Comparing Health Impacts: Cardiovascular vs. Diabetes and Kidney Diseases

Exploring the Global Burden of Disease: Analyzing Leading Causes of DALYs globally, and across Countries over the years

Comparing Health Impacts: Cardiovascular and Diabetes DALYs Across Nations

The line graphs illustrate the trends in DALYs per 1000 persons for Cardiovascular Diseases and Diabetes and Kidney Diseases from 1990 to 2021 in the World, United States, India, and China. The data, sourced from the Global Burden of Disease study and population estimates from the United Nations and Our World in Data, reveals that Cardiovascular DALYs are rising in India and China, reflecting increasing lifestyle-related health issues with rising prosperity. In contrast, the United States shows a decline in Cardiovascular DALYs, indicating effective management and treatment strategies, while the global rate remains stable. For Diabetes and Kidney Diseases, the DALYs per 1000 persons are increasing in all regions, with a notably rapid rise in the United States compared to India, China, and the World, highlighting the growing health impact of these conditions, particularly in the U.S.

Figure 1: This graphic displays the trends in DALYs per 1000 persons for Cardiovascular Diseases and Diabetes and Kidney Diseases from 1990 to 2021. The x-axis represents the years, while the y-axis shows the number of DALYs per 1000 persons. Different colors indicate the data for the World (grey), United States (blue), India (orange), and China (red), providing a comparative view of health burdens across these regions.

How I made these graphics?

Getting the data

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy
library(owidR)                # Get data from Our World in R

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # To lighten and darken colours
library(patchwork)            # Combining plots

# Circle Packing Algorithm
library(packcircles)          # To draw packed circles

search1 <- owidR::owid_search("burden of disease")

df1 <- owid("burden-of-disease-by-cause")

popdf <- owid("population-with-un-projections")

df2 <- df1 |> 
  as_tibble() |> 
  pivot_longer(
    cols = -c(year, entity, code),
    names_to = "indicator",
    values_to = "value"
  ) |> 
  mutate(
    indicator = str_remove(
      indicator,
      "Total number of DALYs from "
    ),
    indicator = str_remove(
      indicator,
      "\n"
    )
  ) |>
  mutate(
    indicator = str_to_title(
      indicator
    ),
    indicator = str_replace(
      indicator,
      "Hiv/Aids",
      "HIV / AIDS"
    ),
    indicator = str_replace_all(
      indicator,
      "And",
      "and"
    )
  )

Visualization Parameters

# Font for titles
font_add_google("Sail",
  family = "title_font"
) 

# Font for the caption
font_add_google("Barlow Condensed",
  family = "caption_font"
) 

# Font for plot text
font_add_google("Poiret One",
  family = "body_font"
) 

# Font for the Strip Text 
font_add_google("Yanone Kaffeesatz",
  family = "strip_font"
)

showtext_auto()

# Colour Palette
set.seed(41)
mypal <- paletteer::paletteer_d("khroma::smoothrainbow")
mypal <- sample(mypal, 25, replace = F)
# Background Colour
bg_col <- "#F1F9F3FF"
text_col <- "grey20"
text_hil <- "#004616FF"

# Base Text Size
bts <- 11

plot_title <- ""

plot_subtitle <- ""

data_annotation <- "About the Data: "

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")

plot_caption <- paste0(
  "**Data:** IHME - Global Burden of Disease (GBD) Study  |  ",
  "**Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )
rm(github, github_username, xtwitter, 
   xtwitter_username, social_caption_1, social_caption_2)

Comparing the rising and falling health burdens of cardiovascular and diabetes diseases across the world, USA, India, and China from 1990 to 2021

# df2 |> 
#   filter(indicator == "Cardiovascular Diseases") |> 
#   ggplot(aes(year, value, group = entity, colour = entity)) +
#   geom_line() +
#   theme(legend.position = "none")
# 
# df2 |> 
#   filter(entity == "India") |> 
#   ggplot(aes(x = year, y = value, fill = indicator)) +
#   geom_col(position = "fill", colour = "white") +
#   geom_text(
#     data = df2 |> filter(entity == "India") |> filter(year == 1990),
#     aes(x = 1989, label = indicator, colour = indicator), 
#     hjust = 1, vjust = 0.5,
#     position = "fill",
#     check_overlap = T) +
#   coord_cartesian(clip = "off") +
#   theme(legend.position = "none")


font_add_google(
  "PT Sans",
  family = "body_font"
)

font_add_google(
  "Trochut",
  family = "title_font"
)

text_col <- "grey25"

text_hil <- "grey35"

popdf1 <- popdf |> 
  as_tibble() |> 
  select(entity, code, year, 
         `Population - Sex: all - Age: all - Variant: estimates`) |> 
  janitor::clean_names() |> 
  rename(population = population_sex_all_age_all_variant_estimates)

df3 <- df2 |> 
  left_join(popdf1)

