Social Vulnerability and COVID-19 Prevalence & Predicting Benzodiazepines Use

# Schedule
Tuesday, May 18, 6:30-8:00 pm (Pacific)
6:30 - Welcome and Networking
6:55 - Announcements
7:00 - Social vulnerability and COVID-19 prevalence in California
7:25 - Q&A
7:30 - Predicting Benzodiazepines Use
7:55 - Q&A
8:00 - Meeting Adjourned

# Social Vulnerability and COVID-19 Prevalence in California
Speakers: Tiffany Feng, Eustina Kim, Diana Pham, Kienna Qin
Seniors, Department of Statistics, UCLA

Bio: Tiffany Feng, UCLA Class of 2021, Statistics Major and Digital Humanities minor; Eustina Kim: UCLA Class of 2021, Statistics Major and Digital Humanities minor; Diana Pham: UCLA Class of 2021, Statistics Major and Global Health minor; Kienna Qin: UCLA Class of 2021, Statistics Major

Abstract:
The goal of this project was to determine if there is a relationship between social vulnerability and COVID-19 case prevalence rates across counties in California. Social vulnerability is defined as the resilience of communities when dealing with external stresses on health, such as age, housing, race/ethnicity, and more. For analysis, we used multiple linear regression to determine which social vulnerability variables are most directly related with hospital capacity in each county in California, using average bed utilization rate as our measure of hospital capacity. The significant predictors from our model were: the proportion of the county’s population under 17, the percentage of individuals reporting severe housing situations, the proportion of the county's population that are Black, the percentage of single parent households, and the percentage of households with more people than rooms. We then developed a county-level social vulnerability index (SVI) and concluded that there was a relationship between the SVI and COVID-19 case prevalence per county after mapping both of them using Tableau. Our findings suggest that more socially vulnerable counties are less equipped to handle impacts of COVID-19 and more resources should be allocated to them to establish equitable health outcomes. This project was originally created in May 2020, with updates to COVID case rates in December 2020.

# Predicting Benzodiazepines Use
Speakers: Tiffany Feng, Eustina Kim, Diana Pham, Kienna Qin
Seniors, Department of Statistics, UCLA

Bio: Tiffany Feng, UCLA Class of 2021, Statistics Major and Digital Humanities minor; Eustina Kim: UCLA Class of 2021, Statistics Major and Digital Humanities minor; Diana Pham: UCLA Class of 2021, Statistics Major and Global Health minor; Kienna Qin: UCLA Class of 2021, Statistics Major

Abstract: This project explores which characteristics among students, health care professionals, and veterans determined the use of benzodiazepines (BZDs) in a sample of participants in the United States during 2018. BZDs are a type of drug that produces sedation and lowers anxiety levels. They are the most commonly prescribed medication in the U.S., and, unfortunately, are frequently abused and taken with other drugs. We used multinomial logistic regression and various data visualizations to determine that having a history of drug use, mental illness, and prescriptions for opioids for treating pain are important in predicting the use of BZD for all three occupations in our sample. The largest impacts for each occupation were a history of drug use for students, mental illness and opioid prescriptions for pain for veterans, and there were similar influences for each category for health professionals. Although our data was not a representative sample, this could still demonstrate the importance of investigating the unique differences in determining BZD use for each occupation, which could lead into interventions such as targeted programs to reduce BZD misuse in each occupation. Hopefully with these insights, we can make a positive impact on the lives of those affected by the misuse of BZDs in the United States.

# Meeting Materials
All meeting materials will be sent to registered attendees after the event.