scholarly journals External validation of a prediction model and decision tree for sickness absence due to mental disorders

2020 ◽  
Vol 93 (8) ◽  
pp. 1007-1012
Author(s):  
Marieke F. A. van Hoffen ◽  
Giny Norder ◽  
Jos W. R. Twisk ◽  
Corné A. M. Roelen
Keyword(s):  
Mental Disorders ◽  
Occupational Health ◽  
Decision Tree ◽  
Prediction Model ◽  
Sickness Absence ◽  
Health Survey ◽  
Characteristic Curve ◽  
External Validation ◽  
Increased Risk

Abstract Purpose A previously developed prediction model and decision tree were externally validated for their ability to identify occupational health survey participants at increased risk of long-term sickness absence (LTSA) due to mental disorders. Methods The study population consisted of N = 3415 employees in mobility services who were invited in 2016 for an occupational health survey, consisting of an online questionnaire measuring the health status and working conditions, followed by a preventive consultation with an occupational health provider (OHP). The survey variables of the previously developed prediction model and decision tree were used for predicting mental LTSA (no = 0, yes = 1) at 1-year follow-up. Discrimination between survey participants with and without mental LTSA was investigated with the area under the receiver operating characteristic curve (AUC). Results A total of n = 1736 (51%) non-sick-listed employees participated in the survey and 51 (3%) of them had mental LTSA during follow-up. The prediction model discriminated (AUC = 0.700; 95% CI 0.628–0.773) between participants with and without mental LTSA during follow-up. Discrimination by the decision tree (AUC = 0.671; 95% CI 0.589–0.753) did not differ significantly (p = 0.62) from discrimination by the prediction model. Conclusion At external validation, the prediction model and the decision tree both poorly identified occupational health survey participants at increased risk of mental LTSA. OHPs could use the decision tree to determine if mental LTSA risk factors should be explored in the preventive consultation which follows after completing the survey questionnaire.

scholarly journals Preventing sickness absence among employees with common mental disorders or stress-related symptoms at work: a cluster randomised controlled trial of a problem-solving-based intervention conducted by the Occupational Health Services

2020 ◽  
Vol 77 (7) ◽  
pp. 454-461 ◽  
Author(s):  
Marijke Keus van de Poll ◽  
Lotta Nybergh ◽  
Caroline Lornudd ◽  
Jan Hagberg ◽  
Lennart Bodin ◽  
...  
Keyword(s):  
Mental Health ◽  
Problem Solving ◽  
Mental Disorders ◽  
Occupational Health ◽  
Sickness Absence ◽  
Primary Outcome ◽  
Common Mental Disorders ◽  
Significant Group ◽  
Secondary Outcomes

ObjectivesCommon mental disorders (CMDs) are among the main causes of sickness absence and can lead to suffering and high costs for individuals, employers and the society. The occupational health service (OHS) can offer work-directed interventions to support employers and employees. The aim of this study was to evaluate the effect on sickness absence and health of a work-directed intervention given by the OHS to employees with CMDs or stress-related symptoms.MethodsRandomisation was conducted at the OHS consultant level and each consultant was allocated into either giving a brief problem-solving intervention (PSI) or care as usual (CAU). The study group consisted of 100 employees with stress symptoms or CMDs. PSI was highly structured and used a participatory approach, involving both the employee and the employee’s manager. CAU was also work-directed but not based on the same theoretical concepts as PSI. Outcomes were assessed at baseline, at 6 and at 12 months. Primary outcome was registered sickness absence during the 1-year follow-up period. Among the secondary outcomes were self-registered sickness absence, return to work (RTW) and mental health.ResultsA statistical interaction for group × time was found on the primary outcome (p=0.033) and PSI had almost 15 days less sickness absence during follow-up compared with CAU. Concerning the secondary outcomes, PSI showed an earlier partial RTW and the mental health improved in both groups without significant group differences.ConclusionPSI was effective in reducing sickness absence which was the primary outcome in this study.

scholarly journals Framingham score and work-related variables for predicting cardiovascular disease in the working population

