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Data for Interactive Figure





In [1]:



    


DT <- read.table("../Data/All_data.txt")



    











In [2]:



    


names(DT)



    


















    







    
	
  1. 'Region'
    2. 
	
  2. 'Disease'
    3. 
	
  3. 'burden_daly'
    4. 
	
  4. 'burden_yll'
    5. 
	
  5. 'burden_yld'
    6. 
	
  6. 'burden_death'
    7. 
	
  7. 'Prop_loc_burden_daly'
    8. 
	
  8. 'Prop_loc_burden_yll'
    9. 
	
  9. 'Prop_loc_burden_yld'
    10. 
	
  10. 'Prop_loc_burden_death'
    11. 
	
  11. 'Prop_glob_burden_daly'
    12. 
	
  12. 'Prop_glob_burden_yll'
    13. 
	
  13. 'Prop_glob_burden_yld'
    14. 
	
  14. 'Prop_glob_burden_death'
    15. 
	
  15. 'Prop_NHI_burden_daly'
    16. 
	
  16. 'Prop_NHI_burden_yll'
    17. 
	
  17. 'Prop_NHI_burden_yld'
    18. 
	
  18. 'Prop_NHI_burden_death'
    19. 
	
  19. 'Nb_RCTs_low'
    20. 
	
  20. 'Nb_RCTs_med'
    21. 
	
  21. 'Nb_RCTs_up'
    22. 
	
  22. 'Nb_Patients_low'
    23. 
	
  23. 'Nb_Patients_med'
    24. 
	
  24. 'Nb_Patients_up'
    25. 
	
  25. 'Prop_loc_RCTs_low'
    26. 
	
  26. 'Prop_loc_RCTs_med'
    27. 
	
  27. 'Prop_loc_RCTs_up'
    28. 
	
  28. 'Prop_loc_Patients_low'
    29. 
	
  29. 'Prop_loc_Patients_med'
    30. 
	
  30. 'Prop_loc_Patients_up'
    31. 
	
  31. 'Prop_glob_RCTs_low'
    32. 
	
  32. 'Prop_glob_RCTs_med'
    33. 
	
  33. 'Prop_glob_RCTs_up'
    34. 
	
  34. 'Prop_glob_Patients_low'
    35. 
	
  35. 'Prop_glob_Patients_med'
    36. 
	
  36. 'Prop_glob_Patients_up'
    37. 
	
  37. 'Prop_NHI_RCTs_low'
    38. 
	
  38. 'Prop_NHI_RCTs_med'
    39. 
	
  39. 'Prop_NHI_RCTs_up'
    40. 
	
  40. 'Prop_NHI_Patients_low'
    41. 
	
  41. 'Prop_NHI_Patients_med'
    42. 
	
  42. 'Prop_NHI_Patients_up'
    43. 



















In [3]:



    


head(DT)



    


















    









RegionDiseaseburden_dalyburden_yllburden_yldburden_deathProp_loc_burden_dalyProp_loc_burden_yllProp_loc_burden_yldProp_loc_burden_death⋯Prop_glob_RCTs_upProp_glob_Patients_lowProp_glob_Patients_medProp_glob_Patients_upProp_NHI_RCTs_lowProp_NHI_RCTs_medProp_NHI_RCTs_upProp_NHI_Patients_lowProp_NHI_Patients_medProp_NHI_Patients_up


	
1All             All             2220063510.800761548796838.65609671266365.04493546250530.2665929100             100             100             100             ⋯               NA              NA              NA              NA              NA              NA              NA              NA              NA              NA              

	
2All                                    Cardiovascular and circulatory diseases287404109.09231                        267807387.790028                       19596607.312309                        14726543.7199449                       12.9457606818034                       17.2913180803241                       2.91934891017476                       31.8408105486781                       ⋯                                      NA                                     NA                                     NA                                     NA                                     NA                                     NA                                     NA                                     NA                                     NA                                     NA                                     

	
3All                         Chronic respiratory diseases112485355.22285             67399684.65133              45085687.018707             3572762.590618              5.06676294059161            4.35174472010244            6.71651215768706            7.72480352122283            ⋯                           NA                          NA                          NA                          NA                          NA                          NA                          NA                          NA                          NA                          NA                          

