Global burden of disease statistics
Isobel Beasley
2025-09-11
Last updated: 2025-09-12
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genomics_ancest_disease_dispar/
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library(data.table)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:data.table':
between, first, lastThe following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
library(stringr)gbd_data <- data.table::fread(here::here("data/gbd/IHME-GBD_2021_DATA-aa22a7fd-1.csv"))Global disease burden - top 15 non-communicable diseases by DALYs (2019)
gbd_data |>
slice_max(n = 15, order_by = val) |>
ggplot(aes(x = reorder(cause, val), y = val)) +
geom_col(fill = "steelblue") +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.3) +
coord_flip() +
labs(
x = "Disease",
y = "DALYs (rate per 100,000)",
title = "Non-communicable diseases with the greatest global disease durden (DALYs - 2019)"
) +
theme_minimal(base_size = 14)
| Version | Author | Date |
|---|---|---|
| f8b699d | IJbeasley | 2025-09-11 |
Distribution of DALYs (rate per 100,000) for non-communicable diseases (2019)
gbd_data |>
ggplot(aes(x = val)) +
geom_histogram(bins = 30, fill = "steelblue", color = "black") +
labs(
x = "DALYs (rate per 100,000)",
y = "# non-communicable diseases",
title = "Distribution of DALYs (rate per 100,000) for non-communicable diseases (2019)"
) +
theme_minimal(base_size = 14)
| Version | Author | Date |
|---|---|---|
| f8b699d | IJbeasley | 2025-09-11 |
top_non_comm_diseases <- gbd_data |>
slice_max(n = 15, order_by = val)
top_non_comm_diseases_daly = top_non_comm_diseases |> pull(val)
top_non_comm_diseases = top_non_comm_diseases |> pull(cause)Global disease burden - Population Attributable Fraction (PAF) for top 15 non-communicable diseases by DALYs (2019)
Load + basic processing of GBD PAF data
gbd_paf_dir <- "data/gbd/IHME_GBD_2021_RISK_1990_2021_RESULTS_APPENDIX_TABLES"
paf_file <- paste0(gbd_paf_dir,
"/IHME_GBD_2021_RISK_1990_2021_RESULTS_APPENDIX_TABLE_S1_GLOBAL_Y2024M05D14.XLSX")
gbd_paf_data <- readxl::read_xlsx(here::here(paf_file),
skip = 1
)
# Clean column names
names(gbd_paf_data) <- names(gbd_paf_data) |>
stringr::str_replace_all("\\r|\\n", " ") |> # remove carriage returns/newlines
stringr::str_squish() |> # trim extra spaces
stringr::str_replace_all(" ", "_") |># replace spaces with underscores
stringr::str_replace("^([0-9]{4})_(.*)$", "\\2_\\1")
# Clean all character cells
gbd_paf_data <- gbd_paf_data |>
dplyr::mutate(across(where(is.character),
~ .x |> stringr::str_replace_all("\\r|\\n", " ") |> stringr::str_squish()))
gbd_paf_data[1:3, 1:4]# A tibble: 3 × 4
Risk_Name Deaths_PAF_1990 Deaths_PAF_2000 Deaths_PAF_2010
<chr> <chr> <chr> <chr>
1 All risk factors: All causes 59·6 (57·1 – 6… 59·9 (57·4 – 6… 59·7 (56·8 – 6…
2 Environmental/occupational ri… 27·3 (24·3 – 3… 25·6 (22·8 – 2… 24·5 (21·8 – 2…
3 Unsafe water, sanitation, and… 6·84 (4·83 – 8… 4·91 (3·30 – 6… 3·64 (2·33 – 4…Are PAFs percentages?
gbd_paf_data |>
# Work just on the DALYs_PAF_2021 column
mutate(DALYs_PAF_2021 := str_replace_all(DALYs_PAF_2021, "·", ".")) |> # replace middle dot with .
