. . . the solutions to our problems lie outside the box.
Aviation Week and Space Technology, July 1975
There are specific patterns that recur in the mapping of diseases in the United States. The reasons for these recurring patterns are probably related to physical demographics or physical medicine and health data (race, ethnicity, genetics), social demographics behavior or sociological attributes data (urbanicity and ruralness, community and social development, cultural/ethnic behaviors and requirements, etc.), physical environmental data (climate, weather, air quality, humidity, water quality, etc.), and natural history based ecological geographical data (the ecology and geography of biological substances, and biological or organismal related beings, namely pathogens, vectors, carriers, hosts, disease-ridden animal populations, etc.)
The following list details the above in a more logical fashion:
- Physical demographics — physical medicine/health linked features
- Social demographics or sociological attributes
- Physical Environmental – physical and energy based
- Natural history ecological – related to or involving life forms
For each feature of the disease, condition, diagnosis, medical event or activity reviewed, there are two major sets of conditions to consider.
The conditions related to human behavior can increase the likelihood that a particular condition will ensue, be diagnosed, or progress in the way that it does spatially and individually, on a per patient basis. These features also have independent, individualistic components as well as socially-related to socially-bound non-independent components. Certain mental behaviors for example have built in sociological and systems-generated diagnoses and behaviors. Certain psychiatric diagnoses have their related sociocultural and independent biological, physiological, neurologically-defined features.
Conditions may also be linked to just the environment, the results of which may or may not in turn be influenced further by the environment or by the social and cultural results of the diagnosis that ensues. Purely environmentally-induced diseases like fungal conditions, elevation induced hypoxia or vertigo, are in theory predictable due to the easily identifiable nature of their potential natural causes. Other diseases of environmental origin but with some dependency upon human behaviors to ensue such as hypothermia, Lyme disease, West Nile fever, and E. coli exposure, all have some human behaviors linked to their cause, but the initiating factor may be totally of environmental origin. In some cases, the human-modified environment is responsible for this disease, a status considered to be human ecological in nature. Other times, only nature’s ecological make up constitutes most of the cause, such as the onset of Mad Cow disease induced by eating elk meat food stuffs.
Somewhere in the middle of these totally human-generated behavior and psychological/psychiatric and culturally-bound diseases, and the purely environmental diseases with and without ecological bases, are the combined human ecological-human behavior disease patterns. These diseases constitute perhaps the largest group of socially-linked disease patterns, with prevention a difficult process in many cases due to their close links to human habits and human behaviors. Examples include diabetes, obesity, heart attack, smoking related COPD and the like, etc.
The kinds of spatial patterns of distribution that recur with certain groups of disease tell us these conditions/diseases etc. have something that links them to similar spatial distributions and outcomes. Defining the causes for these shared medical histories by different people, even with different diseases that appear related, has been an ongoing purpose for the study of human psychology, sociology and anthropology, ethnology or culture when it comes to studying human health and behavior patterns.
The following is an initial attempt to make sense of some recurring patterns in the distribution and shape of disease/medical event peaks seen in the US population. Sometimes there may be no reasons linked to these similar peaks in counts and independent prevalence (IP), other times these peaks are very closely related. Similarly, two peaks which show a broad distribution about a large area may not at all be of this nature due to identical causes for the two different places in the U.S. A broad band peak around Chicago for example for one ICD can be linked to environmental features, whereas a broad band for a completely different type of condition on central Long Island appears to have sociological reasons for this regionalization of its patterns and behaviors.
In spite of the possibility of similar peaks linked to very different causes, the presence of these peaks, and the lack thereof in places where they are expected, raises a flag to epidemiological spatial analysts. These similarities and differences must first be considered coincidental in nature, their potential causes pursued in a way normal to medicine and epidemiology work. But they also have to be evaluated for any reasons possibly linked to these similarities. We need to develop a much deeper insight into the behavior of the disease itself, as well as the people it impacts and the means by which they first develop their behaviors that make way for the condition or disease. We can then more effectively react to the presence of that malady, by way of producing more effective care for such problems.
