What are the consequences of not immunizing your child?


This is an education video I developed for the classroom setting.

I updated it last year to include some additional information on the consequence of catching measles as an older child, because you were not vaccinated.

The presentation style is as a lengthy powerpoint (25 mins perhaps) meant for the classroom or classes at home.  It has no narrative, but an impressive amount of historical photos of events in health that are rarely seen today.

This presentation is automated up until the last text page, before the closing page. (You have to click on that page to finish and see the end page.)

VIDEO >> Conditions linked to Immunizable Diseases, 2015 update


Part I.  Introduction

Over the past twelve years, I have worked on a number of electronic medical records systems, exploring their uses primarily as a spatial analyst.

Now of course, my employment for each of these positions was not as a spatial analyst.  Rather, it was as a typical research associate, quality improvements specialist or compliance officer, data miner or analyst, BI or population health specialist, working for a state MCD/MCR/CHP/COM/Insurance program devoted to assessing the health of anywhere from 50,000 to 100 million members or patients.

Each time, I was engaged in the exploration, mining and production of baseline reporting at the population health level, there were several aspects of these systems that had to be corrected for in order to produce a highly effective spatial analysis tool for medical GIS work.

What stands out about these requirements for engaging in the spatial analysis of population health data is the ease at which such a program could be initiated.  The fact that population health analysis is not carried out in any significant fashion by nearly all healthcare programs, insurance companies and PBMs, tells me that there is some sort of barrier to learning this new technology.  However, working alongside others in this EMR industries, it becomes obvious to all of us that these barriers are linked primarily to middle and upper level management.

If you ask anyone familiar with GIS what such a system can do, you will get an endless list of examples, and most likely be told to go back to the library and look it up, since obviously you are not in touch with this new technological field.

If you ask the same of a healthcare technician, you have some chance of connecting with someone who is either familiar with or experienced with the values GIS offers the healthcare profession.  Any sizable corporation with sufficient medical records, data entry and analytic staff, will probably have two or three individuals familiar with GIS utilization, mostly at the descriptive level.

People engaged in this task, or experimenting with its use, in the standard Cognos or Tableau tools will have some familiarity with how maps can be used as part of a powerful reporting tool.  A more specific use of this technology could once again be lacking in these individuals.

If you ask someone associated with upper level Watson and EPIC tools are unlikely to be familiar with the value of surveillance mapping, at least at the professional, experiential level.

In recent months, there have been outbreaks monthly that could be evaluated using spatial analytic tools.  Some of these outbreaks were very local, like the NYC Legionnaire’s outbreak.  Others have been of national significance, like the Ebola transmission worldwide and the “discovery” that measles vaccinations programs have become a failure in recent decades.  Still other outbreaks have emerged as regional concerns, their distributions predictable and explainable with GIS, by the use of climate, weather, temperature and human ecology mapping methods.

Very few of the tools we currently utilize, originating from the above defined businesses, are capable of depicting the detail of these outbreaks, defining their routes of passage and potential places of origin, or leading to the production of a valid predictable modeling algorithm for detailing their routes of travel, and times of passage versus outbreak.  The current tools being used to handle managed care EMR serve mostly and introspective to retrospective surveillance tools, with just enough resolution to predict models at a fairly reasonable area-size defined level–namely the county, township or zipcode tract level.

Yet, an HIT-GIS is capable of producing valid population and public health data at the small area, very local, neighborhood-based, prediction modeling level.  Exact routes, possible places (including homes) for outbreaks, and even future pathways of travel and diffusion across the country, can be evaluated and defined using the right tools and the right algorithms.

In the typical GIS research program environment, if you ask individuals for examples of these uses, you can easily come up with several dozen examples.  In the health care environment, if you asked the same of these analysts, the experts will more than likely be the public health and epidemiology experts, and teams devoted to disease surveillance, prevention and healthcare maintenance programs, it is atypical to find a program engaged in much more than a couple dozen different types of analyses (PPACA requires 40-6o about).

Still, it is possible to use spatial analysis for managed care population health management and surveillance, for which hundreds to thousands of population health statistics are generated monthly, weekly or even daily. (Although, “thousands” might be pushing it!)

In a number of places, I published my lists of reports that I know by experience are possible.  In particular, I am interested in reports that focus on the most important issues, like social sciences and health, health education/health promotion, cost/savings, inequity and healthcare practices, and quality of care rights for patients.  A program that cannot meet the basic metrics proposed by PPACA (the “obamacare” plan) are in essence a failure when it comes to reaching the agency’s fullest potential.

This means the one third of insurance companies like BCBS, UHC, etc. that have or are about to fail to make the ends meet regarding the ongoing higher costs for care, are in fact the lowest third, the “last leg” in the innovation curve, the worst examples for the managed care insurance profession.

These problems in healthcare, healthcare insurance and certain managed care programs are reviewed and supported by other examples of my work at:

