Identifying Individuals with Eating Disorders Using Health Administrative Data
Bibliographic record
Abstract
AbstractObjective:Eating disorders are common and have a high public health burden. However, existing clinically relevant data sources are scarce, limiting the capacity to accurately measure the burden of eating disorders. This study tests the feasibility of generating a large clinically relevant cohort of individuals with eating disorders using health administrative data.Methods:We developed 3 clinically relevant eating disorder prevalence cohorts using health administrative data from Ontario, Canada, between 1990 and 2014. Cohort 1 included patients with a hospitalization where an eating disorder diagnosis was the primary diagnosis, cohort 2 included patients with a hospitalization where an eating disorder diagnosis was any diagnosis, and cohort 3 included cohort 2 plus any patient with an emergency department visit with an eating disorder diagnosis.Results:Cohort 1 had 7268 patients, cohort 2 had 13,197 patients, and cohort 3 had 17,373 patients. As cohort size increased, the proportion of eating disorder patients with diagnoses of bulimia nervosa and eating disorder not otherwise specified increased. Although the cohorts differed according to demographic and clinical characteristics, these differences were small compared to the degree to which they differed from the Ontario population.Discussion:It is feasible to use health administrative data to measure the clinically relevant burden of eating disorders. The cohorts differed significantly in the eating disorder diagnostic composition. Eating disorders have a high burden, but poor data availability has resulted in fewer public health–related eating disorders studies in comparison to other mental disorders. The use of administrative data can address this evidence gap. RésuméObjectif :Les troubles alimentaires sont communs et constituent un fardeau de santé publique élevé. Toutefois, les sources existantes de données pertinentes sur le plan clinique sont rares, et limitent ainsi la capacité de mesurer avec exactitude le fardeau des troubles alimentaires. Cette étude vérifie la faisabilité de générer une vaste cohorte pertinente sur le plan clinique de personnes souffrant de troubles alimentaires à l’aide des données de santé administratives.Méthodes :Nous avons formé trois cohortes ayant une prévalence de troubles alimentaires pertinente sur le plan clinique à partir des données de santé administratives de l’Ontario, au Canada, entre 1990 et 2014. La cohorte 1 comprenait des patients ayant eu une hospitalisation quand le diagnostic de trouble alimentaire était le principal diagnostic; la cohorte 2 comportait des patients ayant eu une hospitalisation quand un diagnostic de trouble alimentaire était un quelconque diagnostic; et la cohorte 3 regroupait des patients de la cohorte 2 en plus de patients qui comptaient une visite au service d’urgence avec un diagnostic de trouble alimentaire.Résultats :La cohorte 1 comportait 7 268 patients, la cohorte 2, 13 197 patients, et la cohorte 3, 17 373 patients. À mesure que s’accroissait la taille des cohortes, la proportion des patients des troubles alimentaires présentant des diagnostics de boulimie et de trouble alimentaire non spécifié augmentait également. Même si les cohortes différaient à l’égard des caractéristiques démographiques et cliniques, ces différences étaient minimes comparativement au degré auquel elles différaient de la population ontarienne.Discussion :Il est faisable d’utiliser les données de santé administratives pour mesurer le fardeau des troubles alimentaires pertinent sur le plan clinique. Les cohortes différaient significativement en ce qui concerne la composition des diagnostics de troubles alimentaires. Les troubles alimentaires constituent un fardeau élevé, mais la mauvaise disponibilité des données a fait en sorte que moins d’études des troubles alimentaires liées à la santé publique aient été menées en comparaison d’autres troubles mentaux. Le recours aux données administratives peut combler ces lacunes des données probantes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".