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. 2022 Aug;42(8):1834-1851.
doi: 10.1111/risa.13909. Epub 2022 Mar 14.

Quantifying the impact of environment factors on the risk of medical responders' stress-related absenteeism

Mario P Brito  1 , Zhiyin Chen  1 , James Wise  2 , Simon Mortimore  2
Affiliations

Quantifying the impact of environment factors on the risk of medical responders' stress-related absenteeism

Mario P Brito et al. Risk Anal. 2022 Aug.

Abstract

Medical emergency response staff are exposed to incidents which may involve high-acuity patients or some intractable or traumatic situations. Previous studies on emergency response staff stress-related absence have focused on perceived factors and their impacts on absence leave. To date, analytical models on absenteeism risk prediction use past absenteeism to predict risk of future absenteeism. We show that these approaches ignore environment data, such as stress factors. The increased use of digital systems in emergency services allows us to gather data that were not available in the past and to apply a data-driven approach to quantify the effect of environment variables on the risk of stress-related absenteeism. We propose a two-stage data-driven framework to identify the variables of importance and to quantify their impact on medical staff stress-related risk of absenteeism. First, machine learning techniques are applied to identify the importance of different stressors on staff stress-related risk of absenteeism. Second, the Cox proportional-hazards model is applied to estimate the relative risk of each stressor. Four significant stressors are identified, these are the average night shift, past stress leave, the squared term of death confirmed by the Emergency Services and completion of the safeguarding form. We discuss counterintuitive results and implications to policy.

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Figures

FIGURE 1
FIGURE 1
Example of stress‐related leave records
FIGURE 2
FIGURE 2
Flow chart of data generation process
FIGURE 3
FIGURE 3
Left: Position title. Right: Role distribution
FIGURE 4
FIGURE 4
Top: The number of AbnormalMaternityDelivery staff has experienced within a working period. Middle: The number of Child Death staff has experienced within a working Period. Bottom: The number of NormalMaternityDelivery staff has experienced within a working Period
FIGURE 5
FIGURE 5
Top left: The number of Safeguarding_SexualAbuse staff has experienced within a working period. Top right: The number of DeathConfirmedByEMS staff has experienced within a working period. Bottom left: The number of infection status staff has experienced within a working period. Bottom right: The number of MentalHealthDiagnosis staff has experienced within a working period
FIGURE 6
FIGURE 6
Random forest (100 Estimators) receiver operating characteristics (ROC) curve
FIGURE 7
FIGURE 7
Calibration plot of random forest (n estimators = 100)
FIGURE 8
FIGURE 8
Scaled Schonfeld residuals of “Safeguarding Form”
FIGURE 9
FIGURE 9
Survival function of safeguarding form
FIGURE 10
FIGURE 10
Coefficients plot of cox regression
FIGURE 11
FIGURE 11
Calibration plot of cox regression
See this image and copyright information in PMC

References

    1. Antoniadis, A. , Lambert‐Lacroix, S. , & Poggi, J.‐M. , (2021). Random forests for global sensitivity analysis: A selective review. Reliability Engineering & System Safety, 206, 107312
    1. Alba, A. C. , Agoritsas, T. , Walsh, M. , Hanna, S. , Iorio, A. , Devereaux, P. J. , & Guyatt, G. (2017). Discrimination and calibration of clinical prediction models: Users' guides to the medical literature. Journal of the American Medical Association, 318(14), 1377–1384. - PubMed
    1. Boot, C. R. L. , Drongelen, A. v. , Wolbers, I. , Hlobil, H. , Beek, A. J. V. D. , & Smid, T. (2017). Prediction of long‐term and frequent sickness absence using company data. Occupational Medicine, 67(3), 176–181. - PubMed
    1. Brid, R. S. (Producer) . (2018). Introduction to decision trees. https://medium.com/greyatom/decision‐trees‐a‐simple‐way‐to‐visualize‐a‐d...
    1. Burns, C. , & Harm, N. J. (1993). Emergency nurses' perceptions of critical incidents and stress debriefing. Journal of Emergency Nursing, 19(5), 431–436. - PubMed

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