[Algorithm for assessment of exposure to asbestos].
There is no universally approved method in the scientific literature to identify subjects exposed to asbestos and divide them in classes according to intensity of exposure. The aim of our work is to study and develope an algorithm based on the findings of occupational anamnestical information provided by a large group of workers. The algorithm allows to discriminate, in a probabilistic way, the risk of exposure by the attribution of a code for each worker (ELSA Code--work estimated exposure to asbestos). The ELSA code has been obtained through a synthesis of information that the international scientific literature identifies as the most predictive for the onset of asbestos-related abnormalities. Four dimensions are analyzed and described: 1) present and/or past occupation; 2) type of materials and equipment used in performing working activity; 3) environment where these activities are carried out; 4) period of time when activities are performed. Although it is possible to have informations in a subjective manner, the decisional procedure is objective and is based on the systematic evaluation of asbestos exposure. From the combination of the four identified dimensions it is possible to have 108 ELSA codes divided in three typological profiles of estimated risk of exposure. The application of the algorithm offers some advantages compared to other methods used for identifying individuals exposed to asbestos: 1) it can be computed both in case of present and past exposure to asbestos; 2) the classification of workers exposed to asbestos using ELSA code is more detailed than the one we have obtained with Job Exposure Matrix (JEM) because the ELSA Code takes in account other indicators of risk besides those considered in the JEM. This algorithm was developed for a project sponsored by the Italian Armed Forces and is also adaptable to other work conditions for in which it could be necessary to assess risk for asbestos exposure.
Martines
V
V
Direzione Generale della Sanità Militare.
Fioravanti
M
M
Anselmi
A
A
Attili
F
F
Battaglia
D
D
Cerratti
D
D
Ciarrocca
M
M
D'Amelio
R
R
De Lorenzo
G
G
Ferrante
E
E
Gaudioso
F
F
Mascia
E
E
Rauccio
A
A
Siena
S
S
Palitti
T
T
Tucci
L
L
Vacca
D
D
Vigliano
R
R
Zelano
V
V
Tomei
F
F
Sancini
A
A
D000465:Algorithms; D001194:Asbestos / Q000009:adverse effects*; D001195:Asbestosis / Q000150:complications / Q000453:epidemiology* / Q000517:prevention & control; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008654:Mesothelioma / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D015201:Meta-Analysis as Topic; D016273:Occupational Exposure / Q000009:adverse effects*; D010997:Pleural Neoplasms / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D011237:Predictive Value of Tests; D018570:Risk Assessment; D011795:Surveys and Questionnaires
D001194:Asbestos