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.

世界各地的工人将近四分之一的时间都花在工作上。由于对人们长期健康的深远影响，职业健康正变得越来越重要。对于医学地理领域来说，第一产业劳动力的健康状况是一个未知数，那里的健康地图和空间模式无法解释多年来不断变化的疾病发生率。本文提出了一种基于健康状况的可靠描述的新方法，该方法是基于信息论的贝叶斯网络机器学习的目标节点，该学习是从2012年至2016年对西班牙的农村工人进行的13,000次体检的结果。确定健康风险后，将采用监督二元逻辑回归来对不利健康状况进行分类，从而导致不健康的工人。最后，计算区域到点泊松克里金法可提供空间分析，以表示西班牙领土上主要不利医疗状况的发生率和空间格局。该研究说明了如何克服使用离散职业数据的挑战。从概念上讲，可以准确地确定高胆固醇和高葡萄糖，这是第一部门工作人口面临的两个主要健康风险。