  • httpd://        An overview of this process that I developed years ago.
  •     A general overview of why to engage in this new form of managed care HIT-GIS population health monitoring.
  •   The method for implementing quality of care and prediction modeling algorithms as a standard part of the MC HIT-GIS system.  In my current position, I have the tool for analyzing any population, racially, ethnically and age-genderwise, for any ICD subclass applied to overall population health analyses, and special ICD subclasses predefined by past researchers for the evaluation of individual health risks (Charlson Score, Elixhauser score etc.).
  •     This delineates further the steps an agency, business and/or healthcare facility need to undergo to develop a spatial medical GIS program as part of its institution-wide programs.  Ironically, and quite unfortunately, the way managed care works, is that policies prevail when making important changes.  Adding GIS to a managed care system in order to explore thousands of health metrics per day seems to be asking quite a lot of these programs.  The irony is, it takes only a few hours to run a well-scripted program that can pull data, evaluate it, compare it, undergo statistical analysis, produce tables, graphs and maps, and then output these in logical format into a powerpoint or word report that can be mailed to management–ON A DAILY BASIS.   Such a process allows specific days of the week and month to be designated for producing and distributing specific reports, like those devoted to program comparisons (i.e. Monday through Friday as: MCD, MCR, CHP, the Rest, ALL), cultural health (White, African American, Asian, Other, All), special age group studies (Genomic diseases, Developmental, Childhood, MidAge Health, Elderly Health), Culturally bound and rare diseases (White vs. others, Black vs. others, Hispanic vs others, Asian vs. Non, Outstanding or Unusual rare ethnic groups), SES patterns (Area specific reporting, program specific reporting for CHP and other programs successes, Low Income needs special services, QOC, visits and noncompliance reporting for each of these groups, Upper income group health care strategies compared with lowest income groups), and with a financial focus (comparisons across the board to cost, relative to all in-hospital, emergent/urgent care, preventive care/visit procedures, effects of cost on follow up to recommendations, impacts of cost to specific regions, relative to annual amounts allocated to each facility/region.)
  •   This is the exploratory, introductory page to a new technique that I developed the algorithms and math for in 2004.  After posting mention of that algorithm in 2009, the number of people visiting my page skyrocketed almost overnight.  I suspected I hit upon a growing popular topic, and after a few months of seeing the following grow, decided to release my algorithm to the public (I was saving it for a job opportunity), since the GIS community in the workforce setting was still catching up with the basics.  Since then, the Hexagonal Grid page and the Download page for this tool (the original Excel I used, which I keep promosing to upgrade soon to an SQL and SAS version as well), has been viewed by about one fifth of my visitors–the major country interested in this technology is, of all places, Canadian Urban development GIS specialists.  Most other managed care or even traditional health care agencies have yet to understand the value of being 27% more correct in the tool that you use for spatial analysis (which I prove at ).
  •     My criticism of the current system regarding its inability to develop a comprehensive spatial analysis tool and program.  This criticism is not only directed at the programs and overseers (PPACA, NCQA), but also the companies, COs/Pres/VPS, and other agencies with the ability to bring PPACA into a new generation of HIT-GIS success.
  •   In 2015 I transitioned from national health spatial analysis to densely populated, megalopolis regions analytics processes.  This transition enables me to retest my algorithms at the detailed spatial level, even street and neighborhood level.  A number of highly controversial topics were evaluated in the very beginning, in particular local data confirming the asymmetric behaviors of some genomic  disease states, like the male-female survival rate differences noted for one commonly carried disease.  This work also demonstrated THE ROLES THAT RELIGION DOES PLAY on whether or not certain religious families are more likely to bring their patient in for unusual disease screenings and rule outs.  It also allowed me to confirm my suspicions about most of the culturally-linked diseases I have identified, and the unique religious-ethnic post-violence behavioral patterns of patients when it comes to reporting child and adult/wide/spousal abuse; in other words, some religions are more likely to report than others.
  •     A brief recital of the six forces that influence managed care and race-ethnicity focused research approaches, to date.
  •    This page starts with an overview to the value of big data in facilitating what I termed “ACER” analysis of patient populations.  At the bottom are links to my pages on how to design to design a cultural health analytics and public health screening program that is more complete in its approach than the typical NCQA program.  Contemporary HEDIS and NCQA interpretations of the value and meaning of health and EMR analysis for non-caucasion groups is, needless to say, still too “ethnocentric” or focused on white population health metrics.
  •    As the title implies – – my innovations.  Or to be more exact, those “secret” IP related sql and SAS codes that I use to generate my 3D mapping results without GIS.  The majority of algorithms you’ll see at the methods I developed for reclassifying data.  The most unique algorithm is how I recode the religious religious groups, to put those with similar philosophies together in terms of how they relate to disease, its meaning and purpose in life, the reason for healthcare practitioners and their responsibilities.  There are also algorithms here for ICD regroupings (of which I use many) for risk related analyses, the three standard risk scoring algorithms for patient risk assessment (Charlton, Elixhauser, and the Federal Chronic Disease Score), infectious and zoonotic disease surveillance reclass algorithms, race-ethnicity reclass, several culturally-linked and -bound ICDs, etc. etc. Click on the ‘Codes and Population Health’ for a drop down with more on these.
  •   My site devoted to just the non-GIS spatial mapping technology.  How to produce spatial imagery without depending upon a GIS or GIS related cost.  This process requires beginners to intermediate level experience, with intermediate SAS programming experience preferred for complex comparisons between race, ethnicity, gender, religion, age range, gender, SES status, and area.
  •   A site I developed to document this work on the internet.  This was produced following a heated “interview” with on the the nation’s primary EMR companies, responsible for developing HIT systems for numerous managed care facilities and companies.  In essence, this company felt that HIT-GIS was not needed for this profession and the healthcare process to develop, which I contested.  This company simply stated my technology would go nowhere.  Its rivals, also in contact with me, threatened to develop their own IT process for producing these end products devoted to spatial analysis of population health.  Those arguments by the way occurred in 2012.
  •   To date, no major company has been able to produce an effective HIT-GIS tool for analyzing data down to the square inch of an urban setting.  This site was developed and opened up in order to document my discovery of this algorithm and process for 3D modeling of disease and public health patterns, by then a nearly ten year old skillset.
  •    I developed a survey (long, 25+ questions) to document the details about the use (lack thereof) for GIS in the managed care or university hospital/clinical workplace setting.  This is the link to that survey.


Part 2.  Creating your own Managed Care Population Health Surveillance HIT-GIS Program

To produce an effective MC HIT-GIS program, the following steps and actions are recommended.

Step 1.  Evaluate.  Determine, Define and Describe Needs and Potentials.

  • Prepare the EMR for spatial analysis and Medical GIS performance.

To perform spatial analysis, two things are needed:

  1. location or spatial information, preferably in the form of  a latitude-longitude dataset for each member of the patient population (we’ll skip lat-longs for the providers and facilities for the moment).
  2. An analytic tool or process in place that enables researchers to produce spatial information about these data, in the form of descriptive, exploratory, summary visualization products, namely the production of maps depicting the  content and meaning of the environmental, demographic, health, disease, service and financial data stored within the system.

These are the only two additions to an EMR that are required for Medical GIS to be incorporated into a managed care [MC] health information system.

According to the Precision Medical Initiative (PMI) about to be underway,  managed care[MC] system will ideally have several spatial analytic processes in place and being used or experimented with.

The current ways Medical GIS is being promoted is via a single platform, single analytic system process.  Large companies like IBM, Cerner, have platforms in which patient care can be entered, kept track of, monitored, and even reported using primarily their tool.  Reasonable approaches for these tool enable some related company-invested or non-invested software  packages to be included, such as by enabling parts of the ESRI GIS process to be meshed or engaged in as part of the larger HIT platform.

Multiple tools however mean the data itself has to be utilizable as well by other unattached software products or packages.

