The Changing Face of TB Mortality

Africa’s remarkable decline in HIV-associated tuberculosis deaths

TidyTuesday
2025
Africa reduced HIV-associated TB deaths from 60% to 28% of total TB mortality between 2000-2024—a 32 percentage point decline. Despite this progress, Africa’s burden remains 2-6 times higher than other WHO regions. This visualization uses weighted regional estimates and direct labeling to tell a story of remarkable public health progress alongside persistent health disparities.
Published

November 9, 2025

Figure 1: Line chart showing HIV-associated TB deaths as percentage of total TB mortality by WHO region from 2000-2024. Africa’s bold blue line dominates, declining from 60% to 28% (31.9 percentage point decrease). Five other WHO regions shown in gray remain below 35% throughout, with most under 15%. All regions labeled at right endpoint. Chart demonstrates Africa’s significant progress yet persistent disproportionate burden of HIV/TB co-infection.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,     # Easily Install and Load the 'Tidyverse'
    ggtext,        # Improved Text Rendering Support for 'ggplot2'
    showtext,      # Using Fonts More Easily in R Graphs
    janitor,       # Simple Tools for Examining and Cleaning Dirty Data
    scales,        # Scale Functions for Visualization
    glue,          # Interpreted String Literals
    ggrepel        # Non-overlapping text labels
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 7,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

who_tb_data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-11-11/who_tb_data.csv')
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(who_tb_data)
skimr::skim(who_tb_data) |> summary()
```

4. Tidy Data

Show code
```{r}
#| label: tidy-fixed
#| warning: false

### |- calculate HIV-associated mortality proportion (WEIGHTED BY DEATHS) ----
WEIGHTED_BY_DEATHS <- TRUE

tb_hiv_ratio <- who_tb_data |>
  filter(!is.na(e_mort_tbhiv_num), !is.na(e_mort_num), e_mort_num > 0) |>
  mutate(
    hiv_prop = e_mort_tbhiv_num / e_mort_num,
    wt = if (WEIGHTED_BY_DEATHS) e_mort_num else 1
  ) |>
  group_by(g_whoregion, year) |>
  summarise(
    avg_hiv_prop = weighted.mean(hiv_prop, w = wt, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(avg_hiv_prop = 100 * avg_hiv_prop) |>
  filter(!is.na(g_whoregion), !is.na(avg_hiv_prop))

### |- prepare endpoint labels ----
# Africa gets prominent label
endpoint_labels_africa <- tb_hiv_ratio |>
  filter(g_whoregion == "Africa") |>
  filter(year == max(year))

# All other regions get smaller, gray labels for transparency
endpoint_labels_others <- tb_hiv_ratio |>
  filter(g_whoregion != "Africa") |>
  group_by(g_whoregion) |>
  filter(year == max(year)) |>
  ungroup()

# Africa series (sorted)
africa <- tb_hiv_ratio |>
    filter(g_whoregion == "Africa") |>
    arrange(year) |>
    ungroup()

africa_first <- africa |> slice_head(n = 1)
africa_last  <- africa |> slice_tail(n = 1)

africa_change_pp <- africa_last$avg_hiv_prop - africa_first$avg_hiv_prop
start_year <- africa_first$year
end_year   <- africa_last$year
end_val    <- africa_last$avg_hiv_prop
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Define color palette - Africa as hero, others as context
region_colors <- c(
    "Africa" = "#1E3A8A",                    
    "Americas" = "gray60",                   
    "Eastern Mediterranean" = "gray60",      
    "Europe" = "gray60",                     
    "South-East Asia" = "gray60",            
    "Western Pacific" = "gray60"             
)

# Line widths - Africa bold, others thin
region_linewidths <- c(
    "Africa" = 1.5,
    "Americas" = 0.6,
    "Eastern Mediterranean" = 0.6,
    "Europe" = 0.6,
    "South-East Asia" = 0.6,
    "Western Pacific" = 0.6
)

colors <- get_theme_colors(
    palette = list(
        col_africa = "#1E3A8A",
        col_gray = "gray60"
    )
)