tidyr::extract(
DALYs_PAF_2021,
into = c("val", "lower", "upper"),
regex = "([0-9.]+) \\(([-0-9.]+) – ([-0-9.]+)\\)",
convert = TRUE
) |> pull(val) |> summary() Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 0.95 4.01 12.55 12.55 100.00 Grouping and filtering of GBD PAF data
# remove risk factor rows
gbd_paf_data = gbd_paf_data |>
rowwise() |>
mutate(causal_group = ifelse(grepl("All causes", Risk_Name), Risk_Name, NA)) |>
ungroup() |>
tidyr::fill(causal_group, .direction = "down") |>
relocate(causal_group, .before = Risk_Name)
gbd_paf_data = gbd_paf_data |>
filter(!grepl("All causes", Risk_Name)) |>
mutate(causal_group = stringr::str_remove_all(causal_group, ": All causes"))
gbd_paf_data[1:5,1:4]# A tibble: 5 × 4
causal_group Risk_Name Deaths_PAF_1990 Deaths_PAF_2000
<chr> <chr> <chr> <chr>
1 Unsafe water source Diarrhoe… 74·2 (43·8 – 9… 73·7 (43·0 – 9…
2 Unsafe sanitation Diarrhoe… 63·6 (57·9 – 6… 62·1 (56·4 – 6…
3 No access to handwashing facility Diarrhoe… 23·6 (3·36 – 4… 23·8 (3·38 – 4…
4 No access to handwashing facility Lower re… 14·7 (-10·7 – … 14·0 (-10·3 – …
5 Ambient particulate matter pollution Diarrhoe… 0·256 (0·157 –… 0·216 (0·135 –…gbd_paf_data |>
select(causal_group, Risk_Name, DALYs_PAF_2021, 'all-age_DALYs_rate_(per_100,000)_2021') |>
distinct() |>
arrange(Risk_Name) |>
head(1)# A tibble: 1 × 4
causal_group Risk_Name DALYs_PAF_2021 all-age_DALYs_rate_(per_1…¹
<chr> <chr> <chr> <chr>
1 Drug use Acute hepatitis B 1·04 (0·630 – 1·69) 0·3 (0·1 – 0·4)
# ℹ abbreviated name: ¹`all-age_DALYs_rate_(per_100,000)_2021`Looking into 2021 PAF data for top 15 non-communicable diseases by DALYs (2019)
gbd_paf_plot_data <- gbd_paf_data |>
rename(DALY_rate_2021 := 'all-age_DALYs_rate_(per_100,000)_2021') |>
select(causal_group, Risk_Name, DALYs_PAF_2021, DALY_rate_2021)
# gbd_paf_plot_data <- gbd_paf_data |>
# # Work just on the DALYs_PAF_2021 column
# mutate('all-age_DALYs_rate_(per_100,000)_2021' := str_replace_all('all-age_DALYs_rate_(per_100,000)_2021', "·", ".")) |> # replace middle dot with .
# tidyr::extract(
# "all-age_DALYs_rate_(per_100,000)_2021",
# into = c("val", "lower", "upper"),
# regex = "([0-9.]+) \\(([-0-9.]+) – ([-0-9.]+)\\)",
# convert = TRUE
# )
gbd_paf_plot_data =
gbd_paf_plot_data |>
# Work just on the DALYs_PAF_2021 column
mutate(DALYs_PAF_2021 := str_replace_all(DALYs_PAF_2021, "·", ".")) |> # replace middle dot with .
tidyr::extract(
DALYs_PAF_2021,
into = c("daly_paf", "lower_daly_paf", "upper_daly_paf"),
regex = "([0-9.]+) \\(([-0-9.]+) – ([-0-9.]+)\\)",
convert = TRUE
)
gbd_paf_plot_data =
gbd_paf_plot_data |>
# Work just on the DALYs_PAF_2021 column
mutate(DALY_rate_2021 := str_replace_all(DALY_rate_2021 , "·", ".")) |> # replace middle dot with .
tidyr::extract(
DALY_rate_2021,
into = c("daly_rate", "lower_daly_rate", "upper_daly_rate"),
regex = "([0-9.]+) \\(([-0-9.]+) – ([-0-9.]+)\\)",
convert = TRUE
) gbd_paf_data <- data.table::fread(here::here(paste0("data/gbd", "/IHME-GBD_2021_DATA-923822a5-1.csv")))
gbd_paf_data |> head() measure location sex age
<char> <char> <char> <char>
1: DALYs (Disability-Adjusted Life Years) Global Both All ages
2: DALYs (Disability-Adjusted Life Years) Global Both All ages
3: DALYs (Disability-Adjusted Life Years) Global Both All ages
4: DALYs (Disability-Adjusted Life Years) Global Both All ages
5: DALYs (Disability-Adjusted Life Years) Global Both All ages
6: DALYs (Disability-Adjusted Life Years) Global Both All ages
cause rei metric year
<char> <char> <char> <int>
1: Chronic obstructive pulmonary disease All risk factors Percent 2019
2: Idiopathic epilepsy All risk factors Percent 2019
3: Leukemia All risk factors Percent 2019