SAS spatial analysis (versus SAS-GIS), for example, is an easy route to take in producing semi-automated, highly powerful analytics that cannot be produced using the common platforms and packages now being promoted.  SAS alone can be used to produced visualizations that neither EPIC, nor most traditional GISs, nor IBM Watson-Cognos, nor Alteryx-Tableau are capable of producing.  SAS has the ability to produce these visualizations within the everyday work setting at hundreds to thousands of objects (products or maps) per day.  The advantage to self-scripted queries using this method is that the math is traceable, provable, and can be modified or upgraded with ease.  There are no limits to the type of math use, nor the details at which the data are developed.  Whereas very large platforms provide the data at an areal level that is hard to change (i.e. attached to town and zip centroids, usually not to patient place of work or stay), it is up ti the developer/user of SAS programming and tools to determine the limits of the applications for any new algorithms that are developed.

Step 2. EMR/HIT Preparation.

  • Of what value is the current HIT EMR system?

There are a number of related factors for HIT and the EMR that impact whether or not MC can develop a functional GIS.  Nearly all of these are frequently reviewed and re-reviewed in respect to HIT, PPACA requirements, and the EMR.

Quality of data within the EMR is the most basic example of this.   We have the three most basic problems with EMR data:

  • absence or presence of data,
  • data content and structure, and
  • data form.

Arguments concerning the absence or presence of data are obvious.  Data can be missing because they were not included in the plans to develop a system, or they were included, but their content, structure and format weren’t well planned and so they are inconsistently entered, or they are not entered for other reasons, such as

  • a lack of engagement at the data entry (office or clinician) level,
  • a lack of understanding of why data are important, resulting in reduced query for the data (worker:patient relationship), and subsequent entry of that by employee, or in some cases by the patient, and
  • the unwillingness of the clinician to ask a question or unwillingness of the patient to answer the questions needed for the EMR to be of maximum use.

In a recent review five very densely populated urban government boundary settings within a single megalopolis contained within a single state border, one of the five areas had a 45% lack of data completion when compared with the others, that missed only 20% of the data or much less.  Any of the above three reasons, or a combination thereof, may be responsible for this severe lack of important population health related background data on the patient population.

Step 3.  The Planning Stage.

  • Plan. Plan!  PLAN!!!

Developing a plan for the implementation of a Medical GIS for a managed care system requires that measures be developed and engaged in at multiple levels.

Contemporary MC systems use their EMR to monitor population health in accordance with PPACA requirements.  During the past ten or fifteen years, the ‘meaningful use’ [MU] part of the PPACA was preceded by quality improvement activities (QIAs), quality improvement programs (QIPs), performance improvement projects (PIPs).

In a managed care system where the plan is to monitor an entire population health, the HIT-EMR and Medical GIS processes must include projects focused on the individual patient, patients’ quality of life, disease groups, surveillance/monitoring, service industry, providers/programs, health care specialty, interventions, public health, environmental health, “precision”-based (genomic), regional, community, neighborhood, special interest group,  financial, and government/potential investors levels.  PPACA, HEDIS, NCQA programs alone do not satisfy this more complete population health focused approach to MC programs.  The “Big Data” version of EMR utilization requires that all these levels be assessed.

Step 4.  Implement HIT-GIS.

Implementation of a successful managed care HIT-GIS requires that a number of methods of analyses be established, maintained, further developed, and improved on a regular basis.  Ideally, these plans are somehow monitored and/or managed by a single director who is responsible for maintaining the breadth at which Medical GIS in being engaged in, and the various values it can be used to demonstrate and the technology, IT, HIT, mathematical, statistical, BI, and institutional corporate level.  BI, EMR, Medical GIS may together be used to produce an effective predictive modeling program for use in redirecting and improving upon long term quality of care standards and the resulting cost-effectiveness of care relationship.

Step 5  Testing and Production.

  1. Diversify the implementation, testing and production process.
  2. Have two levels of engagement established.
  3. Utilize a variety of methods to establish spatial analysis as part of the MC program.

There are many HIT-EMR processes that are required of programs, such as the HEDIS, PPACA MU and internal QOCs/QIs used to define the value for a MC program.  These standard, usually annual assessment processes occur and recur at and institutional level, and should be integrated with a Medical GIS program developed to meet institutional needs.

Step 6 (if necessary).

Contact me!



Part 3. Critique and Conclusion

Can GIS be included in the next phase of Managed Care development, and the program known as the Precision Medical Initiative (PMI)?

The value of GIS in the PMI is that for the first time, we can assign particular human genomic traits related to illness to a map.  The mapping of illness has been around for several centuries.  When it was first developed in the 18th century, there were few comparisons made between cultures, and the focus was on latitude, climate and disease.

But during the 1800s, physicians went beyond the latitude theory for disease and began incorporating topographic and meteorological features into their pre-bacterial, pre-microbe way of understanding disease.  Once the bacteria was discovered and proven, our focus on disease patterns and causes turned to the microcosm of the human body, and focused on our anatomy, physiology and cellular chemistry.  All of these in turn were related to the bug, germ, microbe, bacterium, virus, what have you, that came to infect us, cause us to become ill.

With the human genome project now to the point where genetic counseling has led to the development of genomic pathway treatment modalities, the older notion of race and temperament, those causes which led to Asians and eastern Europeans bearing one type of illness, and the Prussians another, is now back in the medical literature to such an extent that “mapping” disease bears that double entendre–mapping the gene within and mapping the disease linked to that and where they are in the world, where they evolved as a part of some local society.

The Precision Medical Initiative is going to make GIS an even more important part of the HIT system, combined to form a HIT-GIS.  HIT=GIS programs may in fact have some of their predecessors already in place in the medical profession.  The younger more innovative, potential management and CO employees are more valuable in the long run than the current leaders of Managed Care.  Unless a CO, VP or President of a MC system is learned enough to profess expertise in how to design and HIT-GIS to successfully produce both microcosmic and macrocosmic maps depicting human health, such leaders may soon be seriously outdated in their intellect and understanding of the architectural design of human health, population health, genomic health.

There are many institutions that have smaller special topic, exploratory or experimental, and even self-defined HIT-GIS groups established that are testing the value of these newer, alternative methods of analyzing health data.

Once these smaller GIS programs are established in a MC system, they are usually functional at the office, group, even small department or sub-department level.  Moving this analysis process from lower level to upper level defines whether or not it is a success at the inventor, special team, or departmental level.    The ability of an institution to use a Medical GIS as a symbol of accomplishment should be the long term goal of any team or individual engaged in this process.  The limiter to this success is demonstrating some value for the end products developed using Medical GIS techniques.

