Relationships between health conditions - Spearman's correlation? I am examining the relationships between health issues in pigs. I want to know which conditions tend to co-occur. All animals have at least one condition and 5 at most. I have coded each condition in SPSS as  0 = absent, 1 = present. How should I examine the relationships between these? I have tried a Spearman's correlation but something is not right, maybe I have organised the data in the wrong way.  
This is an example of how I have organised the data:
Pig ID  Tail lesions    Lameness    Bursitis
1            1              0           0
2            0              1           0
3            0              0           1

When I am looking at the output in SPSS I can't tell if/how these conditions are related to each other when I carry out a Spearman's correlation.       

 A: Peter's answer has addressed the technical part. For SPSS, you can do a few things to improve the readability of the output:
First, go to Edit > Options > Pivot Tables, and specify the output table to be in Academic format:

Academic format inserts some horizontal lines into the table and is generally more compact than the default SPSS output table. It'll make reading your matrix more enjoyable.
Second, because your sample size seems to be 171 with one missing in one variable, you can exclude that case and keep the sample size at 170. With that decision, you can simplify the output by clicking the Options button at the bivariate panel, and check the listwise option:

This should give a prettier table with correlation in the first row and p-value in the second.

To better visualize a correlation matrix, there are some tricks as well.
First, generate a matrix that doesn't have any significant correlation flagged by unchecking the flagging option:

Copy and paste your output table to MS Excel. Now, hold down your "Ctrl" key and highlight the cells that contain correlation coefficients. Do not highlight the diagonal cells because they are all 1.0 by default and are going to make visualizing harder:

Now, apply conditional formatting to give different gradients of color to the coefficient cells, that way you should be able to spot some pattern quite quickly:

A: If you just want to see which ones co-occur, and you don't have a huge number of conditions, relative to the number of pigs, you can make a matrix, with conditions in both rows and columns and the cells filled with "number co-occurences". Something like
                 tail lesions lameness bursitis
tail lesions        X            2       1  
lameness            X            X       3
bursitis            X            X       X

You would only need the upper triangle of the matrix (or lower triangle). 
Whether you should include pigs that had no conditions depends on what you are trying to find out, but I'd guess you don't need to include them; in the above matrix, they would add nothing. However, you might want to include them in the denominator. E.g. you could say
Conditions 1 and 2 occurred together in 5 pigs, which is 12% of all pigs that had any condition, and 2% of all pigs in the sample. 
What else you might do also depends on what you want; possibly cluster analysis or factor analysis or even multidimensional scaling. But the above might be enough.
