notebook.community

Edit and run

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





In [1]:



    


library(gdata)



    


















    






gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.

gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.

Attaching package: ‘gdata’

The following object is masked from ‘package:stats’:

    nobs

The following object is masked from ‘package:utils’:

    object.size

The following object is masked from ‘package:base’:

    startsWith


















In [2]:



    


GBD <- read.table("../Data/DALY_YLL_deaths_per_region_and_27_diseases_2005.txt")
RCT_regs <- read.table("../Data/RCTs_and_Patients_Nb_local_prop_median_UI_per_region_and_disease.txt")
RCT_dis <- read.table("../Data/RCTs_and_Patients_prop_among_all_and_HI_median_UI_across_regions_per_disease.txt")



    











In [3]:



    


head(GBD)



    


















    









metrRegionDiseaseburden


	
1daly                                            Central Europe, Eastern Europe, and Central AsiaCardiovascular and circulatory diseases         53161406.515                                    

	
2daly                                            Central Europe, Eastern Europe, and Central AsiaChronic respiratory diseases                    5644608.1271                                    

	
3daly                                            Central Europe, Eastern Europe, and Central AsiaCirrhosis of the liver                          4090337.297                                     

	
4daly                                            Central Europe, Eastern Europe, and Central AsiaCongenital anomalies                            2079422.787                                     

	
5daly                                            Central Europe, Eastern Europe, and Central AsiaDiabetes, urinary diseases and male infertility 5049824.6483                                    

	
6daly                                                                                    Central Europe, Eastern Europe, and Central Asia                                        Diarrhea, lower respiratory infections, meningitis, and other common infectious diseases7281615.92163                                                                           





















In [4]:



    


head(RCT_regs)



    


















    









RegionDiseaseNb_RCTs_lowNb_RCTs_medNb_RCTs_upNb_Patients_lowNb_Patients_medNb_Patients_upProp_RCTs_lowProp_RCTs_medProp_RCTs_upProp_Patients_lowProp_Patients_medProp_Patients_up


	
1Central Europe, Eastern Europe, and Central AsiaTuberculosis                                    4                                               17                                              28                                              817.791035280509                                3727.45887445887                                8286.37821455813                                0.0670184629223417                              0.279881461969048                               0.465039030061452                               0.0541345253732852                              0.246996228187276                               0.548102268596756                               

	
2High-income       Tuberculosis      28                95                186               4760.52142857143  20935.8779220779  80181.1191734     0.04456973410046330.152             0.298837432345686 0.02461451167538080.106212756460855 0.408072232458222 

	
3Latin America and CaribbeanTuberculosis               8                          29                         42                         2031.4689484127            41578.255952381            46855.1901044703           0.185393444249661          0.67421141343607           0.985567832207902          0.25044366594425           4.89484230548107           5.74768206602598           

	
4North Africa and Middle EastTuberculosis                4                           17                          31                          426.4375                    2930.46428571429            4985.14201868919            0.050657984871941           0.214646464646465           0.393272107025709           0.0509666269158626          0.351318357907269           0.598363560953588           

	
5South Asia       Tuberculosis     11               40               56               3184.94642857143 14501.0103479853 23036.5803571428 0.24519372262736 0.8831971737690441.26012776852171 0.1715347922852040.7813390179105591.28564993265724 

	
6Southeast Asia, East Asia and OceaniaTuberculosis                         15                                   53                                   76                                   5566.83035714286                     28046.521031746                      41850.471984127                      0.169275419396122                    0.603575902516798                    0.872978293733491                    0.17204138884611                     0.869588012581335                    1.32455313091434                     





















In [5]:



    


head(RCT_dis)



    


















    









RegionDiseaseProp_all_RCTs_lowProp_all_RCTs_medProp_all_RCTs_upProp_all_Patients_lowProp_all_Patients_medProp_all_Patients_upProp_NHI_RCTs_lowProp_NHI_RCTs_medProp_NHI_RCTs_upProp_NHI_Patients_lowProp_NHI_Patients_medProp_NHI_Patients_up


	
1Central Europe, Eastern Europe, and Central AsiaTuberculosis                                    3.69003740785565                                5.80474934036939                                8.13568226393947                                0.366473691455004                               0.839127809160704                               2.83936826324646                                5.08474576271187                                8.01526717557252                                11.486262637802                                 0.380957658815296                               0.889686757284583                               3.33797750372922                                

	
2High-income     Tuberculosis    25.229856024248532.608695652173943.75           2.0993861537554 5.0135830676225720.7015918123193NA              NA              NA              NA              NA              NA              

