The Confidence Cascade

For some major diseases, uncertainty spans nearly the size of the estimated burden (GBD 2023). Dot = estimate (1.0), Span = 95% uncertainty interval

A point-interval chart showing the 6 global disease groups with the highest relative uncertainty in the 2021 GBD estimates. Each 95% uncertainty interval is normalized to its own central estimate, placing the dot at 1.0 and expressing the span as a ratio. Built with ggdist’s geom_pointinterval in R.

Figure 1: A horizontal point-interval chart showing the 6 disease groups with the widest uncertainty in the 2021 Global Burden of Disease estimates. Each row shows a 95% uncertainty interval normalized to the central estimate at 1.0. Neglected tropical diseases and malaria have the widest span at 95% of their estimate; Musculoskeletal disorders have the narrowest at 60%. All intervals extend further right than left, indicating the upper bound is consistently farther from the estimate than the lower bound.

Steps to Create this Graphic

1. Load Packages & Setup

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

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse, ggtext, showtext, janitor,      
  scales, glue, camcorder, ggdist 
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 8,
  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

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

raw_gbd <- read_csv(
  here::here("data/30DayChartChallenge/2026/gbd_2021_dalys_level2_global.csv"),
  show_col_types = FALSE
) |>
  clean_names()
```

3. Examine the Data

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

glimpse(raw_gbd)
raw_gbd |> count(cause_name)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

gbd <- raw_gbd |>
  rename(cause = cause_name) |>
  select(cause, val, lower, upper) |>
  mutate(
    ui_width = upper - lower,
    rel_ui = ui_width / val * 100,
    # normalize: express as ratio of central estimate
    # dot always at 1.0; span = how far the CI reaches in either direction
    val_norm = 1.0,
    lower_norm = lower / val,
    upper_norm = upper / val,
    # retain billions for caption reference
    val_b = val / 1e9,
    lower_b = lower / 1e9,
    upper_b = upper / 1e9
  ) |>
  arrange(desc(rel_ui)) |>
  mutate(rank_ui = row_number())

# Top 6 by relative uncertainty
focus_gbd <- gbd |>
  slice_head(n = 6) |>
  mutate(
    cause_plot = factor(cause, levels = rev(cause)),
    label_text = paste0(round(rel_ui, 0), "% of estimate")
  )
```

5. Visualization Parameters

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    bg     = "#F5F3EE",        
    neutral = "#B0AAA2",     
    accent = "#8B1A2A",       
    text   = "#2C2C2C",      
    subtext = "#7A7570",     
    grid   = "#E4E0DA"        
  ) 
)

# Extract scalars for annotate() 
col_bg      <- colors$palette$bg
col_subtext <- colors$palette$subtext
col_accent  <- colors$palette$accent
col_grid    <- colors$palette$grid
col_text    <- colors$palette$text
col_neutral <- colors$palette$neutral

### |- titles and caption ----
title_text    <- "The Confidence Cascade"

subtitle_text <- "For some major diseases, uncertainty spans nearly the size of the estimated burden \u00b7 GBD 2023\nDot = estimate (1.0) \u00b7 Span = 95% uncertainty interval"

caption_text <- create_dcc_caption(
  dcc_year    = 2026,
  dcc_day     = 30,
  source_text = "Global Burden of Disease Collaborative Network \u00b7 GBD 2021 Results \u00b7 IHME, 2022"
)

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

### |- plot theme ----
base_theme <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.background = element_rect(fill = col_bg, color = NA),
    panel.background = element_rect(fill = col_bg, color = NA),
    plot.title = element_text(
      size = 26, face = "bold",
      family = fonts$title, color = col_text,
      margin = margin(b = 5)
    ),
    plot.subtitle = element_text(
      size       = 9.5, family = fonts$subtitle,
      color      = col_subtext,
      lineheight = 1.4, margin = margin(b = 18)
    ),
    plot.caption = element_markdown(
      size = 7.0, family = fonts$caption,
      color = col_subtext, linewidth = 1.3,
      hjust = 0, margin = margin(t = 14)
    ),
    axis.text.y = element_text(
      size = 8.5, family = fonts$text,
      color = col_text, hjust = 1
    ),
    axis.text.x = element_text(
      size = 8, family = fonts$text,
      color = col_subtext, lineheight = 1.2
    ),
    axis.title.x = element_text(
      size = 8.5, family = fonts$text,
      color = col_subtext, margin = margin(t = 8)
    ),
    axis.ticks = element_blank(),

