Heat wave and Ozone, Mediation analysis on mortality (Meteo France definition)

Temperature Data Management

Creating Database only with summer months

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

## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
##   0   0   0   0   0 480 496 496 480   0   0   0

Creation of Heat Wave Variable

## First condition for heat wave
for (i in 1:length(villes_s)){
  villes_s[[i]]$heat_wave1<-NA
  for (r in 3:nrow(villes_s[[i]])){
  villes_s[[i]]$heat_wave1[r]<-  if (villes_s[[i]]$tempmoy[r] >= trshld975[i] & villes_s[[i]]$tempmoy[r-1] >= trshld975[i] & villes_s[[i]]$tempmoy[r-2] >= trshld975[i] ) 1 else 0
  }}

## Second condition for heat wave
for (i in 1:length(villes_s)){
  for (r in 3:nrow(villes_s[[i]])){
  villes_s[[i]]$heat_wave[r]<-  if (villes_s[[i]]$heat_wave1[r] == 1 & (villes_s[[i]]$tempmoy[r] >= trshld995[i] | villes_s[[i]]$tempmoy[r-1] >= trshld995[i] | villes_s[[i]]$tempmoy[r-2] >= trshld995[i])) 1 else 0
  }}

Number of Heat Wave for each city:

	Heat wave=0	Heat wave=1
bordeaux	1924	26
clermont	1926	24
grenoble	1914	36
lehavre	1927	23
lyon	1918	32
marseille	1915	35
montpellier	1922	28
nancy	1918	32
nantes	1926	24
nice	1924	26
paris	1923	27
rennes	1918	32
rouen	1925	25
strasbourg	1912	38
toulouse	1921	29

Descriptive Statistics

Number of Observations for each urban agglomeration:

V1	V2
bordeaux	1927
clermont	1944
grenoble	1935
lehavre	1943
lyon	1950
marseille	1948
montpellier	1937
nancy	1948
nantes	1950
nice	1948
paris	1950
rennes	1909
rouen	1950
strasbourg	1947
toulouse	1950

Temperature and Ozone statistics per City
	Temperature			Ozone
city	Temp 25 Perc	Mean Temp	Temp 75 Perc	o3 25 Perc	Mean o3	o3 75 Perc
bordeaux	17.95	20.29	22.30	52.44	65.02	76.18
clermont	16.35	19.00	21.50	53.17	65.23	76.07
grenoble	16.30	18.76	21.35	42.21	57.83	71.98
lehavre	15.26	16.97	18.20	51.40	60.67	68.09
lyon	18.00	20.71	23.38	46.44	61.52	75.30
marseille	21.18	23.25	25.32	59.62	70.45	80.98
montpellier	20.70	22.62	24.60	62.50	73.17	83.70
nancy	15.70	18.20	20.76	38.84	52.01	62.28
nantes	16.33	18.42	20.23	50.22	62.75	72.59
nice	21.25	22.96	24.70	66.13	77.64	89.11
paris	16.67	19.18	21.36	40.24	52.58	62.78
rennes	15.90	17.93	19.75	42.07	54.10	62.82
rouen	14.54	16.72	18.46	38.24	50.10	60.06
strasbourg	16.30	18.79	21.35	41.52	56.51	69.95
toulouse	18.70	21.18	23.61	54.50	66.82	77.79

Temperatures Distribution

## Picking joint bandwidth of 0.633

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Ozone concentrations Distribution

## Picking joint bandwidth of 3.43

Description of Mortality

Total number of deaths:

## [1] 450727

## [1] 117047

## [1] 25438

Mortality per City During non Heat-wave days
	Non Accidental Causes			Cardiovascual Causes			Respiratory causes
city	Minimum Nocc	M Nocc	Max Nocc	Minimum Cv	M Cv	Max Cv	Minimum Respi	M Respi	Max Respi
bordeaux	2	11.540242	25	0	3.263019	13	0	0.6649132	5
clermont	0	5.166146	15	0	1.464063	7	0	0.2802083	3
grenoble	0	7.230121	19	0	2.012638	11	0	0.3522907	4
lehavre	0	5.276042	14	0	1.368229	8	0	0.2864583	3
lyon	4	16.969239	36	0	4.426486	14	0	0.8868613	5
marseille	7	20.089388	39	0	5.595400	17	0	1.2618923	7
montpellier	0	5.834468	17	0	1.639078	8	0	0.3163960	3
nancy	0	6.288100	16	0	1.663883	7	0	0.4055324	4
nantes	1	9.483385	23	0	2.583074	11	0	0.5145379	6
nice	2	11.142039	24	0	3.059313	10	0	0.6529657	5
paris	60	98.195008	286	8	23.577743	56	0	5.4482579	20
rennes	0	3.567625	12	0	1.067093	5	0	0.2358892	3
rouen	1	9.028571	23	0	2.489351	12	0	0.4987013	5
strasbourg	1	7.862755	18	0	2.211629	9	0	0.4379256	6
toulouse	2	11.014055	34	0	2.966684	11	0	0.5637689	5

