Drug Analysis

Introduction:

In this analysis we will be analyzing the relationship between drugs and variables such as age, sex, education, and income. We will be presenting how drug use is affected by age, sex, income and education by using line graphs and t-tests. We want to analyze this data because it is a very interesting subject that our society consumes itself with. Through statistical analysis there is a lot to be learned about how age, sex, income and education effect drug use in our world. When thinking about what is at stake while doing this analysis, there is a lot that comes to mind. Drugs such as heroin, oxycodone, methamphetamine, and cocaine, which are being tested in this lab, are very addicting and harmful drugs that millions of humans partake in daily. By seeing certain types of relationships that are presented in the line graphs, we are able to get a better understanding of what types of people would use drugs. For example, people of lower incomes tend to use drugs more than people with higher incomes. This type of data is very useful in understanding trends of drug use throughout society. The set-up of this report is to first analyze the use of the drugs heroin, cocaine, methamphetamine, and oxycodone in the form of line graphs. These line graphs compare the drugs related above to the variables age, sex, income, and education. The data in these graphs are taken from the years 2002 to 2015. We will then be creating t-tests of each drug to compare drug use in certain years such as, 2002, 2004, 2014, and 2015. This data will give us a more focused understanding on drug use in the years. This data can be very useful to see how drug use has changed since 2002. Here are our findings:

Analysis of heroin use compared to age from 2002 to 2015

This graph analyzes heroin use compared to age. In this graph, you can see that ages 21 to 34 used the most cocaine from 2002 to 2015, while ages 12 to 13 used the least amount. There is not a clear trend showing which specific age group used the most cocaine from 2002 to 2015, but the highest users were between the ages 21 and 34. Cocaine use decreased for some age groups from 2002 to 2015, while it increased for others.

Analysis of heroin use compared to income from 2002 to 2015

This line graph analyzes heroin use compared to income. It is clearly shown that people earning less than 20,000 a year use more heroin than all others from 2002 to 2015. The trend in this graph is showing that as income decreases for people, there tends to be an increase in heroin use. For example, people earning below 20,000 a year use the most heroin from 2002 to 2015. Whereas, people earning 75,000+ a year use heroin the least amount. The trend in this graph is very evident in showing that as income decreases, the amount of cocaine used increases.

Analysis of Heroin use compared to education from 2002 to 2015

This line graph analyzes the relationship between heroin use and the level of education people have received. Through looking at the visualizations on this graph, you can conclude that people who have earned less than a high school degree use the most heroin. On the other hand, people who have 12-17 years of education or are college graduates have used the least amount of heroin. The main trend shown in this graph is that as the level of education people have received decreases, the amount of heroin used increases.

Analysis of heroin use compared to sex from 2002 to 2015

This line graph analyzes the trends between sex and heroin use. A very clear trend from this graph is that males use more heroin than females from 2002 to 2015. Although this is true, both male and female use of heroin from 2002 to 2015 is increasing which is not a positive trend that we want to see.

Summary of heroin use from 2002 to 2015

In most cases, heroin use has significantly increased from 2002 to 2015. Through looking at the visual trends presented above, it appears that the highest percentage of heroin use is done by males, between the ages 18 and 34, who have received less than a high school education and have an income of less than 20,000. Both male and female heroin use has increased over the past 14 years as seen in the visualizations above. This brings us to the tentative conclusion that heroin use is increasing as a whole from 2002 to 2015.

T-Test comparison of 2002 and 2015 use of heroin

## 
##  Welch Two Sample t-test
## 
## data:  Sub2002$Heroin and Sub2015$Heroin
## t = -4.47, df = 107610, p-value = 7.83e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.002155404 -0.000841386
## sample estimates:
##   mean of x   mean of y 
## 0.002403891 0.003902285

T-Test Results

With heroin we would reject the null hypothesis since the p-value is significantly smaller than 0.05. The alternate hypothesis is that the true difference in means is not equal to 0, which is proven in this t-test. The mean of 2002 in this data set is 0.002403, whereas the mean in 2015 is 0.003902, this is the mean of our sample data. This is a big difference in means and is very extreme. There is a -0.002155 probability for observing a difference in the 2002 mean, and a -0.000841 probability in finding a difference in the 2015 mean. These are the 95 percent confidence intervals. In this t-test we did not find evidence to support the null hypothesis, but instead we found data to reject it.

