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Bayesian latent class modelling to examine the diagnostic accuracy of the first hetero-assessment instrument for occupational burnout

Switzerland, Belgium, 2010 - 2019
Department of occupational and environmental health (DSTE)
Guseva Canu, Irina, Shoman, Yara
Created on February 02, 2023 Last modified February 06, 2023 Page views 597 Download 6 Documentation in PDF Metadata DDI/XML JSON

Identification

Survey ID Number
10.16909-DATASET-35
Title
Bayesian latent class modelling to examine the diagnostic accuracy of the first hetero-assessment instrument for occupational burnout
Country
Name Country code
Switzerland CHE
Belgium BE
Abstract
Occupational burnout has no standardized diagnostic or screening criteria. Following a dozen of patient-reported outcome measures (PROMs) for occupational burnout, Belgian researchers developed the first hetero-assessment instrument (HAI) designed for health professionals’ use. The HAI’s sensitivity and specificity was previously assessed with reference to the OLdenburg Burnout Inventory (OLBI) using frequentist statistics in Belgium (100 participants) and Switzerland (42 participants). This study aimed at assessing the HAI’s diagnostic performance using Bayesian latent class modelling (BLCM). We applied Hui-Walter framework for two tests and two populations and ran models with minimally informative priors, with and without conditional dependency between HAI and OLBI results. We further performed sensitivity analysis by replacing one of the minimally informative priors by the distribution beta (2,1) at each time for all priors. We also performed the analysis using literature-based informative priors for OLBI. Using the BLCM without conditional dependency, the sensitivity and specificity of the HAI was 0.91 (0.77-1.00) and 0.82 (0.59-1.00), respectively. The sensitivity analysis did not yield any significant changes in these results. In all models, the sensitivity was never below 0.82 and the specificity was never below 0.78. The HAI’s sensitivity and specificity determined in this study are better compared to the previous studies conducted using frequentist statistics. These finding suggests that the use of BLCM is preferred in the absence of the diagnostic gold standard and precludes underestimating the diagnostic accuracy of the tested instrument.
Kind of Data
Secondary data in the form of tables based on the analysis of de-identified patient data. The tables were taken from other published articles.
Material made available are composed by tables (image in PDF) and R script.
Unit of Analysis
The unit of analysis is the individual person

Scope

Keywords
Keyword
Burnout
Bayesian analysis
R scripts

Coverage

Geographic Coverage
Switzerland and Belgium
Universe
Patients from medical consultations with general practitioners (GP) and occupational physicians (OP)

Producers and sponsors

Primary investigators
Name Affiliation
Guseva Canu, Irina Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE)
Shoman, Yara Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE)
Producers
Name Affiliation Role
Hartnack, Sonja Section of Epidemiology, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland Statistical analysis and co-author of the manuscript
Leclercq, Céline Human Resources Development Unit, Faculty of Psychology and Education Sciences, University of Liège, 4000 Liège, Belgium Co-author of the manuscript
Hansez, Isabelle Human Resources Development Unit, Faculty of Psychology and Education Sciences, University of Liège, 4000 Liège, Belgium Co-author of the manuscript
Funding Agency/Sponsor
Name Abbreviation
The European Union’s Horizon 2020 research and innovation program (under the Marie Skłodowska-Curie grant agreement No 801076, through the SSPH+ Global PhD Fellowship Program in Public Health Sciences (GlobalP3HS) of the Swiss School of Public Health) partly supported the PhD position of YS. SSPH+ Global PhD
Unisanté supported the other part, via the General Directorate of Health of the Canton of Vaud via the grant of the Commission for Health Promotion and the Fight against Addictions Grant N◦8273/3636000000-801.

Sampling

Sampling Procedure
For the Swiss study: a convenience sample of patients received at the Unisanté “Work and Suffering” Consultation (WSC) between 2010 and 2013. WSC patients for whom a completed OLBI was available in their medical record along with the WSC detailed report were included.
For the Belgian study: The target population concerns people who have consulted a GP or an OP and who have expressed complaints and symptoms of suffering at work. Patients who filled OLBI and their clinical judgement using HAI can be linked were included.

Data Collection

Dates of Data Collection
Start End Cycle
2010-01-01 2013-12-31 for Swiss data
2019-01-01 2019-12-31 for Belgian data
Data Collection Mode
Data were taken from 2 published articles (2 tables available in image). Primary data were collected in the process of a medical consultation through questionnaires : one self reported and on filled in by the physician.

Data Processing

Data Editing
No cleaning operation, data has been directly analyzed.

Data Appraisal

Data Appraisal
No action necessary to appraise data.

Access policy

Access authority
Name Affiliation Email
Guseva Canu, Irina Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE) irina.guseva-canu@unisante.ch
Racine, Céline (Repository manager) Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland udd.data@unisante.ch
Citation requirements
Shoman Y, Hartnack S, Leclercq C, Hansez I, Guseva Canu I. Bayesian latent class modelling to examine the diagnostic accuracy of the first hetero-assessment instrument for occupational burnout. Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland. Version 1.0 of the licensed dataset (01/2023), provided by the Unisanté Research Data Repository. DOI:10.16909/DATASET/35

Metadata production

DDI Document ID
10.16909-DATASET-35
Producers
Name Abbreviation Role
Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland Unisanté Data publisher
DDI Document version
Version 1.0 (January 2023)