Prevalence of Epilepsy in People of Sexual and Gender Minoritized Groups
Bibliographic record
Abstract
Importance: Epilepsy is a highly treatable condition for many people, but there are large treatment gaps with suboptimal seizure control in minoritized groups. The sexual and gender minority (SGM) community is at risk for health disparities, yet the burden of epilepsy in this community is not known. Objective: To estimate the prevalence of active epilepsy among SGM people in the United States. Design, Setting, and Participants: This was a cross-sectional, nationally representative survey study of community-dwelling US adults who answered questions about epilepsy, sexual orientation, and gender identity in the 2022 National Health Interview Survey (NHIS). Exposure: Self-identification of transgender or gender-diverse identity, or sexual orientation including gay, lesbian, bisexual, or other orientation, excluding straight (ie, heterosexual). Main Outcomes and Measures: Participants self-reported epilepsy status, medical treatment, seizure frequency, demographic characteristics, sexual orientation, and gender identity. Logistic regression was used to estimate the association of epilepsy with SGM identification. Results: A total of 27 624 participants (15 050 [54%] women; 3231 [12%] Black; mean [SD] age, 48.2 [18.5] years) completed the NHIS and were included. Active epilepsy was present in 1.2% (95% CI, 1.0%-1.3%) of the population. A higher proportion of SGM adults than non-SGM adults reported active epilepsy (2.4% [95% CI, 1.4%-3.3%] vs 1.1% [95% CI, 1.0%-1.3%], respectively). After adjusting for age, race, ethnicity, income, and education, SGM people were more than twice as likely to report active epilepsy than were non-SGM adults (adjusted odds ratio, 2.14; 95% CI, 1.35-3.37). Conclusions and Relevance: The findings suggest that SGM adults in the United States have a disproportionate prevalence of epilepsy. The reasons for this disparity are likely complex and may be associated with biological and psychosocial determinants of health unique to this population; as such, these individuals are in need of protected access to medical care.
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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 | 0.000 | 0.000 |
| 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".