An Examination of the Amount of People Without Health Insurance Between the Ages 18 to 64 in the United States

Introduction

For this analysis mapping analysis will be conducted of people who lack health insurance between the ages of 18 to 64 in the United States. It is widely known that one of the largest areas of concern in America is healthcare. It was only recently that Barack Obama passed the affordable care act which enabled millions of Americans to obtain health insurance and get health care that they would otherwise not be able to get. As soon as the new president entered office he has been determined to change this. Many Americans are living with serious undiagnosed conditions because of lack of access to health insurance. According to a study it was found that hypertension awareness and treatment was lowest among the uninsured and adults without health insurance therefore leading to not regularly getting healthcare they should be.

Data

The shape file for this analysis was taken from https://www.census.gov/geo/maps-data/data/tiger-line.html. The data for this study was obtained from the County Health Rankings & Roadmaps program. Data was collected from all counties accross the U.S., and the variable examined was noinsurance (Percentage of people aged 18-64 who lack health insurance). The tigris package was used for mapping purposes and the shape file.

library(tidyverse)
library(sf)
library(tmap)
library(tigris)
library(spdep)
library(data.world)
library(magrittr)
library(dplyr)

library(sf)
ct_map <- st_read('./tl_2016_us_county/tl_2016_us_county.shp', stringsAsFactors = FALSE)

Reading layer `tl_2016_us_county' from data source `/Users/robertperez/Documents/Soc. 712 Markdowns/tl_2016_us_county/tl_2016_us_county.shp' using driver `ESRI Shapefile'
Simple feature collection with 3233 features and 17 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -179.2311 ymin: -14.60181 xmax: 179.8597 ymax: 71.44106
epsg (SRID):    4269
proj4string:    +proj=longlat +datum=NAD83 +no_defs

names(ct_map)

 [1] "STATEFP"  "COUNTYFP" "COUNTYNS" "GEOID"    "NAME"     "NAMELSAD" "LSAD"     "CLASSFP" 
 [9] "MTFCC"    "CSAFP"    "CBSAFP"   "METDIVFP" "FUNCSTAT" "ALAND"    "AWATER"   "INTPTLAT"
[17] "INTPTLON" "geometry"

insurance_data <- read_csv('/Users/robertperez/Documents/Soc. 712 Markdowns/Noinsurance2016.csv')
names(insurance_data)

 [1] "Geo_FIPS"     "Geo_NAME"     "Geo_QNAME"    "Geo_STATE"    "Geo_COUNTY"   "SE_T006_001" 
 [7] "SE_T006_002"  "SE_T006_003"  "SE_NV005_001" "SE_NV005_002" "SE_NV005_003"

indata <- rename(insurance_data,
                 "County" = Geo_QNAME,
                 "STATEFP" = Geo_STATE,
                 "noinsurance" = SE_T006_002)
head(indata)

indata1 <- indata %>% 
  mutate(fips = as.integer(Geo_FIPS))
ct_map <- ct_map %>% 
  mutate(fips = parse_integer(GEOID)) 
noinsurancemap <- ct_map %>% 
  left_join(indata1, by = "fips")

Spatial Mapping by County

noinsurancemap1 <- noinsurancemap %>% 
  filter(STATEFP.x != "02") %>% 
  filter(STATEFP.x != "15") %>% 
  filter(STATEFP.x != "60") %>% 
  filter(STATEFP.x != "66") %>% 
  filter(STATEFP.x != "69") %>% 
  filter(STATEFP.x != "72") %>% 
  filter(STATEFP.x != "78") 
##**Excluding Hawaii and Alaska**
tm_shape(noinsurancemap1, projection = 2163) + tm_polygons("noinsurance")

In the map shown above we can see the percentage of Americans that do not have health insurance amongst all the counties in the United States. While the map does help illustrate the persons who lack health insurance, it is rather difficult to look at and make assumptions from.

Adding State Borders

library(tmaptools)
States_16 <- noinsurancemap1 %>% 
  aggregate_map(by = "STATEFP.x")

tm_shape(noinsurancemap1, projection = 2163) + tm_polygons("noinsurance", palette = "-RdBu") + 
  tm_shape(States_16) + tm_borders(lwd = .36, col = "black", alpha = 1)

Highlightings the State Line

tm_shape(noinsurancemap1, projection = 2163) + tm_polygons("noinsurance", palette = "-RdBu", border.col = "grey", border.alpha = .4) + 
  tm_shape(States_16) + tm_borders(lwd = .36, col = "black", alpha = 1)

After highlighting the state line we begin to see the differences alot more clearer in the percentage of Americans who lack health insurance among states more clearly. There seems to be some dark red along the Texas state lines. The map is filled with alot of light colors in the east coast indicating that the percentages of persons who lack health insurance are lower amongst people in the north east as opposed to people in the south and in the west coast.

Non-Spatial Mapping

Below are two examples of a non-spatial approach. Immediatly one can see the differences between the two approaches. The Spatial approach as seen above using the map of the US is aview of the data on health insurance while adding another dimension in providing you with a map. By looking at this map one can see the difference in county, states, american nations and many more just by loooking at a map you can be provided with so much data. By making the map interactive you can even get exact number. Through the non-spatial approach as shown below it is like looking at a graph. By looking at the bar graph below i can clearly see that the average percentage of people who lack health insurance is around 20% which is something i could not determine just by looking at the spatial map above. So in terms of looking at the number and just straight data the Non-Spatial approach has its benefits.

ggplot(data=indata, aes(noinsurance)) + geom_histogram() + labs(title = "Persons with no health insurance") + ylab("Percentage") + xlab("Lack of health insurance in the U.S.")

library(lemon)
ggplot(data = indata, aes(x = STATEFP, y = noinsurance)) +
    geom_point() +
  coord_capped_cart(bottom='both', left='none') +
  theme_light() + theme(panel.border=element_blank(), axis.line = element_line())

Part 2

What does ‘cb=TRUE’ do? The code cb=TRUE simply downloads a smaller file (1:500k). This will allow you to enter a resolution size custom of your choice. If cb=False tigris will download the default normal shapefile which will not be able to be changed if it is to detailed.