Overview of Outcomes

On this page, we’ll explore overall trends for our key outcomes – COVID prevalence and COVID death rate – across counties in California, as well as the high-level trends for how these outcomes correlate with census predictors, including major demographic variables such as age, gender, and race, and other independent variables, such as unemployment rate, hospitalization percentage, vaccination rate, and land areas.[TBD***]

1. Outcomes by county

1a. Prevalence across county

Considering the different population bases for each county, we decided to convert the values in the total case count into the prevalence proportion. Prevalence is defined as the proportion of persons in a population who have a particular disease or attribute at a specified point in time or over a specified period of time. By utilizing prevalence, we are able to conduct a more comparable analysis of their respective pandemic responses across counties.

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
    
demo_plot = demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>% 
    ungroup() %>% 
    mutate(county_name = fct_reorder(county_name, prevalence)) %>% 
    ggplot(aes(x = county_name, y = prevalence, color = county_name)) +
    geom_point(alpha = 1) +
    theme(
      legend.position = "none"
    ) +
    labs(x = "County",
         y = "Prevalence",
         title = "Prevalence Across County") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    scale_y_continuous(labels = point)

ggplotly(demo_plot, width = 800, height = 500)

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
    
    demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>% 
    mutate(county_name = fct_reorder(county_name, prevalence)) %>%
    ggplot(aes(x = prevalence, fill = county_name)) +
    geom_histogram(alpha = 0.7) +
    labs(x = "Prevalence",
         y = "Count") +
    theme(
      legend.position = "bottom"
    ) + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point) +
          scale_x_continuous(labels = point)

Until November 2022 (information given by data source), we can observe that the COVID prevalence ranges from as low as 0.094 to nearly 0.386 by county in California, suggesting a roughly 4.1x difference between the minimum and maximum. As a new study from the Centers for Disease Control and Prevention (CDC) revealed, the percentage of Americans who have been infected with COVID-19 is approximately 30% in November 2022. Taking the prevalence of 30% as the baseline for further comparisons, it is worth noticing that around 80% of counties in California have a moderate level of prevalence falling between around 16% to 29%, and around 10% of counties show a quite low prevalence below 15%. Only 6 out of 58 counties in California shows prevalence of above 30%. The COVID infection in California seems relatively controllable to a large extent compared to the general prevalence level across the United States. As for the prevalence differences, it may be caused by some other differentiable factors across counties, and we would like to further discuss about it in later parts.

1b. Death rate across county

The death rate, or called case-fatality ratio, is defined as the ratio of deaths to the population of a particular area or during a particular period of time. Analyzing death rate help us track the social economical and geographical characteristics of those dying due to COVID in California, and allow comparisons of death trends across counties.

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)

demo_plot_1 = demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rate = total_deaths / total_cases) %>% 
    mutate(county_name = fct_reorder(county_name, death_rate)) %>% 
    ggplot(aes(x = county_name, y = death_rate, color = county_name)) +
    geom_point(alpha = 1) +
    theme(
      legend.position = "none"
    ) +
    labs(x = "County",
         y = "Death Rate",
         title = "Death Rate Across County") +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    scale_y_continuous(labels = point)

ggplotly(demo_plot_1, width = 800, height = 500)

point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)
    
 demo %>%
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rate = total_deaths / total_cases) %>% 
    mutate(county_name = fct_reorder(county_name, death_rate)) %>%
    ggplot(aes(x = death_rate, fill = county_name)) +
    geom_histogram(alpha = 0.7) +
    labs(x = "Death Rate",
         y = "Count") +
    theme(
      legend.position = "bottom"
    ) + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point) +
          scale_x_continuous(labels = point)

We can observe that the death rates range from as low as 0.00% to 1.76% by county in California. The density graph suggests a quite centralized distribution of death rates across counties, that most counties’ death rates fall between 0.25% to 1.25%. As for the minimum value from this data set, there is so far no recorded death so as 0% death rate in Alpine. As for the maximum, Siskiyou’s recorded death rate is 1.76% in November 2022, which is a little bit higher than the average country-wide death rate of 1.09% (data reported by John Hopkins University’s Coronavirus Resource Center). Considering the difference shown in death rates across counties, we will investigate the associations between this outcome variable and other potential factors in the following section.

