The number of people diagnosed with Legionnaires disease has risen to 108 as America’s largest city suffers from a record outbreak of the form of pneumonia, authorities said Saturday. No new deaths have been reported on top of the 10 announced earlier in the week and officials say the outbreak is now on the decline. To date, 94 people have been admitted to the hospital with the infection since the outbreak began on July 10 in the south Bronx, the poorest section of New York state.

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Working in the heart of New York City right now, some thoughts about the nature of what is happening with Legionnaires makes me wonder how the press and people are going to deal with this.  

It is worth mentioning that I am perhaps two steps shy of feeling much concern for my developing this disease.  First and foremost, I use my "sense" of health and especially immune health (mostly subjective assumptions) to keep these concerns at a minimum.  Legionnaire’s erupted in my medical school during my first year there, in 1982; I did not catch it then why might I catch it now?  May I add, this reasoning is identical to what I used to perform my field surveillance work around known houses with cases of West Nile inside; the purpose of being there was to map out the ecology of that setting and evaluate the species of vectors and determine if they were carriers.  The second reason I had was even more pinpoint–all of the cases I investigated then involved people who were much older than me . . . and therefore textbook cases.  

 

Everyday, I take a train past on of the hot spots for the most recent New York City Legionnaires outbreak, and may have even traveled to one or two of these sites as part of my work.  

But seeing the big picture of what’s happening in the immediate one to two block area, around a potential outbreak nidus, and seeing how many people travel those streets and enter those buildings each and every hour, I find it amazing to think that anyone could actually design a model to accurate predict the diffusion activity down to the small area spatial level.  That’s what makes spatial epidemiology as exciting as it is and formula writing the big brainteaser that it is.  

 

Taking a break at lunch, I counted about ten thousand people coming in and leaving a less than one block area in lower Manhattan per hour.  Therefore, it makes sense to guess that this is a place where an infectious disease could very easily come in.  

 

But exactly where the infectious person goes once he lands on Water Street is  a different story.  He or she could head to a hospital in the Bronx by taxi or subway, the many local tourist sites, the 911 monument, to times square, or to a small clinic north of Harlem by bus or company transit because that is where he or she works.  How and where a disease will actually diffuse away from the Old Slip is anyone’s guess.  

 

What’s also important here is to realize that is how Valentine Seaman thought when he was trying to map yellow fever out in 1799 or 1800,  following its repeated return in early October.  Seaman thought his disease had to be either locally airborne or came in by crew and passenger and/or by rotten foodstuffs and stinky ballast water. 

 

So, based upon Seaman’s observations, applying this same logic to Legionnaire’s makes no sense–or does it?  

 

The large air conditioning facility is the suspected culprit in this case–aerosolized water particles being spread about a facility.  But how can such an event commence almost simultaneously in multiple facilities?  Is there another commonality to these places and their people, be it environmentally or ecologically (human ecology that is)?  

 

Once again, that question has to be asked by modern New York epidemiologists–is the disease being spread by people or air-water effects.  The locations point to people.  The pattern suggests an aerosol nature.  The nature of the facilities point to the role or human transportation in all of this.

 

Relating all of this back to GIS–with GIS, we cannot make as accurate prediction for the cause and spread of Legionnaire’s as we would like.   We would have a hard time predicting this outbreak, had we the foresight to think about looking this up months ago. But once it is there, we have a place to begin our spatial prediction modelling routine.

 

Fortunately, we can develop a very accurate model on how a disease can behave once it has erupted, and in retrospect why it is where it is.  

See on Scoop.itEpisurveillance

Childhood Preventive Care Topics for utilizing a 2005 Medical GIS research methods, with examples of results for several 2009 to 2013 test runs  The above is an example of how national population h…

Sourced through Scoop.it from: brianaltonenmph.com

This three layer map I produced for my study of diseases amongst the elderly.  On the top are parts of the US where two types of elderly care related mental health ICDs are found–one American and the other traditionally Asian.  The middle layer depicts the Asian culturally linked illness on its own.  The first layer depicts background mapping data overlain by the US case history of this mental health condition.  

 

These three layer maps are easy to produce, and have the additional value of being useful for mapping a very unique three-dimensional dataset gathered only in urban settings–this can be used to depict people within buildings, with each layer depicting one of the floors of the building.  