The majority of MC programs so have internal small group programs using GIS in some way to evaluate a little of their data.  These programs use the Medical GIS for specific purposes.  One common example of this is the use of Medical GIS in a manner that is already employed by environmental health and disease intervention programs functioning within a public health program.

Common diseases or health conditions evaluated spatially include HIV, diabetes, asthma, smoking, and drug addiction.  The impacts of poverty, race/ethnicity, teen pregnancy, and SES on small regional health statistics are also common applications of GIS to a basic MC like setting.

The barrier to producing a larger Medical GIS in MC is the lack of interest in this new technology at the upper level and the unwillingness of programs to diversify their quality improvement/population health surveillance processes.  These special interest topics like HIV, street drugs, poverty, teen pregnancy, serve as an arguable reason to bring Medical GIS from the department level on down, into the corporate level of development, implementation, use and integration.  Combining the notion of mapping disease genomically and demographically, culturally could greatly impact the need for GIS in a managed care system.

The very common large software packages and Big Data HIT platforms in part allow some of these uses to be implemented, although not at an advantageous small area level. Disease mapping requires more than just point-arc GIS or a raster system that is GIS.  The GIS for HIT-GIS must be polymorphic in its capabilities, activities,  and performance.  These values are what turn partially useful spatial analysis tools, programs or systems serves into a reason for supporting (though not promoting) further development of GIS in Managed Care.  The costs attached to these products may or may not help the institution or corporation develop a medical GIS that is both highly productive and impressive, but they will help us improve our understanding of mankind and mankind’s health, past, present and future, as a product of natural and human ecology, and a result of our interactions with the macro- and microcosmos that make us who we are.


Space, disease and time . . . with only time at a standstill!  

Could 2016  become another year without change?








(Click the following link)



With Managed Care receiving limited support from the masses this presidential election year, one has to wonder if the current PPACA (“Obamacare”) program will survive another year.

When the presidential race is over this coming fall, if either of the two most supported candidates is elected, there will more than likely be changes brewing in how to resolve the issues that hold our healthcare system ransom in the United States.


Same image, different rotations

For example, my personal speculation is that if Hillary Clinton get elected, that the attempts might be to continue riding upon the system already in place, under the claim that some things need improvement, and the financing needs for effective management and control, but in the long run, we will simply take what it now in place and change it to make it more effective, more consumer friendly.

If Donald Trump is elected, the resolutions formed in my mind are less convincing or indicative of attempts to maintain some status quo and stability.  In the worst scenario, healthcare will be placed more into the hands of private businesses again, and those programs devoted to the underpriviledge lower class fortified as they have always beenm being available to those most needy, but considerably more thought our in terms of determining those who are eligible.


This means that in-migrating people will not easily acquire such coverage, those moving here for work related reasons, be it permanent or temporarily, will also have more “hoops” to jump through to earn their coverage.  That will service the current system greatly, in terms of finances, but re-open old wounds as the previously uncovered masses, who briefly entered some sort of healthcare status under Obamacare, will once again be left to fend for themselves.  Some of this impact can be lessened, with certain eligible members maintaining their eligibility due to such things as young mother and child status, or having to deal with some life threatening chronic diseases state.  But the least qualified people who were fortunate enough t obtain coverage in recent years, may again be out of luck by the time a new program is put in place, should the next President-Elect be Donald Trump.


Fortunately, it will likely take one and a half to two years for any new health insurance managed care program to be implemented.  That will lessen to brunt of change this nation might endure, whichever way we go.

Some evidence suggests that Hillary Clinton might opt to produce some formidable changes in the Obamacare plan, were she elected.


This begs the question:

What might the best changes be in such a system, were it to be established as an extension in the current program that exists, yet meet the financial and service industry needs in ho the newer way to manage care gets developed?

For one thing, eliminating “Obamacare” is easy.  Change the program, get rid of that title that now has a tremendous amount of hatred and regret attached to it.


But we might only need to eliminate a very small of the PPACA plan in order to satisfy the public masses.

The most important elements to PPACA are the transfer, change and implementation of healthcare into a high technology supported industry, complete with information technology never perfected before by any single healthcare plan (and yes, none have perfected it, no matter how grandiose their thinking and self-judgment appears to be to colleagues).

The information technology systems in healthcare today, known specifically as health information technology or HIT (like this label avoids to scary innuendo of the title IT), are still struggling now more than 15 years into managed care, and nearly twenty years into their complete development and implementation.

Generation after generation of IT fears and barriers have surfaced and resurfaced, demonstrating that the healthcare insurance companies and masters of the health economics  industry aren’t as wise or able as they want us to think.  A change in technology occurs quite readily and in months to a year longer, through simple acceptance of the technology and willingness to test its application.  But one could argue that insurance companies and businesses fear IT advancement due its financial impact upon their individual financial securities, and morality targeting impacts upon the leaders, people and companies that have to use this new technology.


Insurance companies lose considerably once they see solid evidence for overcharging (we can leave the fraudulent billers and overbillers out of this argument even).  Insurance companies will lose greatly once their additional charges and unnecessary charges are uncovered.  The waivering tendency for insurance to assign value and price to specific procedures will raise some red flags, more than they have been doing since this issue was described to the public several years ago–for example, why is the PET costing $175 for medicaid, but $675 for privately insured individuals?


With presidential-elect changes inevitable in the next year or two, how will IT itself — that skill set which insurance companies have failed to support or engage in —   determine the lifespan of the long-lasting stubborn companies unwilling to engage in the EMR, EHR, IT, HIT, HITECH Act requirements used to define PPACA?

The truth is, these IT developments are here to stay, whether you like them or not.  They have already demonstrated their value, and provided evidence to necessitate their further development and use in the healthcare billing and insurance industries.  Even if meaningful measures lessen to some extent, leaders and common sense workers will not engage in lessening or reducing their production of proofs that changes are happening.

With IT/HIT development, we will learn more and more about effective and ineffective certain companies, programs, facilities, neighborhoods, insurance plans, mail pharma companies, allied health programs (bogus or not), and counseling and family support service agencies will be when included as part of the broader, long term minded, preventive-palliative healthcare programs many places are trying to develop.

So IT/HIT must stay for these companies to document, and brag about their successes.


Adding GIS to this only strengthens whatever arguments are going to be made, and making it easier to convince you audiences, in whatever direction you want.

