Introduction

Title: Relationship Between Education Attainment and Other Factors in California Counties

Subtitle: A Dashboard by Anna Sanderson

Data source: CalEnviroScreen 4.0 through the California Office of Health Hazard Assessment

Background: The data collected through CalEnviroScreen 4.0 provides information about social and environmental measures by census tract. This dashboard will visualize relationships between a few of these factors. Specifically, it is known that educational attainment can correlate with levels of other socioeconomic and environmental factors. As such, this dashboard will focus on the relationship between education and other factors. Education can be an indicator of other socioeconomic factors that can cause health differentials.

Results: All graphs indicate some relationship between lack of education and other factors assessed. As the level of individuals without a high school diploma increases, we see an increase in poverty levels, rate of cardiovascular disease, and lead exposure risk in children. However, this information is not causal, and is only an association. Hopefully the information provided will help the reader visualize correlations between education and other environmental and social factors, and help inspire further research on these associations.

Figures 1 and 2

We can first look at the spread of the mean percentage of population over age 25 with less than a high school diploma, per county, in Figure 1. The histogram shows a peak of 6 counties with 11-12% mean percent of individuals without a high school diploma. The graph also shows that two counties have a high mean value (exceeding 30%) of population over age 25 with less education than a high school diploma.

In Figure 2, we can see the relationship between education and poverty, where poverty is represented by the mean percentage of population living below two times the federal poverty level (FPL), per county. There is a correlation between education and poverty. As there is an increase in mean percentage of individuals with less education than a high school diploma, there is also an increase in the mean percentage of the population living below two times the FPL, per county.

On the next page, we will explore other relationships between education, social, health, and environmental factors aggregated by county. As mentioned, none of the information presented is causal, but it is shared to help the reader see patterns and consider topics for further research.

Figure 1: Education

Figure 2: Education vs. Poverty

Figure 3: Education, Poverty, and Cardiovascular Disease

In Figure 3 above, we can see the relationship between education and poverty, with a third variable of cardiovascular disease. In this graph, education is on the x-axis, poverty is on the y-axis, size represents cardiovascular disease rates, and color represents each county. In our dataset, cardiovascular disease is defined by the mean age-adjusted rate of emergency department visits for heart attacks per 10,000, per county. While the graph shows a clear linear relationship between education and poverty, also seen in Figure 2, the cardiovascular disease variable does not quite fit this linear pattern as clearly, although it still follows the trend. Counties with lower percentages of cardiovascular disease are congregated in the bottom left quadrant of the graph, which means they also have higher percentages of population with a high school diploma and lower poverty levels.

We can also look at the relationship between education level and lead exposure risk in counties shown in Figure 4, with education on the x-axis and lead exposure risk on the y-axis. Lead exposure risk is defined by the potential risk for lead exposure in children living in low-income communities with older housing. In this instance, we see that there is a trend for risk of lead exposure in children living in low income communities with older housing to increase as the percentage of the population with less than a high school education increases. As noted on the first page, education can be an indicator of other socioeconomic factors. In this case, this relationship could be due to the correlation between education and wealth, as wealth could impact the home-buying prospects of a family as well as the neighborhood in which they reside. Further research would be needed to confirm this. The graph shows a positive relationship between increased population with less than a high school diploma and risk of lead exposure for children in low-income communities. The graph also includes a trend line to highlight the linear relationship and a standard error bar with an error of about 5 points max on each side.

Figure 4: Education and Lead Exposure Risk