Description: Focused on measuring exposure based on where and how people spend time to understand health effects, including intermittent and abrupt-onset events, common in injury epidemiology. Emphasis on the temporal-spatial scale relevant to a given exposure-outcome question. Teaches coding to analyze data to test hypotheses while avoiding pitfalls including autocorrelation.
Learning Objectives: Recall the features and strategy of Rothman's sufficient cause model.
Differentiate early and late occurring risk factors for a given injury or disease.
Classify the induction period for an exposure as it related to occurrence of an injury or disease.
Classify data collection methods according to their suitability for a given exposure-outcome relation.
Recognize which data collection methods (eg, realtime monitoring via wearable devices; responses to prompts received on smartphone in ecologic momentary assessment (EMA)) are feasible for a given research question and epidemiologic study design.
Learn basic steps to collect data and learning coding to clean and analyze data that have temporal and spatial components (in R, SAS, or Stata).
List the dimensions of Haddon's Matrix.
Populate Haddon's Matrix for a given injury or disease prevention effort.
