Daouda, Oumou S.

Hocine, Mounia

Saporta, Gilbert

Temime, Laura

English

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HAL Id: hal-02471323
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Submitted on 7 Feb 2020
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How to prioritize work-related psychosocial factors
impacting mental health? Regression and random forest
approaches
Oumou S. Daouda, Laura Temime, Gilbert Saporta, Mounia Hocine
To cite this version:
Oumou S. Daouda, Laura Temime, Gilbert Saporta, Mounia Hocine. How to prioritize work-related
psychosocial factors impacting mental health? Regression and random forest approaches. ISCB 40,
Jun 2019, Leuven, Belgium. ￿hal-02471323￿
How to prioritize work-related psychosocial factors impacting mental health?
Regression and random forest approaches 
Oumou S. Daouda1, Laura Temime1, Gilbert Saporta², Mounia N. Hocine1
Background1
 Mental health disorders are both a major public health and economic issue
 In modern professional life, its prevention and promotion have become a major challenge for decision-makers
 A broad range of work-related psycho-social factors (PSFs) have been documented as having an impact on mental health1
 Decision-makers lack pertinent methodological tools to help them identify key PSFs on which they may act to improve mental health among 
employees
 Most published studies attempting to hierarchize PSFs have focused on their importance only i.e. the strength of their association with mental health
 However, the exposure prevalence to each PSF, that is, its performance, is also important to consider
 To propose a new adjusted ranking index (RI) to hierarchize PSFs, that jointly takes into account their importance and their performance
Objective2
Methods3
Results4
Conclusion5
 Cross sectional study conducted in March 2018
Sample of 3200 individuals, living in France and representative of 
the French workers (according to the age, the gender, the profession, 
and the residence place)
 Measurement of the mental health status : 
GHQ-28 « General Heath Questionnaire with 28 items »
 44 PSFs and 9 covariates were documented 
1. Performance measurement : prevalence of exposure to each PSF
2. Importance measurement : adjusted strength of association between 
mental health and PSF
Data
Ranking methodology
Rank
#PSF
PSF description
Weifila
1 5 Unsatisfactory communication at work
2 18 Inability to depend on work collaborators
3 9 Imbalance private and professional life
4 36 Emotional demands at work
5 22 No good career prospects
6 13 Not feeling valued or recognized at work 
 To our knowledge, this is the first study considering jointly the importance of PSFs and their exposure prevalence for decision making in 
work-related health, using multivariate approaches
 A causal analysis is needed to complete all the results already obtained to identify the drivers of mental health improvement
 These findings have the potential to help improve the quality of life of employee
1Conservatoire National des Arts et Métiers (le Cnam), MESuRS lab, Paris, France
²Le Cnam, CEDRIC lab, Paris, France
Weifila approach2 Random forest (RF) approach3
3. PSF Ranking Index (RI)
Based on variance decomposition
Linear context 
Permutation importance
Non-linear context
• RI = importance x performance
• RI-isocurves to identify PSFs requiring priority actions, with higher RIs
• We identified key PSFs based on a synthesis of our results
 PSF importance was normalized to 100% to ensure a better comparability between the two methods
 The PSF rankings obtained with the two approaches are strongly consistent with each other (Spearman correlation coefficient = 0.73; p-value < .001)
 From the 10 PSFs with the highest RIs, a total of six were found in common (in bold) and thus identified as key for decision making
 These PSFs can also be visually identified from the RI-isocurves
email: oumou-salama.daouda@lecnam.net
RI
Performance
Im
p
o
rt
an
ce
9
RI-isocurves, Weifila approach (#PSF)
References :
1. Karasek RA. Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign. Administrative Science Quarterly. 1979;24(2):285–308
2. Wallard H. Using Explained Variance Allocation to analyse Importance of Predictors. 16th ASMDA, Conference Proceeding. 2015;1043-1054
3. Breiman L. Random Forests. Machine Learning. 2001 Oct 1;45(1):5–32


How to prioritize work-related psychosocial factors impacting mental health? Regression and random forest approaches

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How to prioritize work-related psychosocial factors impacting mental health? Regression and random forest approaches

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