Note: Data for covid-19 last updated 4/22/2020.

Data Set-up

Key for the dfs:

df_unfiltered: USA as states, no as country

df_world: USA as country, no as states

df_filtered: Social variables (population, age, urban, density, HDI) and USA as country, no as states.

VARIABLES

dependent_variables <- names(df_unfiltered)[c(23:28,30:33,35:50)]
dependent_variables
##  [1] "total_deaths_per_million"                   
##  [2] "days_to_reached_0.1_deaths_per_million"     
##  [3] "days_to_reached_1_deaths_per_million"       
##  [4] "mean_daily_deaths_per_million"              
##  [5] "median_daily_deaths_per_million"            
##  [6] "max_daily_deaths_per_million"               
##  [7] "total_deaths_week_3_per_million"            
##  [8] "median_daily_deaths_per_million_week_3"     
##  [9] "mean_daily_deaths_per_million_week_3"       
## [10] "max_daily_deaths_per_million_week_3"        
## [11] "total_deaths_month_1_per_million"           
## [12] "median_daily_deaths_per_million_month_1"    
## [13] "mean_daily_deaths_per_million_month_1"      
## [14] "max_daily_deaths_per_million_month_1"       
## [15] "log_total_deaths_per_million"               
## [16] "log_mean_daily_deaths_per_million"          
## [17] "log_median_daily_deaths_per_million"        
## [18] "log_max_daily_deaths_per_million"           
## [19] "log_total_deaths_week_3_per_million"        
## [20] "log_median_daily_deaths_per_million_week_3" 
## [21] "log_mean_daily_deaths_per_million_week_3"   
## [22] "log_max_daily_deaths_per_million_week_3"    
## [23] "log_total_deaths_month_1_per_million"       
## [24] "log_median_daily_deaths_per_million_month_1"
## [25] "log_mean_daily_deaths_per_million_month_1"  
## [26] "log_max_daily_deaths_per_million_month_1"
independent_variables_continuous <- names(df_unfiltered)[17:18]
independent_variables_continuous
## [1] "BCG_mean_coverage"   "BCG_median_coverage"
independent_variables_categorical <- names(df_unfiltered)[12]
independent_variables_categorical
## [1] "BCG_policy"
confounding_varibales <- names(df_unfiltered)[c(7,51,6,10)]
confounding_varibales
## [1] "population_density_2018" "urban_percentage_2018"  
## [3] "HDI_2018"                "ages_65_up"
dependent_variables_label <- df_labels$New.label[df_labels$Variable %in% dependent_variables]

confounding_varibales_label <- df_labels$New.label[df_labels$Variable %in% confounding_varibales]

Models with UNFILTERED data

All data, including states of USA

Linear Models Unfiltered

model_results <- data.frame(matrix(NA, ncol = length(independent_variables_continuous), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_continuous, "_LMU")


for(i in 1:length(independent_variables_continuous)){
  for(j in 1:length(dependent_variables)){
    linear_model <- lm(df_unfiltered[,dependent_variables[j]]~df_unfiltered[,independent_variables_continuous[i]])
    model_results[j,i] <- glance(linear_model)$p.value
    }
}
BCG_mean_coverage_LMU BCG_median_coverage_LMU
total_deaths_per_million 0.0000000 0.0000000
days_to_reached_0.1_deaths_per_million 0.0149270 0.0078625
days_to_reached_1_deaths_per_million 0.0000129 0.0000100
mean_daily_deaths_per_million 0.0000000 0.0000000
median_daily_deaths_per_million 0.0000000 0.0000000
max_daily_deaths_per_million 0.0000103 0.0000082
total_deaths_week_3_per_million 0.0002023 0.0001891
median_daily_deaths_per_million_week_3 0.0000002 0.0000001
mean_daily_deaths_per_million_week_3 0.0002014 0.0001883
max_daily_deaths_per_million_week_3 0.0066965 0.0063904
total_deaths_month_1_per_million 0.0000050 0.0000046
median_daily_deaths_per_million_month_1 0.0000231 0.0000220
mean_daily_deaths_per_million_month_1 0.0000048 0.0000045
max_daily_deaths_per_million_month_1 0.0001430 0.0001337
log_total_deaths_per_million 0.0000000 0.0000000
log_mean_daily_deaths_per_million 0.0000000 0.0000000
log_median_daily_deaths_per_million 0.0000000 0.0000000
log_max_daily_deaths_per_million 0.0000000 0.0000000
log_total_deaths_week_3_per_million 0.0000000 0.0000000
log_median_daily_deaths_per_million_week_3 0.0000000 0.0000000
log_mean_daily_deaths_per_million_week_3 0.0000000 0.0000000
log_max_daily_deaths_per_million_week_3 0.0000000 0.0000000
log_total_deaths_month_1_per_million 0.0000000 0.0000000
log_median_daily_deaths_per_million_month_1 0.0000000 0.0000000
log_mean_daily_deaths_per_million_month_1 0.0000000 0.0000000
log_max_daily_deaths_per_million_month_1 0.0000000 0.0000000

Anova Models Unfiltered

model_results <- data.frame(matrix(NA, ncol = length(independent_variables_categorical), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_categorical, "_AMU")


for(i in 1:length(independent_variables_categorical)){
  for(j in 1:length(dependent_variables)){
    aov_model <- aov(df_unfiltered[,dependent_variables[j]]~df_unfiltered[,independent_variables_categorical[i]])
    model_results[j,i] <- glance(aov_model)$p.value

