notebook.community

Edit and run

Big table with info per region per disease of RCTs and burden





In [1]:



    


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



    











In [2]:



    


#Format
form <- function(x,type="Prop"){
paste(format(round(x[2],1),nsmall = 1),
           ifelse(type=="Prop","% ["," ["),format(round(x[1],1),nsmall = 1),
           "-",format(round(x[3],1),nsmall = 1),"]",sep="")
    }



    











In [3]:



    


#For a given region, disease and metric of burden and research, we give all data in a readable format
readable_numbers <- function(r,d,metr_burden="daly",metr_res="RCTs"){

x <- DT[DT$Region==r & DT$Disease==d,c("Region","Disease",names(DT)[c(grep(metr_burden,names(DT)),grep(metr_res,names(DT)))])] 
    
print(paste("In ",ifelse(r=="All","all regions",r), ", ", 
            ifelse(d=="All","all diseases",d), " caused ", 
            round(x[grep("^burden",names(x))]/1e6,1), " million ", metr_burden, "s", sep=""))


print(paste("In ",ifelse(r=="All","all regions",r), ", ", 
            ifelse(d=="All","all diseases",d), 
            ifelse(metr_res=="RCTs"," was studied by "," were enrolled "), 
            form(x[grep("Nb_",names(x))],"Nb"), " ", metr_res,sep=""))

print(paste("In ",ifelse(r=="All","all regions",r), ", ", 
            ifelse(d=="All","all diseases",d), 
            ifelse(metr_res=="RCTs"," was studied by "," were enrolled "), 
            form(x[grep("Nb_",names(x))]/rep((x[grep("^burden",names(x))]/1e6),3),"Nb"),
            " per million ", metr_burden, "s", sep=""))

if(d!="All"){
    print(paste("In ",ifelse(r=="All","all regions",r),
                ": the local proportion of burden (in ",metr_burden,") vs research (in ",
                metr_res,") of ",d," was ",
                round(x[intersect(grep(metr_burden,names(x)),grep("Prop_loc",names(x)))],1),
                "% vs ",
                form(x[intersect(grep(metr_res,names(x)),grep("Prop_loc",names(x)))]),sep=""
                ))
    }

if(r!="All"){    
    print(paste("For ",ifelse(d=="All","all diseases",d),
                ": the global proportion of burden (in ",metr_burden,") vs research (in ",
                metr_res,") in ",r," was ",
                round(x[intersect(grep(metr_burden,names(x)),grep("Prop_glob",names(x)))],1),
                "% vs ",
                form(x[intersect(grep(metr_res,names(x)),grep("Prop_glob",names(x)))]),sep=""
                ))
    }

if(!r%in%c("All","High-income","Non-HI")){
    print(paste("For ",ifelse(d=="All","all diseases",d),
                ": the proportion among non-high-income regions of burden (in ",metr_burden,
                ") vs research (in ",metr_res,") in ",r," was ",
                round(x[intersect(grep(metr_burden,names(x)),grep("Prop_NHI",names(x)))],1),
                "% vs ",
                form(x[intersect(grep(metr_res,names(x)),grep("Prop_NHI",names(x)))]),sep=""
                ))
    }

}



    











In [4]:



    


levels(DT$Region)
levels(DT$Disease)



    


















    







    
	
  1. 'All'
    2. 
	
  2. 'Central Europe, Eastern Europe, and Central Asia'
    3. 
	
  3. 'High-income'
    4. 
	
  4. 'Latin America and Caribbean'
    5. 
	
  5. 'Non-HI'
    6. 
	
  6. 'North Africa and Middle East'
    7. 
	
  7. 'South Asia'
    8. 
	
  8. 'Southeast Asia, East Asia and Oceania'
    9. 
	
  9. 'Sub-Saharian Africa'
    10. 












