From ‘Big 4′ to ‘Big 5′: A review and epidemiological study on the relationship between psychiatric disorders and World Health Organization preventable diseases
Bibliographic record
Abstract
Introduction Chronic diseases, such as heart disease, stroke, chronic respiratory diseases and diabetes, are by far the leading causes of mortality in the world, representing 60% of all deaths. However, chronic disease rarely exists in isolation. Nevertheless, study of chronic disease rarely takes into account comorbidity and virtually none examine their occurrence in populations. Objectives and aims To review the association between psychiatric disorders and other medical comorbidities. To study the association between psychiatric diseases and medical comorbidities on a population-scale. To reconsider our approach to medical comorbidities. Methods Using an informatics approach, a dataset containing physician billing data for 764 731 (46% male) individuals spanning sixteen fiscal years (1994–2009) in Calgary, Alberta, Canada was compiled permitting examination of the relationship between Physical Disorders and Mental Disorders, based on the International Classification of Diseases (ICD). Results All major classes of ICD physical disorders had odd ratios with confidence intervals above the value of 1.0. Ranging from 1.47 (Injury poisoning) to Circulatory systems (3.82). More precisely, when a psychiatric disorder is present, the likelihood to develop one of the four preventable diseases is significantly increased: Stroke (4.27), Hypertension (3.34), Diabetes (2.66) and COPD (2.43). Conclusion We postulate that psychiatric disorder should be included in the classification of preventable chronic diseases that have a profound impact on society. Developing a consistent and standardized approach to describe these features of disease has the potential to dramatically shift the format of both clinical practice and medical education. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".