sel_cons <- popdf1 |> 
  filter(!is.na(code)) |> 
  filter(year == 2021) |> 
  slice_max(population, n = 4) |> 
  pull(entity)

df4 <- df3 |> 
  filter(indicator %in% c("Cardiovascular Diseases", 
                          "Diabetes and Kidney Diseases")) |> 
  filter(entity %in% sel_cons) |> 
  mutate(perc_cwd = 1000 * value / population)

plot_qr <- ggplot(
  data = NULL, 
  aes(x = 0, y = 0, label = url_graphics)
  ) + 
  ggqr::geom_qr(
    colour = text_hil, 
    fill = "white",
    size = 2.2
    ) +
  # labs(caption = "Scan for the Interactive Version") +
  coord_fixed() +
  theme_void() +
  theme(plot.background = element_rect(
    fill = NA, 
    colour = NA
    ),
    plot.caption = element_text(
      hjust = 0.5,
      margin = margin(0,0,0,0, "mm"),
      family = "caption_font",
      size = bts/1.8
    )
  )


bts = 90

g <- df4 |> 
  ggplot(
    mapping = aes(
      x = year,
      y = perc_cwd,
      colour = entity
    )
  ) +
  geom_line(
    aes(linewidth = (entity == "United States"))
  ) +
  geom_point(
    #aes(stroke = (entity == "United States")),
    pch = 21,
    fill = "white",
    size = 5
  ) +
  ggrepel::geom_text_repel(
    data = df4 |> filter(year == 2021),
    mapping = aes(label = entity),
    hjust = 0,
    nudge_x = 0.25,
    direction = "y",
    family = "body_font",
    min.segment.length = unit(10, "mm"),
    size = bts / 3
  ) +
  scale_linewidth_manual(
    values = c(0.5, 1.5)
  ) +
  scale_x_continuous(
    expand = expansion(c(0,0.2)),
    breaks = seq(1990, 2020, 5)
  ) +
  scale_colour_manual(
    values = c("red", "orange", "blue", "grey50")
  ) +
  coord_cartesian(clip = "off") +
  facet_wrap(~ indicator, scales = "free_y", nrow = 2) +
  labs(
    title = "Comparing Health Impacts:\nCardiovascular vs. Diabetes and Kidney Diseases",
    subtitle = str_wrap("Disability Adjusted Life Years (DALYs) measure the overall disease burden by accounting for years lost due to illness, disability, or early death. This graphic reveals rising DALYs from cardiovascular diseases in India and China, while the United States shows a decline. Conversely, DALYs from diabetes and kidney diseases are increasing in all regions, with the most rapid rise seen in the United States.", 75),
    caption = plot_caption,
    x = NULL, y = "DALYs per 1000 persons"
  ) +
  theme_classic(
    base_family = "body_font",
    base_size = bts
  ) +
  theme(
    plot.margin = margin(10,10,10,10, "mm"),
    legend.position = "none",
    axis.line = element_line(
      arrow = arrow(length = unit(2, "mm")),
      colour = text_col,
      linewidth = 0.5
    ),
    strip.background = element_blank(),
    panel.grid.major.x = element_line(
      linetype = 2,
      linewidth = 0.5,
      colour = lighten(text_col, 0.75)
    ),
    strip.text = element_text(
      family = "title_font",
      colour = text_hil,
      margin = margin(0,0,0,0,"mm"),
      size = bts * 2
    ),
    plot.caption = element_textbox(
      hjust = 0.5, 
      family = "caption_font",
      colour = text_hil
    ),
    axis.title.y = element_text(
      colour = text_col,
      size = 1.2 * bts
    ),
    axis.text = element_text(
      colour = text_col
    ),
    axis.ticks = element_line(
      linewidth = 0.3
    ),
    axis.ticks.length = unit(2, "mm"),
    plot.title = element_text(
      colour = text_hil,
      size = 2.2 * bts,
      lineheight = 0.35,
      family = "title_font",
      hjust = 0.5
    ),
    plot.subtitle = element_text(
      lineheight = 0.25,
      colour = text_hil,
      hjust = 0
    ),
    plot.title.position = "plot"
  )

g2 <- g + 
    inset_element(
    p = plot_qr,
    left = 0.8, right = 1,
    bottom = 0.75, top = 0.9,
    align_to = "full"
  ) + 
  plot_annotation(
    theme = theme(
      plot.background = element_rect(
        fill = "transparent",
        colour = "transparent"
      ),
      panel.background = element_rect(
        fill = "transparent",
        colour = "transparent"
      )
    )
  )

ggsave(
  filename = here::here("data_vizs", "a4_owid_daly_cvddb.png"),
  plot = g2,
  height = 297 * 2,
  width = 210 * 2,
  units = "mm",
  bg = "white"
)

Save a thumbnail

library(magick)
# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", 
                      "a4_owid_daly_cvddb.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(here::here("data_vizs", "thumbnails", 
                         "owid_daly_cvddb.png"))