load data from google drive

location_levels=c("Jordan", "Global")

ds1<- read.csv("IHME-GBD_2021_DATA-5df25752-1_class1.csv")
ds2<- read.csv("IHME-GBD_2021_DATA-878c1bf3-1_class2.csv")

show types of columns of ds2

sapply(ds2, class)
##     measure    location         sex         age       cause      metric 
## "character" "character" "character" "character" "character" "character" 
##        year         val       upper       lower 
##   "integer"   "numeric"   "numeric"   "numeric"

show all unique column values of the causes excluding the last 3 columns for ds1

unique(ds1[, 5])
## [1] "Communicable, maternal, neonatal, and nutritional diseases"
## [2] "Non-communicable diseases"                                 
## [3] "Injuries"                                                  
## [4] "Other COVID-19 pandemic-related outcomes"

show all unique column values of the dataframem excluding the last 3 columns for ds2

lapply(ds2[, -c(8, 9, 10)], unique)
## $measure
## [1] "Deaths"                                
## [2] "DALYs (Disability-Adjusted Life Years)"
## [3] "YLDs (Years Lived with Disability)"    
## [4] "YLLs (Years of Life Lost)"             
## [5] "Prevalence"                            
## 
## $location
## [1] "Global" "Jordan"
## 
## $sex
## [1] "Male"   "Female" "Both"  
## 
## $age
## [1] "<5 years"    "5-9 years"   "10-14 years" "15-19 years" "20-24 years"
## [6] "25-29 years" "All ages"    "0-14 years" 
## 
## $cause
##  [1] "Neglected tropical diseases and malaria"     
##  [2] "Chronic respiratory diseases"                
##  [3] "Transport injuries"                          
##  [4] "Neoplasms"                                   
##  [5] "Unintentional injuries"                      
##  [6] "Mental disorders"                            
##  [7] "Nutritional deficiencies"                    
##  [8] "Digestive diseases"                          
##  [9] "Cardiovascular diseases"                     
## [10] "Musculoskeletal disorders"                   
## [11] "Other non-communicable diseases"             
## [12] "Neurological disorders"                      
## [13] "Self-harm and interpersonal violence"        
## [14] "Skin and subcutaneous diseases"              
## [15] "Respiratory infections and tuberculosis"     
## [16] "Enteric infections"                          
## [17] "Other infectious diseases"                   
## [18] "Maternal and neonatal disorders"             
## [19] "Substance use disorders"                     
## [20] "Diabetes and kidney diseases"                
## [21] "HIV/AIDS and sexually transmitted infections"
## [22] "Sense organ diseases"                        
## 
## $metric
## [1] "Number"  "Percent" "Rate"   
## 
## $year
## [1] 1980 1990 2010 2000 2021 2020

#Tables and figures

Table. Rates of deaths, disability-adjusted life years (DALYs), and years lived with disability (YLDs) among children aged 0-14 years in Jordan in 2021. Data are presented per 100,000 population with 95% uncertainty intervals (UIs) in parentheses, stratified by sex and level 1 causes.

