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

Gerassis, Saki; Boente, Carlos; Albuquerque, M.T.D.; Ribeiro, M.M.A.; Abad, A.; Taboada, Javier

Publication

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

2020Journal article

dc.contributor.author	Gerassis, Saki
dc.contributor.author	Boente, Carlos
dc.contributor.author	Albuquerque, M.T.D.
dc.contributor.author	Ribeiro, M.M.A.
dc.contributor.author	Abad, A.
dc.contributor.author	Taboada, Javier
dc.date.accessioned	2020-04-07T15:05:20Z
dc.date.available	2020-04-07T15:05:20Z
dc.date.issued	2020
dc.description.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.	pt_PT
dc.description.version	info:eu-repo/semantics/acceptedVersion	pt_PT
dc.identifier.citation	GERASSIS, S. [et al.] (2010) - Mapping occupational health risk factors in the primary sector: a novel supervised machine learning and area-to-point poisson kriging approach. Spatial Statistics. ISSN: 2211-6753. Doi: 10.1016/j.spasta.2020.100434	pt_PT
dc.identifier.doi	10.1016/j.spasta.2020.100434	pt_PT
dc.identifier.issn	2211-6753
dc.identifier.uri	http://hdl.handle.net/10400.11/7052
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Elsevier	pt_PT
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/abs/pii/S2211675320300282	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by-nd/4.0/	pt_PT
dc.subject	Occupational data	pt_PT
dc.subject	information theory	pt_PT
dc.subject	Area-to-point poisson kriging	pt_PT
dc.subject	Logit model	pt_PT
dc.subject	Target analysis	pt_PT
dc.title	Mapping occupational health risk factors in the primary sector: a novel supervised machine learning and area-to-point poisson kriging approach	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.title	Spatial Statistics	pt_PT
person.familyName	Albuquerque
person.familyName	Ribeiro
person.givenName	Maria Teresa
person.givenName	Maria Margarida
person.identifier	349097
person.identifier.ciencia-id	5A1C-8956-4C0A
person.identifier.ciencia-id	AD12-4D32-7A48
person.identifier.orcid	0000-0002-8782-6133
person.identifier.orcid	0000-0003-4684-1262
person.identifier.rid	B-1536-2013
person.identifier.rid	M-4235-2013
person.identifier.scopus-author-id	55507421600
person.identifier.scopus-author-id	7201715611
rcaap.rights	restrictedAccess	pt_PT
rcaap.type	article	pt_PT
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