Rapid Fire Australian Epidemiology Association ASM 2018

Risk of Autoimmune Diseases from Occupational Silica and Diesel Exposure (#149)

Istvan Kabdebo 1 , Peter Franklin 1 , William Musk 1 , Nicholas De Klerk 2 , Fraser Brims 3 , Edward Harris 3 , Nita Sodhi-Berry 1
  1. The University of Western Australia, Perth, WA, Australia
  2. Biostatistics, Telethon Kids Institute, Perth, WA, Australia
  3. Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia

Background: Exposure to respirable crystalline silica (RCS) has been found to increase risk of common autoimmune diseases (AIDs) such as: Rheumatoid Arthritis, Systemic Lupus Erythematosus and Systemic Sclerosis. However, the dose-response relationship of these associations is still largely unknown. Exposure to diesel engine exhaust (DEE) has been found to induce allergic responses, promote inflammation and possibly contribute to the development of AIDs. However, few studies have investigated this relationship, despite DEE being a common exposure in the occupational and environmental setting. In spite of mounting evidence documenting the harmful potential of DEE, there are currently no legal exposure limits in Australian workplaces.

Aims: To determine an association between DEE exposure and various AIDs. To estimate the potential dose-response relationship of RCS and DEE exposure and various AIDs.

Methods: This study uses data from a large modern-day mining cohort (N=153,922) in Western Australia. Data collected on subjects includes comprehensive health, demographic, smoking and employment information, with further access to linked administrative health data to identify AIDs cases. Additionally, quantitative personal estimates of cumulative RCS and DEE exposure were calculated for the entire cohort, most of whom were employed during an era of improved mining practices and decreasing exposure concentrations. Using a nested case-control study design, prevalent cases are age, sex and period matched to eligible controls from the entire cohort. Analyses include the construction of multivariate conditional logistic regression models to produce exposure odds ratios for DEE and RCS. 

Results: Data analysis is ongoing with results anticipated in August.