Exponent’s multidisciplinary team of epidemiologists, biostatisticians, scientists and engineers bring expertise in health and safety programming, ergonomics, human factors, and industrial engineering leading to innovative approaches to identify, evaluate and prevent injuries.
Injury epidemiology is the characterization of injury occurrence, the identification of risk factors and the strength of effect of those factors, as well as potentially protective factors related to the development and evaluation of injury prevention strategies and programs. Injuries can occur in every environment from homes to the workplace, recreational settings including sports settings, and to transportation settings between these environments. Many injury interventions are already in place (e.g., transportation requirements such as setting speed limits, safe automobile design, seatbelt and other safety restraint use, helmet and protective equipment use, workplace safety program implementation, and ergonomic design) and have achieved significant public health improvements including reduction of injury occurrence.
Exponent's services include:
- Occupational/workplace injury evaluation
- Traffic safety research
- Sports injury research
- Ergonomic evaluations
- Occupational health & safety assessment
- Product safety evaluation
Our team has expertise in creating and implementing optimal study designs to answer injury-related questions with efficiency and an eye towards future research and prevention needs. Strong study design requires expert knowledge of data management and utilization. Exponent scientists have experience in designing and maintaining injury surveillance systems; for example, workplace surveillance data systems which can be used to analyze injury trends specific to occupational environments. Exponent scientists are well-versed in the use of OSHA and BLS databases and also have experience using numerous surveillance databases such as traffic surveillance databases (e.g., FARS, NASS GES, NASS, CDS, and state motor vehicle crash datasets).