Projects per year
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.
|Title of host publication||Case Studies in Applied Bayesian Data Science|
|Subtitle of host publication||CIRM Jean-Morlet Chair, Fall 2018|
|Editors||Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert|
|Place of Publication||Cham Switzerland|
|Number of pages||17|
|Publication status||Published - 2020|
|Name||Lecture Notes in Mathematics|
- 1 Finished
ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights
Hall, P., Bartlett, P., Bean, N., Burrage, K., DeGier, J., Delaigle, A., Forrester, P., Geweke, J., Kohn, R., Kroese, D., Mengersen, K. L., Pettit, A., Pollett, P., Roughan, M., Ryan, L., Taylor, P., Turner, I., Wand, M., Garoni, T., Smith-Miles, K. A., Caley, M., Churches, T., Elazar, D., Gupta, A., Harch, B., Tam, S., Weegberg, K., Willinger, W. & Hyndman, R.
Australian Research Council (ARC), Monash University – Internal Department Contribution, University of Melbourne, Queensland University of Technology , University of Adelaide, University of New South Wales, University of Queensland , University of Technology Sydney, Monash University – Internal University Contribution, Monash University – Internal Faculty Contribution, Monash University – Internal School Contribution, Roads Corporation (trading as VicRoads) (Victoria)
1/01/17 → 31/12/21