Modeling Chronic Pain Interconnections Dashboard

ORIGINAL RESEARCH article
Front. Pain Res., 27 May 2025
Sec. Pain Research Methods
Volume 6 - 2025 | https://doi.org/10.3389/fpain.2025.1573465

Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study

Aisha Al-Khinji, Dhafer Malouche

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Abstract

This study examines the interdependencies among different chronic pain locations and their relationships with age and gender using data from 2,400 adult participants from the Qatar Biobank (QBB). A Bayesian network approach identified direct and indirect associations among pain locations and demographic factors, quantified by odds ratios (ORs). Key findings include strong correlations between hand pain and hip pain (OR 8.69), headache and facial pain (OR 11.30), and the significant role of back pain as a predictor of systemic pain.

Key Findings

• Headache → Face: OR 11.30 (95% CI: 6.02–21.19)
• Hip → Hand: OR 8.69 (95% CI: 6.07–12.43)
• Hand → Foot: OR 8.25 (95% CI: 6.08–11.20)
• Foot → Knee: OR 4.76 (95% CI: 3.70–6.11)

Citation

Al-Khinji AAMA and Malouche D (2025) Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study. Front. Pain Res. 6:1573465. doi: 10.3389/fpain.2025.1573465