Excess mortality associated with eating disorders: population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Individuals with eating disorders have a high mortality risk. Few population-based studies have estimated this risk in eating disorders other than anorexia nervosa. AIMS: To investigate all-cause mortality in a population-based cohort of individuals who received hospital-based care for an eating disorder (anorexia nervosa, bulimia nervosa or eating disorder not otherwise specified) in Ontario, Canada. METHOD: We conducted a retrospective cohort study of 19 041 individuals with an eating disorder from 1 January 1990 to 31 December 2013 using administrative healthcare data. The outcome of interest was death. Excess mortality was assessed using standardised mortality ratios (SMRs) and potential years of life lost (PYLL). Cox proportional hazards regression models were used to examine sociodemographic and medical comorbidities associated with greater mortality risk. RESULTS: The cohort had 17 108 females (89.9%) and 1933 males (10.1%). The all-cause mortality for the entire cohort was five times higher than expected compared with the Ontario population (SMR = 5.06; 95% CI 4.82-5.30). SMRs were higher for males (SMR = 7.24; 95% CI 6.58-7.96) relative to females (SMR = 4.59; 95% CI 4.34-4.85) overall, and in all age groups in the cohort. For both genders, the cohort PYLL was more than six times higher than the expected PYLL in the Ontario population. CONCLUSIONS: Patients with eating disorders diagnosed in hospital settings experience five to seven times higher mortality rates compared with the overall population. There is an urgent need to understand the mortality risk factors to improve health outcomes among individuals with eating disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 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".