 

There are a few places in the country where this kind of mapping is powerful.  The most obvious example for me is the outbreak of heat stroke and exhaustion cases several decades ago in Chicago.   Another use pertains to V-code and E-code claims for such events as domestic abuse, crime and drug use for high rise buildings set up in low income areas.  Occupancy of a building can be evaluated using this building.  Outbreaks due to contagious disease may also be mapped.  

 

The recent outbreak of Legionnaire’s reminded me of the value of this algorithm.  In large bulding settings, where a disease is suspected to be generated by the local environment setting, you can use this procedure to illustrate in three dimensions the cases that are reported.

 

This type of mapping uses non-GIS software to be produced, and common formulas to generate the algorithm.  The value of that algorithm is detailed more extensively at my site nationalpopulationhealthgrid.com.

See on Scoop.itMedical GIS Guide

SAN ANTONIO (AP) — U.S. Homeland Security investigators dismantled a South Texas ring that illegally smuggled thousands of immigrants across the border from Mexico and on to other parts of the state — often tucked in small, dangerous truck crawl spaces.

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This is in fact the surface of a much greater social problem that exists in Texas.  Some of the most useful findings that studies of V-codes, E-codes and ICDs demonstrate pertain to preventable human behavior and cultural and poverty-linked diseases and health issues.  In this NPHG study, pathways leading form Mexico into Texas also brought into this country many other controversial health problems, such as communicable disease, zoonotic diseases, illegal drugs, and a host of very unique health related matters. In the n-squared image displayed here, notice the small town in Texas at the Tex-Mex border.  It is very indicative of the main route taken by kids into this country.  It is also where the greatest amount of physical and sexual abuse of teenagers prevails. This may be due to a number of reasons, but two of the most commonly cited ones are teen age prostitution and "domestic servant" contracts. 

See on Scoop.itMedical GIS Guide

PopnHealth_RareDiseases.jpg

The people in healthcare are the patient, the physicians, the nurses and other allied health care givers, the administrative staff, the individuals in charge of facilities upkeep, pretty much every person who sets foot in a healthcare facility or setting.

There are a few people that are virtually present within a healthcare environment.  The most important of such are those that comprise an individual’s past medical or health history.  This includes past physicians and surgeons, and all of their associates who saw you name, you medical history, your state of being, your financial state and healthcare debt status.

The indirect, almost invisible people, also present in the healthcare equivalent of Big Data and its collection of people’s health trivia and other unexpected tidbits of personal knowledge, are the financiers, investors, innovators (if there are any), outside thinkers, members of the Boards and Committees who have minimal business relationships with the system, and as many other people one can think of that contribute to the quality of life and health in the health care environment setting.  There are the brownies perhaps, or the dog breeders with their patient friendly doses of animal medicine, or the volunteer groups often interacting with the children’s leukemia floor patients, or the post-op epilepsy wards where patient need to readjust to their recent physical and possibly cognitive changes.

Some of the the events I mention are very rare.  Others are quite common, so common they happen numerous times every day.

The rarest examples are those outlier cases that come into a facility or unit, the likes of which may never be seen again by that team due to its low incidence rate.   There is that hemophililia patient, whose cost amounts to more than 1 million dollars per year, just for the costly pharmaceuticals.  Such a cost may be covered personally, or by some government program like medicaid, or by the patient’s insurance agency (rather rarely so, however).  There are those 6 infants per year who present with a fracture of their arm near the elbow, for which there is usually only one way that happens–child abuse.  There are those people whose health history demonstrates an exceptional outcome, so exceptional the system has to determine how to treat such an event, quietly or supportingly and even outwardly, whatever way being the patient’s choice.

PopnHealth_TallShortPeople

The rarest medical events occur on a scale of a few per 100,000.  There are even rare events, but for this review, we will stick with the handful of cases per 100,000 patients as a realistic prospect when you are reviewing two million people.

Two million people has within it 20 groups of 100,000 people.  If the incidence of something is 3 per 100,000, that means for your 2 population population, you have 2,000,000/100,000,  or 20 times 3 possible cases that might exist in your patients’ list.  That’s 60 cases, and is possibly enough to do a mixed study of, in terms of the quantitative-qualitative research sense.

The more common events documented in medical records are those in the n/10,000 ratio, one logarithmic level greater than the previous example.  These patients provide researchers of that population with the most valuable insight once the 2 million begins to be studied.  These kinds of events (diagnoses, complications, etc.) can be situations which are too often ignored of not reviewed.  Again, with a mixed approach to this sort of study, you could produce a very valuable set of insights into this population, enough to advance the special programs defined for these individuals further along.

