These four maps demonstrate one of the most controversial things about disease mapping.   Sickle Cell is a disease strongly linked to Slavery history.  It is less correlated with other African or African American cultural features such as Muslim in-migration communities, international African missionary program locations (some ICDs are strongly associated with these due to special humanitarian programs) and contemporary African American cultural behaviors and features.

 

This begins my discussion of examples of how to apply these maps to contemporary research, using the Mixed Model or combined qualitative-quantitative research approach to evaluating population health. 

 

The two cultures most in need of this Mixed approach right now are the Hispanic/Latino and African American/black/negro groups.

There is a very important argument that population health monitoring leads many of the critics to.  When, if at all, does this procedure violate HIPAA?  

 

How does grid modeling actually correct for this problem through a  change in spacial parameters and region or place definitions?  

 

Traditional GIS using standard base maps enables zip codes, towns and census and voting blocks to be linked to the data–this link is expressed in the data used to produce the map.  

 

When grid mapping is used (like my NPHG method), you eliminate that ability to link a diagnosis to a particular site as we know it.  To know a particular case is from a particular town or hamlet, you have to know as well that the areas next door are lacking these cases.  No healthcare professional or expert in a disease knows with absolute certainty where every case resides in a region, not matter how much he/she thinks the program knows, since it is the "expert" in the field.

 

In other words, the likelihood for a "mile away" example, that people often forget, is always there.

 

The two maps on the left depict a special formula I used to evaluate Sickle Cell carrier identification. (The IP process converts the numbers to relational concepts, a method more useful today when analyzing certain disease patterns and mimics the prevalence methods always in use).   When IP maps and numbers maps are close together, the result is a very strong spatial association in terms of magnitude.    

 

The maps on the right depict where the African Americans were residing during the slavery period in U.S. history, and how African American households are distributed today.    

 

There is a mid-Atlantic density for carrier traits situated along the densest population density region in the U.S..  This is either due to population density itself or the primary in-migration routes for slaves early in U.S. history.  Virginia, the heart of slave trade and in-migration early on in U.S. history, remains the center of the carriers for this genetic trait.   

 

The isolated high peaks along the west coast demonstrate a more recent outcome due to socio-cultural behaviors, situated in fairly small, perhaps even isolated African American communities.     

 

This map brings up two ideas for a qualitative-quantitative research of African American diseases patterns.  Are the East coast social aspects or variables similar to the same for the west coast?      

 

If we were to select several small communities on the east coast, and compare each to the single isolated community on the west coast, will latitude (north versus south) differences be detected that are distinct when compared to the west coast patterns?    

 

We know that the southern belt, stretching from Mississippi to Florida, has social, cultural and health and human behavior differences when compared with the Atlantic states.  Do statistically significant differences exist between these two regions as well?    

 

For researchers and students – – my mixed methods pages are :    

 

 https://brianaltonenmph.com/biostatistics/grounded-theory/combined-qualitative-quantitative-methods/     

 

https://brianaltonenmph.com/biostatistics/grounded-theory/combined-qualitative-quantitative-research-methods/     

See on Scoop.itMedical GIS Guide