Those reports are old, and we rely on the cohort characterization for that type of function. To use it, you create features (here are 2 exaples for prevalence and distribution types (you can’t mix them in the same feature definition):
Once they are defined, you can apply them to a characterization, which I show here.
Note: when using criteria features this way, you can only choose one ‘idea’ of a measurement at a time. Ie: it does not take every distinct measurement in the measurement table and produce one row per measurement. However, there is a ‘preset’ measurement characterization that I included in the characterization: Measurements Long Term, which will give you the identity and prevalence of all measurements found in the cohort. From this, if you want to make a distribution, you can create a distribution feature for each of the measurements you want to capture, but the downside to this is that you have to create a concept set for each type of measurement you want to calculate.