Factors Associated with Migraine in the General Population of Spain: Results from the European Health Survey 2014
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.
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 22,842 participants or records.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.