My 2010 SAS spatial analysis 3D map -video of the U.S., predicting the hotspots for outbreaks due to lack of childhood immunization (posted numerous times since my study).  This technology combined a grid mapping algorithm I developed with a 3D modeling method that bypasses the need for SAS-GIS and other traditional GIS tools, considered costly by managed care companies and healthcare agencies or institutions.

We are in the midst of a crisis in managed care.  This crisis is due to the major  discrepancies in the definition of healthcare needs and the policies developed by the White House, by Congress, and by insurance companies, regarding how to best monitor local and national health and determine where changes need to be made.

The first reason for this current state of failure we are in is the inability of any one business, agency, institution, program or leading healthcare institution to effectively monitor the long term health of their patients, and thereby define the terms for future performance of this program.   If we refer for a moment to the 100th Monkey Effect, which states that once one business starts to engage in employing an innovative discovery, others will follow (see ).  The practice of health care is a culture that has become stagnant in recent decades.

Since  Feb. 17, 1971 (Pres. Richard Nixon, and Edgar Kaiser), the official definition of improved health care and ‘better managed care’ has been in the minds and speeches of numerous politicians.  None have made a progressive change that has endured and resulted in long lasting care, that improves over the years along with patient and population health.

This legislation plan was developed in response to the same barriers to effective healthcare that exist today, namely long surviving programs that still exist today, like Blue Cross Blue Shield (see CSPAN. 1971. Birth of the HMO.  Large healthcare insurance industries (most still existing today) were the first perpetrators of this prejudicial treatment, which specifically targeted the patients born with an illness or birth defect, the young and potentially lifetime disabled, the poor unable to afford a healthcare path to improved health, or the aged and ill who cost more and more each year to keep alive and healthy.

Surveillance of a local genomic condition

The result of this attitude about the sick is that the sick, and those who are healthy and not yet sick, can undergo numerous changes in healthcare coverage.  We are thus left with a population that we can primarily monitor on a year by year basis.  Few patients can be monitored for their entire life experience with healthcare.  This means prediction models have to be developed in a fairly segmented way.

Today, we rely upon population level historical health care data to evaluate groups of peoples with similar disease experiences.  We try to employ the group data for developing prediction models about the next patients future care may become, and more importantly, what forms of care will they need and what costs may be incurred?

To date, prediction modeling has also failed to implement several forms of new technology and techniques related to spatial analyses.  Zip code related analyses are often mentioned, but true spatial analysis and the development of spatial risk models (maps of risk, cause and effect, cost, etc) are lacking from the system.  Grid modeling relies upon square grids with 43% spatial uncertainty error, versus hexagonal grids which only produce 17% spatial uncertainty error (see my hexagonal grids pages).

Institutions are now barely capable of mapping their entire patient population, at the detailed level (points, not zip codes).  To spatially surveil and analyze their patients, they have to implement a system wide approach to GIS utilization and development.  It is possible to map all of that data obtained from EMR and better target care, better target the changes needed, and target and monitor specific audiences and people who are most in need of change.

The following technologies applicable to health surveillance were developed 10 and 17 years ago (two creations: grid mapping health, and then 3D mapping the health care population’s status).  This first posting details applications of GIS to surveillance for biological and ecological diseases; the next posting will be focused on culture, human behavior and healthcare concepts.


Disease Ecology 

The most traditional use of GIS for disease analysis is perhaps Disease Ecology related, or the evaluation of diseases linked to the environmental setting.  In recent months, the best example of this disease type is the Zika virus brought into the periurban settings of in-migration towns and cities.  Prior to Zika, there were the Chikungunya and West Nile disease, which like the Zika can be carried and introduced to humans via the mosquito vector.  The following is one of the first 3D rotating maps I produced of the US devoted to these ecological disease patterns.  It is based upon a programming tool I developed for use by systems that lack, cannot afford, or will not invest in a GIS.

An example of some early Ecological Disease mapping I applied my formulas to.  This is a zip code case count map with 3D used to display the number of cases for each long-lat linked to a zip code.  It was also used to experiment with the use of the rotating imagery, tilt and video generation protocols and formulas that I developed, to produce 3d mapping videos for every condition, finding, case, etc. that was analyzed, as part of a regular reporting tool.  This tool produced 24,000 or more maps per day, with 700 to 1000 figures used to produce each 360 degree spin of the United States.

Now it ends up, this methodology works many times faster than your standard GIS system.  At peak performance for one company, I was able to produce thousands of results per day; but mind you, this was produced using a Terradata workstation, analyzing between 40 million and 90 million (120 million some say) patients’ medical records (not claims) nationally; it took 20 minutes to run each program; an hour to perform a quality review (not including rerun if need be) and convert the outcomes into a video.