The second benefit of this incidence and the 2 million population size is that we now have 10 times more examples than the previous example.  Instead of 60, we have 600 cases.  Perhaps the entire population can be reviewed, for general features such as age range and gender, but now also more specific features such as income level, neighborhood setting, type of job.

PopnHealth_Epielpsy

The n/1000 group provides us with still better opportunities to explore health and health related events at the small subgroup level.  For a 2 million population, with the same incidence noted before but increased tenfold, we have 3/1000 or 0.3% of the population eligible for the review–totalling 6000 cases (from 0.003 x 2 million).

Now we definitely have to sample for our study, unless we are dealing with only EMR/EHR data.  Then we can explore the 6000 cases and explore their features, mine their other records in search of unique cases and/or outliers.    A common example of this kind of scenario is studying the epileptic patient, the frequency of which is about 4 out of 1000.  A two million patient population will not doubt have about 3000 to 6000 patients, of which if we select the most active and present examples, we are still provided with a large enough population to apply mixed methods research to.  The question on how to deal with this possible study population size is as follows:

  1. First, you need to deal with specifically what it is that you want to study, and how big are the related subgroups.  With epilepsy, we could divide these people into socioeconomic groups and/or ethnic groups, and probably come up with a completely new insight into managing these patients.
  2. Second, you need to know what topics can be studied that can be linked to an intervention or improvement process.  For the treatment of epilepsy, these could include such metrics as waiting room time, frequency of hospitalizations, comparisons of health and performance of patients between major treatment facilities or groups, comparisons of the smaller subgroups to each other (the many kinds of epilepsy), to see which programs are effective, and which are not.
  3. Third, you need to evaluate when, where and how qualitative reviews will carry your study further into the unknown.  Case studies, focus group activities, surveys are all methods  available to these patients for further exploration of their care process, and how well it meets their needs.

PopnHealth_Asthma

For the n/100 group. . . that seems like it could be too many cases for your clinical teams to deal with for a study.  Selection is definitely needed, or requests for voluntary participation.  But at the EMR/EHR level, these groups also allow for a stabilization of data quality in the EMR/EHR world.  One can evaluate all of these patients, filter down to smaller groups by finding the percent of good versus bad records, and then come up with a set of rules for evaluating this kind of population in general.  Examples of this would be the diabetics and heart disease patients common to healthcare programs.

It could also be demonstrated as certain forms of poor patient compliance, poor physician performance, poor follow-up activities, poor long term quality of life consequences.  Again, the mixed approach to studying this group is possible.  Defining subgroups or specific aspects of the care process that you wish to improve can be added to the overall study design.  This is the situation in which performance improvements can be made in quality of care offered for certain ethnicities or minority groups.   Such work on these patients will also significantly impact the cost of care overall, first by increasing healthcare system engagement processes, themselves requiring more money, and then secondly by targeting and actively engaging doctors and patients in this quality of service, quality of life related process.  As before, sampling may help, but it is more useful to subdivide your patients into smaller groups, define your priorities, and continue this study with an emphasis on the differences between the forms of patient care provided for each of these sets of patients.

PopnHealth_GrowingTallerwithYoga

So, the number 2 million is certain a benefit to a healthcare system, if an when I can evaluate such a population size.

Next, we have to answer the question: ‘What are the ways to initiate this form of research?’

PopnHealth_Research

COLUMBUS, Ohio (AP) — Doug Hamilton is just fine with plans to put a woman’s portrait on U.S. paper money, but he’d prefer that the Treasury Department leave the $10 bill alone — particularly the prominent visage of his great-great-great-great-great grandfather, Alexander Hamilton.

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How naive people are about history. 

 

If we’re going to make a change, I say "REPLACE JACKSON, NOT HAMILTON".

 

According to many, Jackson did much to deserve heavy criticisms in retrospect.   He was not always the best example for being a president.  And when it came to medicine, Jackson felt the MD wasn’t worth his few bits for a simple bloodletting, which he routinely did on his own to himself at night.

 

Alexander Hamilton, on the other hand, was of utmost important to national security.  In medicine, Hamilton was the first to take control of the tropical fever problem developing in troops.  Due to yellow fever, he established protocols for packing and unpacking or exchanging goods from ships, military wagons and carts.  All goods were sealed in fabric, placed in a vehicle under a secured cover that was sealed and had to be recorded each time it was opened.  In just one year, this proved beyond doubt that yellow fever could not be carried by the military wagons, and therefore was not transmitted by air that got caught in sealed containers and wrapping materials.

