Creating Database only with summer months

villes_s<-list()

for(i in 1:length(villes)) {
  villes_s[[i]]<-villes[[i]][villes[[i]]$saison=="summer",]
  villes_s[[i]]$temps <- ave(villes_s[[i]]$time,villes_s[[i]]$annee,  FUN = seq_along)
  }

names(villes_s)<-cities

table of N obs per months to check to have only summer month

for (i in 1:length(villes)){
print(table(villes_s[[i]]$mois))
}
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 540 558 558 540   0   0   0

Creating Heat Wave variable

trshld95<-c()

for (i in 1:length(villes_s)){
  trshld95[i]<-quantile(villes_s[[i]]$tempmax, probs=c(0.975),na.rm=TRUE)
  villes_s[[i]]$heat_wave<-0
  villes_s[[i]]$heat_wave[which(villes_s[[i]]$tempmax > trshld95[i])]<- 1 
}

 

Number of Heat Wave for each city:

Heat wave=0 Heat wave=1
bm 2143 53
bordeaux 2143 53
clermont 2142 54
grenoble 2141 55
lehavre 2142 54
lille 2143 53
lyon 2142 54
marseille 2142 54
montpellier 2145 51
nancy 2142 54
nantes 2141 55
nice 2142 54
paris 2144 52
rennes 2142 54
rouen 2143 53
strasbourg 2143 53
toulouse 2141 55 
# creation of no2 variable of today and previous day
#function filter {stats}
for (i in 1:length(villes_s)){
villes_s[[i]]<-transform(villes_s[[i]], no2moy = as.vector(filter(no2,sides = 1, filter = rep(1, 2))/2), no2Lag1=Lag(no2,1),no2Lag2=Lag(no2,2))
}

Analysis

BOOTSTRAP METHOD

Code:

## create dataframe without missing values
for (i in 1:length(villes_s)){
  villes_s[[i]]<-na.omit(villes_s[[i]][, c("respi","o3","Jours","Vacances","hol","heat_wave","no2moy")])
  #villes_s[[i]]<-villes_s[[i]][villes_s[[i]]$Dates>="2007-06-01",]
  villes_s[[i]]$time<-1:nrow(villes_s[[i]])
} 
   

    
  # start bootstrap
     
  nreps=1000 #number of bootstrap reps
  results<-matrix(NA,nrow = 17,ncol=10)
  colnames(results)<-c("city","NDE","NDE_2.5","NDE_97.5","NIE","NIE_2.5","NIE_97.5","PMM","PMM_2.5","PMM_97.5")
  
for (i in (1:length(villes_s))){
  boot.nde<- rep(NA,nreps)
  boot.nie <- rep(NA,nreps)
  boot.pmm <- rep(NA,nreps)
  for (rep in 1:nreps)
      
    {
      dat_resample <- sample(1:nrow(villes_s[[i]]),nrow(villes_s[[i]]),replace=T)
      dat_boot <- villes_s[[i]][dat_resample,]
        
      # outcome model - Poisson Regression: Mortality, HW and O3
      m.outcome<-glm(respi~heat_wave+o3+ns(time,df=round(8*length(time)/365.25))+Jours+Vacances+hol,data=dat_boot,family=quasipoisson)
      
      # mediator model - multinomial linear regression
      m.mediator<-lm(o3~heat_wave+no2moy+ns(time,df=round(8*length(time)/365.25))+Jours+Vacances+hol,data=dat_boot) 
      
      # save coefficients
      theta1<-coef(m.outcome)[2]
      
      theta2<-coef(m.outcome)[3]
     
      beta1<-coef(m.mediator)[2]
      
      # estimate CDE and NIE
      
      boot.nde[rep] <- exp(theta1) #NDE
      
      boot.nie[rep] <-exp(theta2*beta1) #NIE 
      
      boot.pmm[rep] <-boot.nde[rep]*(boot.nie[rep]-1)/(boot.nde[rep]*boot.nie[rep]-1) #NIE 
                   
      
    } #end bootstrap

  results[i,1]<-cities[[i]]
  results[i,2]<-median(boot.nde, na.rm=T)
  results[i,3]<-quantile(boot.nde, 0.025, na.rm=T)
  results[i,4]<-quantile(boot.nde, 0.975, na.rm=T)
  results[i,5]<-median(boot.nie, na.rm=T)
  results[i,6]<-quantile(boot.nie, 0.025, na.rm=T)
  results[i,7]<-quantile(boot.nie, 0.975, na.rm=T)
  results[i,8]<-median(boot.pmm, na.rm=T)
  results[i,9]<-quantile(boot.pmm, 0.025, na.rm=T)
  results[i,10]<-quantile(boot.pmm, 0.975, na.rm=T)
  
     }
  
  # output CDE and NIE and confidence intervals

Results:

res<-data.frame(results)

for (i in (2:10)){
res[,i]<-as.numeric(as.character(res[,i]))
}

res[,2:10]<-round(res[,2:10],digits = 4)

kable(res)%>%kable_styling()%>%
  column_spec(1, bold = T, border_right = T)%>%
  column_spec(4, border_right = T)%>%
  column_spec(7, border_right = T)
city NDE NDE_2.5 NDE_97.5 NIE NIE_2.5 NIE_97.5 PMM PMM_2.5 PMM_97.5
bm 0.9919 0.7786 1.2040 1.0599 0.9930 1.1256 0.3931 -6.8935 6.5696
bordeaux 1.0906 0.9907 1.2024 1.0238 0.9822 1.0602 0.2165 -0.2060 1.0342
clermont 1.1177 0.9140 1.3362 1.0021 0.9515 1.0520 0.0135 -1.4060 2.1329
grenoble 1.0960 0.9396 1.2568 1.0234 0.9823 1.0627 0.1944 -1.4543 1.9870
lehavre 1.0680 0.8924 1.2634 1.0004 0.9494 1.0493 -0.0217 -2.0391 2.5687
lille 1.0553 0.9576 1.1529 1.0097 0.9774 1.0414 0.1258 -1.8731 1.7490
lyon 0.9873 0.8820 1.0890 0.9878 0.9609 1.0114 0.1023 -4.0928 4.4940
marseille 0.9732 0.8834 1.0643 1.0045 0.9993 1.0141 -0.0529 -1.9860 2.0289
montpellier 1.0963 0.9303 1.2674 1.0002 0.9971 1.0046 0.0014 -0.1289 0.1017
nancy 1.0668 0.9189 1.2303 1.0139 0.9813 1.0515 0.1355 -1.9882 2.3868
nantes 1.0167 0.9131 1.1334 1.0229 0.9830 1.0649 0.2421 -6.0829 6.3498
nice 1.0778 0.9508 1.2061 0.9980 0.9833 1.0077 -0.0174 -0.4912 0.6649
paris 1.0307 0.9666 1.0943 1.0047 0.9875 1.0213 0.0990 -1.8650 2.3706
rennes 1.0238 0.8086 1.2507 1.0342 0.9795 1.1017 0.1915 -4.1711 4.8056
rouen 0.8738 0.7602 0.9777 0.9866 0.9401 1.0366 0.0793 -0.3002 0.6272
strasbourg 1.1211 0.9759 1.2735 1.0432 1.0062 1.0832 0.2721 0.0016 1.1900
toulouse 1.2249 1.1089 1.3380 0.9991 0.9740 1.0234 -0.0051 -0.1707 0.1476

Forestplot

Natural Direct Effect

Natural Indirect Effect