qwl & psychosocial risks...this project has received funding from the european union's...

This project has received funding from the European Union's Horizon 2020 Research and Innovation Programmeunder Grant Agreement no 730998

Laboratoire MESuRS “Modélisation, Epidémiologie et Surveillance des RisquesSanitaires” - Cnam

QWL & psychosocial risks:How to identify and prioritize

psychosocial risk factorsimpacting stress level

Mounia N. HOCINEWilliam DAB

Patrick LEGERONGilbert SAPORTA

www.inclusivegrowth.eu 2

Outline

Introduction

Data

Statistical methodology

Resultats

Current work: alternative approaches

Conclusion

www.inclusivegrowth.eu

INTRODUCTION

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Work-related stress

• A major public health issue

has negative effects on physical and psychological health

is an inevitable part of organizational life

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The aim: Reduce work-related stress level

Decision makers would like to be provided with

statistical tools that can help them identify risk factors

requiring a priority action


Quantitative assessment

• Theoretical frameworks have been successful in generating and collecting data on work-related stressors: – Demand/Control Imbalance Model

– Effort/Reward Imbalance Model

M. Gollac recommendation: setting up a mental health monitoring system

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Motivating data

A system of data collection on stress and stressorsduring the yearly compulsory routine visit with the company’s occupational physician

Measurement tools 2 anonymous online questionnaires (response rate > 80%)

Database: 10 000 anonymous employees randomly drawn from Stimulus data collected over 2011-2012.

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Stress measurement tool

• 1st questionnaire: 25 items to measure individual psychological stress at work 8-point Likert scale.

• Example:

“I'm confused and I lack focus and concentration” , answer varies from 1 “not at all” to 8 “enormously”

Build a stress score = responses (25-200)

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• 2nd questionnaire: 58 items to measure the impact of job characteristics (stressors) 6-point Likert scale.

Example 1: “My company does not care about employees well-being” (negative) 0 “totally disagree” to 5 “totally agree”

Example 2: “I know clearly what I am expected to do at work” (positive) 0 “totally agree” to 5 “totally disagree“

Stressors measurement tool


• The 58 stressors are grouped into five blocks:

- Work Context (14)- Job control (14)- Relationships at workplace (12)- Performed tasks (12)- Recognition (6)

Stressors blocks


Cooper index

• Developped by Cooper & Clarke, Health, Risk & Society 2000

• Provides companies a quantitative risk assessment approach to prioritize psychosocial risks at work.

• Approach: identify stressors related to high stress level:

Risk level = exposure x consequences

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Stressor level Cor² (stressor; stress)


Managers priority action

High

Medium

Low


Advantage & Limits

1) Easy to use but questionnable! r² x m is not a risk(or an impact) measure

2) Strong hypothesis: Acting on a given stressor does not impact the other stressors ! No consideration of inter-correlations between stressors

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STATISTICAL METHODOLOGY

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Suggested approach (1st step)

• Partial-Least Square-Structural Equation modeling that allows:– To investigate the impact of job characteristics on

perceived work-related stress in a multidimensional way

– To predict blocks of stressors requiring priority action from managers in preventing high level of work-related stress.

Stressors are correlated and grouped into a block structure generated from a conceptual model

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Conceptual model

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Latent variables No. of Items Cronbach a

1st questionnaire: « stress »

Work-related stress 25 0.94

2nd questionnaire: « stressors »

Work context 14 0.88

Job control 14 0.80

Relationship 12 0.87

Tasks 12 0.62

Recognition 6 0.81

Homogeneity of the blocks


Validity of the blocks

LV MV Outer weight 95% Confidence Interval

Work context

Context_01 0,116 0,110 0,122

Context_08 -0,069 -0,074 -0,064

Context_15 -0,077 -0,082 -0,072

Context_21 -0,096 -0,100 -0,092

Context_23 -0,096 -0,100 -0,092

Context_30 -0,088 -0,092 -0,084

Context_32 -0,085 -0,089 -0,081

Context_34 0,090 0,086 0,095

Context_40 -0,080 -0,084 -0,076

Context_41 0,096 0,090 0,102

Context_43 -0,080 -0,085 -0,076

Context_52 0,092 0,087 0,097

All outer weights are statistically significant (a = 5%)


Predict stress: PLS regression

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Context Control Recognition Relationship Tasks Stress

Context 1.00

Control 0.78 1.00

Recongnition 0.72 0.63 1.00

Relationship 0.69 0.67 0.60 1.00

Tasks 0.61 0.72 0.54 0.53 1.00

Stress -0.52 -0.63 -0.43 -0.51 -0.52 1.00

• Correlation between latent variables:

• A high multi collinearity between the 5 stressorsblocks PLS regression


Path coefficients

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0.15

Path coef.

