RT Journal Article
T1 Unpacking Occupational Health Data in the Service Sector: From Bayesian Networking and Spatial Clustering to Policy-Making
A1 Pazo Rodríguez, María
A1 Boente López, Carlos
A1 Albuquerque, Teresa
A1 Gerassis, Saki
A1 Roque, Natália
A1 Taboada Castro, Javier
AB The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers.
PB Springer
SN 1874-8961
SN 1874-8953 (electrónico)
YR 2023
FD 2023-08
LK https://hdl.handle.net/10272/23899
UL https://hdl.handle.net/10272/23899
LA eng
NO Pazo, M., Boente, C., Albuquerque, T., Gerassis, S., Roque, N., & Taboada, J. (2023). Unpacking Occupational Health Data in the Service Sector: From Bayesian Networking and Spatial Clustering to Policy-Making. In Mathematical Geosciences (Vol. 56, Issue 3, pp. 465–485). Springer Science and Business Media LLC. https://doi.org/10.1007/s11004-023-10087-5
NO This study was funded by CERNAS-IPCB [UIDB/00681/2020] from the Foundation for Science and Technology (Fundação para a Ciência e Tecnologia—FCT)] and by ICT [UIDB/04683/2020] also from FCT. Carlos Boente obtained a post-doctoral contract within the program PAIDI 2020 (Ref. 707 DOC 01097).Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026