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

Abstract

Introduction: This study examines the interdependencies among different chronic pain locations and their relationships with age and gender, critical for effective clinical strategies. Methods: A Bayesian network approach was applied to 2,400 adult participants (18+ years; 50% male, 50% female) from the Qatar Biobank (QBB). Participants were categorized into young (18–35 years, 40.9%), middle-aged (36–60 years, 50.6%), and seniors (61+ years, 8.5%). Results: The model identified direct and indirect associations among pain locations and demographic factors, quantified by odds ratios (ORs). Younger females had the highest probability of headaches or migraines (48.6%) compared to younger males (31.2%), with probabilities decreasing across age (OR 1.917; 95% CI 1.609–2.284). Hand pain strongly correlated with hip pain (OR 8.691; 95% CI 6.074–12.434) and neck or shoulder pain (OR 4.451; 95% CI 3.302–6.000). Back pain was a key predictor of systemic pain, with a 37.9% probability of generalized pain when combined with hand pain (OR 7.682; 95% CI 5.293–11.149), dropping to 6.6% for back pain alone. Age, back pain, and foot pain collectively influenced knee pain, which reached 72.7% in older individuals with foot and back pain (OR 4.759; 95% CI 3.704–6.114). Discussion: These Bayesian network parameters explicitly reveal probabilistic interdependencies among pain locations, suggesting that targeted interventions for key anatomical regions could effectively mitigate broader chronic pain networks. The model also elucidates how demographic predispositions influence downstream pain patterns, providing a clear and actionable framework for personalized chronic pain management strategies.