  }
}
BCG_policy_AMU
total_deaths_per_million 0.0000000
days_to_reached_0.1_deaths_per_million 0.0001479
days_to_reached_1_deaths_per_million 0.0000135
mean_daily_deaths_per_million 0.0000000
median_daily_deaths_per_million 0.0000000
max_daily_deaths_per_million 0.0000129
total_deaths_week_3_per_million 0.0004875
median_daily_deaths_per_million_week_3 0.0000001
mean_daily_deaths_per_million_week_3 0.0004854
max_daily_deaths_per_million_week_3 0.0172147
total_deaths_month_1_per_million 0.0000418
median_daily_deaths_per_million_month_1 0.0000708
mean_daily_deaths_per_million_month_1 0.0000405
max_daily_deaths_per_million_month_1 0.0008810
log_total_deaths_per_million 0.0000000
log_mean_daily_deaths_per_million 0.0000000
log_median_daily_deaths_per_million 0.0000000
log_max_daily_deaths_per_million 0.0000000
log_total_deaths_week_3_per_million 0.0000000
log_median_daily_deaths_per_million_week_3 0.0000000
log_mean_daily_deaths_per_million_week_3 0.0000000
log_max_daily_deaths_per_million_week_3 0.0000000
log_total_deaths_month_1_per_million 0.0000000
log_median_daily_deaths_per_million_month_1 0.0000000
log_mean_daily_deaths_per_million_month_1 0.0000000
log_max_daily_deaths_per_million_month_1 0.0000000

T-test Models Unfiltered

df_unfiltered$BCG_TF <- 1
df_unfiltered[which(df_unfiltered$BCG_policy == "never"),]$BCG_TF <- 0
df_unfiltered[which(df_unfiltered$BCG_policy == "interrupted"),]$BCG_TF <- 0

df_unfiltered$BCG_TF_2 <- 1
df_unfiltered[which(df_unfiltered$BCG_policy == "never"),]$BCG_TF_2 <- 0
df_unfiltered[which(df_unfiltered$BCG_policy == "interrupted"),]$BCG_TF_2 <- NA
independent_variables_t_test <- names(df_unfiltered)[c(ncol(df_unfiltered)-1, ncol(df_unfiltered))] #BCG_TF
independent_variables_t_test
## [1] "BCG_TF"   "BCG_TF_2"
model_results <- data.frame(matrix(NA, ncol = length(independent_variables_t_test), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_t_test, "_TMU")

for(i in 1:length(independent_variables_t_test)){
  for(j in 1:length(dependent_variables)){
    t_test_model <- t.test(df_unfiltered[,dependent_variables[j]]~df_unfiltered[,independent_variables_t_test[i]], paired = FALSE, na.action = na.pass, alternative="greater", var.equal = TRUE)
    model_results[j, i] <- glance(t_test_model)$p.value
  }
}
BCG_TF_TMU BCG_TF_2_TMU
total_deaths_per_million 0.0000000 0.0000000
days_to_reached_0.1_deaths_per_million 0.9999510 0.9999586
days_to_reached_1_deaths_per_million 0.9999589 0.9999911
mean_daily_deaths_per_million 0.0000000 0.0000000
median_daily_deaths_per_million 0.0000000 0.0000000
max_daily_deaths_per_million 0.0000027 0.0000030
total_deaths_week_3_per_million 0.0000603 0.0000776
median_daily_deaths_per_million_week_3 0.0000000 0.0000000
mean_daily_deaths_per_million_week_3 0.0000601 0.0000772
max_daily_deaths_per_million_week_3 0.0032160 0.0030934
total_deaths_month_1_per_million 0.0000038 0.0000039
median_daily_deaths_per_million_month_1 0.0000042 0.0000013
mean_daily_deaths_per_million_month_1 0.0000037 0.0000037
max_daily_deaths_per_million_month_1 0.0002156 0.0001829
log_total_deaths_per_million 0.0000000 0.0000000
log_mean_daily_deaths_per_million 0.0000000 0.0000000
log_median_daily_deaths_per_million 0.0000000 0.0000000
log_max_daily_deaths_per_million 0.0000000 0.0000000
log_total_deaths_week_3_per_million 0.0000000 0.0000000
log_median_daily_deaths_per_million_week_3 0.0000000 0.0000000
log_mean_daily_deaths_per_million_week_3 0.0000000 0.0000000
log_max_daily_deaths_per_million_week_3 0.0000000 0.0000000
log_total_deaths_month_1_per_million 0.0000000 0.0000000
log_median_daily_deaths_per_million_month_1 0.0000000 0.0000000
log_mean_daily_deaths_per_million_month_1 0.0000000 0.0000000
log_max_daily_deaths_per_million_month_1 0.0000000 0.0000000

Models without USA states (UNFILTEREDish data)

All data, excluding states of USA

Linear Models without USA states

model_results <- data.frame(matrix(NA, ncol = length(independent_variables_continuous), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_continuous, "_LMU2")


for(i in 1:length(independent_variables_continuous)){
  for(j in 1:length(dependent_variables)){
    linear_model <- lm(df_world[,dependent_variables[j]]~df_world[,independent_variables_continuous[i]])
    model_results[j,i] <- glance(linear_model)$p.value

