Example 1 We examine the association between gender (x) and the number of online healthcare visits per year (y) by means of a simple Poisson regression analysis. Gender has the values 0=Man and 1=Woman, whereas the number of online healthcare visits ranges between 0 and 25. The IRR we get is 1.72. This would mean that women have a higher rate of online healthcare visits per year in comparison to men.
Example 2 In this study, the association between employment status (x) and the number of coffee cups consumed per day (y) is examined. Employment status is coded as 0=Unemployed and 1=Employed. The number of coffee cups consumed per day ranges between 0 and 15. We get an IRR of 0.67. In other words, employed individuals have a lower rate of coffee cups consumed per days as compared to unemployed individuals.
Practical example
Dataset
StataData1.dta
Variable name
children
Variable label
Number of children (Age 40, Year 2010)
Value labels
N/A
Variable name
sex
Variable label
Sex
Value labels
0=Man 1=Woman
sum children sex if pop_poisson==1
poisson children sex if pop_poisson==1, irr
When we look at the results for sex, we see that the incidence rate ratio (IRR) is 1.32. Thus, one unit increase in sex is associated with a higher rate of children. This means that women have a rate of children that is 1.32 times higher compared to that of men.
The association between sex and children is statistically significant, as reflected in the p-value (0.000) and the 95% confidence intervals (1.28-1.37).
Summary Women have a statistically significantly higher rate of children, compared to men (IRR=1.32; 95% CI=1.28-1.37).
