As I indicated earlier, public health surveillance encompasses data collection for infectious and chronic diseases, injuries, environmental and occupational exposures, as well as personal behaviors that promote health and prevent disease. Throughout the remainder of my presentation, I will use examples from all these areas to demonstrate that the data analytic and summary techniques discussed are not disease specific but instead can be applied to surveillance data from a variety of health conditions, exposures, and behaviors within public health. Throughout the remainder of my presentation, the terms “disease”, “health condition”, “exposure”, and “health behaviors” can be used interchangeably when discussing surveillance data.

First, let’s talk about data analyses. Analysis of surveillance data often begins by summarizing it according to person, place, and time. That is, looking for patterns of disease or health outcomes among different populations, in different places, at different times. Identifying who got sick, where did they went prior to and while sick, and when and for what duration they were sick will tell us valuable information about the types of disease control and prevention efforts we need to undertake, and who we should target in order to most efficiently prevent, if not, limit, the spread of disease.

Examining surveillance data focused on health promotion and disease prevention behaviors for trends is also important. For example, knowing about patterns of vaccination coverage helps us understand why cases of disease are declining, while patterns of caloric intake and exercise help explain changing trends in the prevalence of obesity.