        }
}
BCG_mean_coverage_LMU2 BCG_median_coverage_LMU2
total_deaths_per_million 0.0000000 0.0000000
days_to_reached_0.1_deaths_per_million 0.3319204 0.5242558
days_to_reached_1_deaths_per_million 0.4110823 0.3643631
mean_daily_deaths_per_million 0.0000000 0.0000000
median_daily_deaths_per_million 0.0000000 0.0000000
max_daily_deaths_per_million 0.0000104 0.0000070
total_deaths_week_3_per_million 0.0001675 0.0001593
median_daily_deaths_per_million_week_3 0.0000091 0.0000056
mean_daily_deaths_per_million_week_3 0.0001675 0.0001593
max_daily_deaths_per_million_week_3 0.0017025 0.0016524
total_deaths_month_1_per_million 0.0000016 0.0000016
median_daily_deaths_per_million_month_1 0.0002930 0.0002737
mean_daily_deaths_per_million_month_1 0.0000016 0.0000016
max_daily_deaths_per_million_month_1 0.0000747 0.0000751
log_total_deaths_per_million 0.0014301 0.0003249
log_mean_daily_deaths_per_million 0.0029308 0.0006819
log_median_daily_deaths_per_million 0.0000195 0.0000029
log_max_daily_deaths_per_million 0.0006394 0.0001056
log_total_deaths_week_3_per_million 0.0121553 0.0030733
log_median_daily_deaths_per_million_week_3 0.0067429 0.0021655
log_mean_daily_deaths_per_million_week_3 0.0121553 0.0030733
log_max_daily_deaths_per_million_week_3 0.0055685 0.0013596
log_total_deaths_month_1_per_million 0.0115453 0.0037608
log_median_daily_deaths_per_million_month_1 0.0056246 0.0021765
log_mean_daily_deaths_per_million_month_1 0.0115453 0.0037608
log_max_daily_deaths_per_million_month_1 0.0011728 0.0002773

Anova Models without USA states

model_results <- data.frame(matrix(NA, ncol = length(independent_variables_categorical), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_categorical, "_AMU2")

for(i in 1:length(independent_variables_categorical)){
  for(j in 1:length(dependent_variables)){
    aov_model <- aov(df_world[,dependent_variables[j]]~df_world[,independent_variables_categorical[i]])
    
    model_results[j,i] <- glance(aov_model)$p.value

  }
}
BCG_policy_AMU2
total_deaths_per_million 0.0000000
days_to_reached_0.1_deaths_per_million 0.3720762
days_to_reached_1_deaths_per_million 0.4838823
mean_daily_deaths_per_million 0.0000000
median_daily_deaths_per_million 0.0000000
max_daily_deaths_per_million 0.0000000
total_deaths_week_3_per_million 0.0000005
median_daily_deaths_per_million_week_3 0.0000001
mean_daily_deaths_per_million_week_3 0.0000005
max_daily_deaths_per_million_week_3 0.0000074
total_deaths_month_1_per_million 0.0000020
median_daily_deaths_per_million_month_1 0.0004344
mean_daily_deaths_per_million_month_1 0.0000020
max_daily_deaths_per_million_month_1 0.0000063
log_total_deaths_per_million 0.0000000
log_mean_daily_deaths_per_million 0.0000000
log_median_daily_deaths_per_million 0.0000000
log_max_daily_deaths_per_million 0.0000000
log_total_deaths_week_3_per_million 0.0000000
log_median_daily_deaths_per_million_week_3 0.0000100
log_mean_daily_deaths_per_million_week_3 0.0000000
log_max_daily_deaths_per_million_week_3 0.0000001
log_total_deaths_month_1_per_million 0.0000000
log_median_daily_deaths_per_million_month_1 0.0000015
log_mean_daily_deaths_per_million_month_1 0.0000000
log_max_daily_deaths_per_million_month_1 0.0000000

T-test Models without USA states

df_world$BCG_TF <- 1
df_world[which(df_world$BCG_policy == "never"),]$BCG_TF <- 0
df_world[which(df_world$BCG_policy == "interrupted"),]$BCG_TF <- 0

df_world$BCG_TF_2 <- 1
df_world[which(df_world$BCG_policy == "never"),]$BCG_TF_2 <- 0
df_world[which(df_world$BCG_policy == "interrupted"),]$BCG_TF_2 <- NA
independent_variables_t_test <- names(df_world)[c(ncol(df_world)-1, ncol(df_world))] #BCG_TF
independent_variables_t_test
## [1] "BCG_TF"   "BCG_TF_2"
model_results <- data.frame(matrix(NA, ncol = length(independent_variables_t_test), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_t_test, "_TMU2")


for(i in 1:length(independent_variables_t_test)){
  for(j in 1:length(dependent_variables)){
    t_test_model <- t.test(df_world[,dependent_variables[j]]~df_world[,independent_variables_t_test[i]], paired = FALSE, na.action = na.pass, alternative="greater", var.equal = TRUE)
    
    model_results[j,i] <- glance(t_test_model)$p.value
  }
}
BCG_TF_TMU2 BCG_TF_2_TMU2
total_deaths_per_million 0.0000000 0.0000000
days_to_reached_0.1_deaths_per_million 0.9063435 0.7439748
days_to_reached_1_deaths_per_million 0.8284547 0.6533272
mean_daily_deaths_per_million 0.0000000 0.0000000
median_daily_deaths_per_million 0.0000000 0.0000000
max_daily_deaths_per_million 0.0000029 0.0000000
total_deaths_week_3_per_million 0.0001552 0.0000001
median_daily_deaths_per_million_week_3 0.0000000 0.0000000
mean_daily_deaths_per_million_week_3 0.0001552 0.0000001
max_daily_deaths_per_million_week_3 0.0022963 0.0000021
total_deaths_month_1_per_million 0.0000124 0.0000001
median_daily_deaths_per_million_month_1 0.0000391 0.0000040
mean_daily_deaths_per_million_month_1 0.0000124 0.0000001
max_daily_deaths_per_million_month_1 0.0006775 0.0000026
log_total_deaths_per_million 0.0000000 0.0000000
log_mean_daily_deaths_per_million 0.0000000 0.0000003
log_median_daily_deaths_per_million 0.0000000 0.0000105
log_max_daily_deaths_per_million 0.0000000 0.0000006
log_total_deaths_week_3_per_million 0.0000000 0.0000708
log_median_daily_deaths_per_million_week_3 0.0000008 0.0095828
log_mean_daily_deaths_per_million_week_3 0.0000000 0.0000708
log_max_daily_deaths_per_million_week_3 0.0000001 0.0000937
log_total_deaths_month_1_per_million 0.0000000 0.0000838
log_median_daily_deaths_per_million_month_1 0.0000001 0.0105758
log_mean_daily_deaths_per_million_month_1 0.0000000 0.0000838
log_max_daily_deaths_per_million_month_1 0.0000000 0.0000214