	
4All                   Cirrhosis of the liver30462721.1164         29883176.0549         579531.730192         990159.446125         1.37215538961821      1.9294445410174       0.0863340933450771    2.14086074347173      ⋯                     NA                    NA                    NA                    NA                    NA                    NA                    NA                    NA                    NA                    NA                    

	
5All                 Congenital anomalies43254504.439        40150785.446        3103715.2332        553886.39191        1.94834536167834    2.59238555011768    0.462367160760727   1.19757846821937    ⋯                   NA                  NA                  NA                  NA                  NA                  NA                  NA                  NA                  NA                  NA                  

	
6All                                            Diabetes, urinary diseases and male infertility75821480.094146                                48042869.768465                                27778586.1421424                               2082326.848938                                 3.41528428016898                               3.10194781971226                               4.13823596543272                               4.50227670241887                               ⋯                                              NA                                             NA                                             NA                                             NA                                             NA                                             NA                                             NA                                             NA                                             NA                                             NA                                             





















In [4]:



    


levels(DT$Disease)



    


















    







    
	
  1. 'All'
    2. 
	
  2. 'Cardiovascular and circulatory diseases'
    3. 
	
  3. 'Chronic respiratory diseases'
    4. 
	
  4. 'Cirrhosis of the liver'
    5. 
	
  5. 'Congenital anomalies'
    6. 
	
  6. 'Diabetes, urinary diseases and male infertility'
    7. 
	
  7. 'Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases'
    8. 
	
  8. 'Digestive diseases (except cirrhosis)'
    9. 
	
  9. 'Gynecological diseases'
    10. 
	
  10. 'Hemoglobinopathies and hemolytic anemias'
    11. 
	
  11. 'Hepatitis'
    12. 
	
  12. 'HIV/AIDS'
    13. 
	
  13. 'Leprosy'
    14. 
	
  14. 'Malaria'
    15. 
	
  15. 'Maternal disorders'
    16. 
	
  16. 'Mental and behavioral disorders'
    17. 
	
  17. 'Musculoskeletal disorders'
    18. 
	
  18. 'Neglected tropical diseases excluding malaria'
    19. 
	
  19. 'Neonatal disorders'
    20. 
	
  20. 'Neoplasms'
    21. 
	
  21. 'Neurological disorders'
    22. 
	
  22. 'Nutritional deficiencies'
    23. 
	
  23. 'Oral disorders'
    24. 
	
  24. 'Sense organ diseases'
    25. 
	
  25. 'Sexually transmitted diseases excluding HIV'
    26. 
	
  26. 'Skin and subcutaneous diseases'
    27. 
	
  27. 'Sudden infant death syndrome'
    28. 
	
  28. 'Tuberculosis'
    29. 



















In [5]:



    


DT$Dis_lab <- DT$Disease

levels(DT$Dis_lab) <- c("",
               "Cardiovasc.\n& circulatory",
               "Chronic\nrespiratory",
               "Cirrhosis",
               "Congenital\nanomalies",
               "Diabetes, urin.\nmale infertility",
               "Common\ninfect. dis.",
               "Digestive\ndis.",
               "Gynecol.\ndis.",
               "Hemoglob. &\nhemolytic\nanemia",
               "Hepatitis",
               "HIV",
               "Leprosy",
               "Malaria",
               "Maternal\ndisorders",
               "Mental and\nbehavioral",
               "Musculosk.\ndisorders",
               "Neglected trop.\ndiseases.",
               "Neonatal\ndisorders",
               "Neoplasms",
               "Neurological\ndisorders",
               "Nutritional\ndeficiencies",
               "Oral\ndisorders",
               "Sense organ\ndiseases",
               "STD",
               "Skin and\nsubcutan.",
               "Sudden infant\ndeath synd.",
               "Tuberculosis")

