Introduction

According to the Center for Disease Control and Prevention, “While sexually transmitted diseases (STDs) affect individuals of all ages, STDs take a particularly heavy toll on young people. CDC estimates that youth ages 15-24 account for almost half of the 26 million new sexually transmitted infections that occurred in the United States in 2018.”

About 1 in 4 college students have an STD, either being actively treated or undetected yet. Significant stigma and stress surround reproductive healthcare, as people often feel they are seen as promiscuous or “like that” when they have to open up about being sexually active. This stigma leads to a lack of proper sex education and understanding which furthers this high rate of STDs in young adults.

Study Intention and Hypothesis

Using data from the CDC and NCES, this study looks at the prevalence of three common and treatable STDs from 2016-2020, with a possible correlation to college student populations. These STDs are chlamydia, gonorrhea, and syphilis, which make up a majority of the STDs in this age group. The hypothesis for this project is that states with higher young adult populations (specifically, college populations) will have higher cases and overall rates per 100K population. These two variables will be examined to adjust for population discrepancies among states and examine the overall distribution within a given area. It is noted, however, before beginning this project that there are severe limitations and this is just a foundation exploration. The data that would be best for this project would be data pertaining specifically to distribution of cases by age by state to then filter for only the selected age population and properly compare to college populations. That data is not available from the CDC at this time, in a workable format. No concrete conclusions can or will be drawn from this study; all conclusions are further hypotheses to be explored with later data.

Packages and Data

Below are the following packages necessary for this project.

library(tidyverse)
library(billboard)
library(tidytext)
library(readxl)
library(gridExtra)
library(cowplot)

Data Sources

Below is the combined data for the three of the most prevalent STDs in the U.S., syphilis, gonorrhea, and chlamydia. This data was gathered from the CDC Sexually Transmitted Diseases Report of 2020. The tables on the website provide information related to specific states over a period of years and the prevalence among gender and age, but does not provide the intersection between state, gender, and age. For this specific project, although higher refinement would have been preferred, a correlation possibility will be examined between STD rates over time and college populations over time distinguished by state. A second data set, also below, provides the undergraduate enrollment populations by state for 2016-2020. This data came from the National Center for Education Statistics.

std <- read_excel("std.xlsx")
college <- read_excel("collegeEnrollment.xlsx")

Chlamydia

Chlamydia is a curable STD that transmits through oral, vaginal, or anal sex without the presence of a condom when one partner is infected with the disease. Symptoms include painful burning, discharge, or smells in intimate areas. It requires a clinical diagnosis but is treatable through antibiotics.

This map is interactive; the play button to the right side of the map, underneath the legend, visualizes the data over the set of years being examined.

Over the course of five years, no state had a dramatic increase or decrease in number of cases of chlamydia. No particular region stood out as having been a hotbed for chlamydia from 2016-2020. There is some possible increase in the southern region, but nothing statistically significant at this time. The min and max for chlamydia cases remain at a constant of 1117 and 236,719 respectively, for all five years.

In the CDC data, there are two different values: the total number of cases by state per year, and the rate per 100,000 people by state. The rate variable helps to adjust for population discrepancies and illuminate the areas with the highest density of chlamydia by population. This is important in understanding the prevalence of chlamydia by state; areas with lower populations but higher rates of chlamydia indicate a higher rate of infection among residents, and thus, an area of concern.

First, we will evaluate the number of cases of chlamydia by state from 2016-2020, looking at only the highest 60 values over the 5 year time span to evaluate possible trends in states. This is to prevent an overwhelming mass of data on a graph as these changes in cases can be seen on the map.

std %>%
  filter(Disease == "Chlamydia") -> chlamydia

chlamydia %>%
  group_by(State) %>%
  arrange(desc(Cases)) %>%
  head(60) -> c1

ggplot(c1, aes(Year, Cases, color = State)) +
  geom_point() + geom_line() +
  theme_classic() +
  labs(title = "Total Chlamydia Cases by Selected States 2016-2020",
       subtitle = "Determined by descending order of total cases, filtered for chlamydia")

States with high populations, such as California, New York, Florida, and Texas are all on this graph, as one would assume. Notably, other than Texas, all states had a decline in reported chlamydia cases in 2020, likely due to the pandemic. With many students home from school and in quarantine, less sex is had and thus, less sexually transmitted diseases are transmitted.