Models with FILTERED data

All data, filtered as follows

df_filtered <- subsoet(df_unfiltered, 
                covid19_cumulative_deaths > 0
                  & population_million > 1
                  & ages_65_up > 15 
                  & urban_percentage_2018 > 60 
                  & population_density_2018 < 300 
                  & HDI_2018 > 0.7
                  & ISO3 != "USA_state")

Linear Models Filtered

model_results <- data.frame(matrix(NA, ncol = length(independent_variables_continuous), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_continuous, "_LMF")


for(i in 1:length(independent_variables_continuous)){
  for(j in 1:length(dependent_variables)){
    linear_model <- lm(df_filtered[,dependent_variables[j]]~df_filtered[,independent_variables_continuous[i]])
    model_results[j,i] <- glance(linear_model)$p.value
    }
}
BCG_mean_coverage_LMF BCG_median_coverage_LMF
total_deaths_per_million 0.0200810 0.0130794
days_to_reached_0.1_deaths_per_million 0.3098944 0.2972757
days_to_reached_1_deaths_per_million 0.1846061 0.1933569
mean_daily_deaths_per_million 0.0167913 0.0098991
median_daily_deaths_per_million 0.0235405 0.0213611
max_daily_deaths_per_million 0.0234976 0.0152219
total_deaths_week_3_per_million 0.2688315 0.2886136
median_daily_deaths_per_million_week_3 0.2758677 0.2921062
mean_daily_deaths_per_million_week_3 0.2688315 0.2886136
max_daily_deaths_per_million_week_3 0.2381408 0.2603534
total_deaths_month_1_per_million 0.0606641 0.0598189
median_daily_deaths_per_million_month_1 0.4176019 0.4500354
mean_daily_deaths_per_million_month_1 0.0606641 0.0598189
max_daily_deaths_per_million_month_1 0.0176593 0.0119085
log_total_deaths_per_million 0.0688078 0.0468943
log_mean_daily_deaths_per_million 0.1123067 0.0793990
log_median_daily_deaths_per_million 0.0872487 0.0706727
log_max_daily_deaths_per_million 0.0982779 0.0711571
log_total_deaths_week_3_per_million 0.7169883 0.6815864
log_median_daily_deaths_per_million_week_3 0.3915494 0.3858247
log_mean_daily_deaths_per_million_week_3 0.7169883 0.6815864
log_max_daily_deaths_per_million_week_3 0.6176365 0.5975727
log_total_deaths_month_1_per_million 0.2300306 0.1978414
log_median_daily_deaths_per_million_month_1 0.6460633 0.6170304
log_mean_daily_deaths_per_million_month_1 0.2300306 0.1978414
log_max_daily_deaths_per_million_month_1 0.0707810 0.0494013

Anova Models Filtered

model_results <- data.frame(matrix(NA, ncol = length(independent_variables_categorical), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_categorical, "_AMF")

for(i in 1:length(independent_variables_categorical)){
  for(j in 1:length(dependent_variables)){
    aov_model <- aov(df_filtered[,dependent_variables[j]]~df_filtered[,independent_variables_categorical[i]])
    
    model_results[j,i] <- glance(aov_model)$p.value
  }
}
BCG_policy_AMF
total_deaths_per_million 0.0182780
days_to_reached_0.1_deaths_per_million 0.5066961
days_to_reached_1_deaths_per_million 0.7897329
mean_daily_deaths_per_million 0.0244175
median_daily_deaths_per_million 0.0704910
max_daily_deaths_per_million 0.0098238
total_deaths_week_3_per_million 0.2682578
median_daily_deaths_per_million_week_3 0.1632630
mean_daily_deaths_per_million_week_3 0.2682578
max_daily_deaths_per_million_week_3 0.4246833
total_deaths_month_1_per_million 0.2091805
median_daily_deaths_per_million_month_1 0.1337124
mean_daily_deaths_per_million_month_1 0.2091805
max_daily_deaths_per_million_month_1 0.1755753
log_total_deaths_per_million 0.0074763
log_mean_daily_deaths_per_million 0.0140145
log_median_daily_deaths_per_million 0.0175344
log_max_daily_deaths_per_million 0.0108388
log_total_deaths_week_3_per_million 0.5383513
log_median_daily_deaths_per_million_week_3 0.2756231
log_mean_daily_deaths_per_million_week_3 0.5383513
log_max_daily_deaths_per_million_week_3 0.5994709
log_total_deaths_month_1_per_million 0.1763712
log_median_daily_deaths_per_million_month_1 0.0199775
log_mean_daily_deaths_per_million_month_1 0.1763712
log_max_daily_deaths_per_million_month_1 0.1035297