Analysis of cocaine use compared to age from 2002 to 2015

This graph analyzes cocaine use compared to age. In this graph, you can see that ages 21-25 used the most cocaine from 2002 to 2015, while ages 12 to 13 used the least amount. A positive trend that can be taken from this graph is that all age groups have decreased the amount of cocaine they used since 2002.

Analysis of cocaine use compared to income from 2002 to 2015

This line graph analyzes cocaine use compared to income. The trend in this graph is showing that as income decreases for people, there is an increase in cocaine use. For example, people earning below 20,000 a year use the most cocaine from 2002 to 2015. Whereas, people earning 50,000 to 75,000+ a year use heroin the least amount. The trend in this graph is very evident in showing that as income decreases, the amount of cocaine used increases.

Analysis of cocaine use compared to education from 2002 to 2015

This line graph analyzes the relationship between cocaine use and the level of education people have received. There is not a clear trend in the visualization that shows what level of education used the most cocaine, but the level of education that used the least amount of cocaine were people who received 12-17 years of education. A positive trend that is seen in this graph is that since 2002 all levels of these educations have decreased their cocaine use significantly.

Analysis of cocaine use compared to sex from 2002 to 2015

The trends in this line graph analyze cocaine use compared to sex. Male use of cocaine is significantly higher than female use of cocaine from 2002 to 2015. A positive trend that can be taken from this graph is that both male and female cocaine use from 2002 to 2015 significantly decreased.

Summary of cocaine use from 2002 to 2015

Cocaine use has significantly decreased from 2002 to 2015. Based on the visual representations above, you can conclude that males, between the ages 21 and 25, with some college education and an income of less than 20,000 use cocaine the most. Although this is true, male use has significantly decreased since 2002. This can also be said about female use which has also significantly decreased since 2002. All variables related to cocaine use have significantly decreased, and these results will hopefully continue to head in the same direction.

T-Test comparison of 2002 and 2015 use of cocaine

## 
##  Welch Two Sample t-test
## 
## data:  Sub2002$Cocaine and Sub2015$Cocaine
## t = 14.222, df = 102390, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01239008 0.01635086
## sample estimates:
##  mean of x  mean of y 
## 0.03626177 0.02189130

T-Test Results

With cocaine we would reject the null hypothesis since the p-value is significantly smaller than 0.05. The alternate hypothesis is that the true difference in means is not equal to 0, which is proven in this t-test. The mean of 2002 in this data set is 0.03626, whereas the mean in 2015 is 0.02189, this is the mean of our sample data. This is a big difference in means and is very extreme. There is a 0.01239 probability for observing a difference in the 2002 mean, and a 0.01635 probability in finding a difference in the 2015 mean. These are the 95 percent confidence intervals. In this t-test we did not find evidence to support the null hypothesis, but instead we found data to reject it.

Analysis of oxycodone use compared to age from 2004 to 2014

This line graph analyzes the trends between oxycodone and age. There is not a clear understanding on which age group uses the most oxycodone, but it can be seen in the visual representation that ages 16 to 25 use oxycodone the most. Age group 12 to 13 use this drug the least amount. Age groups 16-17, 18-20, and 21-25 have significantly decreased the amount of oxycodone they have used, while age groups 26-34, 35+, 12-13, and 14-15 have increased their use.

Analysis of oxycodone use compared to income from 2004 to 2014

This line graph analyzes the trends between oxycodone and income. In this visual representation, it can be seen that people earning less than 20,000 use oxycodone the most, while people earning 50,000 to 75,000+ use oxycodone the least. A positive trend that can be taken from this is that all groups of incomes have decreased the amount of oxycodone they have used. A tentative conclusion that you could come to from this line graph is that as education level decreases, the amount of oxycodone used increases.

