Search

Introduction: Although low back pain (LBP) is a high-impact health condition, its burden has not been examined from the syndemic perspective. Objective: To compare and assess clinical, socioeconomic, and geographic factors associated with LBP prevalence in low-income and upper-middle-income countries using syndemic and syndemogenesis frameworks based on network and cluster analyses. Methods: Analyses were performed by adopting network and cluster design, whereby interrelations among the individual and social variables and their combinations were established. The required data was sourced from the databases pertaining to the six Latin-American countries. Results: Database searches yielded a sample of 55,724 individuals (mean age 43.38 years, SD = 17.93), 24.12% of whom were indigenous, and 60.61% were women. The diagnosed with LBP comprised 6.59% of the total population. Network analysis showed higher relationship individuals' variables such as comorbidities, unhealthy habits, low educational level, living in rural areas, and indigenous status were found to be significantly associated with LBP. Cluster analysis showed significant association between LBP prevalence and social variables (e.g. Gender inequality Index, Human Development Index, Income Inequality). Conclusions: LBP is a highly prevalent condition in Latin-American populations with a high impact on the quality of life of young adults. It is particularly debilitating for women, indigenous individuals, and those with low educational level, and is further exacerbated by the presence of comorbidities, especially those in the mental health domain. Thus, the study findings demonstrate that syndemic and syndemogenesis have the potential to widen the health inequities stemming from LBP in vulnerable populations. Key points • Syndemic and syndemogenesis evidence health disparities in Latin-American populations, documenting the complexity of suffering from a disease such as low back pain that is associated with comorbidities, unhealthy habits, and the social and regional context where they live. • The use of network and cluster analyses are useful tools for documenting the complexity and the multifaceted impact in health in large populations as well as the differences between countries. • The variability and impact of socioeconomic indicators (e.g., Gini index) related to low back pain and comorbidities could be felt through the use of cluster analysis, which generates evidence of regional inequality in Latin America. • Populations can be studied from different models (network and cluster analysis) and grouping, presenting new interpretations beyond geographical groupings, such as syndemic and inequity in health. [ABSTRACT FROM AUTHOR]