	
3Latin America and CaribbeanTuberculosis               6.90279038112523           9.6969696969697            12.5560538116592           0.984953513555074          7.43862809488715           24.4784120543199           9.86401964418087           13.3333333333333           17.0268295534253           1.0378086262667            7.78820711422704           27.9627474021534           

	
4Non-HI          Tuberculosis    62.056419012877572.980501392757780.080281187090179.298408187680794.986416932377497.9006138462446NA              NA              NA              NA              NA              NA              

	
5North Africa and Middle EastTuberculosis                3.59039767997637            5.86510263929619            8.48484848484849            0.186777744586216           0.634283140727821           2.09269030195581            4.84384164222874            8.05369127516778            12.5                        0.192695325477562           0.670666221682141           2.42120570680917            

	
6South Asia      Tuberculosis    9.7613376524390213.231552162849916.49716494845361.549686761091873.1853288250145510.475800200161114.166666666666718.232044198895 22.22222222222221.623354206128253.3621094629957612.183418694834 





















In [6]:



    


levels(GBD$Region)
levels(RCT_regs$Region)
levels(RCT_dis$Region)



    


















    







    
	
  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. 












    







    
	
  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. '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 [7]:



    


levels(GBD$Disease)
levels(RCT_regs$Disease)
levels(RCT_dis$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. 












    







    
	
  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. 'Hepatitis'
    11. 
	
  11. 'HIV/AIDS'
    12. 
	
  12. 'Malaria'
    13. 
	
  13. 'Maternal disorders'
    14. 
	
  14. 'Mental and behavioral disorders'
    15. 
	
  15. 'Musculoskeletal disorders'
    16. 
	
  16. 'Neglected tropical diseases excluding malaria'
    17. 
	
  17. 'Neonatal disorders'
    18. 
	
  18. 'Neoplasms'
    19. 
	
  19. 'Neurological disorders'
    20. 
	
  20. 'Nutritional deficiencies'
    21. 
	
  21. 'Oral disorders'
    22. 
	
  22. 'Sense organ diseases'
    23. 
	
  23. 'Sexually transmitted diseases excluding HIV'
    24. 
	
  24. 'Skin and subcutaneous diseases'
    25. 
	
  25. 'Tuberculosis'
    26. 












    







    
	
  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. 'Hepatitis'
    11. 
	
  11. 'HIV/AIDS'
    12. 
	
  12. 'Malaria'
    13. 
	
  13. 'Maternal disorders'
    14. 
	
  14. 'Mental and behavioral disorders'
    15. 
	
  15. 'Musculoskeletal disorders'
    16. 
	
  16. 'Neglected tropical diseases excluding malaria'
    17. 
	
  17. 'Neonatal disorders'
    18. 
	
  18. 'Neoplasms'
    19. 
	
  19. 'Neurological disorders'
    20. 
	
  20. 'Nutritional deficiencies'
    21. 
	
  21. 'Oral disorders'
    22. 
	
  22. 'Sense organ diseases'
    23. 
	
  23. 'Sexually transmitted diseases excluding HIV'
    24. 
	
  24. 'Skin and subcutaneous diseases'
    25. 
	
  25. 'Tuberculosis'
    26. 



















In [8]:



    


#Adding to GBD data , burden of all diseases in all regions
gbd_all <- tapply(GBD$burden[GBD$Region=="All"],paste(GBD$metr[GBD$Region=="All"],GBD$Region[GBD$Region=="All"]),sum)
gbd_all <- data.frame(metr=levels(GBD$metr),
                      Region="All",
                      Disease="all",
                     burden=as.numeric(gbd_all))
GBD <- rbind(GBD,gbd_all)
levels(GBD$Disease)[1] <- "All"



    











In [9]:



    


GBD <- GBD[order(GBD$Region,GBD$Disease),]



    











In [10]:



    


#Adding burden in Non-High-income regions
gbd_nhi <- GBD[GBD$Region=="All",]
gbd_nhi$Region <- "Non-HI"
gbd_nhi$burden <- gbd_nhi$burden - GBD$burden[GBD$Region=="High-income"]
GBD <- rbind(GBD,gbd_nhi)



    











In [11]:



    


GBD$Region <- reorder(GBD$Region,new.order=sort(levels(GBD$Region)))



    











In [12]:



    


#Different columns for each metric of burden
GBDdaly <- GBD[GBD$metr=="daly",]
GBDdaly$burden_daly <- GBDdaly$burden
GBDyll <- GBD[GBD$metr=="yll",]
GBDyll$burden_yll <- GBDyll$burden
GBDyld <- GBD[GBD$metr=="yld",]
GBDyld$burden_yld <- GBDyld$burden
GBDdeath <- GBD[GBD$metr=="death",]
GBDdeath$burden_death <- GBDdeath$burden



    











In [13]:



    