children<- c("<5 years" ,"5-9 years","10-14 years")
measures<- c("Deaths", "DALYs (Disability-Adjusted Life Years)", "YLDs (Years Lived with Disability)" )
ds1 %>% 
  filter(year==2021, age =="0-14 years" , location=="Jordan", metric=="Rate", measure %in% measures ) %>% 
   mutate(val=paste(round(val, 2), "(UI:", round(lower, 2), "-", round(upper, 2), ")")) %>% 
  select(sex, cause, measure, val) %>% 
  tidyr::spread(measure, val) %>% 
  arrange( cause, sex) %>% 
  gt()
sex cause DALYs (Disability-Adjusted Life Years) Deaths YLDs (Years Lived with Disability)
Both Communicable, maternal, neonatal, and nutritional diseases 4668.44 (UI: 3994.4 - 5567.48 ) 39.61 (UI: 32.96 - 47.73 ) 1137.25 (UI: 802.26 - 1566.36 )
Female Communicable, maternal, neonatal, and nutritional diseases 4388.72 (UI: 3707.02 - 5199.88 ) 35.46 (UI: 29.29 - 42.27 ) 1228.25 (UI: 859.55 - 1759.7 )
Male Communicable, maternal, neonatal, and nutritional diseases 4933.04 (UI: 4093.15 - 5935.8 ) 43.55 (UI: 35.17 - 53.37 ) 1051.17 (UI: 728.62 - 1473.27 )
Both Injuries 1033.4 (UI: 887.04 - 1216.3 ) 11.12 (UI: 9.41 - 13.32 ) 109.54 (UI: 79.07 - 148.26 )
Female Injuries 781.65 (UI: 667.25 - 926.03 ) 7.96 (UI: 6.64 - 9.51 ) 111.01 (UI: 78.3 - 150.04 )
Male Injuries 1271.54 (UI: 1074.5 - 1534.13 ) 14.11 (UI: 11.8 - 17.11 ) 108.15 (UI: 76.87 - 148.09 )
Both Non-communicable diseases 6064.51 (UI: 5031.98 - 7301.76 ) 35.49 (UI: 29.26 - 44.31 ) 2967.17 (UI: 2125.72 - 4037.57 )
Female Non-communicable diseases 6000.08 (UI: 4977.35 - 7270.26 ) 33.11 (UI: 27.73 - 40.03 ) 3104.89 (UI: 2219.53 - 4259.35 )
Male Non-communicable diseases 6125.45 (UI: 5001.11 - 7520.75 ) 37.74 (UI: 29.78 - 48.35 ) 2836.9 (UI: 2029.19 - 3864.67 )
Both Other COVID-19 pandemic-related outcomes 46.15 (UI: 0 - 168.12 ) 0.55 (UI: 0 - 1.93 ) NA
Female Other COVID-19 pandemic-related outcomes 34.79 (UI: 0 - 159.77 ) 0.39 (UI: 0 - 1.8 ) NA
Male Other COVID-19 pandemic-related outcomes 56.9 (UI: 0 - 184.06 ) 0.69 (UI: 0 - 2.21 ) NA

Table. Rates of deaths, disability-adjusted life years (DALYs), and years lived with disability (YLDs) among children aged 0-14 years in Jordan in 2021. Data are presented per 100,000 population with 95% uncertainty intervals (UIs) in parentheses, stratified by sex and level 2 causes.

