Uncovering mental well-being profiles in urban slums of Gorakhpur, India: A cluster-based approach using SWEMWBS.

Mental well-being is a growing but underrecognized public health priority in rapidly urbanizing, resource-constrained settings. Conventional mean-based analyses obscure important heterogeneity within vulnerable populations. We aimed to identify distinct mental well-being profiles among adults living in urban slums of Gorakhpur, India, using a person-centered approach. A cross-sectional survey (2023-2024) was conducted among 406 adults (≥18 years) from eight randomly selected slum settlements. Mental well-being was measured using the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS). Standardized item scores were analyzed using K-means clustering, with the optimal cluster solution determined by the elbow method and validated using silhouette and Davies-Bouldin indices. Associations with sociodemographic and psychological factors were examined using chi-square tests, ANOVA, and multiple linear regression. Three profiles emerged: High (n = 133), Moderate (n = 137), and Low well-being (n = 136). SWEMWBS scores differed significantly across clusters (F(2,403) = 482.1; p < 0.001). The Low well-being group reported substantially higher stress, depression, and anxiety, and women were disproportionately represented (χ 2(2) = 29.30; p < 0.001). Longer sleep duration, higher household education, and lower stress independently predicted better wellbeing. Mental well-being is highly heterogeneous within urban slum populations. Cluster-based profiling enables more precise, equitable, and context-sensitive mental health interventions.