neurodevelopmental health of Métis children from a functional perspective, which is essential for culturally sensitive service planning, and policy development. This population-based retrospective birth cohort study linked provincial administrative health data of Métis and non-Métis singleton live births (2006-2016) to follow them up to 10 years of age. A random 1:4 sample of non-Métis children served as a reference group. Neurodevelopmental disorders and disabilities (NDD/D) were examined across six functional NDD/D domains. Prevalence odds ratios (pOR) with 95% confidence intervals (CI) were calculated using logistic regression models, adjusted for maternal and neonatal characteristics. Incidence rates (IR) per 1,000 person-years were estimated, and age-specific IR was modeled using longitudinal Poisson regression, adjusting for covariates. Associations between maternal and neonatal characteristics and NDD/D incidence among Métis children were examined using multivariable longitudinal Poisson regression models, with adjusted incidence rate ratios (IRR) and 95% CI reported. A total of 38,958 singleton live births were included (7,853 Métis and 31,105 non-Métis). Overall NDD/D prevalence among Métis (3.3%) and non-Métis (2.8%) children did not differ significantly after adjustment (adjusted pOR: 1.1, 95% CI: 0.9, 1.3). Learning-cognition was the most prevalent NDD/D domain. Métis children had a higher IR of NDD/D at age 2 (5.5 vs. 2.8 cases per 1,000 person-years, rate difference: 2.7 [95% CI: 0.8, 4.6]). Among Métis children, higher NDD/D incidence was associated with maternal age younger than 20 or older than 35 years, high pre-pregnancy weight, male sex, preterm birth, and congenital anomalies. While overall NDD/D prevalence was similar between Métis and non-Métis children, Métis children were more likely to be diagnosed at age 2, suggesting potential differences in early diagnosis, access to care, or underlying risk factors. A functional classification approach of neurodevelopmental health supports culturally responsive early screening and intervention strategies to address these differences.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.001 | 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".