    







    
	
  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]:



    


readable_numbers(r = "High-income", d = "Cardiovascular and circulatory diseases", metr_burden = "daly", metr_res = "RCTs")



    


















    






[1] "In High-income, Cardiovascular and circulatory diseases caused 43.5 million dalys"
[1] "In High-income, Cardiovascular and circulatory diseases was studied by 7959.0 [7101.0-8901.0] RCTs"
[1] "In High-income, Cardiovascular and circulatory diseases was studied by 182.8 [163.1-204.4] per million dalys"
[1] "In High-income: the local proportion of burden (in daly) vs research (in RCTs) of Cardiovascular and circulatory diseases was 18.8% vs 12.9% [11.6-14.4]"
[1] "For Cardiovascular and circulatory diseases: the global proportion of burden (in daly) vs research (in RCTs) in High-income was 15.2% vs 74.5% [74.0-75.1]"

















In [6]:



    


readable_numbers(r = "All", d = "All", metr_burden = "daly", metr_res = "RCTs")



    


















    






[1] "In all regions, all diseases caused 2220.1 million dalys"
[1] "In all regions, all diseases was studied by 82179.0 [78661.8-85358.2] RCTs"
[1] "In all regions, all diseases was studied by 37.0 [35.4-38.4] per million dalys"

















In [7]:



    


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 [8]:



    


readable_numbers(r = "All", 
                 d = "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases", 
                 metr_burden = "daly", 
                 metr_res = "RCTs")



    


















    






[1] "In all regions, Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases caused 329.3 million dalys"
[1] "In all regions, Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases was studied by 3497.0 [2855.0-4273.0] RCTs"
[1] "In all regions, Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases was studied by 10.6 [8.7-13.0] per million dalys"
[1] "In all regions: the local proportion of burden (in daly) vs research (in RCTs) of Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases was 14.8% vs 4.2% [3.4-5.2]"

















In [9]:



    


readable_numbers(r = "All", 
                 d = "Neonatal disorders", 
                 metr_burden = "daly", 
                 metr_res = "RCTs")



    


















    






[1] "In all regions, Neonatal disorders caused 220.3 million dalys"
[1] "In all regions, Neonatal disorders was studied by 1424.0 [639.0-3612.8] RCTs"
[1] "In all regions, Neonatal disorders was studied by 6.5 [2.9-16.4] per million dalys"
[1] "In all regions: the local proportion of burden (in daly) vs research (in RCTs) of Neonatal disorders was 9.9% vs 1.7% [0.8-4.3]"

















In [10]:



    


readable_numbers(r = "Non-HI", 
                 d = "All", 
                 metr_burden = "daly", 
                 metr_res = "Patients")



    


















    






[1] "In Non-HI, all diseases caused 1988.5 million dalys"
[1] "In Non-HI, all diseases were enrolled 11117752.1 [10376596.3-11813323.1] Patients"
[1] "In Non-HI, all diseases were enrolled 5591.0 [5218.3-5940.8] per million dalys"
[1] "For all diseases: the global proportion of burden (in daly) vs research (in Patients) in Non-HI was 89.6% vs 36.9% [35.6-38.3]"

















In [11]:



    


readable_numbers(r = "High-income", 
                 d = "All", 
                 metr_burden = "daly", 
                 metr_res = "Patients")



    


















    






[1] "In High-income, all diseases caused 231.6 million dalys"
[1] "In High-income, all diseases were enrolled 18964732.8 [17965002.7-19913082.6] Patients"
[1] "In High-income, all diseases were enrolled 81903.0 [77585.4-85998.6] per million dalys"
[1] "For all diseases: the global proportion of burden (in daly) vs research (in Patients) in High-income was 10.4% vs 63.1% [61.7-64.4]"

















In [12]:



    


readable_numbers(r = "All", 
                 d = "Mental and behavioral disorders", 
                 metr_burden = "daly", 
                 metr_res = "RCTs")



    


















    






