title: How to account for categorical variables in calculating a risk score from regression model?
body: <p>I have a data set which has a number of variables that I'd like to use to generate a risk score for getting a disease.</p>
  
  <p>I have created a basic version of what I'm trying to do.</p>
  
  <p>The dataset looks like this:</p>
  
  <pre><code>ID  DISEASE_STATUS  AGE  SEX  LOCATION
  1   1               20   1    FRANCE
  2   0               22   1    GERMANY
  3   0               24   0    ITALY
  4   1               20   1    GERMANY
  5   1               20   0    ITALY
  </code></pre>
  
  <p>So the model I ran was:</p>
  
  <pre><code>glm(disease_status ~ age + sex + location, data=data, family=binomial(link='logit'))
  </code></pre>
  
  <p>The beta values produced by this model were as follows:</p>
  
  <pre><code>bage = −0.193
  bsex = −0.0497
  blocation= 1.344
  </code></pre>
  
  <p>To produce a risk score, I want to multiply the values for each individual by the beta values, eg:</p>
  
  <pre><code>risk score = (-0.193 * 20 (age)) + (-0.0497 * 1 (sex)) + (1.344 * ??? (location))
  </code></pre>
  
  <p>However, what value would I use to multiply the beta score for location by?</p>
  
  <p>Thank you!</p>