children<- c("<5 years" ,"5-9 years","10-14 years")
measures<- c("Deaths", "DALYs (Disability-Adjusted Life Years)", "YLDs (Years Lived with Disability)" )
ds2 %>% 
  filter(year==2021, age =="0-14 years" , location=="Jordan", metric=="Rate", measure %in% measures ) %>% 
   mutate(val=paste(round(val, 2), "(UI:", round(lower, 2), "-", round(upper, 2), ")")) %>% 
  select(sex, cause, measure, val) %>% 
  tidyr::spread(measure, val) %>% 
  arrange( cause, sex) %>% 
  gt()
sex cause DALYs (Disability-Adjusted Life Years) Deaths YLDs (Years Lived with Disability)
Both Cardiovascular diseases 272.56 (UI: 228.81 - 321.33 ) 2.7 (UI: 2.22 - 3.31 ) 42.28 (UI: 29.01 - 60.05 )
Female Cardiovascular diseases 241.56 (UI: 200.55 - 289.51 ) 2.32 (UI: 1.87 - 2.89 ) 42.41 (UI: 28.69 - 61.44 )
Male Cardiovascular diseases 301.89 (UI: 248.55 - 361.69 ) 3.06 (UI: 2.48 - 3.75 ) 42.15 (UI: 28.63 - 60.37 )
Both Chronic respiratory diseases 257.81 (UI: 166.57 - 392.62 ) 0.51 (UI: 0.37 - 0.7 ) 214.5 (UI: 122.49 - 349.55 )
Female Chronic respiratory diseases 222.47 (UI: 144.99 - 341.95 ) 0.47 (UI: 0.32 - 0.76 ) 181.74 (UI: 101.85 - 292.52 )
Male Chronic respiratory diseases 291.24 (UI: 185.41 - 447.77 ) 0.54 (UI: 0.38 - 0.76 ) 245.49 (UI: 137.74 - 404.22 )
Both Diabetes and kidney diseases 99.35 (UI: 82.95 - 119.36 ) 1.01 (UI: 0.82 - 1.23 ) 14.43 (UI: 9.16 - 21.24 )
Female Diabetes and kidney diseases 101.37 (UI: 82.45 - 123.57 ) 1.02 (UI: 0.81 - 1.29 ) 15.11 (UI: 9.94 - 22.54 )
Male Diabetes and kidney diseases 97.43 (UI: 76.48 - 124.72 ) 0.99 (UI: 0.74 - 1.29 ) 13.79 (UI: 8.48 - 20.71 )
Both Digestive diseases 85.89 (UI: 68.94 - 107.79 ) 0.51 (UI: 0.42 - 0.64 ) 42.96 (UI: 29.17 - 61.18 )
Female Digestive diseases 85.66 (UI: 67.44 - 108.16 ) 0.48 (UI: 0.38 - 0.64 ) 45.25 (UI: 29.94 - 63.98 )
Male Digestive diseases 86.11 (UI: 67.39 - 109.62 ) 0.54 (UI: 0.42 - 0.71 ) 40.81 (UI: 27.64 - 57.91 )
Both Enteric infections 196.43 (UI: 150.02 - 257.45 ) 1.47 (UI: 1.03 - 2.05 ) 68.17 (UI: 40.94 - 104.43 )
Female Enteric infections 217.68 (UI: 159.96 - 295.07 ) 1.62 (UI: 1.04 - 2.47 ) 75.17 (UI: 43.61 - 117.49 )
Male Enteric infections 176.32 (UI: 128.86 - 234.69 ) 1.32 (UI: 0.89 - 1.9 ) 61.55 (UI: 36.16 - 94.06 )
Both HIV/AIDS and sexually transmitted infections 116.14 (UI: 45.41 - 238.49 ) 1.29 (UI: 0.51 - 2.66 ) 0.38 (UI: 0.21 - 0.65 )
Female HIV/AIDS and sexually transmitted infections 112.23 (UI: 45.28 - 231.17 ) 1.25 (UI: 0.51 - 2.58 ) 0.26 (UI: 0.14 - 0.43 )
Male HIV/AIDS and sexually transmitted infections 119.84 (UI: 45.18 - 249.78 ) 1.33 (UI: 0.5 - 2.78 ) 0.51 (UI: 0.24 - 0.94 )
Both Maternal and neonatal disorders 2899.07 (UI: 2430.48 - 3442.56 ) 28.54 (UI: 23.32 - 34.64 ) 332.47 (UI: 240.18 - 435.89 )
Female Maternal and neonatal disorders 2540.65 (UI: 2132.25 - 2987.71 ) 24.67 (UI: 20.13 - 29.63 ) 322.96 (UI: 230.04 - 423.8 )
Male Maternal and neonatal disorders 3238.13 (UI: 2630.5 - 3930.31 ) 32.21 (UI: 25.29 - 39.58 ) 341.47 (UI: 241.94 - 452.75 )
Both Mental disorders 1094.5 (UI: 749.57 - 1530.03 ) 0 (UI: 0 - 0 ) 1094.5 (UI: 749.57 - 1530.03 )
Female Mental disorders 1102.96 (UI: 722.75 - 1577.47 ) 0 (UI: 0 - 0 ) 1102.96 (UI: 722.75 - 1577.47 )
Male Mental disorders 1086.51 (UI: 762.96 - 1500.71 ) 0 (UI: 0 - 0 ) 1086.51 (UI: 762.96 - 1500.71 )
Both Musculoskeletal disorders 202.52 (UI: 132.47 - 294.26 ) 0.08 (UI: 0.07 - 0.11 ) 195.69 (UI: 125.72 - 286.19 )
Female Musculoskeletal disorders 272.81 (UI: 180 - 396.61 ) 0.12 (UI: 0.08 - 0.16 ) 263.52 (UI: 171.96 - 387.63 )
Male Musculoskeletal disorders 136.02 (UI: 83.84 - 199.79 ) 0.05 (UI: 0.03 - 0.07 ) 131.53 (UI: 79.27 - 195.02 )
Both Neglected tropical diseases and malaria 52.56 (UI: 32.39 - 79.14 ) 0.03 (UI: 0.02 - 0.07 ) 49.89 (UI: 30.32 - 76.73 )
Female Neglected tropical diseases and malaria 62.01 (UI: 38.08 - 96.22 ) 0.03 (UI: 0.02 - 0.06 ) 59.22 (UI: 35.36 - 93.63 )
Male Neglected tropical diseases and malaria 43.62 (UI: 25.68 - 68.44 ) 0.03 (UI: 0.01 - 0.07 ) 41.06 (UI: 23.4 - 66.32 )