Analysis of oxycodone use compared to education from 2004 to 2014

This line graph analyzes the trends between oxycodone and education. What can be taken away from this line graph is that college graduates use the least amount of oxycodone, while people earning less than a high school education use oxycodone the most. All levels of education have decreased the amount of oxycodone they have used since 2004.

Analysis of oxycodone use compared to sex from 2004 to 2014

This line graph analyzes the trends between oxycodone and sex. In the visual representation we are able to understand that males use oxycodone more than females. With this being said, both males and females have decreased the amount of oxycodone they have used since 2004.

Summary of oxycodone use from 2004 to 2014

Oxycodone use has significantly decreased from 2004 to 2014. Based on the visual trends above, it is apparent that males, between the ages 16 to 25, with an education less than high school and an income less than 20,000 use oxycodone the most. While males do tend to use oxycodone more frequently, male and female use since 2004 has significantly decreased. Oxycodone use overall has significantly decreased since 2004.

T-Test comparison of 2004 and 2014 use of oxycodone

## 
##  Welch Two Sample t-test
## 
## data:  Drug2004$Oxycodone and Drug2014$Oxycodone
## t = 5.25, df = 107850, p-value = 1.524e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.001767198 0.003872749
## sample estimates:
##   mean of x   mean of y 
## 0.009478076 0.006658103

T-Test Results

With oxycodone we would reject the null hypothesis since the p-value is significantly smaller than 0.05. The alternate hypothesis is that the true difference in means is not equal to 0, which is proven in this t-test. The mean of 2004 in this data set is 0.009478, whereas the mean in 2014 is 0.006658, this is the mean of our sample data. This is a big difference in means and is very extreme. There is a 0.001767 probability for observing a difference in the 2004 mean, and a 0.003872 probability in finding a difference in the 2014 mean. These are the 95 percent confidence intervals. In this t-test we did not find evidence to support the null hypothesis, but instead we found data to reject it.

Analysis of methamphetamine use compared to age from 2002 to 2014

This line graph analyzes the trends between methamphetamine and age. There are no clear trends in use by age for this drug, with all age levels demonstrating roughly similar trends. One trend for the year 2014 is that 12 to 13-year old’s used the least amount of methamphetamine, while 18 to 25 year old’s used the most methamphetamine.

Analysis of methamphetamine use compared to income from 2002 to 2014

This line graph analyzes the trends between methamphetamine and income. Based on the visual trends in this graph, people earning less than 20,000 a year use methamphetamine the most, while people earning greater than 75,000 use methamphetamine the least. An evident trend in this graph is that as income decreases, the amount of methamphetamine consumed increases.

Analysis of methamphetamine use compared to education from 2002 to 2014

This line graph analyzes the trends between methamphetamine and education. Based on the visual trends in this graph, people who received less than a high school education used methamphetamine the most, while college graduates used methamphetamine the least. An evident trend in this graph is that as education level decreases, the amount of methamphetamine consumed increases.

Analysis of methamphetamine use compared to sex from 2002 to 2014

This line graph analyzes the trends between methamphetamine and sex. Based on the visual representations, you can see that male and female methamphetamine use is relatively the same in terms of both of them decreasing from 2002 to 2014. An interesting thing to point out is that in 2003, methamphetamine use for females was higher than it was for males, but then by 2014, methamphetamine use for males was higher. Overall, the trends of both male and female use of methamphetamine was relatively similar both decreasing over the 12 years.

Summary of methamphetamine use from 2002 to 2015

Methamphetamine use has significantly decreased from 2002 to 2014. Based on the visual trends above, it is apparent that people, with an income less than 20,000 and an education less than high school used methamphetamine more than all others. There are no apparent trends in determining which sex used methamphetamine more, but from the graph we are able to understand that both male and female methamphetamine use has decreased since 2002. There are also no apparent trends in terms of age and drug use, but from the graph it is apparent that all age groups declined the amount of methamphetamine they used.

