Trajectories of Self-Rated Health Among Industrially Disabled Individuals: A Latent Class Growth Analysis.

Abstract

Understanding the self-rated health of industrially disabled individuals is an important variable that significantly affects their quality of life, satisfaction, and return to work after an industrial accident. Since the health of people with industrial disabilities is affected by various environments and variables, interventions and policies that are suitable for their characteristics are needed. This study aimed to identify changes in self-rated health among industrially disabled individuals, distinguish between different latent classes, and verify predictive factors for each latent class. Four time-point datasets from the 2018-2021 panel study of Korean workers' compensation insurance were used. Using the latent growth curve model, an overall trajectory of self-rated health of industrially disabled individuals was confirmed, and the number and characteristics of different trajectories were identified using the latent class growth model. Multinomial logistic regression analysis was used to identify the predictive factors for each class. Four classes of self-rated health trajectories were identified: low-decreasing (21.7%), moderate-increasing (15.7%), high-decreasing (56.1%), and low-stable (6.5%) classes. A multinomial logistic regression analysis revealed that significant determinants (age, capacity, type of industrial accident, grade of disability, mental activity, outdoor activity, and social relationships) were different for each latent class. Capacity level affected all potential class classifications. To improve the self-rated health of industrially disabled individuals, it is necessary to develop an appropriate strategy that considers the characteristics of the latent class.