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Rmd e692d81 IJbeasley 2025-09-11 Initial investigation into gbd paf 
library(data.table)
library(dplyr)
library(ggplot2)
library(stringr)

Global burden of disease statistics

gbd_data <- data.table::fread(here::here("data/gbd/ihme_gbd_2019_global_disease_burden_rate_all_ages.csv"))

gbd_data[1:5, 1:5]
                                  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
                                                 cause
                                                <char>
1:                                              Asthma
2: Interstitial lung disease and pulmonary sarcoidosis
3:                  Other chronic respiratory diseases
4:          Cirrhosis and other chronic liver diseases
5:                                   Esophageal cancer

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] "Oral disorders"                                                   
 [2] "Oral disorders"                                                   
 [3] "Headache disorders"                                               
 [4] "Hemoglobinopathies and hemolytic anemias"                         
 [5] "Fungal skin diseases"                                             
 [6] "Cirrhosis and other chronic liver diseases"                       
 [7] "Gynecological diseases"                                           
 [8] "Age-related and other hearing loss"                               
 [9] "Blindness and vision loss"                                        
[10] "Total burden related to Non-alcoholic fatty liver disease (NAFLD)"
[11] "Bacterial skin diseases"                                          
[12] "Upper digestive system diseases"                                  
[13] "Headache disorders"                                               
[14] "Gynecological diseases"                                           
[15] "Chronic kidney disease"

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
64d6c1c IJbeasley 2025-09-12
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
64d6c1c IJbeasley 2025-09-12

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

Load PAF data

gbd_paf_sep <- data.table::fread(here::here("data/gbd/ihme_gbd_2019_global_paf_rate_percent_all_ages.csv"))

gbd_paf_data <-
  gbd_paf_sep |>
  filter(metric == "Percent") |>
  filter(rei == "All risk factors")

gbd_paf_sep[1:5, 1:5]
                                  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
                                 cause
                                <char>
1:              Chronic kidney disease
2:              Chronic kidney disease
3:                 Idiopathic epilepsy
4:                 Idiopathic epilepsy
5: Tracheal, bronchus, and lung cancer

Integrate with DALY data

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"
       )

Version Author Date
64d6c1c IJbeasley 2025-09-12

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)")

Version Author Date
64d6c1c IJbeasley 2025-09-12
6caf721 IJbeasley 2025-09-12
f8b699d IJbeasley 2025-09-11

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)

Version Author Date
64d6c1c IJbeasley 2025-09-12

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)")

Version Author Date
64d6c1c IJbeasley 2025-09-12
6caf721 IJbeasley 2025-09-12

PAF separated by risk factor type

gbd_paf_sep <-
  gbd_paf_sep |>
  filter(metric == "Percent") |>
  filter(rei != "All risk factors")

gbd_paf_sep[1:5, 1:5]
                                  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
                                   cause
                                  <char>
1:                Chronic kidney disease
2:                   Idiopathic epilepsy
3:   Tracheal, bronchus, and lung cancer
4:                              Leukemia
5: Chronic obstructive pulmonary disease
# check does this match the total PAF data?
gbd_paf_sep_top = 
gbd_paf_sep |>
  filter(rei != "Drug use") |>
  group_by(cause) |>
  summarise(val = 1 - prod(1 - val)) |>
  arrange(desc(val), cause) 

gbd_paf_sep_top |> head()
# A tibble: 6 × 2
  cause                      val
  <chr>                    <dbl>
1 Alcohol use disorders        1
2 Cervical cancer              1
3 Chronic kidney disease       1
4 Diabetes mellitus type 1     1
5 Diabetes mellitus type 2     1
6 Drug use disorders           1
causes = gbd_paf_sep_top$cause

dplyr::all_equal(
        gbd_paf_sep_top,
         gbd_paf_data |> 
          dplyr::filter(cause %in% causes) |> 
          dplyr::select(cause, val) |> 
          dplyr::arrange(desc(val), cause)
         )
Warning: `all_equal()` was deprecated in dplyr 1.1.0.
ℹ Please use `all.equal()` instead.
ℹ And manually order the rows/cols as needed
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
[1] "Different number of rows."

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     crayon_1.5.3       compiler_4.3.1    
 [5] renv_1.0.3         promises_1.3.3     tidyselect_1.2.1   Rcpp_1.1.0        
 [9] git2r_0.36.2       callr_3.7.6        later_1.4.2        jquerylib_0.1.4   
[13] scales_1.4.0       yaml_2.3.10        fastmap_1.2.0      here_1.0.1        
[17] R6_2.6.1           labeling_0.4.3     generics_0.1.4     knitr_1.50        
[21] tibble_3.3.0       rprojroot_2.1.0    RColorBrewer_1.1-3 bslib_0.9.0       
[25] pillar_1.11.0      rlang_1.1.6        utf8_1.2.6         cachem_1.1.0      
[29] stringi_1.8.7      httpuv_1.6.16      xfun_0.52          getPass_0.2-4     
[33] fs_1.6.6           sass_0.4.10        cli_3.6.5          withr_3.0.2       
[37] magrittr_2.0.3     ps_1.9.1           grid_4.3.1         digest_0.6.37     
[41] processx_3.8.6     rstudioapi_0.17.1  lifecycle_1.0.4    vctrs_0.6.5       
[45] evaluate_1.0.4     glue_1.8.0         farver_2.1.2       whisker_0.4.1     
[49] rmarkdown_2.29     httr_1.4.7         tools_4.3.1        pkgconfig_2.0.3   
[53] htmltools_0.5.8.1