Hot Spots

Are there places where particular diseases appear to produce cases and/or exist in isolation from other potentially high risk populations?  Several such diseases were uncovered by mapping the majority of ICDs known to be rare in their distribution.  Louis Bar sydnrome (above) was one such example.  Hereditary Choroid Dystrophy of Children was another, and Werdung Hofmann Spinal Degradation the third with a highly localized dense disease pattern.

Hereditary Choroid Dystrophy

Werdung Hoffman Spinal Degeneration



Years ago, after reviewing most of the ICDs for my US patient population, and producing maps of these diseases related to specific environments, parts of the world and climates, I defined four types of relationships that exist between disease and culture.

The first is the cultural ecological link, resulting in cultural tendencies to be exposed to common environmental infectious, organismal, and zoonotic disease patterns.

An organismal disease

The second are culturally-bound–they are conditions, usually psychological, that exist mostly because the cultural way of being and thinking think they exist.   They are most often treated for based upon the western medicine psychiatric diagnoses assigned.

The third kind of disease or condition linked to culture are cultural human behavior types, which related to foodways, personal cleaning habits, person living habits and behaviors based of socially predefined expectations.  Infibulation is the best example of this–it is neither necessary nor essential to life, but important to [sometimes] personal, social and community life requirements, and often has negative health related effects linked to infection, promiscuity problems, and relationship/family maintenance behaviors.

The Muslim practice of Infibulation or Female Genital Mutilation, based upon an N-N2 technique later described on this page. 

The third type are also commonly related to western culture diseases like diabetes and heart disease.  It is possible that some cases of Epilepsy, due to their similarities with other cultural-bound syndromes, may have cultural influences and in some cases be both physiological and western-culturally influenced in their patterns.

The fourth pattern of diseases related to cultural are culturally-linked, and are somehow physically linked to a culturally defined group of people, like a culture and genomic conditions, are as follows.  Each has possible genetic or genomic related causes, which are somehow exacerbated or facilitated in their genomic expression, due to cultural and sociocultural behavioral patterns.:

Takayasu is a congenital-, developmental-, and probably genomic-related disease linked to the maldevelopment (significant narrowing or stricture development) of blood vessels by the heart.  The genomic and epigenomic aspects of this condition have not been explored.  This condition is most linked to Hawaiian borne families, but may have other international genomic characteristics as well.  This spatial model used zip code counts, suggesting in-migration regions where people from this cultural group, with this condition, tend to aggregate.


Obscure African Cardiomyopathy is a heart condition that is frequent missed and/or uncoded in the patients’ records.  The underlying problem, cardiomyopathy, already has a specific code that may be linked to claims for billing purposes.  The related mostly African condition is therefore not entered in many places, since to specific billing actions may be linked just to this condition.  It is therefore detectable by the nature and amount of care provided to these patients, which hints at two potential Spatial Analysis Epidemiology research questions:  do African Americans with this condition have specific genomic patterns? and do they receive a different amount and form of care unknowingly than African Americans with traditional cardiomyopathy onset, linked to the more common heart disease conditions throughout our population?


Until recently, with the development of more useful EMRs, the limiter to developing decent spatial models has been the limits of the availability of useful data, the knowledge of its potential users, and especiually the knowledge of the value of these uses by upper level management.

In recent years, managed care programs have turned to looking at ways to develop prediction models, for cost modeling and population health modeling purposes. The development of GIS or geographic information systems has made it possible for place to be related to illness, leaving it up to the observers of any findings to add place and spatial analysis techniques to their review if the cause and effect of health and disease.  In the genomic world of study, we define the causes for diseases within individuals upon their genetic make up.   The counterapproach to this is using an epigenomics approach, where we look at how objects and events that occur outside the body relate in turn to the body itself (focusing on its genetics that are produced due to these “environmental stimuli”).  These features are then related to disease, disease producing (inducing) living patterns, disease producing (inducing) behaviors, or the initiation of a disease development process through stimulating the expression of a gene.

In healthcare, GIS enables us to make the best use of our EMR data.  GIS may also be applied to other managed care processes, such as financial analysis (probably already being done).  GIS enables us to better understand how the environment and epigenomics have an impact on disease, human ecology (the relationship between living spaces and health) and human behaviors.

The following are more examples of its application.

Human Ecology

Pinta is a skin diagnosis induced by an organism native to certain parts of Mexico–Treponema carateum.  It is also an indicator of the cultural movement of people who carry it across the United States.  Genetic studies of the organism and the different clinical expression of the disease may be linked either to the genetics of the immigrants bringing it to the U.S. and/or the strain of Treponema.  This particular video was used to display the effects of zooming in and out of a hot spot for the condition, as part of a surveillance program.

The most basic examples of spatially understandable diseases are the natural ecological induced zoonotic diseases, the vector-host driven ecological/human ecological diseases, the human or cultural behavioral induced diseases, and the pure culturally-bred, culturally-bound disease conditions.