4: Tracheal, bronchus, and lung cancer All risk factors Percent 2019
5: Pneumoconiosis All risk factors Percent 2019
6: Multiple sclerosis All risk factors Percent 2019
val upper lower
<num> <num> <num>
1: 0.74874539 0.8003623 0.69003213
2: 0.07309841 0.0943966 0.05180396
3: 0.13063773 0.1717641 0.09369614
4: 0.76230820 0.8034076 0.71682671
5: 1.00000000 1.0000000 1.00000000
6: 0.11669746 0.1321659 0.10062968gbd_paf_data |>
slice_max(n = 25, order_by = val) |>
ggplot(aes(y = val, x = reorder(cause, val))) +
geom_col(fill = "steelblue") +
theme_bw() +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.3) +
coord_flip() +
labs(y = "% DALYs explained by all risk factors",
x = "Disease"
)
left_join(gbd_paf_data,
gbd_data |> select(cause, daly_rate = val)) |>
mutate(across(c(val, lower, upper), ~ ifelse(.x < 0, 0, .x))) |>
mutate(paf_total = daly_rate * (1- val)) |>
mutate(paf_total_lower = daly_rate * (1-lower) ) |>
mutate(paf_total_upper = daly_rate * (1-upper) ) |>
slice_max(n = 15, order_by = paf_total) |>
ggplot(aes(y= paf_total, x = reorder(cause, paf_total))) +
geom_col(fill = "steelblue") +
theme_bw() +
geom_errorbar(aes(ymin = paf_total_lower,
ymax = paf_total_upper), width = 0.3) +
coord_flip() +
labs(y = "DALYs (rate per 100,000) not explained by measured environmental risk factors",
x = "Disease",
title = "Non-communicable diseases with the greatest global disease durden (DALYs - 2019)")Joining with `by = join_by(cause)`
top_non_comm_diseases_pattern = c("cirrhosis", "liver disease", "depressive", top_non_comm_diseases)
top_non_comm_pattern = paste0(top_non_comm_diseases_pattern, collapse = "|")
gbd_paf_plot_data =
gbd_paf_plot_data |>
filter(grepl(pattern = top_non_comm_pattern, Risk_Name, ignore.case = T)) |>
group_by(Risk_Name) |>
# see formula in page 40 of: https://www.thelancet.com/cms/10.1016/S0140-6736(24)00933-4/attachment/e175b500-3467-4cc5-aff8-ded0c0eea399/mmc1.pdf
summarise(daly_paf = 1 - prod(1 - daly_paf / 100))
# left_join(gbd_paf_plot_data,
# data.frame(Risk_Name = top_non_comm_diseases,
# daly = top_non_comm_diseases_daly),
# by = "Risk_Name") |>
# filter(!is.na(daly)) |>
# mutate(paf_total = 100 * daly_paf_rate / daly) |>
gbd_paf_plot_data |>
mutate(Risk_Name = factor(Risk_Name, levels = unique(Risk_Name))) |>
ggplot(aes(x = reorder(Risk_Name, daly_paf), y = daly_paf)) +
geom_col(fill = "steelblue") +
# geom_errorbar(width = 0.3) +
coord_flip() +
labs(
x = "Risk factor",
y = "Population Attributable Fraction (%)",
title = "Risk factors with the greatest global disease burden (PAF - 2021)"
) +
theme_minimal(base_size = 14)
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Los_Angeles
tzcode source: internal
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] stringr_1.5.1 ggplot2_3.5.2 dplyr_1.1.4 data.table_1.17.8
[5] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.10 utf8_1.2.6 generics_0.1.4 tidyr_1.3.1
[5] renv_1.0.3 stringi_1.8.7 digest_0.6.37 magrittr_2.0.3
[9] evaluate_1.0.4 grid_4.3.1 RColorBrewer_1.1-3 fastmap_1.2.0
[13] cellranger_1.1.0 rprojroot_2.1.0 jsonlite_2.0.0 processx_3.8.6
[17] whisker_0.4.1 ps_1.9.1 promises_1.3.3 httr_1.4.7
[21] purrr_1.1.0 scales_1.4.0 jquerylib_0.1.4 cli_3.6.5
[25] rlang_1.1.6 withr_3.0.2 cachem_1.1.0 yaml_2.3.10
[29] tools_4.3.1 httpuv_1.6.16 here_1.0.1 vctrs_0.6.5
[33] R6_2.6.1 lifecycle_1.0.4 git2r_0.36.2 fs_1.6.6
[37] pkgconfig_2.0.3 callr_3.7.6 pillar_1.11.0 bslib_0.9.0
[41] later_1.4.2 gtable_0.3.6 glue_1.8.0 Rcpp_1.1.0
[45] xfun_0.52 tibble_3.3.0 tidyselect_1.2.1 rstudioapi_0.17.1
[49] knitr_1.50 farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.29
[53] labeling_0.4.3 compiler_4.3.1 getPass_0.2-4 readxl_1.4.5