Both Neoplasms 304.91 (UI: 242.75 - 379.12 ) 3.62 (UI: 2.89 - 4.51 ) 6.39 (UI: 4.27 - 9.56 )
Female Neoplasms 247.32 (UI: 198.38 - 312.77 ) 2.92 (UI: 2.34 - 3.69 ) 5.35 (UI: 3.48 - 8.33 )
Male Neoplasms 359.38 (UI: 276.85 - 453.11 ) 4.29 (UI: 3.3 - 5.42 ) 7.38 (UI: 4.71 - 11.02 )
Both Neurological disorders 471.63 (UI: 182.6 - 882.85 ) 0.92 (UI: 0.74 - 1.12 ) 394.3 (UI: 107.84 - 800.67 )
Female Neurological disorders 489.39 (UI: 187.75 - 943.56 ) 0.82 (UI: 0.64 - 1.05 ) 420.14 (UI: 117 - 877.42 )
Male Neurological disorders 454.84 (UI: 187.85 - 836.46 ) 1.01 (UI: 0.79 - 1.28 ) 369.86 (UI: 105.66 - 753.97 )
Both Nutritional deficiencies 419.26 (UI: 258.46 - 636.4 ) 0.14 (UI: 0.11 - 0.19 ) 406.95 (UI: 246.08 - 623.71 )
Female Nutritional deficiencies 500.44 (UI: 300.92 - 783.79 ) 0.16 (UI: 0.12 - 0.21 ) 486.56 (UI: 286.07 - 770.17 )
Male Nutritional deficiencies 342.47 (UI: 200.13 - 544.61 ) 0.13 (UI: 0.09 - 0.17 ) 331.64 (UI: 188.6 - 535.74 )
Both Other infectious diseases 155.31 (UI: 111.23 - 242.07 ) 1.29 (UI: 0.86 - 2.23 ) 44.02 (UI: 28.28 - 66.04 )
Female Other infectious diseases 164.19 (UI: 114.88 - 262.36 ) 1.32 (UI: 0.85 - 2.37 ) 50.21 (UI: 31 - 77.16 )
Male Other infectious diseases 146.91 (UI: 104.16 - 224.88 ) 1.26 (UI: 0.83 - 2.11 ) 38.17 (UI: 24.22 - 57.24 )
Both Other non-communicable diseases 2685.36 (UI: 2208.3 - 3303.73 ) 26.11 (UI: 21.01 - 33.13 ) 374.63 (UI: 268.15 - 525.52 )
Female Other non-communicable diseases 2610.19 (UI: 2170.85 - 3127.03 ) 24.92 (UI: 20.33 - 30.64 ) 405.15 (UI: 287.84 - 564.12 )
Male Other non-communicable diseases 2756.48 (UI: 2170.09 - 3554.58 ) 27.24 (UI: 20.86 - 36.08 ) 345.77 (UI: 242.23 - 481.08 )
Both Respiratory infections and tuberculosis 829.67 (UI: 669.93 - 1102.24 ) 6.85 (UI: 5.71 - 8.3 ) 235.36 (UI: 125.52 - 487.41 )
Female Respiratory infections and tuberculosis 791.52 (UI: 621.2 - 1060.39 ) 6.41 (UI: 5.18 - 7.85 ) 233.86 (UI: 122.09 - 482.09 )
Male Respiratory infections and tuberculosis 865.76 (UI: 680.07 - 1159.47 ) 7.27 (UI: 6 - 8.97 ) 236.78 (UI: 125.52 - 489.51 )
Both Self-harm and interpersonal violence 90.79 (UI: 73.08 - 113.66 ) 0.89 (UI: 0.69 - 1.16 ) 17.59 (UI: 12.58 - 23.51 )
Female Self-harm and interpersonal violence 76.95 (UI: 57.4 - 105.39 ) 0.68 (UI: 0.45 - 0.99 ) 20.93 (UI: 14.46 - 29.14 )
Male Self-harm and interpersonal violence 103.87 (UI: 78.34 - 138.76 ) 1.1 (UI: 0.79 - 1.51 ) 14.44 (UI: 10.58 - 18.76 )
Both Sense organ diseases 129.95 (UI: 85.36 - 189.77 ) NA 129.95 (UI: 85.36 - 189.77 )
Female Sense organ diseases 130.32 (UI: 86.29 - 192.99 ) NA 130.32 (UI: 86.29 - 192.99 )
Male Sense organ diseases 129.6 (UI: 85.53 - 191.03 ) NA 129.6 (UI: 85.53 - 191.03 )
Both Skin and subcutaneous diseases 458.26 (UI: 292.69 - 673.36 ) 0.03 (UI: 0.02 - 0.04 ) 455.77 (UI: 289.8 - 670.92 )
Female Skin and subcutaneous diseases 494.73 (UI: 318.44 - 731.56 ) 0.04 (UI: 0.03 - 0.05 ) 491.65 (UI: 315.22 - 728.23 )
Male Skin and subcutaneous diseases 423.75 (UI: 271.85 - 621.28 ) 0.02 (UI: 0.02 - 0.03 ) 421.82 (UI: 269.63 - 619.02 )
Both Substance use disorders 1.77 (UI: 0.86 - 3.11 ) 0 (UI: 0 - 0 ) 1.77 (UI: 0.86 - 3.11 )
Female Substance use disorders 1.3 (UI: 0.54 - 2.51 ) 0 (UI: 0 - 0 ) 1.3 (UI: 0.54 - 2.51 )
Male Substance use disorders 2.2 (UI: 1.01 - 3.93 ) 0 (UI: 0 - 0 ) 2.2 (UI: 1.01 - 3.93 )
Both Transport injuries 459.4 (UI: 382.07 - 559.55 ) 5.32 (UI: 4.4 - 6.51 ) 19.06 (UI: 13.27 - 26.17 )
Female Transport injuries 323.6 (UI: 261.19 - 395.53 ) 3.62 (UI: 2.87 - 4.47 ) 19.63 (UI: 13.85 - 27.03 )
Male Transport injuries 587.85 (UI: 478.11 - 734.47 ) 6.92 (UI: 5.61 - 8.68 ) 18.52 (UI: 13.06 - 26.04 )
Both Unintentional injuries 483.22 (UI: 410.45 - 577.44 ) 4.91 (UI: 4.1 - 5.93 ) 72.89 (UI: 49.66 - 102.61 )
Female Unintentional injuries 381.11 (UI: 322.4 - 449.91 ) 3.67 (UI: 3.01 - 4.41 ) 70.45 (UI: 47.47 - 99.4 )
Male Unintentional injuries 579.81 (UI: 477.46 - 708.57 ) 6.08 (UI: 4.93 - 7.49 ) 75.2 (UI: 52.34 - 106.05 )

