Quantitative Exposure Assessment is now taught every fall.  It is required for PhD students in OEH, and we do recommend our MS Industrial Hygiene students take the first module (1 credit) to help integrate what they have learned throughout the program to interpret expsoure data and make decisions on what to do.  It is recommended for all graduate students enrolled in one of the many Occupational and Environmental Health Department degree programs.  Other students interested in the course should review the prerequisites listed in the syllabus and meet with the instructor prior to enrolling.

The goal of this course is to build a framework for students to synthesize exposure data and learn how to process information to make health-related decisions.  Previous iterations of the course have focused on occupational exposure data, but environmental data are also suitable for the analytical methods covered in this course.  Students are required to analyze a set of exposure data, either data generated from their own research or from data sets available to them (through the literature, publicly available databases, or other faculty data).  Actual exposure measurements are needed (not dichotomous or "yes/no" data).  The course is structured to allow students to use a variety of analytical tools to process data and interpret results.  Tools used in class will  incorporate Excel and SAS software to complete exercises.  Instructors will be available to mentor students in the use of SAS outside of the classroom hours.

The course is organized into three modules:

• Descriptive statistics and data distributions (1-credit covers this)
• Testing data:  Analyses of variance and determinants of exposure
• Risk Assessment (including Monte Carlo techniques) and advanced analysis, based on projects being undertaken by those in class.

Students will present homework and discuss articles at the end of each module (15%), complete a take-home exam (35%), and complete a self-driven project that analyzes a set of data using techniques learned in the class (50%).   Students are recommended to use their thesis/dissertation data thesis research data for the course project.  However, agreements between student, instructor, and research advisor is needed to ensure everyone is aware that this data may be used to meet multiple educational requirements for the student.

A detailed example syllabus is provided here:  /media/91 

In 2017, alumni were surveyed about their use of statistical methods in their current job.  Several identified this course as useful to apply statistical methods to evaluate data in the field.  A MS IH student commented that "Quantitative Exposure Assessment was very helpful in that we had practice applying statistical methods to real data sets.  Most of the examples used in other statistics courses were "simplified" in order to demonstrate concepts.  This is helpful but when confronted with more complex "real-world" data it is harder to understand how to apply those concepts."   Here is the survey results: /media/116