Mortality per City During Heat-wave days
	Non Accidental Causes			Cardiovascual Causes			Respiratory causes
city	Minimum Nocc	M Nocc	Max Nocc	Minimum Cv	M Cv	Max Cv	Minimum Respi	M Respi	Max Respi
bordeaux	7	18.576923	36	3	5.807692	12	0	1.6538462	6
clermont	4	8.041667	16	0	2.625000	6	0	0.3750000	2
grenoble	3	9.472222	16	0	2.861111	8	0	0.7222222	4
lehavre	2	6.956522	15	0	2.000000	6	0	0.5217391	2
lyon	11	27.781250	75	2	7.093750	22	0	2.4062500	7
marseille	12	24.885714	40	2	6.285714	14	0	1.8857143	5
montpellier	2	6.928571	14	0	1.785714	7	0	0.5000000	2
nancy	0	9.312500	24	0	2.656250	10	0	0.8437500	4
nantes	7	12.875000	32	0	3.541667	10	0	1.0416667	4
nice	4	15.269231	33	0	4.076923	11	0	1.0000000	3
paris	100	207.518518	724	13	50.481482	179	2	14.5185185	52
rennes	2	5.032258	9	0	1.580645	6	0	0.4838710	3
rouen	6	13.720000	28	0	3.400000	8	0	0.9600000	3
strasbourg	7	12.315789	22	0	3.447368	7	0	0.9473684	3
toulouse	6	14.620690	24	0	4.241379	9	0	0.8965517	3

Non accidental Mortality

Cardiovascular Mortality

Respiratory Mortality

Analysis On Non-Accidental Mortality

BOOTSTRAP METHOD

Code:

# some data management
 for (i in 1:length(villes_s)){
   villes_s[[i]]$time<-1:nrow(villes_s[[i]]) # Create a new variable for time 
 } 
   

    
  # start bootstrap
     
  nreps=700 #number of bootstrap reps
  results<-matrix(NA,nrow = 15,ncol=11)
  colnames(results)<-c("city","NDE","NDE_2.5","NDE_97.5","NIE","NIE_2.5","NIE_97.5","PMM","TE","TE_2.5","TE_97.5")
  
for (i in (1:length(villes_s))){
  boot.nde<- rep(NA,nreps)
  boot.nie <- rep(NA,nreps)
  boot.pmm <- rep(NA,nreps)
  boot.te <- 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(nocc_tot~heat_wave+o3+no2moy+ns(time,df=round(3*length(time)/122))+Jours+Vacances+hol+mois,data=dat_boot,family=quasipoisson)
      
      # mediator model - multinomial linear regression
      m.mediator<-lm(o3~heat_wave+no2moy+ns(time,df=round(3*length(time)/122))+Jours+Vacances+hol+mois,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.te[rep] <- boot.nde[rep]*boot.nie[rep] # TE
      
      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]<-median(boot.te, na.rm=T)
  results[i,10]<-quantile(boot.te, 0.025, na.rm=T)
  results[i,11]<-quantile(boot.te, 0.975, na.rm=T)     
    }
  
  # output CDE and NIE and confidence intervals

Results:

res<-data.frame(results)

for (i in (2:11)){
res[,i]<-as.numeric(as.character(res[,i]))
}

res[,2:11]<-round(res[,2:11],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)%>%
  column_spec(8, border_right = T)

