Computing medical adherence is mostly based on medical claims data, hold by the medical insurers or management services. Patients with a certain disease (e.g. Type2 diabetes) are identified based on their diagnosis claims (ICD codes). Looking at related prescription claims, the adherence to a specific drug or drug-class can be calculated through the proportion of days covered (pdc).
We generated a python based programming solution, computing pdc medical adherence on a patient basis, per drug and per year. The data is plotted using the python library bokeh – also allowing for the generation of interactive displays (bokeh server). Some outputs are shown below.
Further, python bokeh is employed for producing a web-based interactive map output, illustrating aggregated medical adherence data (pdc) per drug-class on a census unit level: The application below enables the user to select a specific drug-class, and setting the year of analysis with the time-slider.
Zoom into the feature map is possible, and census units reveal feature information when hovering over.