Our injury epidemiologists focus on defining the nature and magnitude of injuries across populations. They develop accurate estimates of the severity of hazards, define risk factors for injury, and provide tools (such as surveillance systems) for identifying hazards and evaluating the effectiveness of interventions. CIRP's epidemiology research methods and biostatistics training program offers undergraduate and graduate students the opportunity to participate in pediatric injury research projects.
The bulk of our current epidemiology research methods and biostatistics training program efforts are devoted to preventing motor vehicle crashes and resulting injuries by:
- Identifying populations at particularly high risk for injury and determining environmental or social causes underlying these risks
- Helping to develop improved safety designs and technologies
- Increasing awareness and compliance with proper restraint practices
- Evaluating interventions
- Adopting an evidence-based approach to teaching teens to become safe, responsible drivers
Student Responsibilities for CIRP's Epidemiology Research Methods and Biostatistics Training Program:
- Attend weekly project meetings
- Attend regular mentorship meetings (as agreed upon with supervisor)
- Assist in a variety of study-related tasks, including: Institutional Review Board (IRB) submissions, survey and database development, data collection and management, survey administration, data analyses, and manuscript and poster preparation
- Present findings of research activities to CIRP
- Perform other assigned responsibilities as appropriate
Necessary Qualifications CIRP's Epidemiology Research Methods and Biostatistics Training Program::
- Students must be enrolled in or have completed an undergraduate or graduate training program.
- Typical studies include Epidemiology, Public Health, Medicine, Behavioral Science, Psychology, and others leading to health professions.
- Though not required, knowledge of and prior experience with a statistical package (SAS, SPSS, or Stata) is strongly preferred.