Burden of Mental, Neurological, Substance Use Disorders and Self-Harm in North America: A Comparative Epidemiology of Canada, Mexico, and the United States
Bibliographic record
Abstract
OBJECTIVE: To estimate the burden of mental, neurological, substance use disorders and self-harm (MNSS) in Canada, Mexico, and the United States. METHOD: We extracted 2017 data from the Global Burden of Disease online database. Based on a previously developed framework to classify and aggregate the burden of specific disorders and symptoms, we reestimated the MNSS burden to include suicide, alcohol use, drug use, specific neurological, and painful somatic symptom disorders. We analyzed age-sex-specific patterns within and between countries. RESULTS: The MNSS burden is the largest of all disorder groupings. It is lowest in Mexico, intermediate in Canada, and highest in the United States. Exceptions are alcohol use, bipolar, conduct disorders, and epilepsy, which are highest in Mexico; and painful somatic syndromes and headaches, which are highest in Canada. The burden of drug use disorders in the United States is twice the burden in Canada, and 7 times the burden in Mexico. MNSS become the most burdensome of all disorder groups by age 10, staying at the top until age 60, and show a distinct pattern across the lifetime. The top three MNSS disorders for men are a combination of substance use disorders and self-harm (United States), with the addition of painful somatic syndromes (Canada), and headaches (Mexico). For women, the top three are headaches and depression (all countries), drug use (United States), neurocognitive disorders (Mexico), and painful somatic syndromes (Canada). CONCLUSION: MNSS are the most burdensome disease grouping and should be prioritized for funding in Canada, Mexico, and the United States.
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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 | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".