Table 2: Disability-Adjusted Life Years (DALYs) among children aged 0-14 years in Jordan for the specified years. Values are expressed as numbers with 95% Uncertainty Intervals (UI)

ds1 %>% 
  filter(age =="0-14 years" , location=="Jordan", metric=="Number",  measure =="DALYs (Disability-Adjusted Life Years)", sex=="Both" ) %>% 
   mutate(val=paste(round(val, 2), "(UI:", round(lower, 2), "-", round(upper, 2), ")")) %>% 
  select(-c(upper, lower, metric)) %>% 
  tidyr::spread(year, val) %>% select(-c(age, location, measure, sex)) %>%  gt()
cause 1990 2000 2010 2020 2021
Communicable, maternal, neonatal, and nutritional diseases 273225.65 (UI: 239391.15 - 314247.53 ) 236930.65 (UI: 207164.43 - 268125.2 ) 222583.57 (UI: 191544.31 - 257171.45 ) 174502.67 (UI: 148051.31 - 207770.73 ) 169606.16 (UI: 145118.08 - 202268.7 )
Injuries 42387.23 (UI: 37778.5 - 46989.39 ) 47684.99 (UI: 42725.72 - 52929.48 ) 43568.71 (UI: 38943.16 - 48906.24 ) 38594.03 (UI: 33290.19 - 45107.51 ) 37543.82 (UI: 32226.41 - 44188.53 )
Non-communicable diseases 198523.77 (UI: 163797.46 - 229303.97 ) 198173.06 (UI: 172772.97 - 226043.94 ) 214343.7 (UI: 186007.21 - 248780.73 ) 221033.15 (UI: 183077.94 - 268049.54 ) 220325.89 (UI: 182813.85 - 265275.55 )
Other COVID-19 pandemic-related outcomes 0 (UI: 0 - 0 ) 0 (UI: 0 - 0 ) 0 (UI: 0 - 0 ) 545.93 (UI: 0 - 3156.08 ) 1676.8 (UI: 0 - 6107.9 )

