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

## TL;DR

A 14-year study in Spain found that female sex was the only factor consistently linked to migraine consultations, while climate factors had limited independent effects.

## Contribution

The study provides long-term evidence on the climatic sensitivity of migraine using primary care data and compares predictive modeling techniques.

## Key findings

- Female sex was the only variable independently associated with weekly migraine consultations.
- ARIMAX models outperformed other models in predicting migraine consultations.
- Most atmospheric variables did not show significant independent effects after adjustment.

## Abstract

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.