2019 ◽  
Vol 29 (5) ◽  
pp. 832-837
Author(s):  
Albert-Jan van der Zwaard ◽  
Anna Geraedts ◽  
Giny Norder ◽  
Martijn W Heymans ◽  
Corné A M Roelen
Keyword(s):  
Cardiovascular Disease ◽  
Occupational Health ◽  
Sickness Absence ◽  
Health Survey ◽  
Work Motivation ◽  
Framingham Score ◽  
Cognitive Demands ◽  
Work Related ◽  
Work Type

Abstract Background The Framingham score is commonly used to estimate the risk of cardiovascular disease (CVD). This study investigated whether work-related variables improve Framingham score predictions of sickness absence due to CVD. Methods Eleven occupational health survey variables (descent, marital status, education, work type, work pace, cognitive demands, supervisor support, co-worker support, commitment to work, intrinsic work motivation and distress) and the Framingham Point Score (FPS) were combined into a multi-variable logistic regression model for CVD sickness absence during 1-year follow-up of 19 707 survey participants. The Net Reclassification Index (NRI) was used to investigate the added value of work-related variables to the FPS risk classification. Discrimination between participants with and without CVD sickness absence during follow-up was investigated by the area under the receiver operating characteristic curve (AUC). Results A total of 129 (0.7%) occupational health survey participants had CVD sickness absence during 1-year follow-up. Manual work and high cognitive demands, but not the other work-related variables contributed to the FPS predictions of CVD sickness absence. However, work type and cognitive demands did not improve the FPS classification for risk of CVD sickness absence [NRI = 2.3%; 95% confidence interval (CI) −2.7 to 9.5%; P = 0.629]. The FPS discriminated well between participants with and without CVD sickness absence (AUC = 0.759; 95% CI 0.724–0.794). Conclusion Work-related variables did not improve predictions of CVD sickness absence by the FPS. The non-laboratory Framingham score can be used to identify health survey participants at risk of CVD sickness absence.

Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables

2016 ◽  
Vol 40 (2) ◽  
pp. 168-175 ◽  
Author(s):  
Corné Roelen ◽  
Sannie Thorsen ◽  
Martijn Heymans ◽  
Jos Twisk ◽  
Ute Bültmann ◽  
...  
Keyword(s):  
Occupational Health ◽  
Prediction Model ◽  
Sickness Absence ◽  
Health Survey ◽  
Development And Validation

scholarly journals Does seeing a psychologist reduce sickness absence due to mental disorders? A quasi-experiment

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Lahti ◽  
J Harkko ◽  
H Sumanen ◽  
K Piha ◽  
O Pietiläinen ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Sickness Absence ◽  
Work Ability ◽  
Early Stage ◽  
Matched Sample ◽  
Full Sample ◽  
Common Support ◽  
The City ◽  
Quasi Experiment

Abstract Background Mental ill-health in young adults is a major public health and work-life problem. We examined in a quasi-experimental design whether occupational psychologist appointment can reduce subsequent sickness absence due to mental disorders among young Finnish employees. Methods The present study was conducted among 18-39-year-old employees of the City of Helsinki using register data from the City of Helsinki and the Social lnsurance Institution of Finland. We used Wald test to compare the differences in sickness absence days due to mental disorders (ICD-10, F-diagnosed) between those treated (occupational psychologist appointment for work ability support) and the non-treated (no psychologist appointment) during a one year follow-up. The full sample (n = 2156, 84% women) consisted of employees with mental disorder diagnosed sickness absence during 2009-2014. To account for the systematic differences between the treated and non-treated, the participants were matched according to their characteristics (age, sex, occupational class, education, previous sickness absence and psychotropic medication). The matched sample included 886 participants. We excluded those with treatment before the treatment screening time (± 3 months to the end of sickness absence period), non-treated with treatment during the follow-up and those that could not be matched (lack of common support). Results In the full sample, the mean of sickness absence days due to mental disorders was 17.7 (95% CI, 11.4, 24.1) days for those treated (n = 240) and 23.2 (95% CI, 20.5, 25.9) days for non-treated (n = 1916), difference being non-significant. The corresponding figures in the matched sample were (16.8, 95% CI, 9.5-24.1) for those treated (n = 195) and (27.8, 95% CI, 22.6-32.9) for non-treated (n = 691), difference being statistically significant (p = 0.02). Conclusions This quasi-experiment suggests that seeing an occupational psychologist to support work ability may be reduce mental health related sickness absence. Key messages We showed that supporting work ability at an early stage may prevent sickness absence due to mental disorders. More efforts to provide early stage support for maintaining work ability may prove useful in reducing sickness absence rates in younger employees.

