Recently, an important observation came my way when I was rerunning one of my many algorithms in a new data set.
These algorithms are fairly basic–well, at least to me they seem that way–and are done, and can be done in several different analysis settings. I have used them in SQL and SAS environments often, but I invented and discovered some of the math associations in IDRISI. Now, exposed to Cognos BI, I am looking at another way to run these valuable epidemiology research tools.
The Tableau environment is not so good a tool for this work that I do. I am seeing the same limitations with Cognos BI. They allow basic stuff to be done, but the efforts needed to produce a more complex formula are some times–a successful way to test the programmer’s patience and tolerance for over advertised tools, especially when they do little more than the tools already in use.
As I have mentioned before, the production of a 3D algorithm for converting any data to 3D cartographic imagery requires a little time to plan out the process, find the numbers, make new numbers to basemap with, and then the time, patience and effort needed to make your maps (as raster-like images that is).
Most recently, I reran my formula demonstrating the unique assymmetry that can be found between male and female carriers. This assymmetry tells us that women live long than men in they carry this mysterious disease. But more importantly, it tells us that women live well into and through their reproductive years. This means they have the opportunities to potentially bring the gene that causes this disease into the next generation.
Men don’t have the luxury of surviving well into and through their theoretically sexually active years, 30-45. They start dying off in their late 20s. Yet, why are they dying? After all, they are just carriers of the disease, right?
I have no answer to this question, but continue to contemplate its ontogenic nature. Why did nature make it possible for women to bring that gene on for generation after generation, yet result in men simply dying off for being a carrier?
There is that evolutionary reason we were taught about this disease. It remains because a mosquito attempting to take the blood of a carrier of this disease will die off once the red blood cells take on the deadly in that mosquito’s gut. (This mechanism is similar to how Baccilus thurnbergiensis kills mosquitoes, a natural bacterium used to treat potential west nile carrier areas).
But the main point here is that one would think the bizarre shape the population pyramid of patients with this very unique disease takes on. For me to duplicate it with thousands more cases in a very complete different source of medical data was astounding to see. It was the first take I took on once I got my new system operating, in some new places. When I saw the exact same outcomes of years prior, that told me many of the findings produced earlier on a 100M plus population are reliable. They can add value to my background and knowledge based for evaluating the population health work.
So, back to the algorithm. It doesn’t need GIS, and too much time has to be spent on GIS to produce what my algorithm effectively produces, for the nation, reviewing more than 100M patients, for a period of about 5 to 20 minutes. You cannot do that in GIS–produce a map from just thousands of numbers in a three column table, a skill which my algorithm performs quite gracefully.