DT$Dis_tooltip <- DT$Disease

levels(DT$Dis_tooltip) <- c("All diseases",
               "Cardiovascular and circulatory diseases",
               "Chronic respiratory diseases",
               "Cirrhosis of the liver",
               "Congenital anomalies",
               "Diabetes, urinary diseases and male infertility",
               "Common infectious diseases",
               "Digestive diseases",
               "Gynecological diseases",
               "Hemoglobinopathies and hemolytic anemias",
               "Hepatitis",
               "HIV",
               "Leprosy",
               "Malaria",
               "Maternal disorders",
               "Mental and behavioral disorders",
               "Musculoskeletal disorders",
               "Neglected tropical diseases",
               "Neonatal disorders",
               "Neoplasms",
               "Neurological disorders",
               "Nutritional deficiencies",
               "Oral disorders",
               "Sense organ diseases",
               "Sexually transmitted diseases excluding HIV",
               "Skin and subcutaneous diseases",
               "Sudden infant death syndrome",
               "Tuberculosis")


DT$regs_lab <- DT$Region

levels(DT$regs_lab) <-  c("World",
                          "Eastern Europe and Central Asia",
                          "High-income countries",
                          "Latin America and Caribbean",
                          "Non-high-income countries",
                          "North Africa and Middle East",
                          "South Asia",
                          "Southeast Asia, East Asia and Oceania",
                          "Sub-Saharian Africa")



    











In [9]:



    


Mgbd <- read.table("../Data/27_gbd_groups.txt")
Mgbd$tooltip <- Mgbd$x
levels(Mgbd$tooltip) <- levels(DT$Dis_tooltip)[-1]
Mgbd$tooltip



    


















    







    
	
  1. Tuberculosis
    2. 
	
  2. HIV
    3. 
	
  3. Common infectious diseases
    4. 
	
  4. Malaria
    5. 
	
  5. Neglected tropical diseases
    6. 
	
  6. Maternal disorders
    7. 
	
  7. Neonatal disorders
    8. 
	
  8. Nutritional deficiencies
    9. 
	
  9. Sexually transmitted diseases excluding HIV
    10. 
	
  10. Hepatitis
    11. 
	
  11. Leprosy
    12. 
	
  12. Neoplasms
    13. 
	
  13. Cardiovascular and circulatory diseases
    14. 
	
  14. Chronic respiratory diseases
    15. 
	
  15. Cirrhosis of the liver
    16. 
	
  16. Digestive diseases
    17. 
	
  17. Neurological disorders
    18. 
	
  18. Mental and behavioral disorders
    19. 
	
  19. Diabetes, urinary diseases and male infertility
    20. 
	
  20. Gynecological diseases
    21. 
	
  21. Hemoglobinopathies and hemolytic anemias
    22. 
	
  22. Musculoskeletal disorders
    23. 
	
  23. Congenital anomalies
    24. 
	
  24. Skin and subcutaneous diseases
    25. 
	
  25. Sense organ diseases
    26. 
	
  26. Oral disorders
    27. 
	
  27. Sudden infant death syndrome
    28.

Adding observed data for non simulated diseases





In [6]:



    


unique(DT$Disease[is.na(DT$Nb_RCTs_med)])



    


















    







    
	
  1. Hemoglobinopathies and hemolytic anemias
    2. 
	
  2. Leprosy
    3. 
	
  3. Sudden infant death syndrome
    4. 



















In [7]:



    


library(data.table)
data <- read.table("/media/igna/Elements/HotelDieu/Cochrane/MappingRCTs_vs_Burden/database_RCTs_regions_27diseases.txt")



    


















    






Warning message:
“package ‘data.table’ was built under R version 3.3.2”
















In [8]:



    


Lgbd <- lapply(as.character(data$GBD27),function(x){as.numeric(unlist(strsplit(x,"&")))})



    











In [9]:



    


regs <- sort(unique(unlist(strsplit(as.character(data$Regions),"&"))))
LR <- lapply(regs,function(x){1:nrow(data)%in%grep(x,data$Regions)})
LR <- do.call('cbind',LR)
LR <- data.table(LR)
LR$TrialID <- data$TrialID

#Nb of patients per region per trial
#Supressing sample size of trials with sample size below 10 and above 200k
data$Sample[data$Sample<10 | data$Sample>200000] <- NA
#Nb countries per region per trial to distribute sample size equally across countries
nb_ctrs <- lapply(strsplit(as.character(data$Nb_ctr_per_reg),'&'),as.numeric)
RGs <-strsplit(as.character(data$Regions),'&')
pats <- data.frame(TrialID = rep(data$TrialID,sapply(nb_ctrs,length)),
                   Nb_ctrs = unlist(nb_ctrs),
                   Region = unlist(RGs),
                   Tot_sample = rep(data$Sample,sapply(nb_ctrs,length)))

pats$tot_ctrs <- rep(sapply(nb_ctrs,sum),sapply(nb_ctrs,length))
pats$sample_per_reg <- pats$Tot_sample*pats$Nb_ctrs/pats$tot_ctrs
pats <- data.table(pats)
setkey(pats,TrialID)