 

As for his greatest accomplishment outside medicine and his work as a Military leader, Hamilton worked alongside Thomas Jefferson when the nation’s first National Homeland security act was passed and signed. [see https://www.pinterest.com/pin/568790627909905309/ ]  

 

This can be reviewed at the Pinterest site or on pp. 63-65 in ‘A defence of the measures of the administration of Thomas Jefferson’, Volume 40, Issue 3, by John Taylor, Thomas Jefferson Library Collection (Library of Congress), Miscellaneous Pamphlet Collection (Library of Congress). in books.google.com/.

See on Scoop.itGlobal∑os® (GlobalEOS)

USA Today Editorial states:  “Last year, the U.S. saw 668 cases. Midway through 2015, about 180 people across half the states have been sickened. Many cases were linked to an outbreak at California’s Disneyland, and most of those who’ve fallen ill were never vaccinated. Last week, Washington state reported the first death from measles in the U.S. in 12 years.”  and  “States that still allow easy opt-outs should follow Vermont and California. . . . “

Sourced through Scoop.it from: www.usatoday.com

Let’s apply Prochaska’s Transtheoretical Model of Behavioral Change to this public health/healthcare administration problem.

 

The healthcare programs in the US are for the most part either in the pre-contemplative or contemplative stage for GIS implementation.  The more engaged programs are focused on skill building.  Utilization of just a simple spatial program to map your data is early "Preparation"  Unless you have a GIS established that deals with all QIAs, PIPs, MUs, Chronic Diseases, and the majority of HEDIS metrics, reported yearly, your agency, company, or facility is not in the Action stage.

 

Based on my national GIS/NPHG study outcomes, my recommendations are as follows:

 

#1 – regarding refusals to immunize, focus on the Seattle, Portland, San Francisco, and maybe Los Angeles areas.  

 

#2 – approach insurance companies and physicians’ businesses in the Pacific NW urban settings and ask them why they have facilitated this problem for 25 years.

 

#3 – develop mandatory rural health monitoring programs for the Pacific Northwest

 

#4 – research the in-migration track from NYC to Albany where the hot spots develop due to tourism and immigration.

 

#5 – improve public health security programs designed to prevent the spread of immunizable diseases into Canada via the Buffalo area and other Great Lakes related paths into Canada; apply this to all other high fatality diseases capable of crossing borders (this is possibly the chief route for unexpected yellow fever entry).

 

#6 – continue to vamp up Mex-Tex border security, and establish plans for the south the north route these individual take with their disease, from the border to Midwestern cities along the Mississippi and Chicago.

 

#7 – increase public health security along the Haiti/Cuba-to-Florida route (chikungunya and naturalized Ebola/Ebola host routes).

 

#8 – set up a plan for potential polio re-emergence around the Great Lakes; consider Canada or Chicago a possible direct or indirect (via NYC) point of entry.

 

#9 – require the largest health insurance companies like Aetna, Anthem BCBS, Blue Cross Blue Shield, Cambia, Cigna, Emblem, Fortis, Kaiser Permanente, UnitedHealth, to initiate a medical GIS program immediately, that is capable of working in 9 months, to begin quarterly reporting in 12 months.

 

#10 – Retire all CIOs, CTOs, Directors, VPs, managers, in charge of IT/HIT/QI/QA, who lack spatial epidemiological background and experience, and have not published or presented a demonstration of their unique skills by the implementation of new programs and/or publication of spatial epidemiology results that are more than just descriptive statistics.

 

For those highly adventurous, I recommend:

 

#11 develop a program that separates and spatially evaluates sociocultural and socioeconomic classes of diseases and quality of care (in descending order of priority) for: lowest income classes, highest cost CDMs, african/african-american, hispanic and subgroups, asian/asian-american, native american groups.

 

 

See on Scoop.itMedical GIS Guide

PeruCholera

Peru has declared a 60-day state of emergency in towns in 14 regions to brace for possible damage from the climate pattern El Nino in the rainy season, state media reported Sunday. Peru has forecast a “moderate to strong” El Nino in the winter season and has not ruled out an extraordinary event in the summer, which begins in December in the southern hemisphere. The phenomenon, a warming of Pacific sea-surface temperatures, has wreaked havoc on local fishing in Peru and triggered landslides in years past.

