What can we learn about polytrauma typologies by comparing population-representative to trauma-exposed samples: A Nepali example

Potentially traumatic events (PTEs) are common and associated with detrimental outcomes over the life-course. Previous studies exploring the causes and consequences of PTE-exposure profiles are often from high-income settings and fail to explore the implications of sample selection (i.e., population-representative versus PTE-restricted).

Among individuals in the Nepal Chitwan Valley Family Study, latent class analyses (LCA) were performed on 11 self-reported PTEs collected by the Nepali version of the World Mental Health Consortium's Composite International Diagnostic Interview 3.0 from 2016 to 2018, in a population-representative sample (N = 10,714), including a PTE-restricted subsample (N = 9183). Multinomial logistic regressions explored relationships between sociodemographic factors and class membership. Logistic regressions assessed relationships between class membership and psychiatric outcomes.

On average, individuals were exposed to 2 PTEs in their lifetime. A five-class solution showed optimal fit for both samples; however, specific classes were distinct. No single sociodemographic factor was universally associated with PTE class membership in the population-representative sample; while several factors (e.g., age, age at incident PTE, education, marital status, and migration) were consistently associated with class membership in the PTE-subsample. PTE class membership differentiated psychiatric outcomes in the population-representative sample more than the PTE-subsample.

Primary limitations are related to the generalizability to high-income settings, debate on LCA model fit statistic usage for final class selection, and cross-sectional nature of data collection.

Although population-representative samples provide information applicable to large-scale, population-based programming and policy, PTE-subsample analyses may provide additional nuance in PTE profiles and their consequences, important for specialized prevention efforts.