T-test Filtered

df_filtered$BCG_TF <- 1
df_filtered[which(df_filtered$BCG_policy == "never"),]$BCG_TF <- 0
df_filtered[which(df_filtered$BCG_policy == "interrupted"),]$BCG_TF <- 0

df_filtered$BCG_TF_2 <- 1
df_filtered[which(df_filtered$BCG_policy == "never"),]$BCG_TF_2 <- 0
df_filtered[which(df_filtered$BCG_policy == "interrupted"),]$BCG_TF_2 <- NA
independent_variables_t_test <- names(df_filtered)[c(ncol(df_filtered)-1, ncol(df_filtered))] #BCG_TF
independent_variables_t_test
## [1] "BCG_TF"   "BCG_TF_2"
model_results <- data.frame(matrix(NA, ncol = length(independent_variables_t_test), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(independent_variables_t_test, "_TMF")

for(i in 1:length(independent_variables_t_test)){
  for(j in 1:length(dependent_variables)){
    t_test_model <- t.test(df_filtered[,dependent_variables[j]]~df_filtered[,independent_variables_t_test[i]], paired = FALSE, na.action = na.pass, alternative="greater", var.equal = TRUE)
    
    model_results[j, i] <- glance(t_test_model)$p.value
  }
}
BCG_TF_TMF BCG_TF_2_TMF
total_deaths_per_million 0.0086223 0.0006706
days_to_reached_0.1_deaths_per_million 0.1863422 0.1108277
days_to_reached_1_deaths_per_million 0.3963441 0.2462513
mean_daily_deaths_per_million 0.0064404 0.0005282
median_daily_deaths_per_million 0.0249060 0.0097696
max_daily_deaths_per_million 0.0025747 0.0000008
total_deaths_week_3_per_million 0.0619118 0.1587155
median_daily_deaths_per_million_week_3 0.0337576 0.1170317
mean_daily_deaths_per_million_week_3 0.0619118 0.1587155
max_daily_deaths_per_million_week_3 0.1029118 0.2022459
total_deaths_month_1_per_million 0.0367781 0.0391609
median_daily_deaths_per_million_month_1 0.0384985 0.1567297
mean_daily_deaths_per_million_month_1 0.0367781 0.0391609
max_daily_deaths_per_million_month_1 0.0326939 0.0346584
log_total_deaths_per_million 0.0020591 0.0003713
log_mean_daily_deaths_per_million 0.0031508 0.0013188
log_median_daily_deaths_per_million 0.0027675 0.0065103
log_max_daily_deaths_per_million 0.0023940 0.0026049
log_total_deaths_week_3_per_million 0.1425635 0.3462628
log_median_daily_deaths_per_million_week_3 0.0758333 0.3812898
log_mean_daily_deaths_per_million_week_3 0.1425635 0.3462628
log_max_daily_deaths_per_million_week_3 0.1593286 0.3371390
log_total_deaths_month_1_per_million 0.0294479 0.0574266
log_median_daily_deaths_per_million_month_1 0.0152696 0.4500793
log_mean_daily_deaths_per_million_month_1 0.0294479 0.0574266
log_max_daily_deaths_per_million_month_1 0.0166482 0.0385099

Models with Confounding varibales

confounding_varibales <- names(df_unfiltered)[c(7,51,6,10)]
confounding_varibales
## [1] "population_density_2018" "urban_percentage_2018"  
## [3] "HDI_2018"                "ages_65_up"

Linear Models (with UNFILTERED data, including USA states)

df_confounding_models <- data.frame()

model_results <- data.frame(matrix(NA, ncol = length(confounding_varibales), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(confounding_varibales, "_LMU")


for(i in 1:length(confounding_varibales)){
  for(j in 1:length(dependent_variables)){
    lm_model <- lm(df_unfiltered[,dependent_variables[j]]~df_unfiltered[,confounding_varibales[i]], df_unfiltered)
    model_results[j,i] <- glance(lm_model)$p.value
  }
}
population_density_2018_LMU urban_percentage_2018_LMU HDI_2018_LMU ages_65_up_LMU
total_deaths_per_million 0.5881310 0.0000006 0.0000001 0.0000000
days_to_reached_0.1_deaths_per_million 0.6254575 0.0001316 0.0000017 0.0029274
days_to_reached_1_deaths_per_million 0.3043795 0.9947058 0.0003671 0.0360163
mean_daily_deaths_per_million 0.3852901 0.0000036 0.0000007 0.0000022
median_daily_deaths_per_million 0.9394696 0.0000422 0.0000092 0.0000030
max_daily_deaths_per_million 0.0152616 0.0001172 0.0000175 0.0005292
total_deaths_week_3_per_million 0.1418436 0.0004850 0.0000162 0.0000638
median_daily_deaths_per_million_week_3 0.9286466 0.0003179 0.0000172 0.0001131
mean_daily_deaths_per_million_week_3 0.1418689 0.0004843 0.0000161 0.0000637
max_daily_deaths_per_million_week_3 0.0009550 0.0016727 0.0000153 0.0000221
total_deaths_month_1_per_million 0.5960968 0.0002247 0.0002153 0.0027748
median_daily_deaths_per_million_month_1 0.7350231 0.0035340 0.0021708 0.0100217
mean_daily_deaths_per_million_month_1 0.5967231 0.0002231 0.0002112 0.0027711
max_daily_deaths_per_million_month_1 0.5264177 0.0007549 0.0000487 0.0034942
log_total_deaths_per_million 0.3112114 0.0000000 0.0000000 0.0000000
log_mean_daily_deaths_per_million 0.2487730 0.0000000 0.0000000 0.0000000
log_median_daily_deaths_per_million 0.4021833 0.0000075 0.0000000 0.0000000
log_max_daily_deaths_per_million 0.0733813 0.0000000 0.0000000 0.0000000
log_total_deaths_week_3_per_million 0.1764563 0.0000000 0.0000000 0.0000000
log_median_daily_deaths_per_million_week_3 0.3126177 0.0000008 0.0000000 0.0000000
log_mean_daily_deaths_per_million_week_3 0.1770712 0.0000000 0.0000000 0.0000000
log_max_daily_deaths_per_million_week_3 0.0425772 0.0000000 0.0000000 0.0000000
log_total_deaths_month_1_per_million 0.7880514 0.0000000 0.0000000 0.0000000
log_median_daily_deaths_per_million_month_1 0.3477729 0.0000033 0.0000000 0.0000000
log_mean_daily_deaths_per_million_month_1 0.7800000 0.0000000 0.0000000 0.0000000
log_max_daily_deaths_per_million_month_1 0.8309074 0.0000000 0.0000000 0.0000000

