Category: College of Public Health

Breast cancer is the second-leading cause of cancer-related death among women in the United States, but mortality rates vary across the population. Previous studies use individual and county level data to examine the association between breast cancer mortality and various socio-demographic and environmental variables. However, global regression models assume spatial stationarity meaning that associations between explanatory variables and breast cancer mortality are the same across geographic space and scales. Therefore, the objective of the study is to use county-level data that describes the social and built environment across the contiguous United States to explain breast cancer mortality rates, as reported in the Surveillance, Epidemiology, and End Results (SEER) database, using a multi-scale geographically weighted regression model (MGWR) that accounts for spatial heterogeneity. We compare our MGWR model with a baseline global linear regression model (OLS). The MGWR outperformed the global linear regression model with an adjusted R2 of 0.91, explaining about 43% more variance than the OLS. The R2 for each county is high in the western part of the US and decreases towards the east. In comparison to global linear regression, MGWR had the same overall trends in terms of relationships between explanatory variables and mortality. For example, as mammogram screenings, health food index, and primary healthcare physician ratio increased, breast cancer mortality decreased. However, MGWR reveals spatial heterogeneity associated with the magnitude and direction of each relationship across counties. Such an approach allows for greater consideration of where certain variables are most influential in breast cancer mortality allowing for location-specific interventions.

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Abigail G Kokkinakis0:09

Hello everyone. The objective of this study is to examine the association between the urban built environment and sociodemographic variables and breast cancer mortality using an approach that will capture spatial heterogeneity and those associations. According to the American Cancer Society, breast cancer is most common cancer in American females. One in eight females will be diagnosed with breast cancer and their lifetime, and one in 39 females will die from breast cancer. A lot of studies look at individual level, social demographic data, and regression techniques. However, these regression techniques assume spatial stationarity which ignores local heterogeneities and associations between variables and breast cancer mortality.

Ours created a US model focusing on urban built environment variables without the limitations of most global regression techniques.

We collected Seer data from the most recent five-year interval, which was 2015 to 2019. Note the white space in the map shown. These are the counties that did not have data provided, which was roughly 1/3 of the data.

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Ha Dao1:10

Due to privacy reasons, counties with the count of deaths less than 10 were omitted by SEER. Therefore, we decided to assume these missing counts as 9 and calculate the crude rate based on this. Moving forward with data collection and preprocessing. In the beginning of our research, 65 urban-built and social-demographic breast cancer determinants were obtained. Throughout the process of data cleaning and data wrangling, log transformation and scaling were applied on selected variables to reduce skewness and normalize the range of independent variables. Upon running Pearson correlation and Variance Inflation Factor, we removed variables that were highly correlated so we can avoid multicollinearity in the regression model. Lastly, the leap and bounds algorithm was used to find the variables that explain the models best which you can see on the screen here

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Amanda A Webber1:59

Our model contained 18 variables. For the sake of time, we’re showing the three most significant variables as well as the variables that have no significance. The three most significant variables are access to mammograms, access to primary care doctors, and percent of population that is uninsured. The variables that are not significant based on their P value are textile manufacturing, commute, radiance, and unemployment.

You can see from this map of our county residuals that predictive accuracy varied across counties and appears spatially correlated. The R square value reflects the amount of variation in the data set that is explained by our model. On a scale from 0 to 1, this models R Square explains roughly 50% of the variation within the data.

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Kai Barner2:47

Big picture, this project wanted to account for spatial heterogeneity. A primary limitation of OLS regression is that it assumes that each explanatory variable has the same relationship with the dependent variable across the entire study area. So when one unit change of an explanatory variable occurs, that results in the same change to the dependent variable, no matter the location. Multiscale Geographically Weighted Regression – MGWR – takes location into account by taking the concept of neighborhoods from standard Geographically Weighted Regression and allowing their size, or bandwidth, to vary between different variables in the data set. Multiple scales reflect diversity in distribution and extent of influence.

This project examined data at the county level, so when analyzing the influence of a variable on an individual county, the bandwidth took into account that many neighbors at the same time. There are just over 3100 counties in the continental US, and we can see that some of the neighborhoods actually encompassed all of them. But at the other end of the spectrum, a small bandwidth indicates that the spatial process changes quickly from location to neighboring locations. And we see this with food index, exercise, access to primary health care, and mammograms.

Where our OLS model had an adjusted R-squared of 0.478, our MGWR model explained about 44 percentage points more variance, with an adjusted R-squared of 0.918.

Here we have our MGWR results for mammograms. The mean coefficient is negative 0.14, so the standard behavior nationwide is an inverse relationship. As the number of mammograms increase, breast cancer mortality decreases and we see this norm in cornflower blue. As we move through the purples into magenta, this negative correlation becomes even stronger. But on the other hand, areas in the cyan extend from having a weaker negative correlation to actually having a positive coefficient. In these areas as mammograms increase, breast cancer mortality also increases.

These local differences warrant further examination of other variables and other factors of underlying influence, alluding to the local complexities behind trying to improve breast cancer health outcomes. There is no one-size-fits-all solution.

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Alexandra Diaz Merida5:05

Another variable that we looked at was access to exercise, which measures how close an individual is to a location for physical activity. This can be either parks or recreation centers in this map we see that in general there is a negative correlation, which means that the higher the access to exercise opportunities, there is a decrease in breast cancer mortality. Looking at these graphs, we can see which counties need more access to exercise in order to decrease breast cancer mortality.