[1] "In all regions, Mental and behavioral disorders caused 171.9 million dalys"
[1] "In all regions, Mental and behavioral disorders was studied by 9346.0 [8684.0-10040.0] RCTs"
[1] "In all regions, Mental and behavioral disorders was studied by 54.4 [50.5-58.4] per million dalys"
[1] "In all regions: the local proportion of burden (in daly) vs research (in RCTs) of Mental and behavioral disorders was 7.7% vs 11.3% [10.5-12.3]"

Gaps





In [12]:



    


gaps <- function(r,metr_burden="daly",metr_res="RCTs"){
    print("-------------------------------------------")
    print(paste("Gaps comparing local proportion of burden in ",metr_burden,
                "s vs local proportion of research in ",metr_res,sep=""))
    x <- DT[DT$Region==r,c("Region","Disease",names(DT)[c(grep(metr_burden,names(DT)),grep(metr_res,names(DT)))])] 

    x <- x[!is.na(x[,intersect(intersect(grep(metr_res,names(x)),grep("Prop_loc",names(x))),grep("up",names(x)))]) 
      & x[,intersect(grep(metr_burden,names(x)),grep("Prop_loc",names(x)))] > 
      2*x[,intersect(intersect(grep(metr_res,names(x)),grep("Prop_loc",names(x))),grep("up",names(x)))],]

    x <- x[order(x[,intersect(grep(metr_burden,names(x)),grep("Prop_loc",names(x)))],decreasing=TRUE),]
    print(paste(r,": ",nrow(x)," gaps found:",sep=""))
    if(nrow(x)!=0){
    for(i in 1:nrow(x)){
    k <- x[i,]
    print(paste(k$Disease,": ",form(k[intersect(grep(metr_res,names(x)),grep("Prop_loc",names(x)))]), " vs ",
    round(k[intersect(grep(metr_burden,names(x)),grep("Prop_loc",names(x)))],1),"%",sep=""))
    }
    }
}



    











In [13]:



    


r <- 'Sub-Saharian Africa'
metr_burden <- "daly"
metr_res <- "RCTs"



    











In [14]:



    


gaps(r,metr_burden,metr_res)



    


















    






[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "Sub-Saharian Africa: 2 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 5.8% [4.7-6.9] vs 22.9%"
[1] "Neonatal disorders: 2.0% [0.9-4.5] vs 11.6%"

















In [15]:



    


regs <- levels(DT$Region)
regs <- regs[regs!="Non-HI"]
lapply(regs,gaps)



    


















    






[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "All: 5 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 4.2% [3.4-5.2] vs 14.8%"
[1] "Neonatal disorders: 1.7% [0.8-4.3] vs 9.9%"
[1] "Malaria: 0.5% [0.3-0.6] vs 4.6%"
[1] "HIV/AIDS: 1.7% [1.3-2.1] vs 4.3%"
[1] "Tuberculosis: 0.4% [0.1-0.5] vs 2.5%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "Central Europe, Eastern Europe, and Central Asia: 2 gaps found:"
[1] "Cardiovascular and circulatory diseases: 12.5% [11.1-14.0] vs 35.1%"
[1] "Tuberculosis: 0.3% [0.1-0.5] vs 1.8%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "High-income: 0 gaps found:"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "Latin America and Caribbean: 1 gaps found:"
[1] "Neonatal disorders: 1.6% [0.7-4.0] vs 8.7%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "North Africa and Middle East: 3 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 3.3% [2.6-4.2] vs 10.5%"
[1] "Tuberculosis: 0.2% [0.1-0.4] vs 0.9%"
[1] "Malaria: 0.0% [0.0-0.1] vs 0.5%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "South Asia: 3 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 7.0% [5.8-8.3] vs 21.4%"
[1] "Neonatal disorders: 2.3% [1.1-4.9] vs 16.5%"
[1] "Tuberculosis: 0.9% [0.2-1.3] vs 3.9%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "Southeast Asia, East Asia and Oceania: 1 gaps found:"
[1] "Tuberculosis: 0.6% [0.2-0.9] vs 2.8%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in RCTs"
[1] "Sub-Saharian Africa: 2 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 5.8% [4.7-6.9] vs 22.9%"
[1] "Neonatal disorders: 2.0% [0.9-4.5] vs 11.6%"