Linear Models (with UNFILTEREDish data, excluding USA states)

model_results <- data.frame(matrix(NA, ncol = length(confounding_varibales), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(confounding_varibales, "_LMU2")


for(i in 1:length(confounding_varibales)){
  for(j in 1:length(dependent_variables)){
    lm_model <- lm(df_world[,dependent_variables[j]]~df_world[,confounding_varibales[i]], df_world)
    model_results[j,i] <- glance(lm_model)$p.value
  }
}
population_density_2018_LMU2 urban_percentage_2018_LMU2 HDI_2018_LMU2 ages_65_up_LMU2
total_deaths_per_million 0.6552885 0.0000692 0.0000007 0.0000000
days_to_reached_0.1_deaths_per_million 0.5318725 0.0018491 0.0053167 0.1350306
days_to_reached_1_deaths_per_million 0.2634260 0.9671671 0.2247596 0.4427651
mean_daily_deaths_per_million 0.4043781 0.0003804 0.0000113 0.0000000
median_daily_deaths_per_million 0.7254814 0.0019110 0.0001183 0.0000001
max_daily_deaths_per_million 0.0057970 0.0036729 0.0000026 0.0000000
total_deaths_week_3_per_million 0.2081375 0.0084138 0.0014368 0.0000001
median_daily_deaths_per_million_week_3 0.6667958 0.0096835 0.0007402 0.0000000
mean_daily_deaths_per_million_week_3 0.2081375 0.0084138 0.0014368 0.0000001
max_daily_deaths_per_million_week_3 0.0043473 0.0096377 0.0037324 0.0000015
total_deaths_month_1_per_million 0.9336853 0.0033483 0.0063512 0.0000583
median_daily_deaths_per_million_month_1 0.7665812 0.0288492 0.0375058 0.0000422
mean_daily_deaths_per_million_month_1 0.9336853 0.0033483 0.0063512 0.0000583
max_daily_deaths_per_million_month_1 0.8394024 0.0102175 0.0030207 0.0002915
log_total_deaths_per_million 0.2369341 0.0000000 0.0000000 0.0000000
log_mean_daily_deaths_per_million 0.1737385 0.0000000 0.0000000 0.0000000
log_median_daily_deaths_per_million 0.1841849 0.0002954 0.0000000 0.0000016
log_max_daily_deaths_per_million 0.0425377 0.0000000 0.0000000 0.0000000
log_total_deaths_week_3_per_million 0.1211820 0.0000000 0.0000000 0.0000000
log_median_daily_deaths_per_million_week_3 0.2561719 0.0000279 0.0000000 0.0000000
log_mean_daily_deaths_per_million_week_3 0.1211820 0.0000000 0.0000000 0.0000000
log_max_daily_deaths_per_million_week_3 0.0232416 0.0000000 0.0000000 0.0000000
log_total_deaths_month_1_per_million 0.5362953 0.0000013 0.0000000 0.0000000
log_median_daily_deaths_per_million_month_1 0.2844749 0.0000923 0.0000000 0.0000002
log_mean_daily_deaths_per_million_month_1 0.5362953 0.0000013 0.0000000 0.0000000
log_max_daily_deaths_per_million_month_1 0.5433118 0.0000014 0.0000000 0.0000000

Linear Models (with FILTERED data)

model_results <- data.frame(matrix(NA, ncol = length(confounding_varibales), nrow = length(dependent_variables)))
rownames(model_results) <- dependent_variables
colnames(model_results) <- paste0(confounding_varibales, "_LMF")


for(i in 1:length(confounding_varibales)){
  for(j in 1:length(dependent_variables)){
    lm_model <- lm(df_filtered[,dependent_variables[j]]~df_filtered[,confounding_varibales[i]], df_filtered)
    model_results[j,i] <- glance(lm_model)$p.value
  }
}
population_density_2018_LMF urban_percentage_2018_LMF HDI_2018_LMF ages_65_up_LMF
total_deaths_per_million 0.0227421 0.5085246 0.3891885 0.3483063
days_to_reached_0.1_deaths_per_million 0.6886736 0.5336460 0.6137551 0.1430033
days_to_reached_1_deaths_per_million 0.7635746 0.5111901 0.5103232 0.0102821
mean_daily_deaths_per_million 0.0249032 0.4359951 0.2954470 0.3663080
median_daily_deaths_per_million 0.0349675 0.9181378 0.5207464 0.2067582
max_daily_deaths_per_million 0.1693494 0.1260287 0.1220730 0.5081068
total_deaths_week_3_per_million 0.4027611 0.7819172 0.4732807 0.2513538
median_daily_deaths_per_million_week_3 0.3046428 0.6456844 0.2745237 0.1615026
mean_daily_deaths_per_million_week_3 0.4027611 0.7819172 0.4732807 0.2513538
max_daily_deaths_per_million_week_3 0.5230424 0.8793002 0.5697286 0.2959691
total_deaths_month_1_per_million 0.2678673 0.6345460 0.4253593 0.3957908
median_daily_deaths_per_million_month_1 0.3180395 0.8671323 0.4643671 0.3406577
mean_daily_deaths_per_million_month_1 0.2678673 0.6345460 0.4253593 0.3957908
max_daily_deaths_per_million_month_1 0.1105504 0.5639718 0.3165927 0.4168637
log_total_deaths_per_million 0.0114176 0.4352474 0.0688101 0.1655063
log_mean_daily_deaths_per_million 0.0177705 0.4992911 0.0641028 0.1208885
log_median_daily_deaths_per_million 0.0159156 0.6372384 0.0836058 0.1017734
log_max_daily_deaths_per_million 0.1095580 0.2623762 0.0230553 0.1542857
log_total_deaths_week_3_per_million 0.1935794 0.9654785 0.2088619 0.0314163
log_median_daily_deaths_per_million_week_3 0.4769506 0.4230607 0.1162568 0.0582856
log_mean_daily_deaths_per_million_week_3 0.1935794 0.9654785 0.2088619 0.0314163
log_max_daily_deaths_per_million_week_3 0.3198832 0.9735288 0.2055565 0.0370244
log_total_deaths_month_1_per_million 0.0830269 0.7359661 0.1199333 0.0818872
log_median_daily_deaths_per_million_month_1 0.2237801 0.3638491 0.0850376 0.0280949
log_mean_daily_deaths_per_million_month_1 0.0830269 0.7359661 0.1199333 0.0818872
log_max_daily_deaths_per_million_month_1 0.0530406 0.6210655 0.0724144 0.1610295