scholarly journals Seeing an occupational health psychologist reduces sickness absence due to mental disorders: A quasi-experimental study

2021 ◽  
pp. 106611
Author(s):  
Jouni Lahti ◽  
Jaakko Harkko ◽  
Hilla Nordquist ◽  
Kustaa Piha ◽  
Olli Pietiläinen ◽  
...  
Keyword(s):  
Experimental Study ◽  
Mental Disorders ◽  
Occupational Health ◽  
Sickness Absence ◽  
Quasi Experimental ◽  
Health Psychologist

scholarly journals Clinical prediction of thrombectomy eligibility: A systematic review and 4-item decision tree

2018 ◽  
Vol 14 (5) ◽  
pp. 530-539 ◽  
Author(s):  
Gaia T Koster ◽  
T Truc My Nguyen ◽  
Erik W van Zwet ◽  
Bjarty L Garcia ◽  
Hannah R Rowling ◽  
...  
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
External Validation ◽  
Facial Asymmetry ◽  
Decision Support Tool ◽  
Patient Characteristics ◽  
Group Lasso ◽  
Lasso Regression ◽  
Vessel Occlusion ◽  
Support Tool

Background A clinical large anterior vessel occlusion (LAVO)-prediction scale could reduce treatment delays by allocating intra-arterial thrombectomy (IAT)-eligible patients directly to a comprehensive stroke center. Aim To subtract, validate and compare existing LAVO-prediction scales, and develop a straightforward decision support tool to assess IAT-eligibility. Methods We performed a systematic literature search to identify LAVO-prediction scales. Performance was compared in a prospective, multicenter validation cohort of the Dutch acute Stroke study (DUST) by calculating area under the receiver operating curves (AUROC). With group lasso regression analysis, we constructed a prediction model, incorporating patient characteristics next to National Institutes of Health Stroke Scale (NIHSS) items. Finally, we developed a decision tree algorithm based on dichotomized NIHSS items. Results We identified seven LAVO-prediction scales. From DUST, 1316 patients (35.8% LAVO-rate) from 14 centers were available for validation. FAST-ED and RACE had the highest AUROC (both >0.81, p < 0.01 for comparison with other scales). Group lasso analysis revealed a LAVO-prediction model containing seven NIHSS items (AUROC 0.84). With the GACE (Gaze, facial Asymmetry, level of Consciousness, Extinction/inattention) decision tree, LAVO is predicted (AUROC 0.76) for 61% of patients with assessment of only two dichotomized NIHSS items, and for all patients with four items. Conclusion External validation of seven LAVO-prediction scales showed AUROCs between 0.75 and 0.83. Most scales, however, appear too complex for Emergency Medical Services use with prehospital validation generally lacking. GACE is the first LAVO-prediction scale using a simple decision tree as such increasing feasibility, while maintaining high accuracy. Prehospital prospective validation is planned.

scholarly journals Occupational exposures and 20-year incidence of COPD: the European Community Respiratory Health Survey

Thorax ◽  
2018 ◽  
Vol 73 (11) ◽  
pp. 1008-1015 ◽  
Author(s):  
Theodore Lytras ◽  
Manolis Kogevinas ◽  
Hans Kromhout ◽  
Anne-Elie Carsin ◽  
Josep M Antó ◽  
...  
Keyword(s):  
European Community ◽  
Health Survey ◽  
Respiratory Health ◽  
Population Attributable Fraction ◽  
Occupational Exposures ◽  
Evidence Base ◽  
Job Exposure Matrix ◽  
Increased Risk ◽  
Lower Limit Of Normal

