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In [1]:

    
#On va mettre dans une base de données, pour chaque région, les share across 28 maladies du burden et des essais

#1- On fait matrice burden par catégorie
#################################################################################
library(foreach)
library(doParallel)

#GBD 2005
#Data as downloaded from GBD 2010 study, not included in the repository
gbds <- list.files('/media/igna/Elements/HotelDieu/Cochrane/GBD 2010/GBD_2005_2010_by_cause_country_level_GBD2010/2005')
gbds <- gbds[grep('csv',gbds,ignore.case=TRUE)]

cl<-makeCluster(4)
registerDoParallel(cl)

t0 <- proc.time()
A <- foreach(k = gbds) %dopar% {
Mcause <- read.csv(paste('/media/igna/Elements/HotelDieu/Cochrane/GBD 2010/GBD_2005_2010_by_cause_country_level_GBD2010/2005/',k,sep=""))
#Restriction aux données: toutes les ages, tous les sexes, toutes les metriques (dalys, morts, yll, yld), nombre total (sans intervalle de confiance),nb par mill inhab
Mcause[Mcause$age_name=='All ages' & Mcause$sex=='Both sexes',c(1:8,11,12,18)]
}

stopCluster(cl)

DT <- do.call('rbind',A)

(proc.time()-t0)/60
#1.5min









    



Loading required package: iterators
Loading required package: parallel






    





        user       system      elapsed 
0.0538166667 0.0004833333 3.0323000000



In [15]:

    
table(is.na(DT$causelevel4))
table(DT$causelevel4=="")

Burden is evaluated at least at level 4



In [16]:

    
dis4 <- unique(paste(DT$causelevel3,DT$causelevel4))
#diseases evaluated at level 4
isn <- dis4%in%paste(DT$causelevel3,DT$causelevel4)[is.na(DT$causelevel5) | DT$causelevel5==""]
table(isn)

All diseases are evaluated at level 4, and in the 27-class grouping there is no need of level 5, so we suppress it



In [20]:

    
DT$causelevel5[is.na(DT$causelevel5)] <- ""
DT <- DT[DT$causelevel5=="",]



In [21]:

    
#Taxonomie à 27 catégories
Mgbd <- read.table("../Data/27_gbd_groups.txt")



In [25]:

    
table(is.na(DT$causelevel4))









    





 FALSE 
106756



In [26]:

    
#We add diseases evaluated at level 4
dt <- DT[DT$causelevel4%in%as.character(Mgbd$x),]

#List of diseases added
ML <- dt[!duplicated(paste(dt$causelevel1,dt$causelevel2,dt$causelevel3,dt$causelevel4)),]

table(Mgbd$x%in%c(as.character(dt$causelevel4)))
#FALSE  TRUE 
#   15    13









    





FALSE  TRUE 
   14    13



In [28]:

    
#We have to do manually for the others
Mgbd$x[!Mgbd$x%in%c(as.character(dt$causelevel4))]









    





	Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases
	Neglected tropical diseases excluding malaria
	Maternal disorders
	Neonatal disorders
	Nutritional deficiencies
	Neoplasms
	Cardiovascular and circulatory diseases
	Chronic respiratory diseases
	Cirrhosis of the liver
	Digestive diseases (except cirrhosis)
	Neurological disorders
	Mental and behavioral disorders
	Diabetes, urinary diseases and male infertility
	Musculoskeletal disorders



In [29]:

    
#Maternal disorders
Aj <- DT[DT$causelevel3%in%c("Maternal disorders"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [31]:

    
#Neonatal disorders
Aj <- DT[DT$causelevel3%in%c("Neonatal disorders"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [32]:

    
#Nutritional deficiencies
Aj <- DT[DT$causelevel3%in%c("Nutritional deficiencies"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [33]:

    
#Neoplasms
Aj <- DT[DT$causelevel3%in%c("Neoplasms"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [34]:

    
#Cardiovascular and circulatory diseases
Aj <- DT[DT$causelevel3%in%c("Cardiovascular and circulatory diseases"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [35]:

    
#Chronic respiratory diseases
Aj <- DT[DT$causelevel3%in%c("Chronic respiratory diseases"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [36]:

    
#Cirrhosis of the liver
Aj <- DT[DT$causelevel3%in%c("Cirrhosis of the liver"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [38]:

    
#Digestive diseases (except cirrhosis)
Aj <- DT[DT$causelevel3%in%c("Digestive diseases (except cirrhosis)"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [39]:

    
#Neurological disorders
Aj <- DT[DT$causelevel3%in%c("Neurological disorders"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [40]:

    
#Mental and behavioral disorders
Aj <- DT[DT$causelevel3%in%c("Mental and behavioral disorders"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [41]:

    
#Musculoskeletal disorders
Aj <- DT[DT$causelevel3%in%c("Musculoskeletal disorders"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [42]:

    
#Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases
Aj <- DT[DT$causelevel3%in%c("Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [43]:

    
#Neglected tropical diseases excluding malaria
Aj <- DT[DT$causelevel3%in%c("Neglected tropical diseases and malaria") & !DT$causelevel4%in%c("Malaria"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- "Neglected tropical diseases excluding malaria"
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [44]:

    
#Diabetes, urinary diseases and male infertility
Aj <- DT[DT$causelevel4%in%c("Diabetes mellitus","Acute glomerulonephritis","Urinary diseases and male infertility","Chronic kidney diseases"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- "Diabetes, urinary diseases and male infertility"
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [46]:

    
table(Mgbd$x%in%c(as.character(dt$causelevel2[dt$causelevel3==""]),as.character(dt$causelevel3[dt$causelevel4==""]),as.character(dt$causelevel4[dt$causelevel5==""])))
table(Mgbd$x%in%c(as.character(dt$causelevel2[dt$causelevel3!=""]),as.character(dt$causelevel3[dt$causelevel4!=""]),as.character(dt$causelevel4[dt$causelevel5!=""])))
U <- unique(paste(dt$causelevel1,dt$causelevel2,dt$causelevel3,dt$causelevel4,dt$causelevel5))
length(U)

Ok, we have the 27 groups of diseases



In [50]:

    
#Diseases not included
table(dis4%in%paste(ML$causelevel3,ML$causelevel4))
sort(dis4[!dis4%in%paste(ML$causelevel3,ML$causelevel4)])









    





FALSE  TRUE 
   16   139 






    





	'Diabetes, urogenital, blood, and endocrine diseases Other endocrine, nutritional, blood, and immune disorders'
	'Forces of nature, war, and legal intervention Collective violence and legal intervention'
	'Forces of nature, war, and legal intervention Exposure to forces of nature'
	'Other communicable, maternal, neonatal, and nutritional disorders Other infectious diseases'
	'Self-harm and interpersonal violence Interpersonal violence'
	'Self-harm and interpersonal violence Self-harm'
	'Transport injuries Other transport injury'
	'Transport injuries Road injury'
	'Unintentional injuries other than transport injuries Adverse effects of medical treatment'
	'Unintentional injuries other than transport injuries Animal contact'
	'Unintentional injuries other than transport injuries Drowning'
	'Unintentional injuries other than transport injuries Exposure to mechanical forces'
	'Unintentional injuries other than transport injuries Falls'
	'Unintentional injuries other than transport injuries Fire, heat and hot substances'
	'Unintentional injuries other than transport injuries Poisonings'
	'Unintentional injuries other than transport injuries Unintentional injuries not classified elsewhere'

Not included in the 27-class grouping:

Injuries
Other endocrine, nutritional, blood, and immune disorders
Other infectious diseases

These two residual categories are in particular excluded because they are not included in the arrival space of the classifier, because too complicated



In [53]:

