ABSTRACT
Count data regression Methods The measure of self-assessed health used in previous chapters is an example of an ordered categorical variable. For convenience this was coded as y = 0, 1, 2..., but these numerical values are arbitrary. Count data regression applies to dependent variables coded in the same way, where the values are meaningful in themselves; in other words, where the dependent variable represents a count of events. Common examples in health economics include measures of healthcare utilisation, such as the number of times an individual visits their GP during a given period, or the number of prescriptions dispensed to an individual. Count data regression is appropriate when the dependent variable is a non-negative integer valued count, y = 0, 1, 2..., where y is measured in natural units on a fixed scale. Typically, count data regression is applied when the distribution of the dependent variable is skewed. The data will usually contain a large proportion of zero observations, for example those who make no use of healthcare during the survey period, as well as a long right-hand tail of individuals who make particularly heavy use of healthcare.