    











In [10]:



    


dis <- which(Mgbd$x%in%unique(DT$Disease[is.na(DT$Nb_RCTs_med)]))
dis



    


















    







    
	
  1. 11
    2. 
	
  2. 21
    3. 
	
  3. 27
    4. 



















In [11]:



    


A <- list()
for(i in 1:length(dis)){
    d <- dis[i]
    repl <- data.table(
        TrialID = data$TrialID,
        recl_dis = as.numeric(unlist(lapply(Lgbd,function(x){d%in%x}))),
        recl_gbd = as.numeric(unlist(lapply(Lgbd,function(x){length(x)>0})))
        )
    setkey(repl,TrialID)
    replpats <- merge(pats,repl)
    setkey(replpats,Region)

    #Output data
    df <- data.table(Region=c(sort(regs),"All","Non-HI"),Dis=rep(c("dis","all"),each=9),RCTs=as.integer(0),Patients=as.numeric(0))

    #Par région
    #Nb trials par region concernant la maladie and relevant to GBD
    df[Dis=="dis" & Region%in%regs,RCTs:=table(replpats[recl_dis==1,Region])]
    df[Dis=="all" & Region%in%regs,RCTs:=table(replpats[recl_gbd>=1,Region])]
    #Nb patients par région concernant la maladie and relevant to GBD
    df[Dis=="dis" & Region%in%regs,Patients:=replpats[recl_dis==1,][regs,sum(sample_per_reg,na.rm=TRUE),by=.EACHI]$V1]
    df[Dis=="all" & Region%in%regs,Patients:=replpats[recl_gbd>=1,][regs,sum(sample_per_reg,na.rm=TRUE),by=.EACHI]$V1]

    #WorldWide
    #Nb trials worldwide concernant la maladie and relevant to GBD
    df[Dis=="dis" & Region=="All",RCTs:=sum(repl$recl_dis)]
    df[Dis=="all" & Region=="All",RCTs:=sum(repl$recl_gbd>=1)]
    #Nb patients worldwide concernant la maladie and relevant to GBD
    df[Dis=="dis" & Region=="All",Patients:=sum(replpats[recl_dis==1,sample_per_reg],na.rm=TRUE)]
    df[Dis=="all" & Region=="All",Patients:=sum(replpats[recl_gbd>=1,sample_per_reg],na.rm=TRUE)]

    #Non-HI countries
    #Nb trials worldwide concernant la maladie and relevant to GBD
    df[Dis=="dis" & Region=="Non-HI",RCTs:=replpats[Region!="High-income",][recl_dis==1,][!duplicated(TrialID),.N]]
    df[Dis=="all" & Region=="Non-HI",RCTs:=replpats[Region!="High-income",][recl_gbd>=1,][!duplicated(TrialID),.N]]
    #Nb patients worldwide concernant la maladie and relevant to GBD
    df[Dis=="dis" & Region=="Non-HI",Patients:=sum(replpats[Region!="High-income",][recl_dis==1,sample_per_reg],na.rm=TRUE)]
    df[Dis=="all" & Region=="Non-HI",Patients:=sum(replpats[Region!="High-income",][recl_gbd>=1,sample_per_reg],na.rm=TRUE)]

    A[[i]] <- df
    
    }

Within regions





In [12]:



    


data_f <- data.frame()

for(i in 1:length(dis)){
    
    d <- dis[i]
    DF <- A[[i]]

    data <- DF[Dis=="dis",][,lapply(.SD,function(x){quantile(x,probs=c(0.025,0.5,0.975))}),
                        by=c("Region"),
                        .SDcols=c("RCTs","Patients")]
    dataprop <- DF[,lapply(.SD[Dis=="dis",]/.SD[Dis=="all",],function(x){100*quantile(x,probs=c(0.025,0.5,0.975))}),
                        by=c("Region"),
                        .SDcols=c("RCTs","Patients")]

    df <- data.frame(cbind(cbind(unique(data$Region),as.character(Mgbd$x[d])),
          matrix(data$RCTs,ncol=3,byrow=TRUE),
          matrix(data$Patients,ncol=3,byrow=TRUE),
          matrix(dataprop$RCTs,ncol=3,byrow=TRUE),
          matrix(dataprop$Patients,ncol=3,byrow=TRUE)))

    names(df) <- c("Region","Disease",
                   paste(paste("Nb","RCTs",sep="_"),c("low","med","up"),sep="_"),
                   paste(paste("Nb","Patients",sep="_"),c("low","med","up"),sep="_"),
                   paste(paste("Prop","RCTs",sep="_"),c("low","med","up"),sep="_"),
                   paste(paste("Prop","Patients",sep="_"),c("low","med","up"),sep="_"))
    
    data_f <- rbind(data_f,df)
}



    











In [13]:



    


data_f <- data_f[order(as.character(data_f$Region),as.character(data_f$Disease)),]
DT <- DT[order(as.character(DT$Region),as.character(DT$Disease)),]
table(paste(data_f$Region,data_f$Disease)==paste(DT$Region,DT$Disease)[DT$Disease%in%as.character(Mgbd$x[dis])])



    


















    








TRUE 
  27 

















In [14]:



    


names(data_f)
names(DT)



    


















    







    
	
  1. 'Region'
    2. 
	
  2. 'Disease'
    3. 
	
  3. 'Nb_RCTs_low'
    4. 
	
  4. 'Nb_RCTs_med'
    5. 
	
  5. 'Nb_RCTs_up'
    6. 
	
  6. 'Nb_Patients_low'
    7. 
	
  7. 'Nb_Patients_med'
    8. 
	
  8. 'Nb_Patients_up'
    9. 
	
  9. 'Prop_RCTs_low'
    10. 
	
  10. 'Prop_RCTs_med'
    11. 
	
  11. 'Prop_RCTs_up'
    12. 
	
  12. 'Prop_Patients_low'
    13. 
	
  13. 'Prop_Patients_med'
    14. 
	
  14. 'Prop_Patients_up'
    15. 












    







    
	
  1. 'Region'
    2. 
	
  2. 'Disease'
    3. 
	
  3. 'burden_daly'
    4. 
	
  4. 'burden_yll'
    5. 
	
  5. 'burden_yld'
    6. 
	
  6. 'burden_death'
    7. 
	
  7. 'Prop_loc_burden_daly'
    8. 
	
  8. 'Prop_loc_burden_yll'
    9. 
	
  9. 'Prop_loc_burden_yld'
    10. 
	
  10. 'Prop_loc_burden_death'
    11. 
	
  11. 'Prop_glob_burden_daly'
    12. 
	
  12. 'Prop_glob_burden_yll'
    13. 
	
  13. 'Prop_glob_burden_yld'
    14. 
	
  14. 'Prop_glob_burden_death'
    15. 
	
  15. 'Prop_NHI_burden_daly'
    16. 
	
  16. 'Prop_NHI_burden_yll'
    17. 
	
  17. 'Prop_NHI_burden_yld'
    18. 
	
  18. 'Prop_NHI_burden_death'
    19. 
	
  19. 'Nb_RCTs_low'
    20. 
	
  20. 'Nb_RCTs_med'
    21. 
	
  21. 'Nb_RCTs_up'
    22. 
	
  22. 'Nb_Patients_low'
    23. 
	
  23. 'Nb_Patients_med'
    24. 
	
  24. 'Nb_Patients_up'
    25. 
	
  25. 'Prop_loc_RCTs_low'
    26. 
	
  26. 'Prop_loc_RCTs_med'
    27. 
	
  27. 'Prop_loc_RCTs_up'
    28. 
	
  28. 'Prop_loc_Patients_low'
    29. 
	
  29. 'Prop_loc_Patients_med'
    30. 
	
  30. 'Prop_loc_Patients_up'
    31. 
	
  31. 'Prop_glob_RCTs_low'
    32. 
	
  32. 'Prop_glob_RCTs_med'
    33. 
	
