Demographic, Life Style and Job-Related Determinants of Quality of Life of Industrial Manufacturing Employees: An Application of Multilevel Latent Class Regression on a Large Cross-Sectional Study

BACKGROUND: Employees are one of the key elements of an organization and measure the quality of life (QoL) provides reliable assessment of health and wellbeing in this population. This study aimed at investigating the QoL in a large sample of Iranian industrial manufacturing employees and its determinants. METHODS: In a cross-sectional study conducted was in 2015, 3063 people were selected among 16000 Esfahan Seal Company's employees using multistage cluster sampling. QoL was evaluated by EQ-5D questionnaire, mental health by GHQ-12, physical activity by IPAQ, job stress by Effort-Reward Imbalance questionnaire. Self-report questionnaire was used for gathering demographic characteristics. Multilevel latent class regression analysis was used for data analysis using R (3.4.3). RESULTS: The mean (SD) age of the study participants was 36.74 (7.31), and 91.5% of them were males. The mean (SD) sleep duration was 7.11 (1.17), and 95.4% of the participants had normal mental health. Latent class analysis classified employees into two classes (high (82.4%) and low QoL (17.6%)). Also employees' job categories classified into high and low QoL classes (79.55% and 20.45%, respectively). Latent class regression showed that lower age (OR=0.93; P< 0.0001), being male (OR=1.75; p=0.009), lower levels of education (OR=2.1; P< 0.0001), normal mental health (OR=12.4; P< 0.0001), higher sleep duration (OR=1.2; P< 0.0001) and lower BMI (OR=0.96; P=0.016) were significant predictors of being in high QoL class. CONCLUSION: Our study provides data about the QoL of industrial manufacturing employees along with its significant determinants. The findings picture the ways for improving QoL, finally increasing the efficiency and productivity of workforce by directing health policies appropriately.