Imagine for a moment that your are the principal investigator of a recurring outbreak problem or that your are put in change of surveillance for an incoming disease from an international border, or that you are a Director managing a managed care plan, or a CEO trying to make your business provide other companies with a unique service of generating reports of those companies collections of Big Data.

Hundreds to one thousand or more companies in the U.S. right now provide these kinds of services.

As the person overseeing the offering of your company, you get these reports generated on a very regular basis about its findings or progress.  You usually get these reports on a weekly basis if not better.

(The truth is, that in general, that is really pushing it, and very unrealistic!)

You would like to be able to receive daily updates due to changes taking place at one section of these international borders.

So, you request a report for that part of the country, and the analyst in charge of producing it has determined that there are at least 45 diseases that have to be consistently monitored on a daily basis.

The first questions you have are:

  • how long will it take to produce each of these individual disease reports?
  • how many people will this project entail?

Upon closer inspection you find an equal number of allied diseases and other cultural features that could be monitored for the specific boundary in question.  After a few more hours, you uncover a total of 115 metrics that could be used to specifically focus upon penetration of the boundary by infectious diseases + physically and culturally diseases associated with these primary health + another listing of conditions, behaviors, emergency reports or public health codes that can be used to monitor events across that specific border.

The next series of questions becomes:

  • how do you report the 115 major metrics?
  • how many pages do you thing that report will consist of?
  • how long will it take to generate such a report of 115 metrics for one specific cultural or place defined feature?

The old style of report generating could in its worst form make this a one disease per page version and comprise 115+ pages, with some introduction, methodology and conclusion sections adding another 30 pages, meaning it would be about 150 pages in length, printed out and handed in daily, at a rate of 750 pp/week, 3000pp/mo, 25k-30k pp/year.

Is this method of generating a surveillance report cost effective?

Of course not, so you opt for an electronic version instead, meaning the individual receiving it will, first, never review the entire document, second, review summary sections and important graphs and figures, and their, learn what to focus on for the daily review, meaning only 10-20 pages total are essential, but which always need to other 130-140 related pages so the manager can always back track when needed.

Now, if you used NPHG to do this same report, you could produce an Atlas of disease patterns consisting of 3-5 pages of data, and 6-10 pages of reporting, and 5-10 pp of summary, with a 1 page summary at the beginning; and the optional 15 page appendix with yesterday’s data and a last week or two summary of events in graph and picture form.  This changes the 150 page document that requires an hour or two of perusal, if you have the time, into a 15-25 page document with all 115 of you diseases defined for you, in a format that enables you to place it side by side to yesterday’s report for immediate review.

The secret to its success is placing your results on a highly detailed map, instead of lengthy lists of places and numbers, in descending and alphabetical orders, detailing the medical data.  Its accessibility on the web only adds to this valuable product, for it enables you to go back in time any way that you want, looking at any metric that you want.

The first scenario with the 150 to 300 page report is what is being done right now by most PBMs and Insurance Agencies.  Even when they compress their reports into fewer pages, they still either require hundreds of pages to analyze 100s of metrics, or have to pick the top 15 or 25 each time the report is generated, not by the day however, but instead by the week.  The decade old technology being used to produce these reports, and the lack of sufficient knowledge right now with faster, more effective ways to produce reports on Big Data, is why these industries are so far behind.

My NPHG method surpasses these older talents considerably, and represents what in general we refer to as a form of “Disruptive Technology.”

Why use the older system when with NPHG you can produce complete reports, in more details than those currently in use, across larger spans of place, time and data mining outcomes.

NPHG is currently the only way to do this at a high enough speed to be be used for daily reporting.  In even better situations, we can run half and quarter day reporting at small area levels, if live data sources were available.    That is the benefits of knowing and using a highly effective spatial reporting tool like NPHG.

Now in its embryonic state in terms of being applied at the industry level, NPHG is years ahead in terms of the knowledge base and skill sets used to produce the current maps out there being generated for reporting medical, disease, episurveillance data.  NPHG allows for daily reporting, not yearly reporting like most businesses tend to be engaged in.


Examples of this method of evaluating populations are presented with this section.

The bulk of my productivity is presented here.

The results 3D mapping produces have a number of important features to be understood before using this method.

First, error is always a problem with any sort of statistical work.  The datasets used for this project are for the most part very reliable, due primarily to their size and sources.  However, rarer conditions have a tendency to produce uneven findings and results due to any number of clerk, practitioner, data entry methods and practices, recording, reporting systems, software, hardware and technological errors.

Second, as a follow-up to the first item, the more common a condition is, the more reliable the outcomes are that were posted.

Third, the most common diseases, conditions, etc. such as diabetes, rheumatism, obesity, hypertension, are not reviewed using this method.  These ICDs tend to demonstrate very well documented behaviors.  The purpose of this project is to focus upon normally overlooked issues and conditions.  Applying this method to analyzing extremely common conditions produces some fairly generic results, due to the tendency for very common diseases to rely heavily upon national population density features, and less upon environmental and ecological specifics.


The topics to be covered on the pages linked to this page are as follows:

  • Childhood Immunization
  • Environmental Disease Clusters
  • Ecological Disease Clusters
  • Sociocultural Clusters
  • Socioeconomic Clusters
  • Human Behavior
  • Homeland Security
  • Bioterrorism
  • Occupational Lung Disease
  • Parent Child Relationships
  • Suicide
  • Cost
  • Florida
  • Health Scores