Next, using the rate variable of the data set, does this same baseline assumption hold–are states with higher populations more likely to have higher densities of reported chlamydia?

chlamydia %>%
  group_by(State) %>%
  arrange(desc(Rates)) %>%
  head(60) -> c2

ggplot(c2, aes(Year, Rates, color = State)) +
  geom_point() + geom_line() +
  theme_classic() +
  labs(title = "Chlamydia Rates 2016-2020 by State",
       subtitle = "Rates are determined as per 100K population")

Notably, District of Columbia, which has roughly 700,000 people, has the highest rate of chlamydia infections per 100K people. D.C. is home to one of the top colleges in the country – Georgetown University – as well as serving as a hub for political and law internships for undergrad and post-grad students. Surprisingly, neither Texas nor Florida make this distinction; both states have schools with some of the highest undergraduate population. However, Arizona, home to Arizona State University, the most populated college in the country, is on this graph, albeit lower than anticipated given their high college student population. Below is the head 20 values, in descending order of rates, of the graph above.

Below is a bubble chart for college enrollment populations in comparison to chlamydia cases by year.

Much like the other maps and graphs, this data reveals that largely populated states with young adults have higher reported cases of chlamydia.

The above map compares enrolled student populations in comparison to the rates per 100k people with chlamydia, showing the stark contrast between overall student population and how dominant chlamydia is in a given state. Looking specifically at California and D.C., these two states/territories are two of the most influential areas in the United States. These two areas also have a drastic difference in overall population, but similar levels of poverty due to the high level of urbanization. It would be interesting to see if this difference in cases holds up if the data was filtered to only include the selected age population.

Gonorrhea

Gonorrhea is a sexually transmitted disease that often has no symptoms but can do life-long damage if left untreated. This STD is transmitted through unprotected vaginal, oral, or anal sex when at least one partner has gonorrhea. Symptoms include: swelling and pain in genital regions, burning when urinating, and new/increased discharge. A seven day antibiotic treats gonorrhea but must be diagnosed by a healthcare professional. If gonorrhea is not treated properly, it can lead to infertility (in varying forms dependent on genitals)

This map and the changes it demonstrates shows that gonorrhea, generally, from 2016-2020, increased among each state’s population. Though no state had a drastic change (with a potential exception of Texas). The southern region of the U.S. shows the most visible change from 2016-2020, with multiple states moving from a paler shade (less cases) into a darker shade (more cases). There is a wide range of reported cases; the min and max for each year remains at a constant of 126 and 80,301, respectively.

std %>%
  filter(Disease == "Gonorrhea") -> gonorrhea

gonorrhea %>%
  group_by(State) %>%
  arrange(desc(Cases)) %>%
  head(60) -> g1

ggplot(g1, aes(Year, Cases, color = State)) +
  geom_point() + geom_line() +
  theme_classic() +
  labs(title = "Total Gonorrhea Cases by Selected States 2016-2020",
       subtitle = "Determined by descending order of total cases, filtered for Gonorrhea")

Though less common, there is an opposite trend than the one seen for chlamydia. Every state graphed other than California had an increase in gonorrhea from 2019 to 2020. Texas had the sharpest increase in gonorrhea cases from 2019-2020 and the other selected states had a general increase in gonorrhea.

gonorrhea %>%
  group_by(State) %>%
  arrange(desc(Rates)) %>%
  head(60) -> g2

ggplot(g2, aes(Year, Rates, color = State)) +
  geom_point() + geom_line() +
  theme_classic() +
  labs(title = "Gonorrhea Rates 2016-2020 by State",
       subtitle = "Rates are determined as per 100K population")

Much like the chlamydia rates graph, D.C. is the highest territory for gonorrhea cases per 100K people. Mississippi, well known for the University of Mississippi, also known as “Ole Miss” is one of the top party schools in the country.

Much like the graph for the rates of chlamydia, the top several states for gonorrhea per 100k people are D.C., Alaska, and Mississippi. Most states had an overall increase between 2016-2020, much like the trend lines of the case graph indicates as well.

Below is a bubble chart comparing the enrollment populations of states versus the overall reported cases of gonorrhea.

Texas, Florida, New York, and California all have high rates of college enrollment positively correlating with cases of gonorrhea. Though lower in both population and reported cases, North Carolina, Illinois, and Pennyslvania all follow this positive correlation trend.

Though not as strong a positive correlation, there is absolutely some form of correlation between enrollment and overall rates per 100k people. Over the years, it is clear D.C. has a firm standing as the location with the highest rate of gonorrhea. Southern states, notorious for bad sex ed, have large rates per 100k people, and are increasing every year with college populations.

Syphilis

Syphilis is an STD that develops in stages (primary, secondary, latent, and tertiary) and each stage has different signs and symptoms. Like chlamydia and gonorrhea, this STD is treatable with antibiotics. Infection occurs when a person has direct contact with a syphilis sore during anal, vaginal, or oral sex. You cannot get syphilis through casual touch with common objects, which is an urban myth surrounding this disease.