    







    
	
  1. NULL
    2. 
	
  2. NULL
    3. 
	
  3. NULL
    4. 
	
  4. NULL
    5. 
	
  5. NULL
    6. 
	
  6. NULL
    7. 
	
  7. NULL
    8. 
	
  8. NULL
    9. 



















In [16]:



    


readable_numbers(r = "Sub-Saharian Africa", 
                 d = "HIV/AIDS")



    


















    






[1] "In Sub-Saharian Africa, HIV/AIDS caused 73.5 million dalys"
[1] "In Sub-Saharian Africa, HIV/AIDS was studied by 319.0 [245.0-366.0] RCTs"
[1] "In Sub-Saharian Africa, HIV/AIDS was studied by 4.3 [3.3-5.0] per million dalys"
[1] "In Sub-Saharian Africa: the local proportion of burden (in daly) vs research (in RCTs) of HIV/AIDS was 13.4% vs 13.8% [10.8-15.7]"
[1] "For HIV/AIDS: the global proportion of burden (in daly) vs research (in RCTs) in Sub-Saharian Africa was 76.5% vs 21.4% [19.5-23.0]"
[1] "For HIV/AIDS: the proportion among non-high-income regions of burden (in daly) vs research (in RCTs) in Sub-Saharian Africa was 77.5% vs 52.7% [48.8-55.9]"

















In [17]:



    


readable_numbers(r = "Sub-Saharian Africa", 
                 d = "Malaria")



    


















    






[1] "In Sub-Saharian Africa, Malaria caused 91.4 million dalys"
[1] "In Sub-Saharian Africa, Malaria was studied by 274.0 [162.0-316.0] RCTs"
[1] "In Sub-Saharian Africa, Malaria was studied by 3.0 [1.8-3.5] per million dalys"
[1] "In Sub-Saharian Africa: the local proportion of burden (in daly) vs research (in RCTs) of Malaria was 16.7% vs 11.9% [7.3-13.7]"
[1] "For Malaria: the global proportion of burden (in daly) vs research (in RCTs) in Sub-Saharian Africa was 90.2% vs 63.5% [52.5-67.4]"
[1] "For Malaria: the proportion among non-high-income regions of burden (in daly) vs research (in RCTs) in Sub-Saharian Africa was 90.2% vs 74.7% [69.1-77.4]"

















In [18]:



    


regs



    


















    







    
	
  1. 'All'
    2. 
	
  2. 'Central Europe, Eastern Europe, and Central Asia'
    3. 
	
  3. 'High-income'
    4. 
	
  4. 'Latin America and Caribbean'
    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 [19]:



    


lapply(regs,function(x){gaps(r=x,metr_res="Patients")})



    


















    






[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "All: 0 gaps found:"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "Central Europe, Eastern Europe, and Central Asia: 2 gaps found:"
[1] "Cirrhosis of the liver: 0.6% [0.2-1.3] vs 2.7%"
[1] "Tuberculosis: 0.2% [0.1-0.5] vs 1.8%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "High-income: 0 gaps found:"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "Latin America and Caribbean: 0 gaps found:"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "North Africa and Middle East: 2 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 3.9% [3.0-5.2] vs 10.5%"
[1] "Malaria: 0.0% [0.0-0.2] vs 0.5%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "South Asia: 1 gaps found:"
[1] "Tuberculosis: 0.8% [0.2-1.3] vs 3.9%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "Southeast Asia, East Asia and Oceania: 1 gaps found:"
[1] "Tuberculosis: 0.9% [0.2-1.3] vs 2.8%"
[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "Sub-Saharian Africa: 1 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 5.6% [2.3-9.9] vs 22.9%"










    







    
	
