Pain Reliever Misuse and Abuse

Ainsley, Jun, Nick, Rachel, Rosie, Yulia, Aashna

Data Analysis

Table of Contents

01

02

03

04

About the Data

Data Cleaning

Analysis

Conclusions

Dataset – Background

170k+ Observations

Variables

• 26% misuse and abuse of prescription opioids (MUPO)
• 11% misused or abused pain relievers

• Demographic variables, medication abuse, and illicit drug use
• Mostly categorical, binary, and likert scale variables
• Main variable: Prescription opioid pain reliever misuse and abuse (Likert Scale)

Dataset – Variables

Research Question

• How can pain reliever abuse prevention programs increase their impact?
• What demographics should they target?
• Are users of certain drugs more susceptible to pain reliever abuse?

Data Cleaning

Renaming Columns

Encoded Data -> Factors

Missing Value Removal

EMPLOY18 -> Employment_Status

Switched from numeric to labels

Removed any NA’s and rows of 0’s from our dataset

Demographics

01

• PR misuse more common as in those with higher educational backgrounds
• Slope shows + correlation
• Not a significant amount of difference in misuse in males compared to females

Results: According to the data, younger people tend to abuse prescription drugs more frequently than those in older age groups.

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Results: the larger city metro size has the most percentage of prescription misused/abused drugs at 40% compared to the small and rural city metro size

Medication and Drug Use

02

• Results: highly right skewed

• Younger people tend to consider themselves more frequent users of the drug

• The higher a person is on the likert scale, the more severe problems they are face - positive correlation

Case Study: Cocaine

Conclusion

Education

City Size, Gender

Age

Case: Cocaine

Employment

Positive correlation in PR misuse and ed background

Not much impact for PR misuse

Younger age groups tend to undergo more misuse

More misuse lead to more health problems

Employed have highest misuse