Health status and health‐related behaviors in epilepsy compared to other chronic conditions—A national population‐based study
Bibliographic record
Abstract
PURPOSE: The negative impact of epilepsy is disproportionate to its prevalence. Our objectives were to determine if health-related behaviors (HRBs) and health status differ between patients with epilepsy, migraine, or diabetes. METHODS: The 2001-2005 Canadian Community Health Survey (N = 400,055) was used to explore health status and HRBs in patients with epilepsy, migraine, and diabetes and in the general population. Weighted estimates of association were produced as proportions with 95% confidence intervals (CIs). Logistic regression was used to explore the association between demographic variables and HRBs in epilepsy. RESULTS: The prevalence of active epilepsy, migraine, and diabetes was 0.6%, 8.4%, and 3.8%, respectively. Those with epilepsy and diabetes were more likely than migraineurs to perceive their health as poor and to be physically inactive. Obesity and comorbidities were more likely in all chronic conditions studied compared to the general population. Those with epilepsy or migraine were significantly more likely to smoke compared to the general population or to those with diabetes. Those with epilepsy were more likely to ever have consumed more than 12 alcoholic drinks per week. Health monitoring did not differ between groups. In the logistic regression analysis, epilepsy was associated with physical inactivity and lower alcohol consumption in the past 12 months compared to the general population. DISCUSSION: Our study demonstrated that those with epilepsy have a poorer pattern of HRBs and poorer health status compared to the general population. Screening for and managing comorbidities, and promoting exemplary HRBs, should improve overall health and quality-of-life in those with epilepsy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".