2. Worst & Best conties for outcomes

2a. Worst counties for each COVID outcome

Furthermore, we zoom in on the pandemic responses and extract the 10 worst COVID outcome results, i.e. highest prevalence and highest death rates, in corresponding counties, and plot them as bar charts for a more straightforward visualization.

demo_worst_1 =
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>%
    select(county_name, prevalence) %>% 
    arrange(desc(prevalence)) %>% 
    head(10)

demo_worst_2 = 
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rates = total_deaths/total_cases) %>%
    select(county_name, death_rates) %>% 
    arrange(desc(death_rates)) %>% 
    head(10)

cbind(demo_worst_1, demo_worst_2) %>% 
    knitr::kable(digits = 4,
                 caption = "Worst counties for each COVID outcome",
                 col.names = c("County for Prevalence", "Prevelence", "County for Death Rate", "Death Rate"))

Worst counties for each COVID outcome

County for Prevalence	Prevelence	County for Death Rate	Death Rate
Kings	0.3864	Siskiyou	0.0176
Imperial	0.3483	Shasta	0.0168
Lassen	0.3356	Sierra	0.0162
Los Angeles	0.3249	Calaveras	0.0156
San Bernardino	0.3065	Tehama	0.0156
Tuolumne	0.3034	Trinity	0.0147
Riverside	0.2853	Imperial	0.0144
San Diego	0.2785	Inyo	0.0132
Madera	0.2780	Tuolumne	0.0130
Del Norte	0.2722	Stanislaus	0.0128

demo_worst_pre_plot =
    demo_worst_1 %>%
    mutate(county_name = fct_reorder(county_name, prevalence)) %>%
    ggplot(aes(x = reorder(county_name, prevalence, decreasing = T), y = prevalence, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
    labs(x = '',
         y = "Prevalence",
         title = "Worst Prevalence") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_worst_dea_plot =
    demo_worst_2 %>%
    mutate(county_name = fct_reorder(county_name, death_rates)) %>%
    ggplot(aes(x = reorder(county_name, death_rates, decreasing = T), y = death_rates, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
    labs(x = '',
         y = "Death Rate",
         title = "Worst Death Rate") + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_worst_pre_plot + demo_worst_dea_plot

2b. Best counties for each COVID outcome

Also, we extract the 10 best covid outcome results, i.e. lowest prevalence and lowest death rates, in corresponding counties.

demo_best_1 =
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population)) %>%
    select(county_name, prevalence) %>% 
    arrange(prevalence) %>% 
    head(10)

demo_best_2 = 
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              death_rates = total_deaths/total_cases) %>%
    select(county_name, death_rates) %>% 
    arrange(death_rates) %>% 
    head(10)

cbind(demo_best_1, demo_best_2) %>% 
    knitr::kable(digits = 4,
                 caption = "Best counties for each COVID outcome",
                 col.names = c("County for Prevalence", "Prevelence", "County for Death Rate", "Death Rate"))

Best counties for each COVID outcome

County for Prevalence	Prevelence	County for Death Rate	Death Rate
Modoc	0.0945	Alpine	0.0000
Sierra	0.0989	Mono	0.0025
Trinity	0.1067	Plumas	0.0035
Siskiyou	0.1225	Santa Cruz	0.0042
Alpine	0.1235	San Mateo	0.0043
Humboldt	0.1667	Solano	0.0044
Colusa	0.1700	Sonoma	0.0049
El Dorado	0.1712	Napa	0.0049
Mariposa	0.1774	Colusa	0.0057
Marin	0.1775	Alameda	0.0058

demo_best_pre_plot =
    demo_best_1 %>% 
    mutate(county_name = fct_reorder(county_name, prevalence)) %>%
    ggplot(aes(x = reorder(county_name, prevalence, decreasing = F), y = prevalence, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
    labs(x = '',
         y = "Prevalence",
         title = "Best Prevalence") +
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_best_dea_plot =
    demo_best_2 %>% 
    mutate(county_name = fct_reorder(county_name, death_rates)) %>% 
    ggplot(aes(x = reorder(county_name, death_rates, decreasing = F), y = death_rates, fill = county_name)) +
    geom_bar(stat = "identity", width = 0.5) +
    theme(
      legend.position = "none"
    ) +
     labs(x = '',
         y = "Death Rate",
         title = "Best Death Rate") + 
    theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5)) +
          scale_y_continuous(labels = point)

