Respiratory health of municipal solid waste workers in Greece: A cross-sectional study - Megkou Asimoula,; Thanasias Efthymios,; Georgios-Ioannis Poupouzas,; George Dounias,; George Makrynos, (International Journal of Occupational Safety and Health, 2026)
Introduction: Municipal solid waste workers (MSWWs) are routinely exposed to airborne pollutants, including bioaerosols and combustion by-products, which may contribute to respiratory symptoms and reduced lung function. This study aimed to evaluate the respiratory health of MSWWs employed in local government cleaning services across six municipalities in Greece.Methods: A cross-sectional study was conducted among 621 municipal employees, including 407 MSWWs and 214 office-based employees as controls. Participants underwent spirometry according to ERS/ATS 2019 guidelines and completed a structured questionnaire assessing respiratory symptoms, occupational history, and smoking status. Descriptive statistics were calculated. Between group comparisons were performed using unadjusted independent sample t-tests for continuous variables and chi-square tests for categorical variables. Pearson’s correlation coefficient was used for correlation analyses.Results: MSWWs demonstrated significantly lower mean FEV1 and FVC values compared to office employees (p < 0.001 and p = 0.004, respectively). Respiratory symptoms (cough, sputum production, wheezing, dyspnea) were more frequently reported among MSWWs. Descriptive stratified analyses indicated lower spirometric values among current smokers in both occupational groups; however, interaction effects were not formally tested. Spirometric indices showed modest inverse associations with years of employment; these associations were descriptive and may reflect confounding (e.g., age) rather than cumulative exposure effects.Conclusion: These unadjusted findings are consistent with a potential occupational contribution to respiratory health differences. However, causal inference is limited. Further studies incorporating quantitative exposure assessment and multivariable modeling are recommended.