G <- merge(GBDdaly,GBDyll,by=c("Region","Disease"),all=TRUE)
G <- merge(G,GBDyld,by=c("Region","Disease"),all=TRUE)
G <- merge(G,GBDdeath,by=c("Region","Disease"),all=TRUE)



    


















    






Warning message in merge.data.frame(G, GBDdeath, by = c("Region", "Disease"), all = TRUE):
“column names ‘metr.x’, ‘burden.x’, ‘metr.y’, ‘burden.y’ are duplicated in the result”
















In [14]:



    


G <- G[,c("Region", "Disease", "burden_daly", "burden_yll", "burden_yld", "burden_death")]



    











In [15]:



    


head(G)



    


















    









RegionDiseaseburden_dalyburden_yllburden_yldburden_death


	
1All             All             2220063510.800761548796838.65609671266365.04493546250530.2665929

	
2All                                    Cardiovascular and circulatory diseases287404109.09231                        267807387.790028                       19596607.312309                        14726543.7199449                       

	
3All                         Chronic respiratory diseases112485355.22285             67399684.65133              45085687.018707             3572762.590618              

	
4All                   Cirrhosis of the liver30462721.1164         29883176.0549         579531.730192         990159.446125         

	
5All                 Congenital anomalies43254504.439        40150785.446        3103715.2332        553886.39191        

	
6All                                            Diabetes, urinary diseases and male infertility75821480.094146                                48042869.768465                                27778586.1421424                               2082326.848938                                 





















In [16]:



    


#Within regions, local proportions of burden across diseases
G$Prop_loc_burden_daly <- 100*G$burden_daly/rep(G$burden_daly[G$Disease=="All"],as.numeric(table(G$Region)))
G$Prop_loc_burden_yll <- 100*G$burden_yll/rep(G$burden_yll[G$Disease=="All"],as.numeric(table(G$Region)))
G$Prop_loc_burden_yld <- 100*G$burden_yld/rep(G$burden_yld[G$Disease=="All"],as.numeric(table(G$Region)))
G$Prop_loc_burden_death <- 100*G$burden_death/rep(G$burden_death[G$Disease=="All"],as.numeric(table(G$Region)))



    











In [17]:



    


#Within diseases, global proportion of burden across regions
G$Prop_glob_burden_daly <- 100*G$burden_daly/rep(G$burden_daly[G$Region=="All"],times=length(levels(G$Region)))
G$Prop_glob_burden_yll <- 100*G$burden_yll/rep(G$burden_yll[G$Region=="All"],times=length(levels(G$Region)))
G$Prop_glob_burden_yld <- 100*G$burden_yld/rep(G$burden_yld[G$Region=="All"],times=length(levels(G$Region)))
G$Prop_glob_burden_death <- 100*G$burden_death/rep(G$burden_death[G$Region=="All"],times=length(levels(G$Region)))



    











In [18]:



    


#Within diseases, proportion of burden across non-high-income regions
G$Prop_NHI_burden_daly <- 100*G$burden_daly/rep(G$burden_daly[G$Region=="Non-HI"],times=length(levels(G$Region)))
G$Prop_NHI_burden_yll <- 100*G$burden_yll/rep(G$burden_yll[G$Region=="Non-HI"],times=length(levels(G$Region)))
G$Prop_NHI_burden_yld <- 100*G$burden_yld/rep(G$burden_yld[G$Region=="Non-HI"],times=length(levels(G$Region)))
G$Prop_NHI_burden_death <- 100*G$burden_death/rep(G$burden_death[G$Region=="Non-HI"],times=length(levels(G$Region)))

G$Prop_NHI_burden_daly[G$Region%in%c("All","High-income")] <- NA
G$Prop_NHI_burden_yll[G$Region%in%c("All","High-income")] <- NA
G$Prop_NHI_burden_yld[G$Region%in%c("All","High-income")] <- NA
G$Prop_NHI_burden_death[G$Region%in%c("All","High-income")] <- NA



    











In [19]:



    


names(RCT_regs)
names(RCT_dis)
names(G)



    


















    







    
	
  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. '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. 



















In [20]:



    


names(RCT_regs) <- gsub("Prop","Prop_loc",names(RCT_regs))
names(RCT_dis) <- gsub("_all_","_glob_",names(RCT_dis))



    











In [21]:



    


DT <- merge(G,RCT_regs,by=c("Region","Disease"),all=TRUE)
DT <- merge(DT,RCT_dis,by=c("Region","Disease"),all=TRUE)



    











In [22]:



    


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 [23]:



    


write.table(DT,"../Data/All_data.txt")



    











In [ ]:

Content source: iatal/RCTvsBurden

Similar notebooks:

notebook.community | gallery | about