Mapping occupational health risk factors in the primary sector: a novel supervised machine learning and area-to-point poisson kriging approach

Abstract

Workers around the world spend nearly a quarter of their time at work Occupational health is gaining great importance due to the profound impact on people long term health. The health status of the primary sector workforce is a great unknown for medical geography where health maps and spatial patterns have not been able to explain years of changing disease rates. This article proposes a new approach based on a solid characterization of the health status, which is the target node of an information theory-based Bayesian network machine-learnt from 13,000 medical examinations undertook to rural workers in Spain between 2012 and 2016. From the main health risks identified, a supervised binary logistic regression is used to produce a classification of adverse medical conditions giving rise to not healthy workers. Finally, Area-to-Point Poisson kriging is computed to provide a spatial analysis representing the incidence rate and spatial patterns of the main adverse medical conditions over the Spanish territory. The study illustrates how to overcome the challenges of working with discrete occupational data. Conceptually, high cholesterol and high glucose can be pinpointed with accuracy as the two main health risks for the working population in the primary sector.