Factors Associated with Migraine in the General Population of Spain: Results from the European Health Survey 2014
Bibliographic record
Abstract
OBJECTIVE: To identify the modifiable and nonmodifiable variables that are associated with and might moderate the presence of migraine in the general population. DESIGN: Nationally representative cross-sectional survey. SETTING: Noninstitutionalized population of Spain. SUBJECTS: Individuals aged 15 years or older (N = 22,842). METHODS: A secondary analysis of data from the second wave of the European Health Interview Survey conducted in Spain (2014/2015). We estimated the prevalence of migraine and its distribution according to the study variables, and then built a multivariate logistic model encompassing age, sex, depression severity, chronic anxiety, body mass index, physical activity, smoking status, alcohol use, and perceived social support to predict migraine. RESULTS: The one-year prevalence of migraine was 8%. The final multivariate model (Wald χ2 = 693.00, df = 15, P < 0.001) retained depression severity, chronic anxiety, exercising several times a month or week, and alcohol use as predictors of migraine (odds ratios = 2.1-3.5 for positive associations, odds ratios = 0.4-0.9 for negative associations). CONCLUSIONS: Raising awareness among clinicians regarding the fact that many of the variables that potentially contribute to the presence of migraine are modifiable (e.g., psychological problems and lifestyle behaviors) might intensify resources dedicated to assessing and impacting these factors in order to potentially prevent the frequency and severity of migraine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".