BackgroundOccupational exposures have been associated with an increased risk of COPD. However, few studies have related objectively assessed occupational exposures to prospectively assessed incidence of COPD, using postbronchodilator lung function tests. Our objective was to examine the effect of occupational exposures on COPD incidence in the European Community Respiratory Health Survey.MethodsGeneral population samples aged 20–44 were randomly selected in 1991–1993 and followed up 20 years later (2010–2012). Spirometry was performed at baseline and at follow-up, with incident COPD defined using a lower limit of normal criterion for postbronchodilator FEV1/FVC. Only participants without COPD and without current asthma at baseline were included. Coded job histories during follow-up were linked to a Job-Exposure Matrix, generating occupational exposure estimates to 12 categories of agents. Their association with COPD incidence was examined in log-binomial models fitted in a Bayesian framework.Findings3343 participants fulfilled the inclusion criteria; 89 of them had COPD at follow-up (1.4 cases/1000 person-years). Participants exposed to biological dust had a higher incidence of COPD compared with those unexposed (relative risk (RR) 1.6, 95% CI 1.1 to 2.3), as did those exposed to gases and fumes (RR 1.5, 95% CI 1.0 to 2.2) and pesticides (RR 2.2, 95% CI 1.1 to 3.8). The combined population attributable fraction for these exposures was 21.0%.InterpretationThese results substantially strengthen the evidence base for occupational exposures as an important risk factor for COPD.

scholarly journals A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children

2020 ◽  
Vol 31 (6) ◽  
pp. 1348-1357 ◽  
Author(s):  
Ibrahim Sandokji ◽  
Yu Yamamoto ◽  
Aditya Biswas ◽  
Tanima Arora ◽  
Ugochukwu Ugwuowo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Health Record ◽  
Hospitalized Children ◽  
Operating Characteristic Curve ◽  
Electronic Health

BackgroundTimely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges.MethodsWe retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay.ResultsAmong 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points.ConclusionsUsing various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.

scholarly journals External Validation and Update of a Prediction Rule for the Duration of Sickness Absence Due to Common Mental Disorders

2016 ◽  
Vol 27 (2) ◽  
pp. 202-209 ◽  
Author(s):  
Giny Norder ◽  
Corné A. M. Roelen ◽  
Jac J. L. van der Klink ◽  
Ute Bültmann ◽  
J. K. Sluiter ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Sickness Absence ◽  
Common Mental Disorders ◽  
External Validation ◽  
Prediction Rule

External validation of the Pediatric Extracorporeal Membrane Oxygenation Prediction model for risk adjusting mortality

Perfusion ◽  
2020 ◽  
pp. 026765912095297
Author(s):  
David K Bailly ◽  
Jamie M Furlong-Dillard ◽  
Melissa Winder ◽  
Mark Lavering ◽  
Ryan P Barbaro ◽  
...  
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
Prediction Model ◽  
Goodness Of Fit ◽  
Pediatric Patients ◽  
Life Support ◽  
Extracorporeal Life Support ◽  
Characteristic Curve ◽  
External Validation ◽  
Group Model ◽  
Membrane Oxygenation

Introduction: The Pediatric Extracorporeal Membrane Oxygenation Prediction (PEP) model was created to provide risk stratification for all pediatric patients requiring extracorporeal life support (ECLS). Our purpose was to externally validate the model using contemporaneous cases submitted to the Extracorporeal Life Support Organization (ELSO) registry. Methods: This multicenter, retrospective analysis included pediatric patients (<19 years) during their initial ECLS run for all indications between January 2012 and September 2014. Median values from the BATE dataset for activated partial thromboplastin time and internationalized normalized ratio were used as surrogates as these were missing in the ELSO group. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC), and goodness-of-fit was evaluated using the Hosmer-Lemeshow test. Results: A total of 4,342 patients in the ELSO registry were compared to 514 subjects from the bleeding and thrombosis on extracorporeal membrane oxygenation (BATE) dataset used to develop the PEP model. Overall mortality was similar (42% ELSO vs. 45% BATE). The c-statistic after external validation decreased from 0.75 to 0.64 and model calibration decreases most in the highest risk deciles. Conclusion: Discrimination of the PEP model remains modest after external validation using the largest pediatric ECLS cohort. While the model overestimates mortality for the highest risk patients, it remains the only prediction model applicable to both neonates and pediatric patients who require ECLS for any indication and thus maintains potential for application in research and quality benchmarking.

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