What prevents spatial health work from becoming a part of the managed care  system is not just lack of intellect about what it is and what it can do (everyone knows a map can portray, or lie).  What prevents spatial health from becoming a part of the healthcare system is the lack of knowledge, desire, experience, and skillset on behalf of leaders, to put forth the option of developing such a program in order to save their own business significant amounts of money.

There are a few companies out there that are experimenting with spatial analysis.


But I have yet to see one inkling of evidence that any industry out there is able to analyze its entire popular for the 15,ooo ICDs and ICD groups, the 4 fold as many visits as patients, the 10 to 20 fold number of procedures these people engage in, for 10 to 20 years of long term EMR/EHR history held secure as part of the older medical records systems.

A few companies have 20+ years of EMR on file (I work for one).  GIS demonstrates outcomes that these data produce, that no other MC can produce through much of the country.  Likewise, insurance companies and health businesses that manage the insurance aspects as well, such as KP, have developed partially functional spatial analytic systems that may have the ability to perform full reporting for a full healthcare program.

The question is: what will be the first company to emerge that is able to create a full-fledged GIS/Spatial analysis  based version of the QI improvement report, detailing thousands, not hundreds of the most important population health features?


These features must include the following:

  1. Human genomic data and disease history and spatial analysis
  2. Emergent/Urgent care data, past and present, in reference to age, gender, etc.
  3. Religious group data in relation to specific ICDs and medical events, compliance and non-compliance, and social and moral public health related disease or injury related risks
  4. Race-Ethnicity derived reviews of its regions and communities in relation to population health, place, economics, and cultural traditions.
  5. Socioeconomic status and predicted employment pattern related health care coverage, disease and success in relation to place, time and money
  6. Chronic Disease (CD) management databases, devoted to hundreds of CDs, engaging in predictive modeling of these costly populations
  7. Standard endemic disease pattern public health surveillance
  8. Atypical epidemic and foreign born disease patterns and travel in relation to local regional health matters (local Legionnaires, Ebola, Chikungunya, etc. types of studies and prediction models)
  9. The differentiation of standard linear versus hierarchical diffusion modeling behaviors and patterns pertaining to locally-specific disease patterns
  10. A remodeling of epidemiological transition theory to best meet the needs of the local populations being served, with specific attention paid to economics, business and health, using the sequent occupancy model proposed on other pages posted at this site.

This survey is developed to determine how ready the healthcare profession is for the spatial analysis of health and health-related costs by a managed care system.








A depiction of how diseases were theorized around 1812, wtih expected locations of diseases over the Lake Michigan shoreline, Great Lakes area.

At times I get a little self-assured with the way my predictions have panned out over the past five years.

Back in 2013 I stated that measles was due to re-emerge, after discovering the dense cluster of non-immunized families and children along the west coast, centered in the Pacific Northwest.


Immunization Refusal peaks in the Pacific Northwest, 2000-2010.  Individuals in the national dataset evaluated for this work, who had a Measles history.

Like I said in the many posting on that topic, before, during and after the measles outbreak in the Palisades area of New York, followed by the much broader, nationally spreading disease that erupted out of California.  To date, most of the blame for that outbreak has been directed both at people from other countries coming into the U.S. for a short while, unimmunized and carrying the disease or bringing a child who has the disease, or we have blamed the numerous families into alternative medical philosophies and traditions in California, who felt the world was safe, and whp had that malingering phobia about their child getting autistic due to vaccinations–just MMRs mind you–“but hey, what the heck, we might as well skip them all.” I hear them say.

CDC and other major epidemiology, surveillance groups are focused on the California nidus based cause for the measles outbreak.  They are wrong mind you, since the higher likelihood remains untouched due to this mis-directed focus down south.

And that is often what epidemiologists do–jump to conclusions, determine what’s likely to happen, and take action based upon that guess.


For decades, if not centuries (depending upon how far you read into the past), this basic layout of the world was used to define epidemic patterns, especially for fevers.  Yellow fever was natural to the Torrid Zone, but during warm spells migrated into Temperate Zones.  If in the upper north (for the southern hemisphere, lower south), the colder half of the temperate zone kept the fever present either by warmer weather, or by dense populations of people who could become infected.  This important piece of American history is reviewed in more detail at

So, the new Yellow Fever concern is bringing us another of these returning diseases that we are going to perhaps have to deal with.

See recent news article: Lydia Ramsey’s “Another deadly virus is poised to go global“. 

If yellow fever hit the U.S., in epidemic fashion, I can predict that several big mistakes are going to happen and be published.

A section of William Aitken’s 1872 map of the U.S., depicting the Yellow Fever regions of North America   [Link to Aitken’s map]

First, some of the past “yellow fever” outbreaks that weren’t yellow fever at all may be brought up.  Yellow fever is very much a seasonal epidemic that erupts in the northern latitudes during the late summer, early fall–or as one Quaker physician Shadrach Ricketson stated around 1803 or 1804, it can become more likely when summer returns in the months of October and early November– that period of time we call “Indian Summer.”

Periods in Epidemiological Transition, according the the Sequent Occupancy method of interpretation.  For basic background, see    In 1786, the famous patriot Benjamin Rush wrote about the ways in which society evolved in regions, producing a Human Species-based Evolution Theory that preceded Sequent Occupancy.  (for which see )

A very early possible outbreak, isolated on an Island south of Rhode Island is discussed in an article on an “Extraordinary Disease”.  The geographical theoreies as to how an epidemic can strike an area, based on its levels of development, is also presented on this page that is referenced.  This economic geography interpretation of disease explains the epidemiological transition patterns and behaviors we often see, in more detail and in a much more concise way that the traditional epidemiological transition theory.  This philosophy that fits the epidemic changes better than epidemiological transition is called Sequent Occupancy Theory.  (For “Extraordinary Disease” see )

German medical cartographer Friedrich Berghaus produced this 1848 map, the second global disease map to be published.  The yellow sections (barely visible) are the yellow fever areas of North America.

But the biggest mistakes US epidemiologists could very well make is to predict the entry of this disease by traditional mid-southern and southeastern routes into the United States.  Some will believe it can return in a hierarchical way, via the largest, most heavily traveled cities.

Congressman-to-be Samuel Anderson theorized the possibility that yellow fever was being carried by ships.  On one of the ships he managed as a ship surgeon, he documented the conditions that existed and he suspected may have been related to an epidemic brought up from Curacao to Delaware.  This is from my page on this topic

But my review of yellow fever 6 years ago demonstrated more cases striking the northern states than the southern ones.  That can happen in one of several ways.