  1. NULL
    2. 
	
  2. NULL
    3. 
	
  3. NULL
    4. 
	
  4. NULL
    5. 
	
  5. NULL
    6. 
	
  6. NULL
    7. 
	
  7. NULL
    8. 
	
  8. NULL
    9. 



















In [20]:



    


r <- 'Sub-Saharian Africa'
metr_burden <- "daly"
metr_res <- "Patients"



    











In [21]:



    


gaps(r = r,metr_burden = metr_burden, metr_res=metr_res)



    


















    






[1] "-------------------------------------------"
[1] "Gaps comparing local proportion of burden in dalys vs local proportion of research in Patients"
[1] "Sub-Saharian Africa: 1 gaps found:"
[1] "Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases: 5.6% [2.3-9.9] vs 22.9%"

















In [22]:



    


x <- DT[DT$Region==r,c("Region","Disease",names(DT)[c(grep(metr_burden,names(DT)),grep(metr_res,names(DT)))])]



    











In [23]:



    


x <- x[!is.na(x[,intersect(intersect(grep(metr_res,names(x)),grep("Prop_loc",names(x))),grep("up",names(x)))]) 
      & x[,intersect(grep(metr_burden,names(x)),grep("Prop_loc",names(x)))] > 
      2*x[,intersect(intersect(grep(metr_res,names(x)),grep("Prop_loc",names(x))),grep("up",names(x)))],]



    











In [24]:



    


x <- x[order(x[,intersect(grep(metr_burden,names(x)),grep("Prop_loc",names(x)))],decreasing=TRUE),]



    











In [25]:



    


x



    


















    









RegionDiseaseburden_dalyProp_loc_burden_dalyProp_glob_burden_dalyProp_NHI_burden_dalyNb_Patients_lowNb_Patients_medNb_Patients_upProp_loc_Patients_lowProp_loc_Patients_medProp_loc_Patients_upProp_glob_Patients_lowProp_glob_Patients_medProp_glob_Patients_upProp_NHI_Patients_lowProp_NHI_Patients_medProp_NHI_Patients_up


	
231Sub-Saharian Africa                                                                     Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases125531847.741516                                                                        22.9258956705191                                                                        38.1224905842441                                                                        38.9321072337489                                                                        79411.2027175487                                                                        195483.586003594                                                                        344711.512564562                                                                        2.27264257136015                                                                        5.57577450297116                                                                        9.87487447800421                                                                        3.61888273336711                                                                        8.16715517871999                                                                        13.5659020621904                                                                        9.9503418163965                                                                         20.981249246535                                                                         32.3609016906431                                                                        





















In [26]:



    


readable_numbers(r = "Southeast Asia, East Asia and Oceania", 
                 d  = "Cardiovascular and circulatory diseases",
                metr_burden="daly",
                metr_res="Patients")



    


















    






[1] "In Southeast Asia, East Asia and Oceania, Cardiovascular and circulatory diseases caused 81.7 million dalys"
[1] "In Southeast Asia, East Asia and Oceania, Cardiovascular and circulatory diseases were enrolled 627800.0 [490332.8-829407.1] Patients"
[1] "In Southeast Asia, East Asia and Oceania, Cardiovascular and circulatory diseases were enrolled 7683.1 [6000.8-10150.4] per million dalys"
[1] "In Southeast Asia, East Asia and Oceania: the local proportion of burden (in daly) vs research (in Patients) of Cardiovascular and circulatory diseases was 17.7% vs 19.6% [15.3-25.4]"
[1] "For Cardiovascular and circulatory diseases: the global proportion of burden (in daly) vs research (in Patients) in Southeast Asia, East Asia and Oceania was 28.4% vs 13.1% [10.7-16.6]"
[1] "For Cardiovascular and circulatory diseases: the proportion among non-high-income regions of burden (in daly) vs research (in Patients) in Southeast Asia, East Asia and Oceania was 33.5% vs 38.0% [30.9-46.5]"

Content source: iatal/RCTvsBurden

Similar notebooks:

notebook.community | gallery | about