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Organizational Risk Factors for Aircrew Health: A Systematic Review of Observational Studies.

Elaine Cristina MarquezeErika Alvim de Sá E BenevidesAna Carolina RussoMariana Souza Gomes FürstRodrigo Cauduro RoscaniPaulo Cesar Vaz GuimarãesCelso Amorim Salim
Published in: International journal of environmental research and public health (2023)
Addressing the field of health and safety at work, the primary objective of the present systematic review was to analyze the organizational risk factors for aircrew health according to professional category (flight attendants and pilots/co-pilots) and their consequences. The secondary objective was to identify the countries in which studies were carried out, focusing on the quality of content of the publications. The Medline/Pubmed, Cochrane, Web of Science, and Scopus databases were searched for eligible studies according to PRISMA statements. The risk of bias and the methodological quality of the studies were assessed using the Newcastle-Ottawa scale and Loney tools. Of the 3230 abstracts of articles screened, 36 studies met the inclusion criteria. Most of the research conducted on risk factors for the work organization of aircrew was carried out in the United States and the European Union and had moderate or low-quality methodology and evidence. However, the findings are homogeneous and allow the most prevalent organizational risk factors for the health of aircrew to be determined, namely, high work demand, long hours, and night work. Consequently, the most pervasive health problems were sleep disturbances, mental health disorders, musculoskeletal disorders, and fatigue. Thus, the regulation of the aircrew profession must prioritize measures that minimize these risk factors to promote better health and sleep for these professionals and, consequently, to provide excellent safety for workers and passengers.
Keyphrases
  • mental health
  • public health
  • healthcare
  • systematic review
  • risk factors
  • randomized controlled trial
  • mental illness
  • physical activity
  • quality improvement
  • meta analyses
  • machine learning
  • climate change