Utilizing the metabolic syndrome component count in workers’ health surveillance: An example of day-time vs. day-night rotating shift workers
Yu Cheng Lin 1, 2  
I-Chun Hsieh 3
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En Chu Kong Hospital, New Taipei, Taiwan (Department of Occupational Medicine)
Fu Jen Catholic University, New Taipei, Taiwan (School of Medicine)
Taiwan Adventist Hospital, Taipei, Taiwan (Department of Environmental and Occupational Medicine)
National Taiwan University College of Public Health, Taipei, Taiwan (Institute of Occupational Medicine and Industrial Hygiene)
Yu Cheng Lin   

National Taiwan University, College of Public Health, Institute of Occupational Medicine and Industrial Hygiene, Room 733, 17 Syujhou Road, Taipei 10055,
Int J Occup Med Environ Health 2015;28(4):675–688
Objectives: To establish a practical method for assessing the general metabolic health conditions among different employee groups, this study utilized the total count of metabolic syndrome (MetS) elements as a parameter, and performed a retrospective analysis comparing changes of MetS component count (MSC) of 5 years among day-time work (DW) and day-andnight rotating shift work (RSW) employees. Material and Methods: The data of personal histories, physical examinations, blood tests, abdominal sonographic examinations and occupational records were collected from a cohort of workers in an electronics manufacturing company. We first defined the arithmetic mean value of MSC as MSC density (MSCD) for the employee group; then we compared the changes of MSCD over 5 years between DW and RSW workers. Occupational, personal and health records were analyzed for the 1077 workers with an initial mean age of 32.4 years (standard deviation (SD): 6.2 years), including 565 RSW workers (52%). Results: The initial MSCDs were 1.26 and 1.12 (p = 0.06) for DW and RSW workers, respectively; after 5 years, the increments of MSCD for DW and RSW workers were 0.10 and 0.39, respectively (p < 0.01). By performing multivariate logistic regression analyses, and comparing with DW co-workers, final results indicated that the workers exposed to RSW have 1.7-fold increased risk of elevated MSCD (95% confidence interval (CI): 1.28–2.25, p < 0.01); and are 38% less likely (adjusted rate ratio (aRR) 0.62, 95% CI: 0.45–0.86, p < 0.01) to attain decreased MSCD. Conclusions: These observations demonstrate that changes of MSCD are significantly different between DW and RSW workers, and are increasingly associated with RSW exposure. In conclusion, MSCD can represent the general metabolic health conditions of a given employee group; MSC, MSCD and their transitional changes can be applied as simple and standardized tools for monitoring metabolic health risk profiles when managing employee health, at both the individual and company levels.