Observed gonorrhea case rates (number of positive tests per 100,000 individuals) increased by 75 percent in the United States between 2009 and 2017, predominantly among men. However, testing recommendations by the Centers for Disease Control and Prevention (CDC) have also changed over this period with more frequent screening for sexually transmitted infections (STIs) recommended among men who have sex with men (MSM) who are sexually active. In this and similar disease surveillance settings, a common question is whether observed increases in the overall proportion of positive tests over time is due only to increased testing of diseased individuals, increased underlying disease or both. By placing this problem within a counterfactual framework, we can carefully consider untestable assumptions under which this question may be answered and, in turn, a principled approach to statistical analysis. This report outlines this thought process.