  33. 'Prop_glob_RCTs_up'
    34. 
	
  34. 'Prop_glob_Patients_low'
    35. 
	
  35. 'Prop_glob_Patients_med'
    36. 
	
  36. 'Prop_glob_Patients_up'
    37. 
	
  37. 'Prop_NHI_RCTs_low'
    38. 
	
  38. 'Prop_NHI_RCTs_med'
    39. 
	
  39. 'Prop_NHI_RCTs_up'
    40. 
	
  40. 'Prop_NHI_Patients_low'
    41. 
	
  41. 'Prop_NHI_Patients_med'
    42. 
	
  42. 'Prop_NHI_Patients_up'
    43. 
	
  43. 'Dis_lab'
    44. 
	
  44. 'Dis_tooltip'
    45. 
	
  45. 'regs_lab'
    46. 



















In [15]:



    


DT$Nb_RCTs_med[DT$Disease%in%as.character(Mgbd$x[dis])] <- as.numeric(as.character(data_f$Nb_RCTs_med))
DT$Nb_Patients_med[DT$Disease%in%as.character(Mgbd$x[dis])] <- as.numeric(as.character(data_f$Nb_Patients_med))
DT$Prop_loc_RCTs_med[DT$Disease%in%as.character(Mgbd$x[dis])] <- as.numeric(as.character(data_f$Prop_RCTs_med))
DT$Prop_loc_Patients_med[DT$Disease%in%as.character(Mgbd$x[dis])] <- as.numeric(as.character(data_f$Prop_Patients_med))

Within diseases





In [16]:



    


regs <- sort(unique(DF$Region))
regs <- regs[regs!="All"]
regs



    


















    







    
	
  1. 'Central Europe, Eastern Europe, and Central Asia'
    2. 
	
  2. 'High-income'
    3. 
	
  3. 'Latin America and Caribbean'
    4. 
	
  4. 'Non-HI'
    5. 
	
  5. 'North Africa and Middle East'
    6. 
	
  6. 'South Asia'
    7. 
	
  7. 'Southeast Asia, East Asia and Oceania'
    8. 
	
  8. 'Sub-Saharian Africa'
    9. 



















In [17]:



    


data_f <- data.frame()

for(i in 1:length(dis)){
    d <- dis[i]
    DF <- A[[i]]

    DFr <- DF[DF$Region%in%regs & DF$Dis == "dis",]
    DFr$RCTs_all <- rep(DF$RCTs[DF$Dis=="dis" & DF$Region=="All"],each=length(regs))
    DFr$RCTs_NHI <- rep(DF$RCTs[DF$Dis=="dis" & DF$Region=="Non-HI"],each=length(regs))
    DFr$Patients_all <- rep(DF$Patients[DF$Dis=="dis" & DF$Region=="All"],each=length(regs))
    DFr$Patients_NHI <- rep(DF$Patients[DF$Dis=="dis" & DF$Region=="Non-HI"],each=length(regs))

    df <- data.frame(cbind(regs,as.character(Mgbd$x[d]),
        do.call('rbind',by(DFr[DFr$RCTs_all!=0,],
                           DFr$Region[DFr$RCTs_all!=0],
                           function(x){100*quantile(x$RCTs/x$RCTs_all,probs=c(0.025,0.5,0.975))})),
        do.call('rbind',by(DFr[DFr$Patients_all!=0,],
                           DFr$Region[DFr$Patients_all!=0],
                           function(x){100*quantile(x$Patients/x$Patients_all,probs=c(0.025,0.5,0.975))}))))

    if(sum(DFr$RCTs_NHI)!=0){
        df <- cbind(df,cbind(                   
            do.call('rbind',by(DFr[DFr$RCTs_NHI!=0,],
                               DFr$Region[DFr$RCTs_NHI!=0],
                               function(x){100*quantile(x$RCTs/x$RCTs_NHI,probs=c(0.025,0.5,0.975))})),
            do.call('rbind',by(DFr[DFr$Patients_NHI!=0,],
                               DFr$Region[DFr$Patients_NHI!=0],
                               function(x){100*quantile(x$Patients/x$Patients_NHI,probs=c(0.025,0.5,0.975))}))))
    }
    if(sum(DFr$RCTs_NHI)==0){
        df <- cbind(df,matrix(0,nrow=length(regs),ncol=3),matrix(0,nrow=length(regs),ncol=3))
    }


    names(df) <- c("Region","Disease",
               paste(paste("Prop_all","RCTs",sep="_"),c("low","med","up"),sep="_"),
               paste(paste("Prop_all","Patients",sep="_"),c("low","med","up"),sep="_"),
               paste(paste("Prop_NHI","RCTs",sep="_"),c("low","med","up"),sep="_"),
               paste(paste("Prop_NHI","Patients",sep="_"),c("low","med","up"),sep="_"))

    data_f <- rbind(data_f,df)
}



    