From one of my studies of Seaman’s maps of the 1790s-e1800 Yellow Fever in New York City (source: )

First, the disease managed to come into the U.S. in spite of epidemiological surveillance in its natural endemic settings to the south.  In other words, people bypassing the public health security part of the southern border of the U.S. will never be caught, and therefore make their way, as quickly as possible, to the northernmost parts of the US, via the Mississippi River and Great Plains travel routes.

Chiclero’s Ulcer ( see    )

Of all things to relate to yellow fever, Chiclero’s Ulcer or Ear, an infection of the external ear indiced by the heavy humidity of Yucatan, also makes its way into this country via this route.  The same migration route into the United States is followed by another Mexican-originated ecological disease–Pinta (pinto), a relative of STDs Falcoparum.


♥ (link)  <

So, Yellow Fever could make its way into the U.S by a classical in-migration route, that one would think the U.S. government can secure and watch over.

Another route that Yellow Fever could take in is the route in first took as it made its way into this country–by traditional shipping routes.  Though this may not be so likely today as it was between 1740 and 1815, when shipping was a primary way to get around.  Shipping routes enabled the disease to follow the inland waterways as well, meaning that in order to infect parts of inner Canada the way it did, yellow fever had to enter by way of the St. Lawrence Seaway, head across the Great Lakes, and then head in every direction along major tributaries heading west and north into Canada, and south and west into the United States.

The first disease with numerous topographic and geographic associations was the yellow fever, finally put onto a map by New York’s Valentine Seaman, as part of his investigation of the New York City outbreaks.

National Yellow Fever cases in the 2000s (personal research)

Numerous more maps of yellow fever behaviors have been published.  Unlike Malaria or Dengue Fever, two other mosquito related foreign diseases to  hit the United States, yellow fever has more of an ecological niche on this continent, and of the three, it is therefore more likely than them to return, and acclimate to the warming climate and the warmer southern shores of the United States, now managing to become a part of the ecosystems here, unlike back in the late 18th and early 19th century.

An Early Pinta 3D rotating image video I produced using my sql and sas formulas.

The third possible route for yellow fever into this country that is most unusual is akin to the last example just mentioned with the St. Lawrence Seaway.  Today’s peaks of yellow fever seen between 2000 and 2010 possible relate to lesser security to the north, due to lesser expectations for yellow fever to enter the U.S. by way of a northern route.

And so, that’s the hypothesis I am testing here, by watching how the yellow fever behaves if it reaches this country.  If there are weaker security measures for this disease at our north border, accompanied by lesser expectation by Canadian Public Health officials for a tropic disease to enter the heart of their country and establish a seasonal-only nidus there.


For more examples of Ecological Disease Cluster research using GIS, see my NPHG page:





The figures on this part are from my 2000 to 2006 studies of the Ecology of West Nile Fever.  ESRI GIS, Idrisi and several raster-grid and imagery tools were employed over the years for this work.   The research methods presented on this page and the site I provide links for, pertain mostly in New York, and to a lesser extent in Colorado.  To date, the full data set I developed for west nile ecology work has only been partially reviewed.


With Zika now present on the United States continent’s mainland, numerous questions must be considered for the events that may soon follow.

Some mosquito-born diseases like Malaria fail to reach a state where the pathogen, vector, and hosts other than people enable it to become a part of the local ecology.  Just what are the features that prevent such an event from happening?  What parts of the potential ecosystem in which the vectors could survive is incapable of providing the requirements for its pathogens–plasmodia?

With yellow fever, there was limited possibility for the pathogen-vector-host/carrier relationship to exist as part of the natural setting.  The limiting factor much of the time was weather, in particular seasonal climate changes that made it difficult if not impossible for pathogens to remain alive in much the U.S. over the winter.  And although such is not the case in the deep south of the U.S., yellow fever never became the pathogen that it still is within its native tropical settings.  So there’s more at play with this form of mosquito-vectored disease, than just the pathogen-vector-host relationship.  Something in the ecosystem extinguishes the yellow fever virus, as well as limits its principally Aedes and Haemagogus vectors.


If we ignore for a moment the historically important Dengue Fever linked to a similar vector dependency, the next most important mosquito vectored pathogen to strike the U.S. is the West Nile Fever related virus.  Although it also arose from a very different climate and topographic setting, like yellow fever and malaria, West Nile was able to establish itself as part of the natural ecology.  Why did it accomplish this?


The traditional reasoning, from traditional epidemiologists, tends to focus on the human-host-vector-pathogen relationship, paying limited attention to the macrocosm of the pathogen and peoples’ world–the surrounding ecosystem.  West Nile had a number of very distinct ecological behaviors that to date, with the exception of some of the work I published more than ten years ago, have not been developed into a potential method for evaluating and spatially analyzing mosquito-vectored imported disease patterns.


The following are the most important spatial methods for evaluating disease ecology pertaining to mosquito-borne diseases:

  • Plant Ecological research provides insights into the ecosystems that certain species of mosquitoes favor, and/or stay away from in terms of finding the niches for potentially infected mosquito populations.
  • Understanding the relationship that exists between landuse form, plant type, vegetation region or biome type, and the effects of these upon the microclimate of vector-breeding areas, provides a unique set of insights into how to evaluate the ecology of the vector-host relationship, and provides us with a way to initially define, more accurately, high risk regions.
  • As demonstrated by a field study I performed around 2002, using a light sensor, subcanopy light influences the behaviors of vectors and can be used to define the most susceptible natural settings for a vector to engage in specific feeding, mating, breeding and migration activities.
  • The application of normalized difference vegetation index (NDVI) imagery to studying species patterns and the possible locations of swarms or dense clusters of populations provides additional insights into how a region at the small area level becomes disease prone or not. [See ]
  • Satellite imagery may be used to further this method of research on the influences of canopy upon host-vector-pathogen relationships. [ ]
  • Competition between different species in a shared territory influences how they position themselves within the ecosystem, at the elevation and canopy-related level, and in terms of the distance from the water body that they may reproduce and aggregate the most.


My most successful series of studies demosntrated the ecological links between certain species and population density per species, the potential impacts of species competition on the exposure of people to disease carriers, and the important roles of understanding landform relationships with the behaviors of potential mosquito-carriers, a feature that is very microecological nature, and can vary from one moderately sized region to the next.


Most important to this study was the value it demonstrated for the use of NLC imagery and the value of applying other free  (then, not necessarily now) spatial imagery data to this research process.

