Probabilistic pathways from chronic pain to suicidal ideation in Qatar: a Bayesian-network study

Background Chronic pain frequently co-occurs with depressive and anxiety symptoms and doubles or triples the risk of suicidal ideation. Yet the joint pathways linking these conditions remain under-explored in Middle-Eastern settings. We therefore sought to map the probabilistic interdependencies between chronic pain, mental-health symptoms and demographic factors in Qatari adults, with a focus on sequences culminating in suicidal thoughts Methods We analyzed questionnaire data from 2,363 Qatari adults enrolled in the Qatar Biobank. A data-driven Bayesian Network (BN) was learned with hill-climbing and Bayesian Information Criterion scoring; edge stability was checked by bootstrap resampling. Conditional probability tables yielded odds ratios (ORs) with 95% confidence intervals (CIs) for each directed edge, emphasizing chains that ended in suicidal ideation. Results The final BN contained 29 directed edges. Self-regret (OR = 24.89, 95% CI 12.81–48.36) and psychomotor change (OR = 18.84, 9.50–37.37) were the strongest direct predictors of suicidal thoughts. An early-symptom cascade—sleep problems → anhedonia → depression—amplified suicide risk (depression → self-regret OR = 23.14). Fatigue linked chronic pain (all-body pain OR = 3.50) to self-regret (OR = 11.92), illustrating a physical–psychological bridge. Age and gender modified several pathways, with younger women showing the highest conditional probability of the full cascade. Conclusions BN modelling disentangled complex, non-linear relationships between pain, mental health and suicidality in a Middle-Eastern population. The high-risk pathways identified—especially those centered on self-regret and psychomotor disturbance—offer concrete screening targets for integrated pain-mental-health services. Tailoring interventions to demographic sub-groups could further mitigate suicide risk in Qatar.