In [18]:



    


data_f <- data_f[order(as.character(data_f$Region),as.character(data_f$Disease)),]
table(paste(data_f$Region,data_f$Disease)==paste(DT$Region,DT$Disease)[DT$Disease%in%as.character(Mgbd$x[dis]) & DT$Region!="All"])



    


















    








TRUE 
  24 

















In [19]:



    


names(data_f)
names(DT)



    


















    







    
	
  1. 'Region'
    2. 
	
  2. 'Disease'
    3. 
	
  3. 'Prop_all_RCTs_low'
    4. 
	
  4. 'Prop_all_RCTs_med'
    5. 
	
  5. 'Prop_all_RCTs_up'
    6. 
	
  6. 'Prop_all_Patients_low'
    7. 
	
  7. 'Prop_all_Patients_med'
    8. 
	
  8. 'Prop_all_Patients_up'
    9. 
	
  9. 'Prop_NHI_RCTs_low'
    10. 
	
  10. 'Prop_NHI_RCTs_med'
    11. 
	
  11. 'Prop_NHI_RCTs_up'
    12. 
	
  12. 'Prop_NHI_Patients_low'
    13. 
	
  13. 'Prop_NHI_Patients_med'
    14. 
	
  14. 'Prop_NHI_Patients_up'
    15. 












    







    
	
  1. 'Region'
    2. 
	
  2. 'Disease'
    3. 
	
  3. 'burden_daly'
    4. 
	
  4. 'burden_yll'
    5. 
	
  5. 'burden_yld'
    6. 
	
  6. 'burden_death'
    7. 
	
  7. 'Prop_loc_burden_daly'
    8. 
	
  8. 'Prop_loc_burden_yll'
    9. 
	
  9. 'Prop_loc_burden_yld'
    10. 
	
  10. 'Prop_loc_burden_death'
    11. 
	
  11. 'Prop_glob_burden_daly'
    12. 
	
  12. 'Prop_glob_burden_yll'
    13. 
	
  13. 'Prop_glob_burden_yld'
    14. 
	
  14. 'Prop_glob_burden_death'
    15. 
	
  15. 'Prop_NHI_burden_daly'
    16. 
	
  16. 'Prop_NHI_burden_yll'
    17. 
	
  17. 'Prop_NHI_burden_yld'
    18. 
	
  18. 'Prop_NHI_burden_death'
    19. 
	
  19. 'Nb_RCTs_low'
    20. 
	
  20. 'Nb_RCTs_med'
    21. 
	
  21. 'Nb_RCTs_up'
    22. 
	
  22. 'Nb_Patients_low'
    23. 
	
  23. 'Nb_Patients_med'
    24. 
	
  24. 'Nb_Patients_up'
    25. 
	
  25. 'Prop_loc_RCTs_low'
    26. 
	
  26. 'Prop_loc_RCTs_med'
    27. 
	
  27. 'Prop_loc_RCTs_up'
    28. 
	
  28. 'Prop_loc_Patients_low'
    29. 
	
  29. 'Prop_loc_Patients_med'
    30. 
	
  30. 'Prop_loc_Patients_up'
    31. 
	
  31. 'Prop_glob_RCTs_low'
    32. 
	
  32. 'Prop_glob_RCTs_med'
    33. 
	
  33. 'Prop_glob_RCTs_up'
    34. 
	
  34. 'Prop_glob_Patients_low'
    35. 
	
  35. 'Prop_glob_Patients_med'
    36. 
	
  36. 'Prop_glob_Patients_up'
    37. 
	
  37. 'Prop_NHI_RCTs_low'
    38. 
	
