Personal light-at-night exposures and components of variability in two common shift work industries: uses and implications for future research

OBJECTIVES: Shift workers’ increased risk of various adverse health outcomes has been linked to light-at-night (LAN) exposure, but few studies have measured LAN exposure in workplaces. To inform future research methods, this study aimed to (i) measure shift workers’ exposures to LAN across industries, occupations, and work environments and (ii) assess components of variance across different exposure groupings and metrics. METHODS: Between October 2015 and March 2016, 152 personal full-shift measurements were collected from 102 night shift workers in emergency health services (paramedics, dispatchers) and healthcare industries (nurses, care aides, security guards, unit clerks, and laboratory, pharmacy, and respiratory therapy staff) in the province of British Columbia, Canada. Descriptive and variance component analyses were conducted for the 23:00–05:00 period to characterize exposures using multiple metrics of potential biological relevance (median lux, 90^th percentile lux, sum of minutes ≥30 lux, and sum of minutes ≥100 lux). RESULTS: Average exposure levels were highest in the healthcare industry. By occupation, laboratory workers and care aides displayed the highest and emergency dispatch officers displayed the lowest levels for all LAN exposure metrics. Between-group variance was large relative to within-group variance for all exposure groupings and metrics, and increased as grouping specificity increased (moving from industry to occupation). CONCLUSIONS: Results from this study suggest that high-level grouping schemes may provide a simple yet effective way of characterizing individual LAN exposures in epidemiological studies of shift work. Ongoing measurement of LAN exposures and assessment of exposure variability is needed in future studies of shift workers as a means to increase sampling efficiency, reduce measurement error, and maximize researchers’ ability to detect relationships where they exist.

Abstract licence: CC BY

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