Syphilis, unlike chlamydia or gonorrhea, is the most likely to show symptoms in the form of sores. These sores are visual representations of the different stages of syphilis. This disease can be treated though that does not prevent you from getting it again.

The map legend has a constant min and max of 17 and 28,811 respectively, for all years. California and Texas are automatic focuses, with larger numbers of cases than any other state on the map. From 2016-2020, California does increase enough to be visually perceived and therefore, possibly statistically significant.

std %>%
  filter(Disease == "Syphilis") -> syphilis

syphilis %>%
  group_by(State) %>%
  arrange(desc(Cases)) %>%
  head(60) -> s1

ggplot(s1, aes(Year, Cases, color = State)) +
  geom_point() + geom_line() +
  theme_classic() +
  labs(title = "Total Syphilis Cases by Selected States 2016-2020",
       subtitle = "Determined by descending order of total cases, filtered for Syphilis")

Unsurprisingly, the top states for syphilis are California, Florida, Texas, and New York in regards to total case by population. Each of these states has a large overall population as well as a large undergraduate population, with several notable schools being located in each of the given states. Syphilis is less common than both gonorrhea and chlamydia, there are nearly 30,000 cases in California, a state with a population of 40 million people, roughly 1 in every 1340 people. Interestingly, in contrast with the previous two STDs, syphilis tends to track more toward western and midwestern states, rather than southern and southwestern states.

syphilis %>%
  group_by(State) %>%
  arrange(desc(Rates)) %>%
  head(60) -> s2

ggplot(s2, aes(Year, Rates, color = State)) +
  geom_point() + geom_line() +
  theme_classic() +
  labs(title = "Syphilis Rates 2016-2020 by State",
       subtitle = "Rates are determined as per 100K population")

Much like gonorrhea and chlamydia, D.C. has the highest rates per 100k people for syphilis infections. California and Nevada also have high rates of infection per 100k people; there are a variety of possible factors here. This can be attributed to California’s high population, high poverty (and therefore lack of healthcare), or high young adult and college population. For Nevada, sex work (though illegal) is widely done in Las Vegas and has a large potential to contribute to large rates of STD transmission. Overall, this map bears some similarities to chlamydia and gonorrhea, but for the most part, syphilis does not follow the same trends in either cases or rates for distribution among states.

Below is a bubble chart comparing college enrollment populations per state to total reported cases of syphilis per state.

Syphilis follows the same positive correlation over 2016-2020 as chlamydia and gonorrhea with high numbers of reported infections in California, New York, Texas, and Florida, all of which have high undergraduate student enrollments.

Like gonorrhea and chlamydia, D.C. has the highest rates of infection per 100k people, but unlike gonorrhea and chlamydia, there is a higher rate of infection in the west with lower rates of enrollment, a different correlation than seen previously, but noted above with the other graphs for syphilis.

Conclusion

As hypothesized, states with large populations such as California, Texas, Florida, and New York had high numbers of infections overall, but smaller areas with notable colleges such as D.C., Missouri, Louisiana, North Carolina, and Arizona had high rates of infection per 100K people. California, New York, Texas, and Florida all have some of the top colleges in the country and house some of the most students in the country, and also have some of the highest rates of infections; just not the highest rates. Additionally, it is important to note that several southern states, midwestern states, and Alaska – all of which have minimal and often poor access to reproductive health care and information – have high rates and cases for each of the three studied STDs.

Chlamydia has the highest rates of infection, followed by gonorrhea, and finally syphilis. This may be due to the lack of symptoms the first two diseases present and the overwhelmingly easy transmittable nature of the diseases.

Overall, there is a tentative hypothesis that smaller schools may have higher rates of STD infection due to less opportunity for “expansion” as students often have sexual relations within familiar circles. The opposing argument to that is more people provides a higher chance of infection because of the factorial theory. This all provides a larger problem to look at, and this project is just the first look into how STD rates and cases may factor into college populations.

After Thoughts

This is a correlational study; more precise data delving into ages of those with the given STD by state would allow for true and accurate comparison between college population data and STD population data. Another potential factor would be to examine states’ college populations for prevalence of “party and hookup culture” which indicates higher rates of (potentially) unsafe sex and (potentially) more transmission of STDs.

Additionally, changes over time would have been more illuminating for this data and allowed for the deepest understanding of rates and cases of each STD among states over time. Plotting specific changes by year would have shown better detail on the graphs to fully understand the scope and impact of what transmission is like and what populations are affected (this is, of course, under the assumption the data could be filtered for ages and thus college population ages)