Predictors of migraine prevalence among different age groups in Hong Kong Chinese women: Machine learning analyses on the MECH-HK cohort.

Abstract

Purpose: To identify age-specific predictors of migraine prevalence among Chinese women.

Methods: In this cross-sectional analysis, 54 predictors were collected from the MECH-HK cohort. Migraine was assessed by the ICHD 3rd edition. Machine learning was employed to select a streamlined subset of predictors. Participants were categorised as young and middle age group (<60 years) and old age group (≥60 years) for analysis.

Results: The mean age of participants was 54.3 years. Migraine prevalence was higher in women under 60 than in older women (10.7% vs. 6.0%, P< 0.001). Lasso selected seven (<60 years) and twelve (≥60 years) predictors, respectively. The top three predictors among women under 60 were fatigue, migraine family history, and PSQI, explaining 6.6%, 5.0%, and 4.9% of variation, respectively. Their ORs (95% CIs) were 1.61 (1.37-1.89), 3.93 (2.77-5.57), and 1.29 (1.12-1.48), respectively. For older women, the top three predictors were experience of hunger, smartphone usage time, and migraine family history, explaining 2.0%, 1.8%, and 1.6% of variation, respectively, with ORs (95% CIs) of 2.16 (1.21-3.84), 1.24 (1.03-1.48), and 2.26 (1.16-4.40), respectively.

Conclusion: Migraine family history and experience of hunger were shared predictors for migraine prevalence in both ages. Other predictors differentially influence migraine prevalence across ages.