Diabetes, cardiovascular disease, and health care use in people with and without schizophrenia
Bibliographic record
Abstract
PURPOSE: To compare the prevalence of cardiovascular risk factors (CV-RF) and disease (CV-D) and health care use in people with and without schizophrenia. SUBJECTS/MATERIALS AND METHODS: Data from the Canadian Community Health Survey (CCHS), cycle 3.1, were used. Prevalence of CV-RF, CV-D, and health care use were compared in those with and without schizophrenia using logistic regression analysis. Sampling weights and bootstrap variance estimates were used to account for survey design. RESULTS: A total of 399 (0.3%) people with schizophrenia were identified and compared to 120,044 (97.7%) people without. Individuals with schizophrenia were significantly more likely to be obese (34.8% vs. 15.6%) and report diabetes (11.9% vs. 5.3%). After accounting for sociodemographic variables, schizophrenia was not independently associated with diabetes (adjusted odds ratio [aOR]: 0.86; 0.49-1.51). Individuals with schizophrenia were more likely to be hospitalized (21.9% vs. 8.0%; aOR: 2.37; 95% CI: 1.51-3.74) but no more likely to visit their physician (86.7% vs. 85.7%; aOR: 1.23; 95% CI: 0.65-2.35). DISCUSSION/CONCLUSION: Our findings suggest that people with schizophrenia access the primary health care system at least as frequently as someone without schizophrenia, and the opportunity for management of modifiable CV-RF exists in this vulnerable population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".