Association between physical multimorbidity, body mass index and mental health/disorders in a representative sample of people with obesity
Bibliographic record
Abstract
BACKGROUND: Obesity is known to be associated with poor mental health. Studies suggested that multimorbidity might explain the consequences of obesity on mental health. The objective of the present study was to examine to what extent physical multimorbidity and the severity of obesity were associated with mental health and with mental disorders. METHODS: Cross-sectional study including a weighted representative sample of individuals in obesity from the province of Quebec included in the 2013-2014 Canadian Community Health Survey (N=1315) and test of the replicability of the association in the three previous cycles (2011-2012, N=1180; 2009-2010, N=1166; 2007-2008, N=1298). RESULTS: Adjusted logistic regressions showed that when obesity classes and physical multimorbidity were considered, the latter was preferentially associated with poor perceived mental health (OR 3.58, 95% CI 2.07 to 6.22), psychological distress (OR 3.71, 95% CI 2.14 to 6.42), major depressive episode (OR 5.16, 95% CI 2.92 to 9.13), mood disorders (OR 2.31, 95% CI 1.41 to 3.78) and anxiety disorders (OR 2.46, 95% CI 1.46 to 4.16). The same associations were confirmed in the previous cycles. Obesity class was only associated with stress (OR 2.05, 95% CI 1.36 to 3.07), but this association was not confirmed in the other cycles. Both physical multimorbidity and severe obesity were associated with mental multimorbidity. CONCLUSION: Among people with obesity, physical multimorbidity is preferentially associated with poor mental health/disorders. There is an existence of a somatic-mental multimorbidity which should be assessed and prevented in the management of obesity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".