George Washington University, District of Columbia
April 19, 2024
April 19, 2024
April 20, 2024
2
10.18260/1-2--45738
https://peer.asee.org/45738
61
Zuyi (Jacky) Huang is an Associate Professor in the Department of Chemical Engineering at Villanova University. He teaches Chemical Process Control (for senior students) and Systems Biology (for graduate students) at Villanova. He is enthusiastic in apply
Mental health advocacy and awareness have entered the mainstream of culture and society in recent years. Understanding the relationship between individuals' lifestyle choices and their mental well-being can provide valuable insights into coping mechanisms for mental health challenges. One such lifestyle factor worthy of examination is drug use among individuals. With the legalization of marijuana in the United States over the past decade, its prevalence in our culture has surged. There is a plausible connection between individuals grappling with poor mental health and their inclination towards marijuana use as a coping mechanism for stress and anxiety. Examining trends across various age groups can offer valuable insights into how different generations are engaging with marijuana concerning their mental health status. Our hypothesis posits a correlation between mental health and marijuana use, particularly among younger adults, suggesting that individuals with poorer mental health may show increased marijuana consumption compared to previous observations. To investigate this hypothesis, we utilized R-Studio to employ statistical techniques that uncover relationships between mental health status, age, household composition, and marijuana usage. We extracted data from the CDC's NHANES database spanning two time points bracketing the onset of marijuana legalization (2009-2010 and 2017-2018). Through meticulous coding, we organized the data into relevant groups based on age and mental health status, facilitating comprehensive analysis using various statistical methods. Principal component analysis and hierarchical clustering were employed to identify outliers and patterns among different age groups and mental health statuses concerning marijuana use. T-tests were conducted to ascertain significant differences in marijuana consumption between groups with good and poor mental health across different age ranges. Furthermore, dominance analysis was performed to evaluate whether mental health exhibited a more substantial influence on marijuana use post-legalization compared to pre-legalization. Our findings corroborate our hypothesis, indicating that individuals with poor mental health indeed exhibit higher marijuana usage compared to those with good mental health, a trend observed across most age groups before and after legalization. Additionally, our results suggest a general increase in marijuana consumption post-legalization, irrespective of mental health status. While younger adults demonstrate heightened marijuana use, we have yet to definitively establish a causal link between this trend and deteriorating mental health levels over the analyzed period. In summary, our findings provide broad support for the notion that individuals may turn to marijuana as a means of addressing poor mental health, with legalization potentially exacerbating this phenomenon. This project can also be used as an in-class example to illustrate the use of modeling and simulation to enhance engineering education. It is also a prevalent practice within engineering education, particularly in the increasingly popular fields of data mining and machine learning.
Forte, R. J., & Goehring, R., & Fogliano, M., & Huang, Z. (2024, April), Statistical Investigation of the Relationship Between Mental Health & Marijuana Use from NHANES Database Paper presented at ASEE Mid-Atlantic Section Spring Conference, George Washington University, District of Columbia. 10.18260/1-2--45738
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