    
#We add excluded categories to dt to evaluate what amount of burden we are excluding
dt$cats27 <- TRUE



In [54]:

    
#Injuries
Aj <- DT[DT$causelevel2%in%c("Injuries"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- ""
Mttp$causelevel3 <- ""
Mttp$cats27 <- FALSE
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [55]:

    
#Residual categories
Aj <- DT[DT$causelevel4%in%c("Other endocrine, nutritional, blood, and immune disorders","Other infectious diseases"),]
Mttp <- Aj[!duplicated(paste(Aj$country_name,Aj$measure)),]
Mttp <- Mttp[order(paste(Mttp$country_name,Mttp$measure)),]
Mttp$nm_mean <- tapply(Aj$nm_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$rt_mean <- tapply(Aj$rt_mean,paste(Aj$country_name,Aj$measure),sum)
Mttp$causelevel4 <- "Excluded residual categories"
Mttp$cats27 <- FALSE
dt <- rbind(dt,Mttp)
ML <- rbind(ML,Aj[!duplicated(paste(Aj$causelevel1,Aj$causelevel2,Aj$causelevel3,Aj$causelevel4)),])



In [56]:

    
table(dis4%in%paste(ML$causelevel3,ML$causelevel4))

Ok, we included all the burden



In [57]:

    
#We include variable Diseases = last causelevel without ""
dt$Disease <- as.character(dt$causelevel4)
dt$Disease[dt$Disease==""] <- as.character(dt$causelevel3[dt$Disease==""])
dt$Disease[dt$Disease==""] <- as.character(dt$causelevel2[dt$Disease==""])



In [58]:

    
table(dt$Disease%in%as.character(Mgbd$x))
length(unique(dt$Dis))









    





FALSE  TRUE 
 1496 19416 






    




29

Ok: 27 + injuries and residual



In [59]:

    
dt$Sup_region <- dt$region
levels(dt$region)









    





	'Andean Latin America'
	'Australasia'
	'Caribbean'
	'Central Asia'
	'Central Europe '
	'Central Latin America'
	'Central Sub-Saharan Africa'
	'East Asia'
	'Eastern Europe'
	'Eastern Sub-Saharan Africa'
	'High-income Asia Pacific'
	'High-income North America'
	'North Africa and Middle East'
	'Oceania'
	'South Asia'
	'Southeast Asia '
	'Southern Latin America'
	'Southern Sub-Saharan Africa'
	'Tropical Latin America'
	'Western Europe'
	'Western Sub-Saharan Africa'



In [60]:

    
levels(dt$Sup_region) <- c(
"Latin America and Caribbean", "High-income", 
"Latin America and Caribbean", "Central Europe, Eastern Europe, and Central Asia", 
"Central Europe, Eastern Europe, and Central Asia", "Latin America and Caribbean",
"Sub-Saharian Africa", "Southeast Asia, East Asia and Oceania",
"Central Europe, Eastern Europe, and Central Asia", "Sub-Saharian Africa",
"High-income", "High-income",
"North Africa and Middle East", "Southeast Asia, East Asia and Oceania",
"South Asia", "Southeast Asia, East Asia and Oceania",
"High-income", "Sub-Saharian Africa",
"Latin America and Caribbean", "High-income",
"Sub-Saharian Africa")

#Region level
GBD <- dt
meas <- levels(GBD$mes)
dis <- levels(GBD$Dis)
reg <- levels(GBD$Sup_reg)

S <- tapply(GBD$nm_mean,paste(GBD$meas,GBD$Sup_region,GBD$Disease,sep="&"),sum)

L <- strsplit(names(S),"&")

M <- data.frame(do.call('rbind',L))
names(M) <- c("metr","Region","Disease")
M$burden <- as.numeric(S)



In [61]:

    
write.table(M,"../Data/DALY_YLL_deaths_per_region_and_27_and_excluded_diseases_2005.txt")



In [ ]: