Systematic Review and Meta-Analysis: Prevalence of Neurodevelopmental Disorders Among Indigenous Children
Bibliographic record
Abstract
Objective: To summarize epidemiological evidence on the prevalence of neurodevelopmental disorders (NDDs) among Indigenous children in Australia Canada, New Zealand, and the United States compared to that of non-Indigenous children. Method: Comprehensive searches in 5 electronic databases were performed to identify studies evaluating the prevalence of NDDs among Indigenous and non-Indigenous children in Australia, Canada, New Zealand, and the United States. Unadjusted pooled odds ratios (pOR) with 95% confidence intervals were calculated in random-effects meta-analysis by outcome and Indigenous group. Results: From 1,274 non-duplicate citations identified, 12 studies were included in the review. Overall, cohort, ecological, and cross-sectional studies had moderate-to-high risk of bias. Meta-analysis showed no differences in the prevalence of attention-deficit/hyperactivity disorder (ADHD) between American Indian/Alaska Native (AI/AN) children and White children (pOR = 1.02, 95% CI = 0.79, 1.33). In addition, there were no differences in the prevalence of autism spectrum disorder (ASD) between Australian Aboriginal children and non-Aboriginal children (pOR = 0.43; 95% CI = 0.11, 1.58). Non-Indigenous children were found to have higher ASD prevalence compared to Indigenous children in Canada. Some evidence suggests a greater burden of intellectual disabilities affecting Indigenous children in Australia, New Zealand, and the United States, and specific learning disorders among AI/AN children in the United States. Conclusion: Limited evidence suggests a difference in prevalence for specific NDD between Indigenous children and non-Indigenous children in countries with similar colonial histories. More epidemiological research adopting an Indigenous lens in the screening and assessment of NDD is required to better understand the burden of NDDs in Indigenous children. Diversity & Inclusion Statement: One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. Study preregistration information: Prevalence of Neurodevelopmental Disorders among Indigenous Children: A Systematic Review; https://www.crd.york.ac.uk/PROSPERO/view/CRD42021238669.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".