COVID-19 Vaccination Uptake and Flu Infection amongst California Counties

Problem Statement

The development and progress of the COVID-19 pandemic, and subsequent vaccine discovery, have allowed for the opportunity to research vaccine awareness, uptake, and retention. Research that could provide more insight into how promotion of a vaccine impacts behavior with regards to another. Evidence indicating that the promotion of one vaccine may influence uptake of other vaccines could help in future vaccine promotions and further encourage public vaccination.

Our team will study and consider three datasets with the purpose of finding a correlation between COVID-19 vaccination rates and influenza infection rates, one containing weekly influenza infection data, another containing COVID-19 vaccination data, and a data set composed of demographic and geographic data for Los Angeles County. In addition to analyzing the correlations between COVID-19 vaccination rate and flu infection rate by age for California. We will also be examining the infection rate and vaccination rate correlations across the different counties within the state.

Methods

Data Sources:

All three datasets used for this project were sourced from the California Department of Public Health (simulated data). California COVID vaccination rates by quarter (ca_vax_rates_quarter.csv): provided quarterly numbers on Covid-19 vaccination numbers and demographics per county in California from 2020-2023. Simulated flu case reporting for California (sim_flu_CA.csv) sourced: provided influenza infection and severity numbers as well as demographics per county in California from 2022-2023, with the exception of Los Angeles County. Simulated Flu case reporting for Los Angeles County including population (sim_flu_LACounty_pop.csv): provided Los Angeles County specific influenza infection and severity numbers as well as demographics from 2022-2023.

Data Cleaning:

First and foremost, we converted columns in all three datasets to snakecase and coerced data types to collectively match. Next, we evaluated for any missing values and renamed our columns of interest to all correspond for binding. We initially merged our two influenza datasets and created a new variable of the calculated influenza incidence per county. Then, we appended a new variable of the calculated vaccination rate per county to the COVID-19 vaccination data.

To examine the relationship between age and respective rates, we reviewed the age categories present in the data. We integrated the three strata: “Under 5”, “5-11”, and “12-17” within the COVID-19 vaccination data into a “0-17” category to reflect the influenza data. During this process, we discovered that Alpine and Sierra County were both missing data for the “0-17” age group and for these two rows we re-coded those cells as NA. Finally, we merged our influenza data and our COVID-19 vaccination data by county and four age categories into one combined set for analysis.

Data Analysis:

For the analysis stage, we began with generating visualizations of the data. We created three different plots to compare influenza infection rates against COVID-19 vaccination rates by county and age categories. The purpose of our graphics was to visually evaluate density and distribution of data points as well as view which counties within California were on the upper and lower end of our data. Moreover, we decided to calculate two additional rates for analysis: the average influenza infection rate per county and the average COVID-19 vaccination rate per county across all age groups. We compared these two average rates using a mirror bar graph depicting each county’s mean rate. Lastly, we developed an interactive table displaying rates for four age categories by county and their respective populations, COVID-19 vaccination rate per 100 individuals, and influenza infection rate per 100 individuals.

Figure 1.

Figure 1: This table includes all county data with age categories 0-17, 18-49, 50-64, and 65+. It includes the total population, COVID-19 vaccination rates per 100 persons, the total flu infections, and flu infection rate per 100 persons.

Figure 2.

Figure 2: The graph examines COVID-19 Vaccination Rate by flu infection rate per county. The graph does suggest a correlation between higher vaccination rates and lower flu infection rates.

Figure 3.

Figure 3: This graphic shows the COVID-19 vaccination rates by flu infection rates per the four different age categories within each county.

Figure 4.

Figure 4: This scatterplot depicts COVID-19 vaccination rates by flu infection rates across all California counties.

Figure 5.

Figure 5: This dodged bar chart is a side-by-side comparison of COVID-19 vaccination rate and flu infection rate to generate a clearer understanding between the two variables.

Results

Figure 1. contains the complete counts and rates of each age category per county. This table indicates the aggregated data used for calculating cumulative rates per county. When examining the aggregated data, Figure 2. shows the COVID-19 vaccination rates by flu infection rate per California county. There does show some relationship between counties having a considerably higher vaccination rate, between 60-90%, and a moderate flu infection rate that is less than 47.5%. There is also a distribution of counties that have a high flu infection rate, above 55%, and a low COVID-19 vaccination rate compared to other California counties. The following Figure examines the COVID-19 vaccination rates by flu infection rates divided into age categories per California county. Figure 3. shows the relationship between COVID-19 vaccination rates and flu infection rates amongst four age categories. There is some relationship between the age categories of 50-64 and 65+ having a high COVID-19 vaccination rate and low flu infection rates, less than 47.5%. There also is a cluster of 0-17 values that are below the California age category averages for COVID-19 vaccination rate and higher than average flu infection rate. Figure 4. is similar to Figure 1. in that the graph examines the relationship of COVID-19 vaccination rates and flu infection rates among California counties with the county labels representative of data points. This graph does give a clear indication and direction of a negative relationship between COVID-19 vaccination rates and flu infection rates. In Figure 5, this graph shows the numerical values of flu infection rate and COVID-19 vaccination rate per county. This graph shows the relationship between high flu infection rate and low COVID-19 vaccination rate in Modoc, Lassen, and Colusa counties. It also shows that as COVID-19 vaccination rates increase, flu infection rates stay moderate to average, however, this does not result in lower flu infection rates.

Discussion

COVID-19 and influenza are caused by different viruses, and they can be influenced by various factors including the emergence of new variants, public health measures, vaccination efforts, as well as individual behaviors. Rates can vary over time across different age groups and regions of the country. Historically, the COVID-19 vaccination campaign gave preference to certain age groups and high-risk populations at first. Initially, older adults, healthcare workers, and individuals with underlying health conditions were prioritized for vaccination. As vaccine availability increased, eligibility expanded to include younger age groups, which may explain why some of the 0-17 age group have lower COVID-19 vaccination rates. Individuals may choose a COVID-19 vaccine over a flu vaccine for many reasons such as perceiving COVID-19 to be a more severe threat, given its higher potential for severe illness and the global emphasis on its prevention. Others may be subject to mandates or requirements for COVID-19 vaccination for employment or travel. Media coverage and public health campaigns advocating the COVID-19 vaccine could also contribute to its heightened awareness and urgency. It’s important to recognize that public health recommendations generally advocate for both COVID-19 and flu vaccination to maximize individual and community protection against preventable diseases. Comparing vaccine rates for COVID-19 to infection rates for influenza may provide insight into the effectiveness and efficacy of vaccination programs in preventing illnesses.