Self-Reported Lifetime History of Eating Disorders and Mortality in the General Population: A Canadian Population Survey with Record Linkage
Bibliographic record
Abstract
Eating disorders (EDs) are often reported to have the highest mortality of any mental health disorder. However, this assertion is based on clinical samples, which may provide an inaccurate view of the actual risks in the population. Hence, in the current retrospective cohort study, mortality of self-reported lifetime history of EDs in the general population was explored. The data source was the Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2), linked to a national mortality database. The survey sample was representative of the Canadian household population (mean age = 43.95 years, 50.9% female). The survey inquired about the history of professionally diagnosed chronic conditions, including EDs. Subsequently, the survey dataset was linked to the national mortality dataset (for the date of death) up to 2017. Cox proportional hazards models were used to explore the effect of EDs on mortality. The unadjusted-hazard ratio (HR) for the lifetime history of an ED was 1.35 (95% CI 0.70-2.58). However, the age/sex-adjusted HR increased to 4.5 (95% CI 2.33-8.84), which was over two times higher than age/sex-adjusted HRs for other mental disorders (schizophrenia/psychosis, mood-disorders, and post-traumatic stress disorder). In conclusion, all-cause mortality of self-reported lifetime history of EDs in the household population was markedly elevated and considerably higher than that of other self-reported disorders. This finding replicates prior findings in a population-representative sample and provides a definitive quantification of increased risk of mortality in EDs, which was previously lacking. Furthermore, it highlights the seriousness of EDs and an urgent need for strategies that may help to improve long-term outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".