Etude des facteurs prédictifs de la désinsertion professionnelle : l’utilisation des données massives en santé au travail dans la prévention primaire des sorties d’emploi

International audienceWith more than 500,000 employees concerned each year, occupational deinsertion is a public health problem. Although job retention, work toughness and the factors predicting occupational deinsertion are a major issue, these topics remain little studied. Furthermore, the few international studies and publications focus on a very limited number of factors, do not take into consideration the employee as a whole (no consideration of the three WHO dimensions of health. The objective is to provide answers in the identification of predictive factors of occupational deintegration using massive occupational health data. The statistical approaches envisaged are based on models for repeated data (mixed models and trajectory models), models for censored data (survival models and multi-state Markov models), considering the different sources of latent variability (cluster effect (companies) and cluster effect (profiles of employees' common characteristics)).Avec plus de 500 000 salariés par an concernés, la désinsertion professionnelle est un problème de santé publique. Si le maintien dans l’emploi, la pénibilité au travail et les facteurs prédictifs de la désinsertion professionnelle sont un enjeu majeur, ces thématiques restent peu étudiées. D’autre part, les rares travaux et publications internationales se focalisent sur un nombre très limité de facteurs, ne prennent pas en compte le salarié dans sa globalité (pas de prise en compte des trois dimensions de la santé de l’OMS). L’objectif est d’apporter des réponses dans l’identifications des facteurs prédictifs de la désinsertion professionnelle grâce aux données massives de Santé au travail. Les approches statistiques envisagées reposent sur des modèles pour données répétées (modèles mixtes et modèles de trajectoires), des modèles pour données censurées (modèles de survie et modèles multi-états de Markov), prenant en compte les différentes sources de variabilité latentes (effet « grappes » (entreprises) et effet « clusters » (profils de caractéristiques communes des salariés)

Occupational Risk Factors by Sectors: An Observational Study of 20,000 Workers

International audienceObjective: We aimed to assess the prevalence of exposure by sector and the sectors of activity most exposed to each exposure, using routine occupational health data, and to quantify the risk of being exposed. Method: Occupational risk factors were assessed by workers followed by the Occupational Health Service of Cher, using self-reported questionnaires. The sectors of activity were grouped into seven sectors, and the risks were grouped into six occupational exposure groups. Comparisons were made using the Chi-squared test and Cramer’s V, and the odds ratios were calculated by using logistic regression. Results: We included 19,891 workers. The construction sector had the highest prevalence (p < 0.05 vs. all other sectors) of exposure to physical (76%) and biomechanical factors (82%), as well as chemical risks (75%). Human health and social work was the sector with the highest prevalence of exposure to biological factors (69%), psychosocial factors (90%), and atypical working hours (61%). With workers from administrative and support sectors as the reference, construction workers had more chance of declaring exposure to physical factors (OR = 3.28, 95%CI = 2.89 to 3.72), biomechanical factors (1.82, 1.58 to 2.09), and chemical agents (3.83, 3.38 to 4.33). Workers from the human health and social sectors had more chance of being exposed to biological agents (13.4, 11.9 to 15.2), atypical working hours (1.93, 1.75 to 2.14), and psychosocial factors (2.74, 2.38 to 3.16). Conclusion: Psychosocial risk factors were commonly reported in all sectors. Workers in the construction, human health, and social sectors seem to report more exposures than those in other sectors. The analysis of occupational exposures is a necessary basis to build an efficient preventive strategy for occupational health