(R² = 40%)

0.18

0.12

0.14

0.15

efficients cannot be used directly to provide

decision makers with ranked predictors


Importance-Performance Analysis

• A graphical grid for decision making (Martilla & James, The Journal of Marketing 1977)


• Performance: measured as the score mean of 10 000

responses

• Importance: calculated using the suggested formulae:

Importance (kth item) = |Outer weight (kth item in jth block)| x

Path coefficient (jth block, stress)

Importance-Performance Analysis


Graphical decision making

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Stressors to maintain

Task_05: « I frequently see the work pile up without being able to eliminate the backlog »

Task_07: inverse of «My work gives me many opportunities to perform interesting tasks »

Task_01: inverse of «My work has meaning to me »

Task_02: «My job involves monotonous and repetitive tasks »

Contro_03: inverse of « I can achieve professional life—personal life balance »

Contro_14: « I'm undergoing or I expect to undergo an undesirable change that might affect my career »

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Priority action

1. Task_11: « I have to work fast in a short timeframe »

2. Recon_01: «My promotion prospects are weak »

3. Recon_02: inverse of «My company offers me interesting career opportunities »

4. Task_09: « I work in a noisy and hectic atmosphere »

5. Recon_04 : inverse of « I am rewarded when I reach my goals »

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Limitations

1) The validity of the underlying conceptual model is questionable

2) The previous method SEM-path modeling combined with IPA is not easy to use by decision makers

3) Confusion between correlation and causality!

acting on a stressor would cause changes in other stressors

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Alternative methods(Ignore the conceptual model)

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1. Weifila “Weighted first last”

A variance decomposition method in linear regression context (Y=sress score)

𝐸(𝑌) = 𝛼 + 𝛽1𝑋1 +⋯+ 𝛽𝑝𝑋𝑝

Predictor’s importance 𝑊(𝑗) = weighted average between:

• “First” allocation: 𝐹𝑖𝑟𝑠𝑡(𝑗) = 𝑐𝑜𝑣(𝑦1; 𝑋𝑗)

• “Last” allocation: 𝐿𝑎𝑠𝑡(𝑗) = 𝑠𝑟²(𝑗) « semi-partial R² »

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𝑊 𝑗 = 𝐿𝑎𝑠𝑡 𝑗𝐹−𝑅2

𝐹−𝐿+ 𝐹𝑖𝑟𝑠𝑡 𝑗

𝑅2−𝐿

𝐹−𝐿𝑖𝑓 𝐿 < 𝑅2 < 𝐹

𝐿𝑎𝑠𝑡 𝑗𝑅2−𝐹

𝐿−𝐹+ 𝐹𝑖𝑟𝑠𝑡 𝑗

𝐿−𝑅2

𝐿−𝐹𝑖𝑓 𝐹 < 𝑅2 < 𝐿


• A combination of tree predictors of Y=stress score

• Each tree depends on the values of a random vector sampledindependently with the same distribution for all trees in the forest

• Predictor’s importance = the frequency of the item occurrence in the forest trees

• We used “Gini” a local criteria based on the heterogeneity decrease

The importance of an item is a weighted sum of the induced heterogeneity

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2. Random Forest


Consider Y= stress score

The association between Y and the predictors 𝑥𝑗 𝑗 = 1,… , 58

can be estimated using a linear regression

To predict Y1 = 1 if Y>110, 0 if not

Logit (p) = log 𝑝(𝑌1=1)

𝑝(𝑌1=0)= 𝑏0 + 𝑏1𝑥1 +⋯+ 𝑏𝑝𝑥𝑝

Consider now the average exposure E(𝑥𝑗) to the predictor 𝑥𝑗

EF 𝑥𝑗 =𝑒𝑥𝑝 𝑏𝑗 −1 ∗𝐸 𝑥𝑗

1+ 𝑒𝑥𝑝 𝑏𝑗 −1 ∗𝐸 𝑥𝑗“An epidemiological impact measure”

3. Etiologic fraction (EF)


Weifila AR R. Forest

PSRF09 PSRF09 PSRF09






PSRF09: inverse of “I can manage my professional life and personal life”PSRF54: “I am living/expecting an undesirable change likely to affect my career”PSRF25: “I often see the work pile up without being able to absorb the delay”

Priority action: comparison


Conclusion

• The four metrics lead to similar results

• For organizational strategies to prevent and manage stress, decision-makers should act in priority on the level of the 5 “stressors to improve”

• Etiological fraction-based approach is a relevant-easy to implement tool to help decision making