    # vertical grid at ratio breaks — helps read the span
    panel.grid.major.x = element_line(color = col_grid, linewidth = 0.2),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    plot.margin = margin(t = 20, r = 55, b = 20, l = 15)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- main plot ----
p <- ggplot(
  focus_gbd,
  aes(
    y = cause_plot,
    x = val_norm,
    xmin = lower_norm,
    xmax = upper_norm
  )
) +
  # Geoms
  geom_vline(
    xintercept = 1,
    color = "#CECDCA",
    linewidth = 0.35,
    linetype = "solid"
  ) +
  geom_pointinterval(
    color = col_accent,
    point_color = col_bg,
    point_fill = col_accent,
    shape = 21,
    linewidth = 2.0,
    fatten_point = 4.5,
    alpha = 0.92
  ) +
  geom_text(
    aes(x = upper_norm, label = label_text),
    hjust = -0.15,
    family = fonts$text,
    size = 3.0,
    fontface = "bold",
    color = col_accent
  ) +

  # Annotate
  annotate(
    "text",
    x = 1.62, y = 6.4,
    label = "Nearly as large\nas the estimate",
    hjust = 0, vjust = 0.5,
    size = 2.6, family = fonts$text,
    color = col_subtext, fontface = "italic",
    lineheight = 1.25
  ) +

  # Scales
  scale_x_continuous(
    breaks = c(0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0),
    labels = function(x) ifelse(x == 1, "1.0\n(estimate)", paste0(x, "\u00d7")),
    expand = expansion(mult = c(0.02, 0.28))
  ) +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Ratio to estimate (1.0) · Span = 95% uncertainty interval · Global · 2021",
    y = NULL
  )
```

7. Save

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

### |- plot image ----  
save_plot(
  p,
  type = "30daychartchallenge",
  year = 2026,
  day = 30,
  width = 8,
  height = 7
  )
```

8. Session Info

TipExpand for Session Info

R version 4.5.3 (2026-03-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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 utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      ggdist_3.3.3    camcorder_0.1.0 glue_1.8.0     
 [5] scales_1.4.0    janitor_2.2.1   showtext_0.9-8  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5 forcats_1.0.1  
[13] stringr_1.6.0   dplyr_1.2.1     purrr_1.2.2     readr_2.2.0    
[17] tidyr_1.3.2     tibble_3.3.1    ggplot2_4.0.3   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6         xfun_0.57            htmlwidgets_1.6.4   
 [4] tzdb_0.5.0           vctrs_0.7.3          tools_4.5.3         
 [7] generics_0.1.4       curl_7.0.0           parallel_4.5.3      
[10] gifski_1.32.0-2      pacman_0.5.1         pkgconfig_2.0.3     
[13] RColorBrewer_1.1-3   S7_0.2.1             distributional_0.7.0
[16] lifecycle_1.0.5      compiler_4.5.3       farver_2.1.2        
[19] textshaping_1.0.5    codetools_0.2-20     snakecase_0.11.1    
[22] litedown_0.9         htmltools_0.5.9      yaml_2.3.12         
[25] crayon_1.5.3         pillar_1.11.1        magick_2.9.1        
[28] commonmark_2.0.0     tidyselect_1.2.1     digest_0.6.39       
[31] stringi_1.8.7        rsvg_2.7.0           rprojroot_2.1.1     
[34] fastmap_1.2.0        grid_4.5.3           cli_3.6.6           
[37] magrittr_2.0.5       utf8_1.2.6           withr_3.0.2         
[40] bit64_4.6.0-1        timechange_0.4.0     rmarkdown_2.31      
[43] bit_4.6.0            otel_0.2.0           ragg_1.5.2          
[46] hms_1.1.4            evaluate_1.0.5       knitr_1.51          
[49] markdown_2.0         rlang_1.2.0          gridtext_0.1.6      
[52] Rcpp_1.1.1           xml2_1.5.2           svglite_2.2.2       
[55] rstudioapi_0.18.0    vroom_1.7.1          jsonlite_2.0.0      
[58] R6_2.6.1             systemfonts_1.3.2   

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in 30dcc_2026_30.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Sources:
   • Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2024. Available from: https://vizhub.healthdata.org/gbd-results/
2. Key Datasets:
   • gbd_2021_dalys_level2_global.csv — DALYs (Disability-Adjusted Life Years), Level 2 causes, Global, Both sexes, All ages, Year = 2021, Metric = Number. Downloaded from the GBD Results Tool (IHME). 22 cause groups included.

11. Custom Functions Documentation

Note📦 Custom Helper Functions

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