Mental health status and gender as risk factors for onset of physical illness over 10 years
Bibliographic record
Abstract
BACKGROUND: There is a growing interest in understanding the connection between mental illness (MI) and the onset of new physical illnesses among previously physically healthy individuals. Yet the role of gender is often forgotten in research focused on comorbidity of health problems. The objective of this study was to examine gender differences in the onset of physical illness in a cohort of respondents who met criteria for MI compared with a control cohort without mental health problems. METHODS: This cohort study, conducted in Ontario, Canada, used a unique linked dataset with information from the 2000-2001 Canadian Community Health Survey and medical records (n=15,902). We used adjusted Cox proportional survival analysis to examine risk of onset of four physical health problems (chronic obstructive pulmonary disorder, asthma, hypertension and diabetes) for those with and without baseline MI across a 10-year period (2002-2011) among respondents aged 18-74 years. We controlled for socioeconomic and health indicators associated with health. RESULTS: The incidence of physical illness in the MI cohort was 28.5% among women and 29.9% among men (p=0.85) relative to controls (23.8% and 24%, respectively; p=0.48). Women in the MI cohort developed secondary physical health problems a year earlier than their male counterparts (p=0.002). Findings from the Cox proportional survival regression showed that women were at 14% reduced risk of developing physical illness, meaning that men were more disadvantaged (HR=0.89, CI 0.80 to 0.98). Those in the MI cohort were at 10 times greater risk of developing a secondary physical illness over the 10-year period (HR=1.10, CI 0.98 to 1.21). There was no significant interaction between gender and MI cohort (HR=1.05, CI 0.85 to 1.27). CONCLUSIONS: Policy and clinical practice have to be sensitive to these complex-needs patients. Gender-specific treatment and prevention practices can be developed to target those at higher risk of multiple health conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".