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

Abstract
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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.

Geographic coverage
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Switzerland and Belgium

Analysis unit
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The unit of analysis is the individual person

Universe
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Patients from medical consultations with general practitioners (GP) and occupational physicians (OP)

Kind of data
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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.

Sampling procedure
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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.

Mode of data collection
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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.

Cleaning operations
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No cleaning operation, data has been directly analyzed.

Data appraisal
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No action necessary to appraise data.

摘要
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职业倦怠尚无统一的诊断或筛查标准。比利时研究人员根据职业倦怠的十二项患者报告结果指标（PROMs），为医疗专业人员开发了首个异质评估工具（HAI）。此前，HAI的敏感性和特异性已通过频繁主义统计方法在比利时（100名参与者）和瑞士（42名参与者）与OLdenburg倦怠量表（OLBI）进行比较评估。本研究旨在利用贝叶斯潜在类别模型（BLCM）评估HAI的诊断性能。我们对两种测试和两种人群应用了Hui-Walter框架，并使用最小信息先验在存在和不存在HAI与OLBI结果条件依赖的情况下进行模型运行。此外，我们还通过将每个先验的最小信息先验替换为贝塔分布（2,1）进行敏感性分析。我们还使用基于文献的OLBI信息先验进行数据分析。在不考虑条件依赖的BLCM模型中，HAI的敏感性和特异性分别为0.91（0.77-1.00）和0.82（0.59-1.00）。敏感性分析并未导致这些结果产生显著变化。在所有模型中，敏感性从未低于0.82，特异性从未低于0.78。本研究中确定的HAI的敏感性和特异性优于先前使用频繁主义统计方法进行的研究。这些发现表明，在没有诊断金标准的条件下，使用BLCM是更佳的选择，并防止低估测试工具的诊断准确性。
地理覆盖范围
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瑞士和比利时
分析单元
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分析单元为个体
总体
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来自家庭医生（GP）和职业医生（OP）医疗咨询的患者
数据类型
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以表格形式存在的二手数据，基于对去标识化患者数据的分析。这些表格来自其他已发表的文献。
提供材料包括表格（PDF中的图像）和R脚本。
抽样程序
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对于瑞士研究：在2010年至2013年间，于Unisanté“工作与苦难”咨询（WSC）接受便利样本的患者。
对于比利时研究：目标人群包括咨询过GP或OP并表达工作苦难投诉和症状的人。
填写了OLBI并使用HAI进行临床判断的患者可以纳入。
数据收集方式
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数据来源于两篇已发表的文献（提供两个表格）。通过医疗咨询过程中的问卷调查收集原始数据：一项自我报告，一项由医生填写。
数据清洗操作
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无需进行数据清洗，数据已直接进行分析。
数据评估
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无需采取行动对数据进行评估。