Human Exposure, Epidemiology & Data Science
IOM’s Human Exposure Scientists have in recent years been involved in pioneering work to advance the next level of knowledge about exposure to hazardous agents via other experimental and modelling approaches. IOM research also includes work on methods for characterising the exposome, a fast developing research paradigm which encompasses all exposures from environmental and occupational sources in an individual’s lifetime and how they relate to health. Exposome research is a multidisciplinary field which includes use of novel methods of measurement, such as sensors, -omics technologies, bioinformatics, and exposure and life course modelling.
Our exposure assessment work is underpinned by a wide range of environmental modelling tools and chemical, mineral and toxicity analysis techniques.
IOM has a strong reputation in the field of occupational and environmental epidemiology, and plays a prominent role in many of the ICOH, EPICOH and BIOSS membership associations. Our epidemiological investigations help in understanding the effect of environmental and workplace exposures on health. Our pneumoconiosis field research study (PFR) was instrumental in identifying coalworkers’ pneumoconiosis. We have also carried out studies in the following industries: lead, hardmetal, rubber, polyvinyl chloride, hard rock quarries, coke oven and many more.
We recently organised and hosted the 26th International Symposium on Epidemiology in Occupational Health (EPICOH 2017) at the Edinburgh International Conference Centre between the 28th and 31st August 2017.
IOM employs highly skilled data scientists for information capture and analysis across its full range of scientific work. Such skills are now more crucial than ever: The rapidly changing built environment and world of work sees increasing fusion with technology e.g. – ubiquitous use of mobile devices and applications, sensors for monitoring and data capture, the Internet of Things, omics, Big Data, together with the advent of practical artificial intelligence and machine learning applications within innumerable occupational and environmental settings. IOM’s Data Science team analyses and models such data to gain unique insight and extract valuable information and knowledge from our research.