### |- titles and caption ----
title_text <- str_glue("The Changing Face of TB Mortality")

subtitle_text <- str_glue(
    "HIV-associated deaths as a share of total TB mortality, by WHO region<br>",
    "**Africa** shows remarkable progress from {round(africa_first$avg_hiv_prop)}% ",
    "to {round(africa_last$avg_hiv_prop)}%, yet still bears the heaviest burden"
)

caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 45,
    source_text = "WHO Global Tuberculosis Database | Regional estimates weighted by mortality burden"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_markdown(
      face = "bold", family = fonts$title, size = rel(1.4),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_text(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.9), margin = margin(b = 20), hjust = 0
    ),

    ## Grid
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),

    # Axes
    axis.title = element_text(size = rel(0.8), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.85)),
    axis.ticks = element_blank(),

    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(1),
      margin = margin(t = 8, b = 8)
    ),
    panel.spacing = unit(2, "lines"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.8), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |-  main plot ----
p <- 
  ggplot(tb_hiv_ratio, aes(
    x = year, y = avg_hiv_prop,
    color = g_whoregion,
    linewidth = g_whoregion)
    ) +
  # Geoms
  geom_line(alpha = 0.9) +
  geom_text_repel(
    data = bind_rows(endpoint_labels_africa, endpoint_labels_others),
    aes(
      x = year,
      y = avg_hiv_prop,
      label = g_whoregion,
      size = g_whoregion,
      fontface = ifelse(g_whoregion == "Africa", "bold", "plain")
    ),
    hjust = 0,
    color = ifelse(bind_rows(endpoint_labels_africa, endpoint_labels_others)$g_whoregion == "Africa",
      "#1E3A8A", "gray40"
    ),
    lineheight = 0.9,
    nudge_x = 1,
    direction = "y",
    segment.size = 0.2,
    segment.color = "gray70",
    min.segment.length = 0,
    max.overlaps = 20,
    xlim = c(NA, 2038),
    show.legend = FALSE
  ) +
  # Scales
  scale_size_manual(
    values = c(
      "Africa" = 4.5,
      "Americas" = 2.8,
      "Eastern Mediterranean" = 2.8,
      "Europe" = 2.8,
      "South-East Asia" = 2.8,
      "Western Pacific" = 2.8
    )
  ) +
  scale_color_manual(values = region_colors) +
  scale_linewidth_manual(values = region_linewidths) +
  scale_x_continuous(
    breaks = seq(2000, 2024, by = 5),
    limits = c(2000, 2028),
    expand = expansion(mult = c(0.02, 0))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    limits = c(0, 65),
    breaks = seq(0, 65, by = 10)
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "HIV-Associated TB Deaths\n(% of Total TB Mortality)"
  ) +
  # Annotate
  annotate(
    "text",
    x = 2021, y = 22,
    label = glue("{round(abs(africa_change_pp),1)} pp decline since {start_year}"),
    family = fonts$text, size = 3.6, hjust = 0, color = colors$palette$col_africa
  ) +
  # Theme
  theme(
    plot.title = element_markdown(
      size = rel(1.85),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.15,
      margin = margin(t = 8, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.88),
      lineheight = 1.2,
      margin = margin(t = 2, b = 5)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = "Arial",
      color = colors$caption,
      hjust = 0,
      lineheight = 1.3,
      margin = margin(t = 12, b = 5),
    ),
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.2),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 45, 
  width  = 10,
  height = 7,
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      ggrepel_0.9.6   glue_1.8.0      scales_1.3.0   
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] skimr_2.1.5       lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       pillar_1.9.0     
[25] crayon_1.5.3      camcorder_0.1.0   magick_2.8.5      commonmark_1.9.2 
[29] tidyselect_1.2.1  digest_0.6.37     stringi_1.8.4     rsvg_2.6.1       
[33] rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1 
[37] cli_3.6.4         magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4       
[41] withr_3.0.2       bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29   
[45] bit_4.5.0         ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[49] knitr_1.49        markdown_1.13     rlang_1.1.6       gridtext_0.1.5   
[53] Rcpp_1.0.13-1     xml2_1.3.6        renv_1.0.3        svglite_2.1.3    
[57] rstudioapi_0.17.1 vroom_1.6.5       jsonlite_1.8.9    R6_2.5.1         
[61] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_45.qmd.

For the full repository, click here.

10. References

  1. Data Source:

11. Custom Functions Documentation

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

  • fonts.R: setup_fonts(), get_font_families() - Font management with showtext
  • social_icons.R: create_social_caption() - Generates formatted social media captions
  • image_utils.R: save_plot() - Consistent plot saving with naming conventions
  • base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

Back to top