Disparities Across Sexual Orientation in Migraine Among US Adults
Bibliographic record
Abstract
Migraine affects 1 in 6 adults and represents the fifth leading cause of emergency department visits in the US. 1 Despite an increasing recognition of disparities in migraine prevalence by race/ethnicity, sex, and socioeconomic status, 1 there is a paucity of research on disparities by sexual orientation.Sexual minority groups (eg, lesbian, gay, bisexual, and other nonheterosexual people) may experience unique discrimination, stigma, and barriers to health care access, thus leading to disparities in physical and mental health.2,3 The objective of this study was to determine the association between sexual orientation and migraine in a nationally representative sample of US adults.Methods | Cross-sectional, nationally representative data of US adults ages 31 to 42 years old from Wave V (calendar years 2016-2018) of the National Longitudinal Study of Adolescent to Adult Health (Add Health) were analyzed from May 2020 to June 2020.The University of North Carolina institutional review board approved all study procedures, and written informed consent was obtained from all participants.Migraine was measured based on self-report in response to the interview question, "Have you ever had five or more headaches that were at least four hours long; one-sided, pulsating, intense, or worsened by activity; and associated with nausea, vomiting, or sensitivity to light or sound?" which was consistent with the International Classification of Headache Disorders, third edition diagnostic criteria for migraine without aura.4 Sexual orientation was categorized into 3 categories: exclusively heterosexual; mostly heterosexual but somewhat attracted to people of one's own sex; or lesbian, gay, or bisexual, as has been previously categorized.5 Logistic regression analysis was conducted using Stata version 15.1 (Stata-Corp) with sexual orientation as the independent variable and migraine as the dependent variable, adjusting for sex, race/ ethnicity, age, education, income, smoking, and alcohol use 1 and incorporating national sample weighting.6
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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 | 0.000 | 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 | 0.000 | 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 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".