@hlsmith, thanks for the question, which is not so easy to asnwer in few words.

CA is a dimesionality reduction technique used for cross-tabulation.

Essentially, it allows you to display the deviation from independence by displaying the rows and columns of a cross-tab in a low-dimensional space. The spread of point out from the origin is related to the amount of variability in the table (i.e., the "discrepancy" between expected and observed counts).

By and large, row points (diseases) close together feature a similar proportion of column categories (and viceversa). Again by and large, the chart indicates an opposition between diseases more "related" to the "none" category (right-hand side) and diseases that are more "related" to the highest dose of tabacco (left-hand side). As you can see, the first (horizontal) dimension is capturing 80% of the data variability.

Think of CA as a sort of PCA tailored for categorical data.

Hope this helps

Best

Gm

p.s.

a better description in literature (of course) and in my website (link alrteady provided)