demo_best_pre_plot + demo_best_dea_plot

From the above tables and bar charts, Kings County responses to COVID with highest prevalence of 38.64%, and Siskiyou responses to covid with highest death rate of 1.76%. In comparison, Modoc has the lowest prevalence of 9.45% in California, and Alpine has no recorded death until November 2022. Upon observations, two out of ten counties with top prevalence, Imperial and Tuolumne, also have top death rates. And two out of ten counties with lowest prevalence, Alpine and Colusa, also have lowest death rates. While to be highlighted here, Siskiyou County has one of the lowest prevalence but the highest death rate among all counties. This preliminary observation indicates that it is hard to obtain an apparent relationship between the prevalence and death rates. Further steps are required to take in order to explore the associations and correlations between our two outcome variables, COVID prevalence and death rates.

3. Correlation between outcomes

    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population),
              death_rates = total_deaths/total_cases) %>%
    ungroup() %>% 
    dplyr::select(prevalence, death_rates) %>%
    GGally::ggpairs() %>% 
    ggplotly(width = 650, height = 400)

The points fall quite randomly on the plot, which indicates that there is no obvious or strong relationship between the outcome variables. Moreover, the correlation coefficient can range in value from −1 to +1. The larger the absolute value of the coefficient, the stronger the relationship between the variables. In our case, the correlation coefficient of -0.086 means that the relationship between outcomes are very weak and it is not statistically significant at all. Before this procedure, we made a reasonable guess that as prevalence increases, the death rates may increase. However, this plot result contradicts our hypothesis by showing a negative correlation in nature. Hence, in order to gather more comprehensive evidence on this relationship, we decide to take population by county into consideration for a further test on the association between outcomes.

4. Associations between outcomes across county

To find out the associations between two outcomes across counties, we decide to use bubble chart to visualize relationships between three numeric variables, death rates, prevalence proportion, and the population size for each county.

 demo_asso =   
    demo %>% 
    filter(!county_name %in% "Out of state") %>%
    group_by(county_name, population) %>% 
    summarize(total_cases = sum(reported_cases),
              total_deaths = sum(reported_deaths),
              prevalence = total_cases/mean(population),
              death_rates = total_deaths/total_cases) %>%
    ggplot(aes(x = prevalence, y = death_rates)) + 
      geom_point(aes(color = county_name, size = population)) + 
      geom_smooth(method = lm, se = FALSE, color = "red", size = 0.4, aes(weight = population)) + 
      labs(
        x = "Prevalence",
        y = "Death Rate",
        size = "Population",
        col = "County",
        title = "Associations Between Outcomes Across County"
      ) +
        theme(legend.title = element_text(size = 5),
          legend.key.size = unit(0.3, 'cm'),
          legend.text = element_text(size = 4)) +
    theme(axis.text.x = element_text(angle = 60, hjust = 1)) +
    theme(
          axis.title.x = element_text(size = 6),
          axis.text.x = element_text(size = 5),
          axis.title.y = element_text(size = 6),
          axis.text.y = element_text(size = 5))

ggplotly(demo_asso, width = 800, height = 500)

As we plotted, the values for each bubble are encoded by its horizontal position on the x-axis of prevalence, its vertical position on the y-axis of death rates, and the size of the bubble depends on the population size. The color of each bubble represent different counties in California to provide a visual sense.

On one hand, there is a positive best-fit trend line drawn on this plot which indicates a positive relationship between prevalence and death rates across counties, although this relationship is considerably weak. On the other hand, as the values on the horizontal and vertical scale decreases, we can only observe small bubbles on the bottom-left corners. And as the values on the horizontal or vertical scale increases, relatively large bubbles start to appear. To add on, the largest bubble of Los Angeles located on the top-right corner on this graph, indicating its relatively high prevalence and high death rate. These two perspective of findings may coincide with each other, suggesting a positive but weak relationship between outcomes across county.

Combining our findings in part 3 and 4, we can primarily conclude that across counties in California, prevalence and death rate are very weakly correlated.