Mapping health needs

In [30]:

    

library(data.table)


    

In [76]:

    

GBD <- read.table("../Data/DALY_YLL_deaths_per_region_and_27_and_excluded_diseases_2005.txt")
GBD <- data.table(GBD)
names(GBD)


    

    






	
'metr'
	
'Region'
	
'Disease'
	
'burden'
	
'cats27'

In [77]:

    

#Total burden per region (with excluded categories)
dg <- GBD[,list(lapply(.SD,sum),
                lapply(.SD[Disease!="Injuries",],sum),
                lapply(.SD[cats27==TRUE,],sum)),.SDcols=c("burden"),by=c("metr","Region")]


    

In [78]:

    

#Part of injuries among total burden per region per metric
dg$Prop_inj <- 100 - 100*as.numeric(dg$V2)/as.numeric(dg$V1)


    

In [79]:

    

#Part of excluded diseases among total burden of diseases, per region per metric
dg$Prop_excl_dis <- 100 - 100*as.numeric(dg$V3)/as.numeric(dg$V2)


    

In [80]:

    

dg[,c(1,2,6,7)]


    

    






metr
Region
Prop_inj
Prop_excl_dis

	
1
daly                                            
Central Europe, Eastern Europe, and Central Asia
14.1390406120141                                
0.374399537566802                               
	
2
daly             
High-income      
9.95374248395683 
0.874546593018735
	
3
daly                       
Latin America and Caribbean
14.1176535353185           
1.07857397578715           
	
4
daly                        
North Africa and Middle East
10.6130446519093            
1.30766119323818            
	
5
daly            
South Asia      
11.2288229916697
1.41029163665819
	
6
daly                                 
Southeast Asia, East Asia and Oceania
12.1743208640528                     
0.877672086963031                    
	
7
daly               
Sub-Saharian Africa
6.77986985119354   
1.54287339643092   
	
8
death                                           
Central Europe, Eastern Europe, and Central Asia
9.74096749853662                                
0.1420245253546                                 
	
9
death            
High-income      
6.45421392631165 
0.993291331280986
	
10
death                      
Latin America and Caribbean
13.1785927286985           
1.04315602180657           
	
11
death                       
North Africa and Middle East
9.63586256427789            
1.32503150911653            
	
12
death           
South Asia      
11.444711205292 
1.26301539706411
	
13
death                                
Southeast Asia, East Asia and Oceania
9.80633464149911                     
0.613785215382705                    
	
14
death              
Sub-Saharian Africa
7.47074031266474   
1.42484248873917   
	
15
yld                                             
Central Europe, Eastern Europe, and Central Asia
7.11547433245897                                
0.616884249970013                               
	
16
yld              
High-income      
7.0708849504915  
0.402194812080424
	
17
yld                        
Latin America and Caribbean
3.73141412680833           
0.826968094599863          
	
18
yld                         
North Africa and Middle East
6.18063172528117            
0.964996019518679           
	
19
yld             
South Asia      
5.15160738791978
1.41224665210612
	
20
yld                                  
Southeast Asia, East Asia and Oceania
6.22216438537055                     
0.770991084028012                    
	
21
yld                
Sub-Saharian Africa
3.09407142182695   
1.44476407491931   
	
22
yll                                             
Central Europe, Eastern Europe, and Central Asia
17.0762222147661                                
0.260814567550838                               
	
23
yll             
High-income     
12.5297370149571
1.32296061728127
	
24
yll                        
Latin America and Caribbean
20.5024751607031           
1.26587724614045           
	
25
yll                         
North Africa and Middle East
13.3777555825167            
1.53915644631607            
	
26
yll             
South Asia      
13.2294618561729
1.40958828891362
	
27
yll                                  
Southeast Asia, East Asia and Oceania
15.3583586953629                     
0.940901410564933                    
	
28
yll                
Sub-Saharian Africa
7.43176268238633   
1.56103893792361   

In [81]:

    

#Worldwide:
dg <- GBD[,list(lapply(.SD,sum),
                lapply(.SD[Disease!="Injuries",],sum),
                lapply(.SD[cats27==TRUE,],sum)),.SDcols=c("burden"),by=c("metr")]


    

In [82]:

    

#Part of injuries among total burden per metric
dg$Prop_inj <- 100 - 100*as.numeric(dg$V2)/as.numeric(dg$V1)
#Part of excluded diseases among total burden of diseases per metric
dg$Prop_excl_dis <- 100 - 100*as.numeric(dg$V3)/as.numeric(dg$V2)


    

In [83]:

    

dg[,c(1,5,6)]


    

    






metr
Prop_inj
Prop_excl_dis

	
1
daly            
10.5730779448206
1.18506603297897
	
2
death            
9.40952379404409 
0.967403128886815
	
3
yld              
5.60138476935332 
0.955603452962151
	
4
yll             
12.5623477994155
1.2841882674719 

In [84]:

    

GBD <- GBD[GBD$cats27==TRUE,]


    

In [85]:

    

alldis <- GBD[,lapply(.SD,sum),.SDcols="burden",by=c("metr","Region")]
alldis$Disease <- "all"
alldis <- alldis[,c(1,2,4,3)]
allreg <- GBD[,lapply(.SD,sum),.SDcols="burden",by=c("metr","Disease")]
allreg$Region <- "All"
allreg <- allreg[,c(1,4,2,3)]


    

In [86]:

    

GBD <- GBD[,c(1:4)]


    

In [87]:

    

GBD <- rbindlist(list(GBD,alldis,allreg))


    

In [89]:

    

write.table(GBD,"../Data/DALY_YLL_deaths_per_region_and_27_diseases_2005.txt")


    

In [ ]: