Milestone #6

Scenario 3: Environment and asthma in California

Problem Statement

Asthma remains a significant public health challenge, with emergency department (ED) visits serving as critical indicators of acute asthma episodes. In California, demographic and environmental factors influence disparities in asthma outcomes. Identifying and targeting environmental determinants at the county-level can guide interventions to mitigate acute asthma and improve community health.

This project aims to address the need for targeted public health strategies by examining the relationship between environmental measures and asthma ED rates. Using census tract-level data from Cal EnviroScreen (CES) and county-level asthma ED rates, this analysis will summarize environmental measures at the county level and assess their correlation with asthma ED rates. The research will focus on identifying counties where specific environmental factors may disproportionately contribute to acute asthma episodes, providing a foundation for targeted environmental interventions.

Methods

Overall Cleaning

For all three datasets, we standardized column names for consistency and ease of analysis, converting them to lowercase and replacing punctuation (e.g., dots) with underscores using the rename_with() function from the dplyr package. We also addressed data quality issues like inconsistent county names and special characters in categorical variables (e.g., “Multi-race” and age group labels) using str_replace_all(). After cleaning, data was aggregated at the county level to facilitate comparisons and highlight key patterns.

Data Source 1: California Environmental Measures

Source & Years: The “calenviroscreen_measures_2021.csv” dataset from CalEPA includes environmental metrics for California counties, representing 2021 data on air quality, water quality, toxics, and waste.
Cleaning & New Variables: We renamed several column names for clarity (e.g., california_county to county). We aggregated the data at the county level, calculating the median of key environmental measures like PM2.5 and diesel particulate matter.
Analytic Methods: We calculated median values for key pollutants at the county level, providing insights into environmental health disparities across counties.

Data Source 2: California Environmental Score

Source & Years: The “calenviroscreen_scores_demog_2021.csv” dataset from CalEPA includes CES scores and demographic data for each census tract in California, representing 2021 data.
Cleaning & New Variables: We cleaned the county column by removing the “County” suffix. We aggregated the data at the county level, calculating the mean CES score to assess overall environmental vulnerability.
Analytic Methods: We averaged CES scores at the county level, providing a measure of environmental vulnerability for each county that may be useful for exploring health disparities.

Data Source 3: Asthma Emergency Department Visits

Source & Years: The “chhs_asthma_ed.csv” dataset from CalHHS includes age-adjusted asthma ED visits by county, year, and demographic group, with 2020 being the most recent year.
Cleaning & New Variables: We resolved any issues with special characters. We filtered the dataset for 2020 data and asthma rates for children and adults. We pivoted the data to create separate columns for each group.
Analytic Methods: We analyzed asthma ED visit rates for children and adults in 2020, facilitating comparisons with environmental data to explore links between asthma and environmental factors.

Table 1

Table 1 (Age group: 0-17 years): The table displays county-level environmental measures as well as age-adjusted asthma emergency department visit rates for 0-17 year olds in California in 2020. The table provides an overview of various environmental factors—that affect air pollution and thus may affect asthma rates—as well as an overall county-level environmental score.

Figure 1

From Figure 1 we can see that there is a positive association between the California Environmental Score and age adjusted emergency department visit rate for asthma across counties in California for both adults and children in 2020.

Figure 2

Figure 2 shows that a slight negative association between age-adjusted asthma rates and median traffic density in California for both adults and children in 2020, though there is a slightly steeper negative association in adults.

Results

Table 1 presents county-level environmental factors that influence air pollution and potentially impact asthma rates, alongside age-adjusted asthma ED visit rates for people between the ages of 0-17. The four counties with the highest asthma age-adjusted ED visit rates were Modoc, Fresno, Mono, and Madera at 70.1, 51.8, 51.3, and 48.7, respectively. The top 4 highest mean California Environmental Score were Merced, Tulare, Kings, and Fresno.

Figure 1 shows a positive association between California Environmental scores and age-adjusted ED visit rates for asthma across all counties for both children and adults in 2020. A steeper slope was observed in children compared to adults.

Figure 2 shows a negative association between age-adjusted asthma rates and median traffic density in California for both children and adults in 2020. A steeper negative slope was observed in adults compared to children.

Discussion

The results from our analysis highlight the role of environmental factors in shaping asthma-related health outcomes across counties in California. The 4 counties with the highest mean California environmental scores were all located in the Central Valley, which is is known for its combination of high agricultural activity and higher environmental burdens. The geographic location of the Central Valley also exacerbates socioeconomic disparities, and as seen with Fresno and Madera, can also have among the highest age-adjusted ED visit rates for Asthma.

The positive association shown in Figure 1 between environmental scores and age-adjusted asthma ED visit rates further supports the findings from Table 1, and the steeper slope observed in children suggests that younger populations may be more vulnerable to environmental exposures. Conversely, the negative association in Figure 2 between median traffic density and age-adjusted asthma ED visit rates does not support a link between traffic pollution and increased asthma risk, however, this may be due to the ease of healthcare access in urban areas compared to rural areas. It may be worthwhile exploring the association between traffic pollution and asthma rates within Central Valley counties in future analyses to see if there is a within-rural area trend compared to urban areas.

The alignment of high California Environmental scores with elevated asthma ED visit rates in the Central Valley region highlights the cumulative impact of environmental and social stressors. For example, counties like Fresno, which rank among the highest in both environmental scores and asthma ED visit rates, demonstrate the interplay between pollution exposure and adverse health outcomes. Further analyses should focus on this population and explore efforts to reduce socioeconomic disparities.