  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.
