Bayesian Modelling to assist inference on health outcomes in Occupational Health Surveillance

Nicholas J. Tierney, Samuel Clifford, Christopher C. Drovandi, Kerrie L. Mengersen

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Objectives: Occupational Health Surveillance (OHS) facilitates early detection of disease and dangerous exposures in the workplace. Current OHS analysis ignore important workplace structures and repeated measurements. There is a need to provide systematic analyses of medical data that incorporate the data structure. Although multilevel statistical models may account for features of OHS data, current applications in occupational health medicine are often not appropriate for OHS. Additionally, typical OHS data has not been analysed in a Bayesian framework, which allows for calculation of probabilities of potential events and outcomes. This paper’s objective is to illustrate the use of Bayesian modeling of OHS. Three analytic aims are addressed: (1) Identify patterns and changes in health outcomes; (2) Explore the effects of a particular risk factor, smoking and industrial exposures over time for individuals and worker groups; (3) identify risk of chronic conditions in individuals. Method: A Bayesian hierarchical model was developed to provide individual and group level estimates and inferences for health outcomes, FEV1%, BMI, and Diastolic and Systolic blood pressure. Results: We identified individuals with the greatest degree of change over time for each outcome, and demonstrated how to flag individuals with substantive negative health outcome change. We also assigned probabilities of individuals moving into “at risk” health categories 1 year from their last visit. Conclusion: Bayesian models can account for features typically encountered in OHS data, such as individual repeated measurements and group structures. We describe one way to fit these data and obtain informative estimates and predictions of employee health.

Original languageEnglish
Title of host publicationCase Studies in Applied Bayesian Data Science
Subtitle of host publicationCIRM Jean-Morlet Chair, Fall 2018
EditorsKerrie L. Mengersen, Pierre Pudlo, Christian P. Robert
Place of PublicationCham Switzerland
Number of pages17
ISBN (Electronic)9783030425531
ISBN (Print)9783030425524
Publication statusPublished - 2020

Publication series

NameLecture Notes in Mathematics
ISSN (Print)0075-8434
ISSN (Electronic)1617-9692

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