Combination Tables (p-value matrix)

Linear Models

Variable BCG_mean_coverage_LMU BCG_median_coverage_LMU BCG_mean_coverage_LMU2 BCG_median_coverage_LMU2 BCG_mean_coverage_LMF BCG_median_coverage_LMF
total_deaths_per_million Deaths/1 M (Total) 0.0000000 0.0000000 0.0000000 0.0000000 0.0200810 0.0130794
days_to_reached_0.1_deaths_per_million Days to 0.1 Death/1 M 0.0149270 0.0078625 0.3319204 0.5242558 0.3098944 0.2972757
days_to_reached_1_deaths_per_million Days to 1 Death/1 M 0.0000129 0.0000100 0.4110823 0.3643631 0.1846061 0.1933569
mean_daily_deaths_per_million Deaths/day/1 M (mean) 0.0000000 0.0000000 0.0000000 0.0000000 0.0167913 0.0098991
median_daily_deaths_per_million Deaths/day/1 M (median) 0.0000000 0.0000000 0.0000000 0.0000000 0.0235405 0.0213611
max_daily_deaths_per_million Deaths/day/1 M (max) 0.0000103 0.0000082 0.0000104 0.0000070 0.0234976 0.0152219
total_deaths_week_3_per_million Deaths/1 M/week 3 (Total) 0.0002023 0.0001891 0.0001675 0.0001593 0.2688315 0.2886136
median_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (median) 0.0000002 0.0000001 0.0000091 0.0000056 0.2758677 0.2921062
mean_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (mean) 0.0002014 0.0001883 0.0001675 0.0001593 0.2688315 0.2886136
max_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (max) 0.0066965 0.0063904 0.0017025 0.0016524 0.2381408 0.2603534
total_deaths_month_1_per_million Deaths/1 M/month 1 (Total) 0.0000050 0.0000046 0.0000016 0.0000016 0.0606641 0.0598189
median_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (median) 0.0000231 0.0000220 0.0002930 0.0002737 0.4176019 0.4500354
mean_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (mean) 0.0000048 0.0000045 0.0000016 0.0000016 0.0606641 0.0598189
max_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (max) 0.0001430 0.0001337 0.0000747 0.0000751 0.0176593 0.0119085
log_total_deaths_per_million Deaths/1 M (Total) 0.0000000 0.0000000 0.0014301 0.0003249 0.0688078 0.0468943
log_mean_daily_deaths_per_million Deaths/day/1 M log(mean) 0.0000000 0.0000000 0.0029308 0.0006819 0.1123067 0.0793990
log_median_daily_deaths_per_million Deaths/day/1 M log(median) 0.0000000 0.0000000 0.0000195 0.0000029 0.0872487 0.0706727
log_max_daily_deaths_per_million Deaths/day/1 M loglog(max) 0.0000000 0.0000000 0.0006394 0.0001056 0.0982779 0.0711571
log_total_deaths_week_3_per_million Deaths/1 M/week 3 log(Total) 0.0000000 0.0000000 0.0121553 0.0030733 0.7169883 0.6815864
log_median_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(median) 0.0000000 0.0000000 0.0067429 0.0021655 0.3915494 0.3858247
log_mean_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(mean) 0.0000000 0.0000000 0.0121553 0.0030733 0.7169883 0.6815864
log_max_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(max) 0.0000000 0.0000000 0.0055685 0.0013596 0.6176365 0.5975727
log_total_deaths_month_1_per_million Deaths/1 M/month 1 log(Total) 0.0000000 0.0000000 0.0115453 0.0037608 0.2300306 0.1978414
log_median_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(median) 0.0000000 0.0000000 0.0056246 0.0021765 0.6460633 0.6170304
log_mean_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(mean) 0.0000000 0.0000000 0.0115453 0.0037608 0.2300306 0.1978414
log_max_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(max) 0.0000000 0.0000000 0.0011728 0.0002773 0.0707810 0.0494013

