Do Socioeconomic Factors Influence Texans’ Decision to Get Vaccinated? – A cartographic Approach

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.

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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.

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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.

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Average Fine Particulate Matter (PM 2.5) (µg/m³) By County (2011)

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Check out this map that shows the average fine particulate matter (PM 2.5) (µg/m³) by county for the year 2011. From the map we can see clusters with a higher average indicated by the darker shading. For instance, we can see a cluster consisting counties within for Colorado, Nebraska, Kansas and Wyoming. It is also apparent there are higher concentrations in many Southern, Midwestern and Northeastern states compared to western states. States such as, Indiana, Ohio, Tennessee and Kentucky predominantly have a higher average.

By Julia Watson

NHSC and NCQA Certified PCMH Sites In Tennessee

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Check out this map that shows the number of National Health Service Corps (NHSC) sites in Tennessee as of 10/6/17 and the number of National Committee for Quality Assurance certified Patient Center Medical Home (PCMH) sites in Tennessee as of 1/23/18.

For more information on the Patient Centered Medical Home click here.

By Julia Watson

Rate of Self Reported Pesticide Related Illness By State 2014

 

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Today’s map shows the rate of self reported pesticide related illness by state for the year of 2014 per 100,000 person. From the map we can see some states had rates ranging as low as 0.00 to 0.27 indicated by the yellow shading. In contrast some states, such as Alaska, Florida, Alabama, Mississippi, Arkansas, New York, Kansas and Virginia had rates as high as 0.87 to 2.56 indicated by the dark blue shading. However, its important to note that because these are self-reported rates the date is subjected to under-reporting. In addition, because these exposures are self-reported both the type of pesticide and the degree of illness associated with the exposure may be mis-classified since the designation by the poison control center for both is based on the description provided by the caller.

According to the CDC farmworkers are among those when are subjected to pesticide exposure. For more information on migrant workers click here.

By Julia Watson

Number of Homes Built Between 1950 and 1979 By County 2000

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Check out this map that shows the number of homes built between 1950 and 1979 by county for year 2000. From the map we can see there were many new homes built in counties within the eastern states, such as New York, New Jersey, Road Island, Main  indicated by the darker shading.  There were also many new homes built in counties within some western states, such as California and Arizona. We can see states such as North and South Dakota, Nebraska, Montana and Kansas had fewer new homes built within the 29 year period indicated by the yellow/yellowish shading. This makes sense because when compared to the previous map of homes built prior to 1950 for the year of 2000 we see these states had a higher percentage of older homes.

By Julia Watson

Percent of Uninsured Living with HIV By County 2014 (Ages 13 and Older)

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Check out this map which shows the percent of people living with HIV who were uninsured by county for the year of 2014 ages 13 and older. From the map we can see that a large portion of counties within Hawaii, the north east, and some midwest states had some of the lowest percentages of people with HIV who where uninsured indicated by the yellow shading. In contrast states in the south and west had some of the highest percentages of people living with HIV who were uninsured.  We can see Alaska and Texas were predominantly shaded dark, indicating percentages ranging as high as 19 to 39.

By Julia Watson

Percent of People With HIV Living In Poverty By County 2014 ( Ages 13 and Older)

AIDVU_%PovertyCheck out this map that shows the percent of people living with HIV who are living in poverty by county for the year of 2014. From the map we can see that a large portion of counties in the southern and western states have a higher percentage of people with HIV living in poverty, indicated by the dark shading. We can also see that Alaska has counties shaded dark, such as Yukon-Koyukuk Census area, indicating the percentage of those living in poverty range from 22.61 to 47.40.

By Julia Watson