HEP444 Module 7 BRFSS Data Analysis

Compiling data from the BFRSS was easy and interesting. The steps provided in the assignment guided me well through the entire process. Since we had also learned how to compute odds ratio via openepi.com, this was also not challenging. The whole process was comprehensive and interesting.

I believe that the stratification of epidemiologic data into demographic characteristics is very important. This is because it familiarizes the researcher with how key demographic variables and patterns in the data are distributed. Evaluating and controlling confounding (confusion) are the key motivations for data stratification. Examples of demographic characteristics include age and gender. Stratification also minimizes error propagation. If for example, someone made an error representing a number of individuals in a certain age bracket, it is only that stratum that will have the error. All other stratums will be error-free.

A while ago, most individuals did not appreciate the role that fitness professionals play in public health. From the viewpoint of the fitness sector, there is a rising feeling of confidence over their ability to tackle this important public health issue. Getting enough consistent exercise throughout one’s life is now widely accepted as having several health benefits. To promote global health, we must start merging the fitness business with the healthcare industry. This realization is what sparked my interest in epidemiology. As a wellness and fitness trainer, I require knowledge of how diseases are caused and distributed.