Back

Unravelling Somatic Symptoms in Pakistan's Urban Slums: A Sex-Stratified Multilevel Exploration of Individual and Household Factors

Sughra, M.; Irum, A.; Ibrahim, M.; Khan, A. A.

2025-10-09 psychiatry and clinical psychology
10.1101/2025.10.07.25337547 medRxiv
Show abstract

Mental health conditions in low- and middle-income countries (LMICs) often manifest as somatic symptoms--physical complaints lacking clear medical causes--that complicate diagnosis and care. This study examines the prevalence and determinants of somatic symptoms in Dhoke Hassu, a low-income urban settlement in Rawalpindi, Pakistan, with particular attention to gender and household dynamics. Using data from 782 adults (18-75 years), we applied sex-stratified multilevel logistic regression to explore individual and household-level predictors. Overall, 72% of participants reported somatic symptoms. Results revealed that household-level factors explained 18% of the variance among women but were not significant for men, underscoring the influence of family context on womens psychosomatic health. Across both sexes, older age, hypertension, and medical service utilization were strongly associated with symptom reporting. Gender-specific patterns emerged: higher body mass index, access to technology, and a family history of mental illness were protective for men, while womens symptoms were linked to household roles and relational positioning. These findings highlight the need for integrated, gender-sensitive approaches in primary care. Interventions should embed mental health screening in routine healthcare, leverage digital tools for men, and address relational and household stressors for women. By situating somatization within the Social Ecological Model, this study advances understanding of multi-level influences on mental health in Pakistans urban slums and provides actionable pathways for policy and practice.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.