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NEW DATASET

QWL barometer survey: “ Baromètre Travail et Santé Psychique”

• A cross-sectional study: 3200 assets

• A representative sample of the French population according to: age, gender, occupation

• Survey conducted by Ipsos between Fabruary & March 2018

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Sociodemographic profile of the interviewed employees

51% 49%

9

23

27

37

4

25-34 y

35-44 y

60 y or +

18-24 y

21

22

22

11

24

Ile de France Nord OuestNord Est Sud OuestSud Est

Region

# children 18 years old or less

52

22

20

5

1

Aucun

1

2

3

4 et +

45-59 y

- 35 y. old: 32%

+ 35 y. old: 68%

NEW DATASET


Factorial analysis

Somatisation

GHQ-28

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Désadaptation

sociale

Dépression

sévère

Anxiété et

insomnie

General Health Questionnaire

Unidimentionnality of the GHQ-28


• Item’s contribution to the 1st factorial axis

Weighted GHQ score needed


PSRF1

36

PSRF2

….

PSRF43

Score GHQ

PSRF44

44 PRF documented

Psychosocial Risk Factors


Confounding factors

Gender, age

Tobacco, alcohol

Physical health

Sick leave for psychic problem

Drug consumption

Medical check-up

Week-end work, night work

Sport, income, …etc.

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Linear Regression ModelItems Coefficient

PSRF9 1,96**

PSRF18 1,67**

PSRF5 1,64**

PSRF8 1,24**

PSRF43 0,85*

PSRF30 0,81*

Priority action

PSRF9 1) Inverse of “I can manage my professional life and my personal life”

PSRF18 2) Communication and information exchange within my company are

satisfactory

PSRF5 3) Inverse of “I know I can rely on people I work with”

PSRF8 4) I have to deal with a lot of complex and numerous information

PSRF43 5) I’m worried for my professional future

* < .01

** < .001


Conclusion & Perspectives

• Similar main risk factor identified (external validation)

• Causal inference analysis is needed to determine stressors on which to act in order to reduce psychosocial disorders associated with stress.

Follow-up data: individual trajectories on stress and psychosocial risk factors needed

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Work conditions and infectious risk

2013-2016: MOTILIS « Modélisation de l’Organisation du Travail

et de son Impact sur les infections Liées aux Soins »

Impact of organizational factors on stress and fatigue in

French intensive-care healthcare workers

Jones, Hocine et al. Inter J Nursing Studies 2015

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Work conditions and infectious risk

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2017-2020: STRIPPS research project

« modelling work organization and its impact on

healthcare-associated infections »

Partner: CRTD, Cnam

Prospective cohort study

To propose optimal organizational strategies for controlling

infectious risk.


Conceptual framework

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Data collection

Longitudinal data collection over 1 year in 5 hospitals in

Paris (35-40 randomly selected wards)

Data collection started in February 2018

4 time points: T0, T0+4 months, T0+8 months, T0+12

months


Methodology

Quantitative/qualitative approach

Quantitative: Administered questionnaires (~750 HCWs),administrative databases, hand hygiene compliance audits,…

Longitudinal data analyses, hierarchical models, causalityanalyses

Qualitative: interviews with HCWs and days of observationby the ergonomist

Analysis of activity


Concluding sheme

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Conceptual model built by experts

(P.Légeron)

Measurement tools: questionnaires on stress & psychosocial risk factors(validated instruments)

Data collected during a routine visit with the

occupational GP (high quality)

PLS Structural Equation Model: predicted successfully stress

(deal with colinearity)

Importance-performance analysis: prioritized stressors

regarding their impact on stress (graphical tool)

Step 1

Step 2

Causality analysis (Priority action)

Step 3


Some references

1. Hocine MN, Aït Bouziad K, Légeron P, Dab W, Saporta G.How to Identify and Prioritize Psychosocial Factors Impacting Stress Level. PLoS One. 2016 Jun 15 ;11(6):e0157078.

2. Henri Wallard. Using Explained Variance Allocation to Analyse Importance of Predictors. 16th ASMDA Conference Proceedings, 30 June – 4 July 2015, Piraeus, Greece

3. Clarke, S. G., & Cooper, C. L. The risk management of occupational stress. Health, Risk & Society 2000.

4. Vinzi, E, Russolillo, G. Partial least squares algorithms and methods. WileyInterdisciplinary Reviews: Computational Statistics 2013.

5. Martilla, J. A., & James, J. C. Importance-performance analysis. The journal of marketing 1977.

6. Bühlmann, P. Causal statistical inference in high dimensions. Mathematical Methods of Operations Research 2013.

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qwl & psychosocial risks...this project has received funding from the european union's...

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