Mental Health and Social Determinants: An R-Based Analysis Using BRFSS 2020

In this analysis, I explore how social determinants such as education, income, sex, and race relate to self-reported mental health outcomes. Using data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS), I focus specifically on the variable that measures the number of days in the past 30 days when a person’s mental health was “not good.” By visualizing this data and comparing averages across demographic groups, I aim to highlight patterns and disparities that may inform future public health efforts or social policy decisions.

The analysis was performed in R, and a brief interpretation accompanies each visualization to provide context for the findings. My goal is to present this work in a clear, accessible, and meaningful way, combining data analytics with real-world impact.

The BRFSS is a nationally representative telephone survey conducted by the CDC that collects health-related data from adults in all 50 states and U.S. territories. The dataset used includes over 400,000 observations from the 2020 cycle. The analysis was performed using R programming and involved the following steps:

- Importing and cleaning the dataset
- Recoding categorical variables (e.g., education, income, race, sex)
- Removing invalid or out-of-range values from the mental health variable
- Grouping and summarizing data by demographic variables
- Creating bar plots and faceted charts to visualize trends
- Interpreting results within a public health framework

Visualizations were created using `ggplot2` and comparisons were made using simple group means. Each chart focused on a specific dimension of the data, such as education level alone, income alone, or combined factors like education, sex, and race.

Figure 1: Average Poor Mental Health Days by Education Level

This bar chart illustrates the average number of poor mental health days reported in the past 30 days across various education levels, based on the 2020 BRFSS survey data. The trend indicates that individuals with lower educational attainment — particularly those with less than a high school education — report more days of poor mental health on average. Conversely, college graduates report the fewest poor mental health days. This suggests a potential connection between educational attainment and mental well-being.

Figure 2: Average Poor Mental Health Days by Income Level

This chart displays the average number of poor mental health days reported over the past 30 days across different income groups, based on BRFSS 2020 data. A clear gradient appears: respondents with lower incomes report significantly more poor mental health days than those in higher income brackets. Individuals earning less than $10,000 annually report the highest average, while those earning $75,000 or more report the fewest. This pattern reinforces the relationship between economic stressors and mental well-being.

Figure 3: Average Poor Mental Health Days by Education Level and Sex

This grouped bar chart breaks down average poor mental health days by education level and sex. Across nearly all education levels, women report slightly more poor mental health days than men. The disparity is especially noticeable among individuals with some college or a high school diploma. Notably, both men and women with lower educational attainment report more poor mental health days than those with college degrees, reinforcing the connection between education and psychological well-being.

Figure 4: Mental Health Days by Income Level and Race

This faceted bar chart explores the relationship between income and poor mental health days across racial and ethnic groups. In nearly every group, individuals in the lowest income brackets report the highest number of poor mental health days, with a noticeable decline as income increases. The pattern is most pronounced among White, Hispanic, and Multiracial respondents. While the overall trend remains consistent, the degree of disparity varies across groups, suggesting that race and income intersect in complex ways to shape mental health outcomes.

Figure 5: Average Poor Mental Health Days by Education, Sex, and Race

This faceted chart shows how education level, sex, and race intersect in shaping mental health outcomes. In every racial/ethnic group, respondents with lower educational attainment tend to report more poor mental health days. The chart also reveals gender differences within groups, with females generally reporting slightly more poor mental health days than males. The disparity is especially pronounced in the “Never attended” category among Black and Native American/Alaska Native respondents. This visualization emphasizes the layered effects of education and gender within diverse racial and ethnic contexts.

Key Takeaways

• Individuals with lower levels of education and income consistently report more poor mental health days on average.

• Women report slightly more poor mental health days than men across nearly all education and income brackets.

• Racial and ethnic disparities exist, particularly among Multiracial, Black, Hispanic, and Native American groups.

• Higher education appears to buffer mental health challenges across demographic lines, but disparities persist even within the same education levels.

• These findings underscore the importance of addressing mental health through both clinical care and broader, socioeconomic, and equity-focused strategies.

Summary

This project analyzed self-reported mental health data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS), focusing on how education, income, sex, and race influence the average number of poor mental health days reported in the past 30 days. The analysis revealed clear and consistent patterns: individuals with lower levels of education and income tend to report worse mental health outcomes. These disparities are further influenced by gender and race, with certain populations — particularly women and individuals identifying as Black, Hispanic, Native American, or Multiracial — experiencing higher levels of reported distress. By visualizing these patterns, this project highlights the complex interplay between social determinants and mental well-being, reinforcing the importance of equity-driven approaches in public health.