To evaluate this aspect of human disease ecology, I identified and evaluated the distributions of well over 100 diseases, with their vector and or host natural history linked to specific continents or regions of continents.  Evaluating an EMR representing approximately 50-75M US patients across the country, the following distribution map video was developed:

Foreign Zoonotic Disease


Tick-borne diseases – local endemic forms and forms from various parts of the world.


Regionalism is a sensitive word.  It assigns, and to some blames, a given region for a given public health problem.  Regional disease patterns can be looked at ecologically, geographically, zoogeographically, climatically, and sociodemographic or behaviorally.  For example, in the mid 1800s, chiggers were common to eastern European homesteads due to sanitation practices, climate, the organisms’ living requirements, and, strangely, the human hair styles (long hair).  The long hair and dreadlocks habits of slavic cultures made them more suceptible to this, what was then defined as a “regional disease pattern.”

Reviewing the ICD 9s, I was able to related certain diseases to certain countries, and produce US maps demonstrating the inland routes by which these diseases travel. Diseases attached to specific regions of the world had their data merged and the cases mapped.  Two maps demonstrate how two African diseases followed two different routes into the US.   Sickle Cell followed the traditional slave population route, the other (infibulation, shown above) follows a more modern 20th century migration route.

Sickle Cell is biologically linked and genetic.  Infibulation is behaviorally and culturally linked, but only to specific parts of African culture.

Sickle Cell Carriers distribution

We can also simple map all the diseases that come in from a region and see how they are spread and/or diagnosed in the US.

African migration 2

This was done for Central and South America, Africa, Russia, the Middle East, Japan, and Australia, to name a few regions. Separate lists and separate pages devoted to these are also posted.  (Many of these videos appear in my two Youtubes devoted to this–an older Youtube page, which Youtube stopped me from publishing in, and my more recent Youtube.)



And Japan

A number of these have a Version 1 and Version 2.  Version 1 represents the obvious zoonotic diseases (i.e Venezuelan Tick Fever or Encephalitis). Version 2 represents additional diseases specific to that region or country ecologically (Bancroft Filaria), or culturally (i.e. Kuru), due to the nature of the organisms they are linked to.

United State “Regionalism” and Disease Ecology

In the United States, organismal and some microbial or bacterial diseases have specific distributions.  The following example of a disease very specific to Chicago climate and soil illustrates this spatial relationship.

Known colloquially as “Chicago Illness”, this video was developed to demonstrate the different methods of evaluating disease mapping outcomes.  I also use it to demonstrate the value of a method commonly used in remote sensing (satellite imagery analysis), in which N values per cell are squared to cubed to identify ecological disease centroids and “hot spots.”

As discussed in my thesis in 2000, and as introduced by Economic and Medical Geography Gordon Pyle, disease travel can be hierarchical or non-hierarchical.  This was covered as well in the following second version of the Chicago Illness video:

Another environmental disease group I identified for review of Disease Ecology pertained to specific geochemicals, such as metals, and the products of nature that influence our health and foodways.

Natural poisoning diseases (venoms and the like), related to place, occupation and landuse features (mining, domestic settings, occupation, or travel and/or recreational exposures) were assessed

Natural resource industries also produce regional health concerns.  The coal miners, woodworkers working with specific trees, and even farmers expose them selves to plant substances that many chemically, structurally and immunologically cause new diseases to develop.

Notice how the Mushroom Growers Lung is distributed (focus is on midwest, the center of one of the largest Armillariella organisms; is this for occupational or ecological reasons?).  This condition is generated by spore inhalation resulting in fibrosis, scarring, a pneumoconiosis like effect, and a reduction in lung elasticity and expandibility,

Chiclero’s Ulcer is a unique fungal infection of the ear found in workers of highly humid and moist tropical rain forest settings, namely the Yucatan Peninsula.  The distribution of this diagnosis in the US demonstrates the migration routes taken by these former outdoor workers from Mexico.


Again, the application of Medical GIS and disease mapping are numerous for the Managed Care industry.  With the Managed Care philosophy in mind, we can use it to improve our surveillance and design more effectively targeted programs for intervention or prevention purposes. have been defined, to prepare a program for changing rates in specific healthcare practices, disease rates and moral or ethical concerns.

The current failure in this system is once again in the delay of implementation of spatial modeling that is occuring.  The level of spatial modeling required by managed care to be more effective, and innovative, is to use it to monitor all health related facts, practices and costs.  The traditional GIS seems too cumbersome to use for this form of monitoring, which is why I am demonstrating only examples of the automated surveillance system spatial analytics system I currently use in large data warehouses and systems.

The next review of this Medical GIS will focus on sociocultural and human behavioral GIS monitoring practices and examples.