Abstract
OBJECTIVES: Anesthesiologists represent an occupational group exposed to specific occupational hazards, including potential exposure to waste anesthetic gas released during medical procedures. In recent decades, halogenated anesthetic gases, such as desflurane and sevoflurane, have largely replaced nitrous oxide, due to better safety profiles and lower adverse health effects. However, possible long-term effects of low concentration exposures are unknown. A longitudinal analysis of health surveillance data was performed to test for possible changes over time in key markers of liver and kidney function. Moreover, we assessed the appropriateness of applying linear mixed models to occupational health data. MATERIAL AND METHODS: A retrospective cohort study was conducted using health surveillance data from a cohort of anesthesiologists and a cohort of unexposed physicians working at the Polyclinic Hospital San Martino of Genoa, Italy, during 2016-2022. A 2-level linear mixed model with covariance structure of first order autoregressive model (AR(1)) type at the first level and unstructured type at the second level was applied. RESULTS: One hundred seventy subjects were included in the analysis, equally divided between exposed and unexposed. At the first and last periodic examination, liver and kidney markers were not statistically different in the 2 cohorts. The only significant change found related to estimated glomerular filtrate, which was found at the last follow-up to be greater among the exposed (M = 104.18 vs. 90.07, p = 0.007). The linear mixed model showed that anesthetic gas exposure was not associated with any of the outcomes. These results suggest the absence of increase in liver and kidney profile markers in the study population. CONCLUSIONS: Health surveillance data, aggregated and analyzed with appropriate statistical models, allow inferences to be made about potential health effects of workers due to uncontrolled exposures. To this end, the linear mixed model represents a powerful tool for longitudinal analysis of data derived from monitoring workers. Int J Occup Med Environ Health. 2024;37(5):557-68.