My most understandable and therefore successful outcome was of course related to the aerial photo imagery project, the third image above, for describing and demonstrating the spatial relationship between a cluster of positive testing animal hosts, and the probably location of a positive testing pool of vectors.  These vectors overwintered the following seasons, and re-emerged in April of the following year, demonstrating the ability of vectors to retain an active pathogen or virus into the following years.

Important to note here – – for Zika to become ecologically stable within certain areas of the United States, and to become a part of the local ecology, this behavior has to be duplicated.    


With IDRISI (and its subsequent products, IDRISI 32 onward), one is able to evaluate important topographic, elevation-related, and surface-reflection/solar pattern linked features to the behaviors of a disease.  To identify the most susceptible, effective surveillance sites, the SLARs and related elevation datasets available to remote sensing staff can be used to define the more successful topographic regions for trapping possibly infected vectors.  This worked in the field and for lab results (producing positive testing hosts and vectors) in several ways.   The most important was it correlated back to the elevation-distance from water edge study I performed, by setting mosquito traps along a transect of the creek’s flood plain.  It also explained why one creek was not very productive with positive testing species, versus the other.  The two creeks appear the same, but topographically and ecologically, according to elevation and topographic images, their forms influenced canopy and solar energy aspect features, making one creek very stable ecologically and not a carrier due to high vector species diversity, whereas the other was less species rich and therefore more likely to harbor infectable vectors species, predominantly in regions where the disease would be spread by human ecological means.


Different species of vectors have different elevation related behaviors.  Idrisi was also used to produce DEM derived images with overlays, to demonstrate how elevation above water surface does influence the species captured.  The most revealing images and findings about vector species ecology came from my various studies of plant species, canopy type and light penetration, in relation to small water body locations, potential and infected raptor host species that had recently deceased, and the use of algorithms developed for testing the relationships between plants and small areas.


This first study is of a small shady yeard setting, with a nearby pond, a positive testing host, several related dead hosts, and a unique multispecies canopy setting.  The NDVI was used to demonstrate the results of a supervised classification process used to reprocess the original image of this setting.


The trap sites on this property were added as point, converted to grid data, and then overlain of the reclassified NDVI.  Buffers around the trap sites were identified and that data pulled from all subsequent images analyzed.   The results of this analysis was then related back to the entire image to produce an entire map depicting the risk of each grid cell area.  A Theissen Polygon analysis was performed of these trap areas, for use if defining the content of each trap site in terms of landuse, NDVI,  canopy and vegetation related grid cell findings.


Several ecological tools already present in Idrisi were also applied, focusing on biodiversity and species richness, and tested as possible indicators of potential west nile-bearing vector-host settings.


Four types of regions were defined by Idrisi, each with different types of ecological relationships between the plants and other materials or belongings found in those particular regions.  The hot spot was overlain on these four classes of disease inducing locations, and the point related best with Classes 5 and 6. (Above).  This implied that Deciduous trees played an important role in producing the ecological setting that the vector species most desired, followed by a low lying moist vegetative region adjacent to it.  It also demonstrated that the heavy canopy settings had minimal influence on vector swarm patterns.  The heavy canopies at this site were planted evergreens, mainly 30-40 foot tall Fir and Pine trees.


Applying this back to the entire image, a cross-sectional risk area pattern could be produced, using several formulas or dimensions by which risk can be assessed.   Linear, quadratic and cuboid formulas were applied. The cuboid model demonstrated the best fit, especially for Class 5 area, again, reconfirming the point-area derived summarization deduced based simply upon visual inspection of Class 5 of the classification images.


For more on the method for mapping mosquito cector ecology, see my pages at the following links:

West Nile Surveillance


An Automated 3D Mapping process (courtesy NPHG technology).  

Recent results of a new technology for spatial medical cartography that I developed, requiring minimal engagement at the IT developer’s end, including an elimination of time requirements related to the development of videos


We are in the initial stages of implementing GIS for managed care based population health monitoring.  There are a number of software programs out there than enable you to include maps in the displays or presentations you develop as part of the monitoring process of your workstation.  As leaders in the field, the production of “dashboards”, “scorecards” and “reports” have enabled us to increase our understanding of the population we serve and develop the knowledgebase needed to better manage the various types of healthcare programs we provide.

Surveillance is a major reason to establish as GIS workstation that has specific public health and population health and safety related metrics defined.  Most of the uses of GIS to date have been for research purposes, with a few projects actually evolving into standard intervention, health safety, health security and even cost effectiveness reviews of our programs.

There are literally thousands of conditions, events, diagnoses that fit the description of being important to national health security concerns.  Aside from the hundreds or more zoonotic and rare infectious diseases,  there are culturally linked and bound health conditions for which the knowledge of their notations in the EHR or EMR should lead to the raising of a “red flag.”

Included in these issues are those related to domestic violence, spouse or child related mistreatment, malnutrition, unsanitary living conditions, criminal activities, illegal or immoral seclusion to an inside home-setting, etc. etc.    This particular event is one of the most controversial to appear in the US EMR/EHR data.  Its controversy in part related to its reasons for practice, as well as its reasons for continuous practice in spite of international laws passed prohibiting it from being performed.

This new technology enables spatial analysts to define the sociological or sociocultural “hot spots” in health related issues.  The recent re-eruption of measles, the in-migration of mosquito-born diseases like zika, the possible planting of new forms of food-spread antibiotic resistant bacteria in certain parts of the U.S., the increasing density of certain culturally-linked genetic disease traits due to cultural growth, combined with the ongoing forming of you families with shared genetic traits, set the stage for the development of a medical GIS by all managed care institutions.  When such an HIT-GIS station is developed in association with the local public health program activities utilizing spatial health analytics tools, we develop a better understanding of our local population health, and can more quickly use this knowledge to monitor, survey and even predict the health changes expected for a region.

Genetic Disease Carriers, for a very common malady within a heavily populated urban setting.  Note: this was not performed using a GIS, but a simple set of SQL-SAS algorithms

Fortunately, technology does catch up with some of the programs I write.  One of those programs in the combined SQL and SAS needed to automate reporting for managed care companies.

The limiter to producing an automated ‘MAPP’ Program, as I call it (Mine-Analyze-Produce-Present) is the amount of work you put your system through.

My first attempt to go through this process entailed a production of about 3500 popualtion pyramids for most of the major ICD9 disease classes, many broken into specific age groups, in order to determine the highest risk gender-age groups in one year increments for very special social disease patterns, such as anorexia, wife beating, pyromancy, infibulation, and the varous forms of child abuse.  This evaluation took me nearly a half-year to complete, b ut inspired me to automate my processes some more for producing more effective products, more quickly.