  38. 'Prop_NHI_RCTs_med'
    39. 
	
  39. 'Prop_NHI_RCTs_up'
    40. 
	
  40. 'Prop_NHI_Patients_low'
    41. 
	
  41. 'Prop_NHI_Patients_med'
    42. 
	
  42. 'Prop_NHI_Patients_up'
    43. 
	
  43. 'Dis_lab'
    44. 
	
  44. 'Dis_tooltip'
    45. 
	
  45. 'regs_lab'
    46. 



















In [20]:



    


DT$Prop_glob_RCTs_med[DT$Disease%in%as.character(Mgbd$x[dis]) & DT$Region!="All"] <- 
        as.numeric(as.character(data_f$Prop_all_RCTs_med))
DT$Prop_glob_Patients_med[DT$Disease%in%as.character(Mgbd$x[dis]) & DT$Region!="All"] <- 
        as.numeric(as.character(data_f$Prop_all_Patients_med))
DT$Prop_NHI_RCTs_med[DT$Disease%in%as.character(Mgbd$x[dis]) & DT$Region!="All"] <- 
        as.numeric(as.character(data_f$Prop_NHI_RCTs_med))
DT$Prop_NHI_Patients_med[DT$Disease%in%as.character(Mgbd$x[dis]) & DT$Region!="All"] <- 
        as.numeric(as.character(data_f$Prop_NHI_Patients_med))



    











In [21]:



    


head(DT[DT$Disease=="Leprosy",])



    


















    









RegionDiseaseburden_dalyburden_yllburden_yldburden_deathProp_loc_burden_dalyProp_loc_burden_yllProp_loc_burden_yldProp_loc_burden_death⋯Prop_glob_Patients_upProp_NHI_RCTs_lowProp_NHI_RCTs_medProp_NHI_RCTs_upProp_NHI_Patients_lowProp_NHI_Patients_medProp_NHI_Patients_upDis_labDis_tooltipregs_lab


	
13All                 Leprosy             8539.55289646       0                   8539.55289646       0                   0.0003846535405367680                   0.00127215563614428 0                   ⋯                   NA                  NA                  NA                  NA                  NA                  NA                  NA                  Leprosy             Leprosy             World               

	
41Central Europe, Eastern Europe, and Central AsiaLeprosy                                         23.8790711                                      0                                               23.8790711                                      0                                               1.57599038386549e-05                            0                                               4.95251384242701e-05                            0                                               ⋯                                               NA                                              NA                                              20                                              NA                                              NA                                              8.83875367881882                                NA                                              Leprosy                                         Leprosy                                         Eastern Europe and Central Asia                 

	
69High-income          Leprosy              23.88100136          0                    23.88100136          0                    1.03134835933603e-05 0                    2.10771246590008e-05 0                    ⋯                    NA                   NA                   118                  NA                   NA                   173.67164289901      NA                   Leprosy              Leprosy              High-income countries

	
97Latin America and CaribbeanLeprosy                    1060.8730239               0                          1060.8730239               0                          0.000918446042195016       0                          0.00214674772386038        0                          ⋯                          NA                         NA                         12                         NA                         NA                         12.3413448972869           NA                         Leprosy                    Leprosy                    Latin America and Caribbean

	
125Non-HI                   Leprosy                  8515.6718951             0                        8515.6718951             0                        0.000428243371288557     0                        0.00152620610479439      0                        ⋯                        NA                       NA                       100                      NA                       NA                       100                      NA                       Leprosy                  Leprosy                  Non-high-income countries

	
153North Africa and Middle EastLeprosy                     34.2475894                  0                           34.2475894                  0                           3.34863128992032e-05        0                           8.2766124077638e-05         0                           ⋯                           NA                          NA                          14                          NA                          NA                          3.64672822904794            NA                          Leprosy                     Leprosy                     North Africa and Middle East





















In [22]:



    


write.table(DT,"../Interactive_figure/data/data.txt")



    











In [23]:



    


ratio_align <- read.table("../Data/Alignment_ratios_within_regions_across_diseases_wt_sims_patients_metrs_burdens.txt")



    











In [24]:



    


write.table(ratio_align,"../Interactive_figure/data/data_ratios.txt")



    











In [ ]:

Content source: iatal/RCTvsBurden

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