TidyTuesday: Global Health Spending - Public, Private, and Out-of-Pocket

TidyTuesday
R
Data Viz
WHO GHED data on current health expenditure, financing schemes, and curative vs preventive shares for 2023 across countries
Author

chokotto

Published

April 28, 2026

Overview

The WHO Global Health Expenditure Database (GHED) makes it possible to compare how countries pay for care: public schemes, voluntary insurance, and household out-of-pocket spending, alongside what the money buys (curative vs preventive care, and more). This notebook uses the TidyTuesday 2026-04-21 release (WHO GHED via ONE Data).

  • Data Source: TidyTuesday 2026-04-21 (WHO GHED)
  • Site note: Official dataset date is 2026-04-21 (week 16); this post uses 2026-04-28 to pair with the same week’s Makeover Monday update per site rules.
  • Angle: Scale of spending (CHE in constant 2023 US$), financing mix (% of CHE), and curative vs preventive allocation for the largest spenders in 2023

Dataset

Code 
library(tidyverse)
library(scales)
library(glue)
library(ggrepel)
library(showtext)
library(colorspace)
library(forcats)
Code 
data_dir <- file.path(getwd(), "data")
che <- readr::read_csv(
  file.path(data_dir, "health_spending.csv"),
  show_col_types = FALSE
)
fin <- readr::read_csv(
  file.path(data_dir, "financing_schemes.csv"),
  show_col_types = FALSE
)
pur <- readr::read_csv(
  file.path(data_dir, "spending_purpose.csv"),
  show_col_types = FALSE
)
Code 
NOTE_TEXT   <- "2023 cross-section where available; GHED units per TidyTuesday dictionary"
SOURCE_TEXT <- "TidyTuesday 2026-04-21 / WHO GHED (via ONE Data)"
CAPTION     <- glue("Note: {NOTE_TEXT}  |  Source: {SOURCE_TEXT}  |  \u00A9 2026 chokotto")

theme_fm <- theme_minimal(base_size = 12) +
  theme(
    plot.background = element_rect(fill = "white", color = NA),
    panel.background = element_rect(fill = "#f8fafc", color = NA),
    panel.grid.major = element_line(color = "#e2e8f0", linewidth = 0.3),
    panel.grid.minor = element_blank(),
    text = element_text(color = "#334155"),
    axis.text = element_text(color = "#475569"),
    plot.title = element_text(color = "#1e293b", face = "bold", size = 14),
    plot.subtitle = element_text(color = "#64748b", size = 10),
    plot.caption = element_text(
      face = "italic", color = "#94a3b8", size = 9,
      hjust = 0, margin = margin(t = 12)
    ),
    plot.caption.position = "plot",
    legend.background = element_rect(fill = "white", color = NA),
    plot.margin = margin(15, 15, 15, 15)
  )

COL_GOV <- "#0284c7"
COL_OOP <- "#dc2626"
COL_CUR <- "#0d9488"
COL_PRV <- "#f59e0b"

Analysis

The United States still leads absolute current health expenditure

Code 
che_23 <- che |>
  filter(
    year == 2023,
    expenditure_type == "Current Health Expenditure (CHE)",
    unit == "constant 2023 US$"
  )

top15 <- che_23 |>
  slice_max(order_by = value, n = 15) |>
  mutate(
    country_name = fct_reorder(country_name, value),
    dollar_billions = value / 1e9
  )

format_usd_trillions <- function(x) {
  paste0("$", scales::comma(x / 1000, accuracy = 0.1), "T")
}

ggplot(top15, aes(x = dollar_billions, y = country_name)) +
  geom_segment(
    aes(x = 0, xend = dollar_billions, yend = country_name),
    color = "#e2e8f0", linewidth = 0.9
  ) +
  geom_point(color = COL_GOV, size = 4.5) +
  geom_text(
    aes(label = dollar(value, scale = 1e-9, accuracy = 0.1, suffix = "B")),
    hjust = -0.15, size = 3.2, color = "#475569"
  ) +
  scale_x_continuous(
    labels = format_usd_trillions,
    breaks = scales::breaks_extended(n = 5),
    expand = expansion(mult = c(0, 0.08))
  ) +
  labs(
    title = "Fifteen largest national health budgets (CHE, constant 2023 US$)",
    subtitle = "Current health expenditure aggregates all financing sources | Year 2023",
    x = "Current health expenditure (USD, trillions)",
    y = NULL,
    caption = CAPTION
  ) +
  theme_fm +
  theme(panel.grid.major.y = element_blank())

