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Publication
Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making
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| 23.85 MB | Adobe PDF |
Advisor(s)
Abstract(s)
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, ordinary kriging, and clustering, was used to generate national distribution maps for several 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.
Description
Keywords
Health data Information theory Bayesian learning Ordinary kriging Gclusters
Citation
Pazo, Maria [et al] (2023) - Unpacking occupational health data in the service sector: from bayesian networking and spatial clustering to policy-making. Mathematical Geosciences. DOI: 10.1007/s11004-023-10087-5
Publisher
Springer