My second attempt entailed evaluating regional disease patterns.  To accomplish this, I broken the US down ultimately into about 25 regions.  It seemed the NCQA, NIH, US Census and USGS ways of breaking the US population and states down into regions, based on income and/or expeditures and insurance related patterns, wasn’t exact enough to demonstrate the varying family sizes I noted for unique areas, in more unique parts of this country.  The Midwest and Great Lakes areas for example, I divided into north-south and east-west and combined N-S-E-W quadrant patterns to determine where the most statistically significant differences existed across state line/regional or subregional borders.  That project fortunately took only a week or two to perfect, and led me to develop the grid mapping of the entire United States, in detail.


Over the past five years, the status of Medical GIS as practiced within the Managed Care profession has remained at a 5.50-5.75 level.  We don’t use GIS to improve our HEDIS results, nor event to routinely monitor the HEDIS requirements, or event the Obamacare “Meaningful Use” requirements.  There are numerous programs underway to try to convince managers to implement some form of spatial monitoring process.  But due to disinterest, and/or lack of knowledge for this form of research and exploration, this more productive form of Health Information Technology (HIT) management has not become a standard part of any managed care system.  It remains, in what I like to call, an “Experimental Use” category for HIT.

My grid mapping algorithms begin with the zip code mapping style, in which two kinds of maps are developed.  The first is a raw data related zip code choroplethic or 3D columnar like demonstration of where the distributions exist for each particular metric.  These metrics are, like before, of diseases and disease groups based upon ICD9, but also included evaluations of human behavioral patterns like late refills on necessary chronic disease prescriptions, or incidence/prevalance rates for some of the more classical population based disease distributions.

My second way of 3D mapping uses the grid modeling, of one or more resolutions.  I tends towards the “best resolution” models determined using standard comparison analyses of the ability of different grid cell sizes to cluster series of adjacent cases to each other, producing the best fit half-bell curve of smoothly changing frequency distribution half-bell graphs.    Each have their values when used to produce 3D mapping of large regional health care statistics.

My recent years have been spent applying these processes to local disease mapping.  In particular, larger urban areas were needed to test the models that were produced.  This allows the cartographer to test outcomes, and then to quantify the utility of each process relative to the spatial-temporal distribution patterns of diseases or measured events.  Some processes work incredibly rare on rare disease events.  Some appear to be under representative of high density patterns, like diabetes, obesity, hypertension, atrial fibrillation.

The use of prevalence incidence metrics for the spatial grid modeling technique is still very important to work with.  But in too many cases (about one third of the time), this traditional use of “rates” as determined by demographers, epidemiologists, and public health specialists to be the chosen method, are unrealistic and useless at the business level.  It would be a tremendous waste of company money to direct your spending to the higher prevalence/incidence places, which some of these values relate to low population counts, not any true need to healthcare facility or products and care management development.


There are several essential ways to develop your EMR data for automating the managed care population health reporting process.  With the proper use of macros, you can develop the programming needed to make these calculation processes happen.  Most importantly, this methodology doesn’t require the introduction of new software and work station settings to the system.

Granted, these standards to evaluating populations will be around for a while, and unfortunately, they distract from the directions in which more effective evaluations need to be made.  The standard Cognos, Sharp and other systems out there are productive, but too slow.  These processes evolved from the processes I developed are a great deal more productive, and produce lengthy detailed reports, on a daily basis if you wanted.

In the past year, the following algorithms were developed, tested, utilized and produced into report-producing programming end products:

  1. Population pyramid and standard barchart graphical depictions, for detailing standard metrics (demographies, diseases, costs, savings per member, cost, predicted costs, etc.)
  2. Age-Gender Population Pyramid produced statistical comparisons of matched or comparable regions, for any health-related metric
  3. Race-Ethnicity defined grouped data and regrouped data (binomial grouped) differential analytics, per metric
  4. Religion and Health regrouping algorithm used to compared traditional and various non-traditional religion and religion-health profiling methods for evaluating areas, regions, neighborhoods, in relation to treatment patterns, refusal for care, disease outbreak patterns, and performance of preventive care programs.
  5. A method for defining the least healthy patients in any program or population
  6. A method for defining the most important (highest numbers, incidence, costs)  ICD groups for a given population, based upon race, ethnicity, etc.
  7. A method for applying and then combining or merging the three major risk scoring formulas into a single population-area health analysis methodology, specific down to the 1-year age increments for assigning a risk score, for each patient, and then reporting the summary of these data at the age-gender depicting race, ethnicity, religion, regional, program-defined, facility-defined levels [8 x 3 x 10 x n1 regions x n2 insuranceprograms or insured groups (MCD, CHP, MCR, COM, etc.)].
  8. A method for mapping these data (numerically, or fraction/ratio related):
    1. institutionally
    2. facility or office/provider based
    3. by network types (MCD, MCR, CHP, COM, BCBS, Obamacare)
    4. by industry distributions (urban hierarchical modeling processes)
    5. by socioeconomics data, race, ethnicity, and religion
    6. by specific disease classes (ICD, V and E codes)
    7. by specific human behavior patterns (based upon criminal or consumer data)
    8. by potential investor types


We expect some of what we see on the above diagram.  As people get older, they potentially get sicker and require more care.  Those who are the sickest experience increases in care related needs, relative to their number of chronic disease patterns.  Visits include all visits to doctors, hospitals, labs, referrals, ERs, counselors, anytime you walk into a place and are billed for that visit. Procedures are actions taken by whom you isit, like your lab tests, your annual x-ray, PET, MRI or mammography, your routine drug levels screening for seizure control, your monthly drug urinalysis.  VPR is Visits to Patients (per patient) ratio, per 1 year increment of age. ProcVR is the procedures to Visits ration; expected to increase as you get older and more procedures need to be peformed for more reason.  ProcPtR is Procedure Patient Ratio, which is a product of ProcVR and Visits to Patient ration of average visits per patient, by age increments (1 year).  The left column is for all standard office visit procedures; the middle column for Emergency Visits only; the third is of a population with a specific medical history using the ER for a specific reason.  What is most important to note  is the flate VPR that exists throughout the ER visits, relative to age.  Younger people come in for different reasons, but that seems to balance out with the newer reasons that lead older people to come into ER for in order to receive care.  The small spike at 0-2 years of age is for post-delivery problems, that often cause deaths in some children.