Climatic sensitivity of migraine: a 14-year time series analysis of primary care consultations in Spain

Background: Climatic variability has been proposed as a trigger for migraine; however, evidence from long-term primary care datasets remains scarce. Understanding how atmospheric conditions influence healthcare utilization may improve migraine prediction and management. This study aimed to analyze the association between climatic variables and weekly migraine consultations over a 14-year period in Spanish primary care and to identify the most accurate predictive time-series model. Methods: Weekly migraine consultations from 2010 to 2023 were extracted from electronic medical records using the International Classification of Primary Care, Second Edition (ICPC-2) code N89.01. Meteorological variables—temperature, diurnal variability, day-to-day change, wind direction and speed, barometric pressure, and sunshine hours—were obtained from the Spanish State Meteorological Agency (AEMET). Time-series analyses used exponential smoothing state-space models with external regressors (ETSX) and AutoRegressive Integrated Moving Average models with eXogenous regressors (ARIMAX). Model performance was assessed using Root Mean Squared Error (RMSE), Symmetric Mean Absolute Percentage Error (SMAPE), and Mean Absolute Scaled Error (MASE). Results: A total of 3176 migraine consultations were identified (mean age 47.6 ± 15.3 years; 81.7% female). The ARIMAX model showed the best predictive performance (RMSE = 3.485, SMAPE = 73.840, MASE = 0.875). Stationarity was confirmed using the Augmented Dickey–Fuller test (p = 0.01), and residuals showed no autocorrelation (Ljung–Box test, p = 0.833). After multivariable adjustment, female sex was the only variable independently associated with weekly migraine consultations; temperature, barometric pressure, diurnal variability, and wind speed showed no independent effects. Forecasting indicated a stable trend over the subsequent four years. Conclusions: This long-term time-series analysis showed that female sex was the only variable independently associated with weekly migraine consultations in primary care. Although most atmospheric indicators did not retain significance, climate-informed ARIMAX modeling improved prediction accuracy and may support personalized, weather-adapted preventive strategies.