Deaths resulting from drug overdoses are the leading cause of injury death in the United States and are due in part to nonmedical use or misuse of prescription opioids. While strategies exist to combat this epidemic, current monitoring and surveillance systems in place do not provide real-time data. Research shows that substantial changes have taken place over time in the geographic distribution of opioid-related mortality. A need exists for a public health platform to help public health workers predict where in the United States a rise in misuse or overdose may be occurring, so that evidence-informed strategies may be implemented as quickly as possible.
Methods: This study analyzed how Twitter users describe prescription opioid use and misuse on Twitter, whether there were discernible state-specific trends in the quantity of tweets, and whether the variations in tweets by state reflected variations in estimated prescribing rates, overdose rates, and nonmedical use rates. A search was conducted for all tweets mentioning keywords related to prescription opioids during a six-month period. The tweets were filtered to include only those originating from individuals discussing individual prescription opioid use at least five times, indicating potential for misuse. The tweets were sorted by user-identified geographic location and qualitatively analyzed for content indicative of misuse according to four categories: overdose, dependence, co-ingestion, and seeking. Results were compared to state-specific prescribing rates, nonmedical use rates, and overdose rates.
Results: Results demonstrated that Twitter users are engaging in conversation about prescription opioid use (n=809), and the majority (76.9%) mention behaviors that are indicative of misuse. A modest, statistically significant correlation (r=0.303) with state-specific estimates of nonmedical use of prescription pain relievers was found, if one data point considered an outlier was removed. No statistically significant correlation was found for estimated prescribing and overdose rates.
Conclusion: The findings indicate that individuals are discussing their prescription opioid use on Twitter, and there is a wealth of information available to analyze within the content of these discussions. Further research is needed to determine the potential to use Twitter as an early predictor of prescription opioid misuse and overdose.
Table of Contents
CHAPTER 1: INTRODUCTION. 1
Problem Statement. 3
Purpose Statement. 6
Theoretical Framework. 7
Significance Statement. 7
Definition of Terms. 8
CHAPTER 2: REVIEW OF THE LITERATURE. 10
The Potential of Twitter Data for Public Health Analysis. 10
Twitter Data Analysis Related to Specific Public Health Issues. 15
Twitter Data Analysis Related to Prescription Drug Misuse. 18
CHAPTER 3: METHODOLOGY. 23
Population and Sample. 23
Research Design. 25
Plans for Data Analysis. 28
Limitations and Delimitations. 28
CHAPTER 4: RESULTS. 30
Other Findings. 39
CHAPTER 5: CONCLUSIONS, IMPLICATIONS, AND RECOMMENDATIONS. 41
Summary of Study. 41
About this Master's Thesis
|Committee Chair / Thesis Advisor
|An Examination of the Portrayal of Prescription Opioid Use and Misuse on Twitter ()
|2018-08-28 12:00:41 -0400