Out-of-pocket share trades off with compulsory public financing

Code 
fin_pct <- fin |>
  filter(
    year == 2023,
    unit == "% of current health expenditure"
  ) |>
  mutate(
    scheme_short = case_when(
      str_detect(financing_scheme, "Government schemes") ~ "Government",
      str_detect(financing_scheme, "out-of-pocket") ~ "Out_of_pocket",
      str_detect(financing_scheme, "Voluntary") ~ "Voluntary",
      TRUE ~ NA_character_
    )
  ) |>
  filter(!is.na(scheme_short)) |>
  group_by(country_name, scheme_short) |>
  summarise(value = sum(value, na.rm = TRUE), .groups = "drop") |>
  pivot_wider(names_from = scheme_short, values_from = value)

fin_plot <- fin_pct |>
  inner_join(
    che_23 |>
      select(country_name, che_usd = value) |>
      slice_max(che_usd, n = 60),
    by = "country_name"
  ) |>
  filter(!is.na(Government), !is.na(Out_of_pocket))

ggplot(fin_plot, aes(x = Government, y = Out_of_pocket)) +
  geom_point(aes(size = che_usd), alpha = 0.55, color = COL_GOV) +
  geom_smooth(
    method = "lm", se = FALSE, linewidth = 0.4,
    color = "#94a3b8", linetype = "dashed"
  ) +
  geom_text_repel(
    data = fin_plot |> slice_max(che_usd, n = 12),
    aes(label = country_name),
    size = 3, max.overlaps = 20,
    segment.color = "#e2e8f0"
  ) +
  scale_size_continuous(range = c(2, 10), labels = label_dollar(scale = 1e9, suffix = "B")) +
  scale_x_continuous(labels = label_number(suffix = "%")) +
  scale_y_continuous(labels = label_number(suffix = "%")) +
  labs(
    title = "Public compulsory financing vs household out-of-pocket (% of CHE)",
    subtitle = "Bubble size = total CHE (2023) | Labels: top 12 spenders among plotted countries",
    x = "Government & compulsory schemes (% of CHE)",
    y = "Household out-of-pocket (% of CHE)",
    size = "CHE (USD)",
    caption = CAPTION
  ) +
  theme_fm +
  theme(legend.position = "bottom")

Curative care still absorbs most of the on-budget health function mix

Code 
pur_23 <- pur |>
  filter(
    year == 2023,
    unit == "% of current health expenditure",
    spending_purpose %in% c("Curative care", "Preventive care")
  )

lv <- levels(top15$country_name)

cp <- pur_23 |>
  filter(country_name %in% lv) |>
  mutate(
    country_name = factor(country_name, levels = rev(lv)),
    spending_purpose = fct_relevel(spending_purpose, "Preventive care", "Curative care")
  )

ggplot(cp, aes(x = country_name, y = value, fill = spending_purpose)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.72) +
  coord_flip() +
  scale_fill_manual(
    values = c("Curative care" = COL_CUR, "Preventive care" = COL_PRV)
  ) +
  scale_y_continuous(labels = label_number(suffix = "%")) +
  labs(
    title = "Curative vs preventive care as a share of CHE",
    subtitle = "Same 15 countries as the CHE ranking | 2023 | each bar is % of CHE (other functions omitted)",
    x = NULL,
    y = "Percent of current health expenditure",
    fill = NULL,
    caption = CAPTION
  ) +
  theme_fm +
  theme(legend.position = "top")

This post is part of TidyTuesday, a weekly data project.