Spatial disease mapping and learning to recognize large areal distribution patterns for diseases opens several entirely new doors to methods of researching disease ecology, human disease ecology and disease prevention and prediction practices. Likewise, spatial disease mapping has some predictive modeling applications that link it to engaging in prevention practices. The spatial behaviors for diseases previously unidentified or unknown can provide us with further insights into the patterns and behaviors responsible for the spatial behaviors of diseases in general. For example, the mismatch between adult distribution and congenital tuberculosis distribution patterns tell us there has to be a reason why different places of high prevalence exist for these two otherwise seeming identical disease patterns. The fact that different age groups have different peak areas for the condition or syndrome suggests that further studies are needed to come up with reasons why such a pattern exists, and how this discovery can be applied to predicting similar behaviors or diagnoses elsewhere. The highest incidence of some behaviors may not be directly linked to the highest prevalence areas for these diseases, due primarily to the role of behaviors in distribution and causality. As demonstrated by the maps documenting immunization refusal rates and disease rates for those that can be immunized, the spatial relationship between childhood immunization refusal and immunizable disease rates does not imply that one event leads to the other. The two have very different distribution patterns.
All Truths go through three stages of recognition. First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident.
My expectation with this method of disease modeling is that it is not going to get much respect for several years, although I would not be surprised to learn about others whom on their own recognizance have been trying to duplicate this methodology as well. Examples of similar efforts appear of the web for Sonoma College, where it was used to map crime statistics, and the Gecon group at Yale University Department of Global Economics, in order to map the global economy. One problem with this method of mapping is that epidemiologists are too familiar and dependent upon the standard polygon and point methods in use, and rely upon this method of mapping along with standard tables reporting to provide all of the key data they believe they need for a business report.
Epidemiologists typically don’t spend much time focused mostly on money and finances. Big Business has a problem in that it doesn’t understand or try to understand people’s medical needs that much. The role of Big Business is to make money, at the expense of emptying the pockets of its large consumer population. How does this philosophy meet the needs of unhealthy people?
Right now, the epidemiologist has the problem of constantly looking at population statistics relying upon broad band ranges. Better results may be obtained by calculating outcomes for much narrower age band ranges, such as the 1-year age-gender increments models.
The details of a grid map add another dimension to this issue at hand. False 3D models are more spatial and true, more detailed in their presentation, but to some, appear to produce too much information for businesses to deal with. The prejudice that prevent businesses from advancing with these new routines is based on the complexity of the underlying conclusions that can be drawn using these methods. For some business people, more information, no matter how well condensed it is in its presentation, means that more work will be required as a result, even when the results of that work are expected to be tremendous successes.
The more people learn this skill as a part of their GIS training, the more likely these innovative methods of spatial interpretation of disease and public health will be put to use a generation or two from now. Without this knowledge being circulated, (even if in theory, not formulas), the more likely it is to be implemented quite late as part of a public health program. In the long run, students will learn and make the best use of this kind of disease mapping and presentation, not employed workers, managers, CEOs, and or even health officials within the epidemiology sector. Change is not an easy thing for modern day businesses to do.
The insights these maps provide public health workers are very valuable as information sources and as preventive health, program planning tools. They provide more area specific data than the typical county-based and point-area town or city based maps commonly produced using contemporary GIS. Most importantly, unlike standard GIS irregular point-area maps, the regular grid models herewith used and presented make it possible for produce highly useful surface trend maps for socially and physically-born disease types. They are the most important tool for spatial epidemiological research of any given region.
Due to their novelty, they are hard to produce due to insufficient information most of the time for small area studies. But enough information does exist for the production of large area distribution maps, in the form of 3D grids and isolines. These maps can then be supplemented by the addition of small area local research maps of given sections of the large area grid maps. (Large area – 25 mi x 25 mi grid cells). With both of these tools in use, one nationally and the other locally, we can produce a very effective disease monitoring and disease prediction tool for application to many different parts of this country. Due to the nature of the programming that is used, these methods can even be employed in generating sizable information reports on the overall health status of the county, region, county, or small community area.
What follows are examples of recurring disease patterns. These patterns can be compared to the examples of 3D mapping demonstrated on other pages.
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Work in Progress