T-Test comparison of 2002 and 2014 use of methamphetamine

## 
##  Welch Two Sample t-test
## 
## data:  Sub2002$Methamphetamine and Drug2014$Methamphetamine
## t = 11, df = 94507, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.004782269 0.006856021
## sample estimates:
##   mean of x   mean of y 
## 0.010595610 0.004776465

T-Test Results

With methamphetamine we would reject the null hypothesis since the p-value is significantly smaller than 0.05. The alternate hypothesis is that the true difference in means is not equal to 0, which is proven in this t-test. The mean of 2002 in this data set is 0.1059, whereas the mean in 2014 is 0.00477, this is the mean of our sample data. This is a big difference in means and very extreme. There is a 0.004782 probability for observing a difference in the 2002 mean, and a 0.006856 probability in finding a difference in the 2014 mean. These are the 95 percent confidence intervals. In this t-test we did not find evidence to support the null hypothesis, but instead we found data to reject it.

Drug of Greatest Concern

There is specific information that can be taken from the visual representations of all these graphs to set up a profile of what type of people would be at the greatest risk for doing the drugs heroin, oxycodone, methamphetamine and cocaine. Although this information is true for these drugs, we can come to a tentative conclusion that this information would be true for all drugs. The people that are at greatest risk of doing drugs are, males, who earn an income of less than 20,000 and have less than a high school education. It is difficult to decipher what ages would be at greatest risk of using drugs since all of the data relating drugs to age is scattered amongst the type of drug being tested. There are few cases where the profile I present isn’t fully true, but overall that is an accurate profile of individuals who are at higher risk for use. With that being said, heroin is the drug that would be used the most. Heroin is the only drug that increased in use from 2002 to 2015. All other drugs decreased in how much they were being used. This brings us to the unconfirmed conclusion that heroin will be the prevailing drug for males earning less than 20,000 and having less than a high school education in the coming years.

Summary and Recommendation

The drugs heroin, oxycodone, cocaine and methamphetamine have all had a change in prevalence in our world. Heroin was the only drug that increased in prevalence over the past 14 years, whereas oxycodone, cocaine and methamphetamine decreased in prevalence over the past 14 years. With this being said, I would recommend the NIDA to put a focus on prevention efforts against heroin. For heroin, the largest users are males, between the ages 18 and 34, who have received less than a high school education and have an income of less than 20,000. For cocaine, the largest users are males, between the ages 21 and 25, with some college education and an income of less than 20,000. Oxycodone’s highest users are males, between the ages 16 to 25, with an education less than high school and an income less than 20,000. Lastly, methamphetamine’s users are males and females, with an income less than 20,000 and an education less than high school. From this data just presented, you can understand that the people at greatest risk of using drugs are males, who have less than a high school education and earn less than 20,000. Focusing on heroin, the NIDA should target males, between the ages 18 and 34, who have received less than a high school education and have an income of less than 20,000. This is where the majority of heroin users are found, and if the NIDA can make prevention efforts against those type of people they will be able to stop the majority of heroin use.

Possible Interpretation

According to the first article on Narconon.org, state legislatures and law enforcement have been cracking down on doctors who abuse their prescription abilities and have created a new regulated statewide prescription monitoring program. This has forced the price of oxycontin pills to trend up and their usage to trend down. The findings that can be seen in our oxycontin graphs are compatible with the information found in the first article. Use of oxycontin was way up before these new systems were implemented which lead users to turn to the cheaper alternative heroin. The article found on CNN.com depicts the demographics of heroin users. It stated that the average user begins ‘doping up’ at around 23 years old. Our findings are also compatible with CNN’s research because our heroin trend lines are highest for the age group of 21-25 years old in 2013 through 2015. The final article also supports our findings. Ohio has seen an increase in heroin use and overdose. As you can see in our heroin graphs, usage is up.