Sourced through Scoop.it from: news.yahoo.com

What has this got to do with health?  Everything!

Back around the turn of the 19th century (the late 1800s), cycles became a pop culture craze (like dozens of times before, and after).  During the early 1900s, professional journals began talking about the cycling of weather, and its effects upon finances, crop production, the job market, and the resulting political and social turmoil these changes often created.

The American Meteorological Association came to be around 1910, and a number of theories for cyclic weather patterns were published.  The most popular one was the Sunspots Theory, which claims that weather patterns were impacted by the changes in solar radiation related to sunspot activity due to the solar flares and “solar wind” that were produced.  Now, all of these changes in the energy patterns for the Solar System were in fact quite true.  But the association of these natural events with manmade events on the earth’s surface was hard to accept.

So those in favor of this theory found many more ways to support it.  Some even proposed secondary cyclic patterns, which went in and out of resonance with the sunspot cycle.  This explained still more events then awaiting recognition.

The cycling of finances, in particular stock prices were still hard to accept this as an explanation for.  We could easily accept this argument for food industry products like corn, soy bean, grains.   Meteorologists had demonstrated some links between drought and the solar cycles.  That was enough to hush everyone who was against this theory–it had indirect implications–hard to provide either way.

But then out came a new rendering of this sunspot theory in the 1980s–the La Nina-El Nino cycle theory.  It provided another explanation for atmospheric changes, that couldn’t always be correlated with the solar winds (which are true events, the winds are energy related).

When I returned to college it was my intent to link the Asiatic Cholera outbreaks over time to this philosophy.  I spent several years researching this, even returning to some of my dendrochronology work that I did back in the 70s.  But then, I turned to the Cycles journals again, and took issue with how the natural cycles were being compared once again to finances, automobile manufacturing and sales, changes in gas and electricity stock prices.  An ecological approach to studying cholera cyclicity and peak outbreak times, based upon La Nina-El Nino theory, was in fact possible, due to the use of GIS to review these past popular culture themes.

Naturally, over time, my interest in the cyclicity diminished once GIS came to be my tool at hand, instead of my handy increment borer for extracting tree rings and using the tree ring cycle data sets shipped to me from the leader in this field (evaluated on a 286 PC).

EMR/EHR, Big Data, the iCloud, and GIS can now be used to test these older pop culture theories.  We can prove once and for all whether or not the cost for growing coffee beans in parts of Africa will influence the country’s ability to control its other social and economic problems, not to mention the events leading to the next spread of Ebola.

Yes, there is another cycle starting, but now we can begin to monitor it from day one.  If and when there is an outbreak, we can define how and why it had everything to do with El Nino, or nothing at all to do with the oscillation of global energy patterns.

This cyclicity and global energy phenomenon was also used to explain global outbreak patterns in the mid-19th century.  The British Surgeon in Charge at the Military Hospital in Crimea, Ukraine, was removed to Jamaica in the Caribbean following the medical disaster that struck that place during the Crimean War.

In Jamaica, he came up with his theory as to how the magnetic fields generated by the earth could be the cause for the moving and cycling of yellow fever outbreaks around the world.  The earth’s magnetic fields moved about, as did the yellow fever outbreaks.   As a result, his theory–Robert Lawson’s Pandemic Waves Theory–was published by esteemed medical journals. After all, he was a member of some of the Royal medical societies.  He drew up what he called “the World Isoclines Map” ca. 1860-1875, and used it to explain the outbreaks.

Sound familiar?

We are back to square one with evaluating the impacts of climate and natural cycles on disease patterns and outbreak behaviors.

With the right GIS in place, this controversial issue could have been resolved ten or fifteen years ago, all except the cycling periods of indecisiveness that politics and medicine–global health patterns–are riddled with.  Perhaps it may take a recurrence of a past disastrous outbreak, to lead to enough research, to at least resolve this issue once and for all.  And even then. we may still be left not knowing how and why Chikungunya, MERS and Ebola behave the way they do–but of course that may also be due to the lack of GIS implementation, for people health, not just ecological health.

Peru by the way is a hot spot for vibrio ecology studies and a natural setting where vibrio has become naturalized.  It bears the classical and two most dangerous strains are linked to this Asiatic Cholera disease nidus, in particular the El Tor.

See on Scoop.itMedical GIS Guide

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