Anova Models

Variable BCG_policy_AMU BCG_policy_AMU2 BCG_policy_AMF
total_deaths_per_million Deaths/1 M (Total) 0.0000000 0.0000000 0.0182780
days_to_reached_0.1_deaths_per_million Days to 0.1 Death/1 M 0.0001479 0.3720762 0.5066961
days_to_reached_1_deaths_per_million Days to 1 Death/1 M 0.0000135 0.4838823 0.7897329
mean_daily_deaths_per_million Deaths/day/1 M (mean) 0.0000000 0.0000000 0.0244175
median_daily_deaths_per_million Deaths/day/1 M (median) 0.0000000 0.0000000 0.0704910
max_daily_deaths_per_million Deaths/day/1 M (max) 0.0000129 0.0000000 0.0098238
total_deaths_week_3_per_million Deaths/1 M/week 3 (Total) 0.0004875 0.0000005 0.2682578
median_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (median) 0.0000001 0.0000001 0.1632630
mean_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (mean) 0.0004854 0.0000005 0.2682578
max_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (max) 0.0172147 0.0000074 0.4246833
total_deaths_month_1_per_million Deaths/1 M/month 1 (Total) 0.0000418 0.0000020 0.2091805
median_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (median) 0.0000708 0.0004344 0.1337124
mean_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (mean) 0.0000405 0.0000020 0.2091805
max_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (max) 0.0008810 0.0000063 0.1755753
log_total_deaths_per_million Deaths/1 M (Total) 0.0000000 0.0000000 0.0074763
log_mean_daily_deaths_per_million Deaths/day/1 M log(mean) 0.0000000 0.0000000 0.0140145
log_median_daily_deaths_per_million Deaths/day/1 M log(median) 0.0000000 0.0000000 0.0175344
log_max_daily_deaths_per_million Deaths/day/1 M loglog(max) 0.0000000 0.0000000 0.0108388
log_total_deaths_week_3_per_million Deaths/1 M/week 3 log(Total) 0.0000000 0.0000000 0.5383513
log_median_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(median) 0.0000000 0.0000100 0.2756231
log_mean_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(mean) 0.0000000 0.0000000 0.5383513
log_max_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(max) 0.0000000 0.0000001 0.5994709
log_total_deaths_month_1_per_million Deaths/1 M/month 1 log(Total) 0.0000000 0.0000000 0.1763712
log_median_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(median) 0.0000000 0.0000015 0.0199775
log_mean_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(mean) 0.0000000 0.0000000 0.1763712
log_max_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(max) 0.0000000 0.0000000 0.1035297

T-test Models

Variable BCG_TF_TMU BCG_TF_2_TMU BCG_TF_TMU2 BCG_TF_2_TMU2 BCG_TF_TMF BCG_TF_2_TMF
total_deaths_per_million Deaths/1 M (Total) 0.0000000 0.0000000 0.0000000 0.0000000 0.0086223 0.0006706
days_to_reached_0.1_deaths_per_million Days to 0.1 Death/1 M 0.9999510 0.9999586 0.9063435 0.7439748 0.1863422 0.1108277
days_to_reached_1_deaths_per_million Days to 1 Death/1 M 0.9999589 0.9999911 0.8284547 0.6533272 0.3963441 0.2462513
mean_daily_deaths_per_million Deaths/day/1 M (mean) 0.0000000 0.0000000 0.0000000 0.0000000 0.0064404 0.0005282
median_daily_deaths_per_million Deaths/day/1 M (median) 0.0000000 0.0000000 0.0000000 0.0000000 0.0249060 0.0097696
max_daily_deaths_per_million Deaths/day/1 M (max) 0.0000027 0.0000030 0.0000029 0.0000000 0.0025747 0.0000008
total_deaths_week_3_per_million Deaths/1 M/week 3 (Total) 0.0000603 0.0000776 0.0001552 0.0000001 0.0619118 0.1587155
median_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (median) 0.0000000 0.0000000 0.0000000 0.0000000 0.0337576 0.1170317
mean_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (mean) 0.0000601 0.0000772 0.0001552 0.0000001 0.0619118 0.1587155
max_daily_deaths_per_million_week_3 Deaths/1 M/week 3 (max) 0.0032160 0.0030934 0.0022963 0.0000021 0.1029118 0.2022459
total_deaths_month_1_per_million Deaths/1 M/month 1 (Total) 0.0000038 0.0000039 0.0000124 0.0000001 0.0367781 0.0391609
median_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (median) 0.0000042 0.0000013 0.0000391 0.0000040 0.0384985 0.1567297
mean_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (mean) 0.0000037 0.0000037 0.0000124 0.0000001 0.0367781 0.0391609
max_daily_deaths_per_million_month_1 Deaths/1 M/month 1 (max) 0.0002156 0.0001829 0.0006775 0.0000026 0.0326939 0.0346584
log_total_deaths_per_million Deaths/1 M (Total) 0.0000000 0.0000000 0.0000000 0.0000000 0.0020591 0.0003713
log_mean_daily_deaths_per_million Deaths/day/1 M log(mean) 0.0000000 0.0000000 0.0000000 0.0000003 0.0031508 0.0013188
log_median_daily_deaths_per_million Deaths/day/1 M log(median) 0.0000000 0.0000000 0.0000000 0.0000105 0.0027675 0.0065103
log_max_daily_deaths_per_million Deaths/day/1 M loglog(max) 0.0000000 0.0000000 0.0000000 0.0000006 0.0023940 0.0026049
log_total_deaths_week_3_per_million Deaths/1 M/week 3 log(Total) 0.0000000 0.0000000 0.0000000 0.0000708 0.1425635 0.3462628
log_median_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(median) 0.0000000 0.0000000 0.0000008 0.0095828 0.0758333 0.3812898
log_mean_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(mean) 0.0000000 0.0000000 0.0000000 0.0000708 0.1425635 0.3462628
log_max_daily_deaths_per_million_week_3 Deaths/1 M/week 3 log(max) 0.0000000 0.0000000 0.0000001 0.0000937 0.1593286 0.3371390
log_total_deaths_month_1_per_million Deaths/1 M/month 1 log(Total) 0.0000000 0.0000000 0.0000000 0.0000838 0.0294479 0.0574266
log_median_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(median) 0.0000000 0.0000000 0.0000001 0.0105758 0.0152696 0.4500793
log_mean_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(mean) 0.0000000 0.0000000 0.0000000 0.0000838 0.0294479 0.0574266
log_max_daily_deaths_per_million_month_1 Deaths/1 M/month 1 log(max) 0.0000000 0.0000000 0.0000000 0.0000214 0.0166482 0.0385099