The association between depression and epilepsy in a nationally representative sample
Bibliographic record
Abstract
PURPOSE: To determine the: (1) national prevalence of epilepsy and depression; (2) prevalence of depression among those with epilepsy; (3) odds ratio of depression among those with epilepsy compared to those without, controlling for demographic characteristics; (4) demographic correlates of depression among those with epilepsy and those without; and, (5) health services utilization of those with epilepsy and depression. METHODS: The full sample of the nationally representative 2000/2001 Canadian Community Health Survey (n = 130,880) was used to determine prevalence of epilepsy and depression. A subsample of 781 individuals reporting an epilepsy diagnosis and with complete depression data was used to determine prevalence and correlates of depression, and health service utilization patterns. Correlates of depression among those without epilepsy (n = 126,104) were also determined. Chi-square analyses, t-tests, prevalence ratios, and a logistic regression were conducted. RESULTS: Thirteen percent of those with epilepsy were depressed, in comparison to 7% of those without (p < 0.001). Epilepsy was associated with 43% higher odds of depression when adjusting for demographic factors. The odds of depression among individuals with epilepsy were higher for females, visible minorities, older individuals, and individuals experiencing food insecurity. Visible minority and older age appear to be unique risk factors for depression in those with epilepsy as compared to those without. Thirty-eight percent of depressed respondents with epilepsy had no consultations with a mental health professional in the previous year. DISCUSSION: Medical professionals need to regularly assess levels of depression in their patients with epilepsy. This research helps guide which risk groups should be targeted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".