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File Version Author Date Message
Rmd 296be71 IJbeasley 2025-09-12 Updating disease burden plots with new paf information
html 6caf721 IJbeasley 2025-09-12 Build site.
Rmd 18ffbd1 IJbeasley 2025-09-12 workflowr::wflow_publish("analysis/gbd_data_plots.Rmd")
html f8b699d IJbeasley 2025-09-11 Build site.
Rmd e692d81 IJbeasley 2025-09-11 Initial investigation into gbd paf 
library(data.table)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:data.table':

    between, first, last
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(ggplot2)
library(stringr)

Global burden of disease statistics

gbd_data <- data.table::fread(here::here("data/gbd/IHME-GBD_2021_DATA-aa22a7fd-1.csv"))

Top non-communicable diseases by DALYs (2019)

top_non_comm_diseases = 
  gbd_data |>
  slice_max(n = 15, order_by = val) |>
  pull(cause)

print(top_non_comm_diseases)
 [1] "Ischemic heart disease"                    
 [2] "Stroke"                                    
 [3] "Chronic obstructive pulmonary disease"     
 [4] "Diabetes mellitus"                         
 [5] "Low back pain"                             
 [6] "Depressive disorders"                      
 [7] "Headache disorders"                        
 [8] "Cirrhosis and other chronic liver diseases"
 [9] "Tracheal, bronchus, and lung cancer"       
[10] "Other musculoskeletal disorders"           
[11] "Age-related and other hearing loss"        
[12] "Chronic kidney disease"                    
[13] "Anxiety disorders"                         
[14] "Alzheimer's disease and other dementias"   
[15] "Blindness and vision loss"

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)

Population Attributable Fraction (PAF) for risk factors for non-communicable diseases (2021)

Load PAF data + intergrate with DALY data

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 0.10062968
gbd_paf_data =
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)  ) 
Joining with `by = join_by(cause)`

Top 25 non-communicable diseases by % DALYs explained by all risk factors (2019)

gbd_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"
       )

Top 15 non-communicable diseases by DALYs not explained by measured environmental risk factors (2019)

gbd_paf_data |>
  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)")

Distribution of DALYs not explained by measured environmental risk factors (2019)

gbd_paf_data |>
  ggplot(aes(x = paf_total)) +
  geom_histogram(bins = 30, fill = "steelblue", color = "black") +
  labs(
    x = "DALYs (rate per 100,000) not explained by measured environmental risk factors",
    y = "# non-communicable diseases",
    title = "Distribution of DALYs (rate per 100,000) not explained by measured environmental risk factors (2019)"
  ) +
  theme_minimal(base_size = 14)
Warning: Removed 11 rows containing non-finite outside the scale range
(`stat_bin()`).

Version Author Date
6caf721 IJbeasley 2025-09-12
f8b699d IJbeasley 2025-09-11

Dalys not explained by measured environmental risk factors for top non-communicable diseases by DALYs (2019)

gbd_paf_data |>
  filter(cause %in% top_non_comm_diseases) |>
  ggplot(aes(x= reorder(cause, daly_rate), y = paf_total)) +
  geom_col(fill = "steelblue") +
  theme_bw() +
  coord_flip() +
  labs(y = "DALYs (rate per 100,000)",
       x = "Disease",
       title = "DALYs (rate per 100,000) for top non-communicable diseases (2019)")


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] gtable_0.3.6       jsonlite_2.0.0     compiler_4.3.1     renv_1.0.3        
 [5] promises_1.3.3     tidyselect_1.2.1   Rcpp_1.1.0         git2r_0.36.2      
 [9] callr_3.7.6        later_1.4.2        jquerylib_0.1.4    scales_1.4.0      
[13] yaml_2.3.10        fastmap_1.2.0      here_1.0.1         R6_2.6.1          
[17] labeling_0.4.3     generics_0.1.4     knitr_1.50         tibble_3.3.0      
[21] rprojroot_2.1.0    RColorBrewer_1.1-3 bslib_0.9.0        pillar_1.11.0     
[25] rlang_1.1.6        cachem_1.1.0       stringi_1.8.7      httpuv_1.6.16     
[29] xfun_0.52          getPass_0.2-4      fs_1.6.6           sass_0.4.10       
[33] cli_3.6.5          withr_3.0.2        magrittr_2.0.3     ps_1.9.1          
[37] grid_4.3.1         digest_0.6.37      processx_3.8.6     rstudioapi_0.17.1 
[41] lifecycle_1.0.4    vctrs_0.6.5        evaluate_1.0.4     glue_1.8.0        
[45] farver_2.1.2       whisker_0.4.1      rmarkdown_2.29     httr_1.4.7        
[49] tools_4.3.1        pkgconfig_2.0.3    htmltools_0.5.8.1