Characterizing the Distribution of Shift Domains by Demographics and Shift Schedule in the American Manufacturing Cohort

Details

• Personal Author:
  Bradshaw PT ; Costello S ; Cullen MR ; Eisen EA ; Ferguson JM
• Description:
  Introduction: The American Manufacturing Cohort is the largest source of time-registry data on American workers available for research. In this cohort of 23,096 workers with over 23 million shifts, between 2003-2013, we describe the distribution of shift work domains relevant for circadian rhythm disruption by demographics and shift schedules. Methods: We defined the prevalence of shift domains using algorithms for shift type (e.g. day vs. night), shift duration (e.g. shift >/=13 hours), shift intensity (e.g. quick return (<11 hours between shifts) and consecutive work), rotational direction (forward, backward, flipped), and social aspects (e.g. weekend work). Person-years with >/=150 shifts/year were classified into shift schedules by combinations of permanent and rotating day, evening, and night. Shift domains distributions were examined by shift schedules and demographics, and were cross-classified in a matrix to examine co-occurrence. Results: Shifts were classified into morning (6%), day (50%), evening (16%), and night (28%). Approximately 60% of shifts may cause circadian rhythm disruption as they were non-day shifts or day shifts with a quick return, a rotation, or were 13 hours or longer. One third of quick returns were due to a backwards rotation while 42% were attributable to a long shift. Approximately 48% of person-years were non-rotating: day (32%), night (12%), evening (4%), day/evening (11%), day/night (24%), evening/night (4%) and day/evening/night (13%). Men were more likely to work rotational schedules (54.9% vs. 41.4%). White workers worked permanent day shifts most often, while racial minorities worked more day/night schedules. Older workers worked more permanent day and fewer day/evening/night schedules. Distribution of shift domains such as quick returns, shift length, and rotations varied by schedule type.Conclusions: We identified variations in the joint distributions of shift domains by shift schedules, demographics, and schedule type. These shift domains are important to identify, as they may impact circadian rhythm disruption. Description provided by NIOSH
• Subjects:
  • Circadian Rhythm
  • Demography
  • Manufacturing and Industrial Facilities
  • Occupational Health
  • Safety
  • Shift Work Schedule
• Keywords:
  • Shift work
  • Shift workers
  • Circadian rhythms
  • Manufacturing
  • Demographic characteristics
  • Work schedules
  • Biological rhythms
• ISSN:
  1984-0063
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Abstract or Summary
• Place as Subject:
  California ; Idaho ; OSHA Region 10 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  10
• Volume:
  12
• NIOSHTIC Number:
  nn:20057985
• Citation:
  Sleep Sci 2019 Aug; 12(Suppl 3):10
• Federal Fiscal Year:
  2019
• NORA Priority Area:
  Manufacturing
• Performing Organization:
  Stanford University
• Peer Reviewed:
  True
• Start Date:
  20110901
• Source Full Name:
  Sleep Science
• Supplement:
  3
• End Date:
  20200831
• Download URL:
  https://stacks.cdc.gov/view/cdc/215179/cdc_215179_DS1.pdf
• File Type:
  [PDF - 179.85 KB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:360b76439ee050b3072321473fa0615f96e7b7bb8338fbe35fd89bf2801ee74970fc134d35c718932555320e0824fdef143d15455e9873685f7f8a031e57dc4b