This is an example of a choropleth map. From the map we can see the percent of teeth that have been extracted due to tooth decay or gum disease for 2006, 2008 and 2010. A large percent of teeth extractions occurred in southern states indicated by the counties in red compared to Western states indicated by the counties in yellow.
Interestingly, the South is known for having poorer health outcomes and higher rates of chronic disease. Maps like this allow for this information to be displayed in a picture.
The CDC has created a map that shows the Rates of Adults and Adolescents living with a diagnosed HIV infection by the area of residence. This map was created in 2015 with data taken from 2013. Though there are many areas where there is no data seen, The areas with the highest rates are located in the Southeastern USA.
GIS a relatively new and is finding its way to improve health. Developing countries deal with more technological challenges to make use of GIS. But nonetheless, a very effective project with implication and impact on the local community of Ahmedabad, India is shown below. The project was impactful – was able to communicate the uneducated & also the educated mass on the issue. It went on to be heard by local policymakers too.
The above map developed by UMC in help with AMC staff shows the spatial distribution of slums and community health centers across the city.
UMC staff visited AMC’s existing UHCs and CHCs to understand their functioning and understand the requirements for upgrading facilities. UMC developed a methodology which involved meetings and interviews with health staff including medical officers, pharmacists, lab technicians, multi-purpose workers (MPW) etc. Separate SWOT analysis was conducted with medical health workers and with pharmacists, lab technicians and MPWs. UMC with assistance of AMC staff, identified slum pockets and communities on the base map of Ahmedabad and also marked existing health facilities provided by the AMC. These maps were later transferred to a GIS environment to analyze the accessibility of health facilities by slum dwellers. This assisted in locating newer health facilities as proposed under the NUHM. UMC also has prepared model layouts for the new proposed health centres for the AMC. A detailed phase-wise budget and a proposal was prepared for the AMC for submission to Government of India under the NUHM.
Source: Office of Urban Development Authority of Ahmedabad, India.
There are several factors that contribute to obesity, one of the prominent ones is walkability. With the second world countries, having more space in general, they lack the environment that promotes walkability. These countries are well equipped with parks but they lack the facility for easy commute. One of the articles below shows the influence of walkability on the obesogenic environment.
The above maps compare the number of intersections in a European city vs Los Angeles and also Irvine, CA. Irvine is a city that is lush with many parks but unfortunately, it does not help people who could commute to places through walking.
This is an eye opener for city planners as such as a challenge for them to try to reduce obesogenic environment which would fit other lifestyle choices.
Hotspot analysis is a way fo finding the geographical areas with high and also low (called the cold spots) distribution of the specific variable. The below map shows the hotspot analysis of the distribution of non- vaccination rates in Texas. It is obvious the high non-vaccination rates are around central Texas which geographically correlates with San Antonio and Austin. The cold spots are interesting that they are more around the counties bordering Mexico.
Hotspot Analysis of Unvaccinated Rates in Texas 2016
The above map was created using data from DSHS Texas based on their report on vaccination among school children. The analysis is usually done to raise the research question around the variable based on its pattern of distribution.
In this map specifically, the area of interest is the cold spots which are the counties bordering Mexico. We expect more people not getting vaccinated because of cultural and socioeconomic factors. Since it is otherwise, it gets interesting to analyze the phenomenon. One more fact to be taken into consideration is maybe the unvaccination rates can be underreported because of a high immigrant population around the counties.
Texas has one of the highest vaccination rates for childhood diseases overall, 97.4%, according to CDC. But the number of children not vaccinated because of their parents’ “personal beliefs”—as opposed to medical reasons—has risen since 2003, when such exemptions were introduced, to more than 44,000 so far in 2017 according to CDC. The 4:3:1:3:3:1:4 series is an overall measure that encompasses many vaccines that are recommended for children. Various demographic factors (sex, gender, race, availability of commercial health insurance) influence the decision to get vaccinated, were looked at.
The county-level data on the socioeconomic factors were obtained from US Census Bureau (American Factfinder). The health insurance data was obtained from Small Area Health Insurance Estimates (SAHIE). The vaccination rates were obtained from Texas Immunization registry through DSHS. The data was cleaned and geocoded to be analyzed in ArcGIS to produce maps as shown in Figure 1. Pearson’s correlation coefficient was used to analyze the relationship between vaccination rates and independent variable.
The non-vaccination rates are higher around the major cities of Dallas, Austin-San Antonio, Houston and some northwest Texas counties. Population density has a positive correlation with the non-vaccination rate. Other demographic factors have a positive correlation in certain counties as opposed to others.
Source: American FactFinder, Texas Immunisation Registry
The limitation on the immunization data is it being an optional registry so it would not be accurate to run statistics off this information to estimate an immunization rate. In future, it is productive to expand this concept to use regression analysis to try to find the odds of the relationship expressed in the maps and to find if there is a significant association.