Fig. Distribution of causes of disability-adjusted life years (DALYs) and deaths among children aged 0-14 years in Jordan and globally in 2021. Panels A and B display the proportion of causes of DALYs in Jordan and globally, respectively. Panels C and D show the proportion of causes of deaths in Jordan and globally, respectively.

children <- c("<5 years" ,"5-9 years","10-14 years")
measures <- c("Deaths", "DALYs (Disability-Adjusted Life Years)", "YLDs (Years Lived with Disability)")

create_plot <- function(data, Location, Measure, y_label) {
  data %>% 
    mutate(age = factor(age, levels = children)) %>% 
    filter(age %in% children, location == Location, metric == "Number", measure == Measure, sex == "Both") %>% 
    select(-c(upper, lower, metric)) %>% 
    ggplot(aes(x = age, y = val, fill = cause)) +
    geom_col(position = "fill") +
    theme_classic() +
    labs(y = y_label, x = "Age Group", fill = "Cause of DALYs") +
    theme(
      axis.title.x = element_text(size = 12),
      axis.title.y = element_text(size = 12),
      legend.title = element_text(size = 12),
      legend.text = element_text(size = 10)
    )+
    guides(fill = guide_legend(nrow = 2, byrow = TRUE))  # Set legend to two rows
}

A <- create_plot(ds1, "Jordan", "DALYs (Disability-Adjusted Life Years)", "Proportion of Total DALYs by Cause")
B <- create_plot(ds1, "Global", "DALYs (Disability-Adjusted Life Years)", "Proportion of Total DALYs by Cause")
C <- create_plot(ds1, "Jordan", "Deaths", "Proportion of Total Deaths by Cause")
D <- create_plot(ds1, "Global", "Deaths", "Proportion of Total Deaths by Cause")

ggpubr::ggarrange(A,B,C,D, labels="AUTO", legend="top", common.legend = T)

Percentages of DALYs and causes of death among different sex groups in Jordan and globally in 2021 ( I do not like this figure)

library(ggplot2)
library(dplyr)
library(tidyr)
library(ggpubr)

children <- c("<5 years", "5-9 years", "10-14 years")
measures <- c("Deaths", "DALYs (Disability-Adjusted Life Years)", "YLDs (Years Lived with Disability)")

create_plot_by_sex <- function(data, Location, Measure, y_label) {
  data %>% 
    filter(age %in% children, location == Location, metric == "Number", measure == Measure) %>% 
    select(-c(upper, lower, metric)) %>% 
    ggplot(aes(x = sex, y = val, fill = cause)) +
    geom_col(position = "fill") +
    theme_classic() +
    labs(y = y_label, x = "Sex", fill = "Cause of DALYs") +
    theme(
      axis.title.x = element_text(size = 12),
      axis.title.y = element_text(size = 12),
      legend.title = element_text(size = 12),
      legend.text = element_text(size = 10)
    ) +
    guides(fill = guide_legend(nrow = 2, byrow = TRUE))  # Set legend to two rows
}

A_sex <- create_plot_by_sex(ds1, "Jordan", "DALYs (Disability-Adjusted Life Years)", "Proportion of Total DALYs by Cause")
B_sex <- create_plot_by_sex(ds1, "Global", "DALYs (Disability-Adjusted Life Years)", "Proportion of Total DALYs by Cause")
C_sex <- create_plot_by_sex(ds1, "Jordan", "Deaths", "Proportion of Total Deaths by Cause")
D_sex <- create_plot_by_sex(ds1, "Global", "Deaths", "Proportion of Total Deaths by Cause")

ggpubr::ggarrange(A_sex, B_sex, C_sex, D_sex, labels = "AUTO", legend = "top", common.legend = TRUE)