Similar to access to exercise, we also looked at food index, which is described as how close an individual is to healthy foods and the and the ability to access due to cost barriers. It is also negatively correlated, which again means that the higher the food index, there’s a decrease in mortality. These maps will help inform local governments about breast cancer mortality rates and how improving these variables can decrease mortality.

In summary, MGWR explains the results better than OLS for spatial heterogeneity, providing better explanations for how urban built environment variables affect breast cancer mortality. Thank you.

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Kai Barner6:12

Thank you.

AK

Abigail G Kokkinakis6:13

Thank you.

This research study aims to identify how alcohol, marijuana, and nicotine consumption behaviors have shifted in under-resourced/underrepresented college students at George Mason University since the pandemic. While alcohol and substance use appears customary in the lives of college students, the COVID-19 pandemic has created a concern about regulating these uses. The stresses that the pandemic has presented have caused even more attention to college student’s mental health and how that correlates to changes in substance use and alcohol. As a result of the COVID-19 pandemic, increasing mental health issues along with changes in the environment (i.e housing and social constructs) might have caused a shift in alcohol, marijuana, and nicotine consumption behaviors in under-resourced/underrepresented college students at George Mason University.

[Title Page Slide] Clarissa: Hello everyone. My name is Clarissa Montes along with Zimako Chuks. This summer, we have had the opportunity to work with fellow undergraduate and graduate research assistants, volunteers, and Mason faculty advisors on our Summer Team Impact Project titled COVID-19’s Impact on under-resourced/underrepresented college students and their peers at George Mason University. [Slide 1] Clarissa: Although our team is made up of several individuals, this specific project consists of Zimako Chuks, Myself, and Samara Tahmid. Zimako is a Graduate Research Assistant working towards her MPH in Epidemiology. I am an Undergraduate Research Assistant going into my senior year majoring in Global Community Health. And Samara Tahmid is a Research Volunteer also working toward her MPH. We are very excited to be working on this project and sharing it with everyone. [Slide 2] Clarissa: So what is COVID-19? Well according to the CDC, coronavirus, or more commonly known as COVID-19, is a disease caused by a virus. Those who contract the virus typically experience mild symptoms, however, it is highly possible for people to experience life-threatening symptoms. Older populations and those with underlying medical conditions are more at risk for severe illness from COVID-19. Ways to protect yourself from infection include regular hand washing, physical distancing, face coverings, and most importantly getting vaccinated. [Slide 3] Clarissa: In the United States alone, there have been almost 35 million COVID-19 cases, over 600,000 deaths, and almost 164 million fully vaccinated individuals. Sadly, new variants are emerging causing even more reported cases. As a result, it is important that we all continue protecting ourselves and others to prevent spreading the virus even further. [Slide 4] Clarissa: While conducting the research, we as a group have recognized how impactful the COVID-19 pandemic has been on many populations, especially college students. Since the onset of the pandemic, several students reported challenges and changes pertaining to academics, economics, social relationships, moods, behaviors, substance use, and beliefs. What my group is specifically focusing on is substance use and how consumption behaviors have shifted as a result of the pandemic, particularly among under-resourced/underrepresented undergraduate students. [Slide 5] Zimako: Substance Use It is important to examine substance use because use and addiction can be dangerous and cause lifelong negative health outcomes. According to the National Institute on Alcohol Abuse and Alcoholism, alcohol use among college students is a popular custom that several students see as an integral part of their college experience. After reviewing articles that examined substance use in college students around the world during the pandemic, they found that rates of alcohol, marijuana, and nicotine were extremely mixed. Many factors such as living at home, losing close relationships with friends, etc were associated with lower rates of drinking. Social distancing was associated with a decrease in group marijuana use, however, stay-at-home orders saw slight increases in solitary and at-home use. Some college students quit nicotine during the pandemic while others used the nicotine to cope with stress. [Slide 6] Zimako: Methods The overarching goal of this study was to collect oral histories to gain a better understanding of how Mason students’ lives were altered due to the COVID-19 pandemic. We recruited participants using Facebook Groups and email listservs and reached out to over 3,000 students, successfully interviewing 133 participants. We conducted semi-structured in-person interviews all led by student researchers. Participants asked open-ended questions during the interview about topics such as academic life, financial stability, substance use behaviors, beliefs, mental health, and general behaviors. Interviews were audio-recorded and automatically transcribed through zoom. Student researchers were tasked with editing the transcript and performing preliminary analyses to separate common themes. Our small group analyzed common substance use behavior themes. [Slide 7] Zimako: Findings So now, we will share some of our preliminary findings. [Slide 8] Zimako: Results After performing preliminary analysis, we found that 34% of participants did not use substances during the pandemic. Some participants stated that use decreased because they were away from social groups. This caused some participants to drink less and to quit substances such as nicotine and marijuana. Around 15% of participants stated they did not use substances for religious reasons. During the pandemic, Some participants grew stronger in their religious beliefs while others moved home and had religions and strict home environments that did not allow substance use. [Slide 9] Zimako: We hope to continue our project this year and write formal research papers for publication. Our next steps include using NVivo software to code qualitative data to find the specific frequencies of themes. We hope to incorporate the screener data in our analysis to draw comparisons between participants’ living situations and substance use. Finally, we hope to perform 2 to 5 person focus groups to more in-depth questions pertaining to substance use. Slide 10 Zimako: Thank you Thank you for listening to our presentation. Should you have any questions please reach out to me at [email protected]