This story is pretty easy to follow. Life expectancy varies by income level. The story becomes a good example for a statistics class because in the interview, the researcher describes a multivariate model. One in which multiple different independent variables (drug use, medical insurance, smoking, income, etc.) could be used to explain the disparity the exists in lifespan between people with different incomes.
As such, this story could be used as an example of multivariate regression. And The Third Variable Problem. And why correlation isn't enough.
In particular, this part of the interview (between interviewer Ari Shapiro and researcher Gary Burtless) refers to the underlying data as well as the Third Variable Problem as well as the amount to variability that can be assigned to the independent variables he lists).
I also like this example because it can be used to open your students minds up with how to study specific hypotheses after a result is known. Alright, a correlation has demonstrated this disparity. Why? How can we study this better? The author presents a list of factors that have already been examined. What else could there be?
You could also talk about how to model this data to allow for moderators. The interviewer says that health insurance helps offset this disparity. But does it effect everyone equally? Perhaps number of cigarettes smoked per day moderates this relationship, such that health insurance narrows the disparity with non-smokers but doesn't assist smokers.