Revisiting the story

In the last post, early April, I described some preliminary thinking about twin proposals for a Virtual Medical Observatory.   Well, we decided to winnow the ideas into one proposal--and we await the decisions on that front (probably several months from now).

Meanwhile, our Bucky Beaver team cannot sit still.   We took the Chronic Illness idea on, and chased it to an interesting point.   Turns out that CDC has a host of major chronic illnesses 'on-line' as choropleth maps (well, not perfectly designed choropleth maps, but certainly maps built along that line.  Did I ever define choropleths within this blog, especially the issues involved in their proper design and display?

Ahh, choropleths--a fancy word, to be sure, but an important one for cartographers, demographers, or epidemiologists.   Here's the Wikipedia defiinition: A choropleth map (from Ancient Greek χῶρος (khôros) 'area, region', and πλῆθος (plêthos) 'multitude') is a type of statistical thematic map that uses pseudocolor, meaning color corresponding with an aggregate summary of a geographic characteristic within spatial enumeration units, such as population density or per-capita income.

Choropleth maps provide an easy way to visualize how a variable varies across a geographic area or show the level of variability within a region. A heat map or isarithmic map is similar but uses regions drawn according to the pattern of the variable, rather than the a priori geographic areas of choropleth maps. The choropleth is likely the most common type of thematic map because published statistical data (from government or other sources) is generally aggregated into well-known geographic units, such as countries, states, provinces, and counties, and thus they are relatively easy to create using GIS, spreadsheets, or other software tools.

I authored a descriptive essay to illustrate key features for choropleths using our COVID maps.  See.  https://www.researchgate.net/publication/377777227_Choropleth_design.   

Well, the point of this discussion is to say that we have adopted the COVID mapping models for a slew of chronic diseases.  29 of them, yup!   Here is the first view, showing all tractable illnesses on the left and a COPD choropleth map for California, Oregon, and Colorado (including ranked counties @ right)

Another choice is to compare two diseases for the same geographies.  Here, for example, we show the same states for COPD and Diabetes.   Note that Colorado and Oregon have much lower Diabetes rates than COPD rates, while counties in Southern California climb.  

Imagine this kind of comparative illustration for the panoply of chronic diseases, mapped to the granularity of counties for every nation on earth, able to be time-sequenced so that disease and treatment efficacy can be longitudinally evaluated.   This is the dream of the Worldwide Medical Observatory.