city	NDE	NDE_2.5	NDE_97.5	NIE	NIE_2.5	NIE_97.5	PMM	TE	TE_2.5	TE_97.5
bordeaux	1.4920	1.2917	1.7223	1.0247	0.9995	1.0529	0.0708	1.5283	1.3259	1.7682
clermont	1.4570	1.2084	1.7407	1.0203	0.9917	1.0496	0.0621	1.4814	1.2317	1.7741
grenoble	1.1509	1.0206	1.3147	1.0311	1.0078	1.0572	0.1897	1.1886	1.0528	1.3534
lehavre	1.2710	1.0159	1.5816	1.0329	0.9881	1.0867	0.1375	1.3163	1.0503	1.6311
lyon	1.3516	1.1443	1.5884	1.0280	1.0130	1.0462	0.0971	1.3887	1.1783	1.6253
marseille	1.1412	1.0566	1.2416	1.0032	0.9976	1.0110	0.0262	1.1454	1.0591	1.2517
montpellier	1.1035	0.9457	1.2826	1.0084	0.9957	1.0256	0.0705	1.1128	0.9582	1.2937
nancy	1.2035	1.0317	1.4235	0.9955	0.9723	1.0178	-0.0295	1.1970	1.0254	1.4167
nantes	1.1933	0.9896	1.4448	1.0753	1.0307	1.1235	0.3158	1.2856	1.0696	1.5410
nice	1.2162	1.0548	1.3999	1.0040	0.9985	1.0162	0.0222	1.2222	1.0623	1.4041
paris	1.6504	1.3663	2.0617	1.0353	1.0170	1.0651	0.0839	1.7089	1.3966	2.1337
rennes	1.3123	1.0966	1.5479	1.0599	1.0183	1.1154	0.2023	1.3932	1.1550	1.6500
rouen	1.3062	1.1121	1.5184	1.0684	1.0346	1.1097	0.2277	1.3972	1.1946	1.6364
strasbourg	1.4232	1.2833	1.5889	1.0197	0.9966	1.0506	0.0636	1.4534	1.3189	1.6149
toulouse	1.2400	1.1046	1.3737	1.0352	1.0147	1.0610	0.1582	1.2850	1.1447	1.4239

# write_xlsx(res,"C:/Users/Anna/Dropbox/Heat wave and O3/Meteo France/mortality/nonaccidental/results.xlsx")

Meta-Analysis and Forestplot

Natural Direct Effect

## 
## Random-Effects Model (k = 15; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0084 (SE = 0.0066)
## tau (square root of estimated tau^2 value):      0.0918
## I^2 (total heterogeneity / total variability):   50.60%
## H^2 (total variability / sampling variability):  2.02
## 
## Test for Heterogeneity:
## Q(df = 14) = 29.1531, p-val = 0.0100
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   1.2722  0.0348  36.5505  <.0001  1.2040  1.3404  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Natural Indirect Effect

## 
## Random-Effects Model (k = 15; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value):      0.0156
## I^2 (total heterogeneity / total variability):   74.45%
## H^2 (total variability / sampling variability):  3.91
## 
## Test for Heterogeneity:
## Q(df = 14) = 46.9917, p-val < .0001
## 
## Model Results:
## 
## estimate      se      zval    pval   ci.lb   ci.ub 
##   1.0233  0.0052  195.4659  <.0001  1.0131  1.0336  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Analysis On Cardiovascular Mortality

Results:

city	NDE	NDE_2.5	NDE_97.5	NIE	NIE_2.5	NIE_97.5	PMM	TE	TE_2.5	TE_97.5
bordeaux	1.5487	1.2798	1.8886	1.0109	0.9680	1.0610	0.0305	1.5706	1.2933	1.9016
clermont	1.7088	1.3208	2.2297	0.9999	0.9520	1.0535	-0.0004	1.7070	1.3192	2.2266
grenoble	1.2397	0.9698	1.5638	1.0489	1.0096	1.0994	0.1988	1.3042	1.0150	1.6206
lehavre	1.3145	0.8587	1.8159	1.1347	1.0447	1.2523	0.3537	1.4903	0.9908	2.0300
lyon	1.3785	1.0973	1.7082	1.0253	1.0003	1.0558	0.0851	1.4144	1.1229	1.7527
marseille	1.0666	0.9242	1.2335	1.0039	0.9975	1.0157	0.0391	1.0725	0.9249	1.2389
montpellier	1.0622	0.7207	1.5311	1.0069	0.9812	1.0392	0.0171	1.0695	0.7213	1.5313
nancy	1.4707	1.1122	1.9119	0.9846	0.9402	1.0297	-0.0491	1.4551	1.1008	1.8627
nantes	1.2334	0.9289	1.6271	1.1022	1.0259	1.1913	0.3547	1.3600	1.0278	1.7720
nice	1.1176	0.8578	1.4101	1.0064	0.9958	1.0260	0.0410	1.1229	0.8645	1.4200
paris	1.6794	1.3090	2.1246	1.0367	1.0132	1.0710	0.0863	1.7444	1.3498	2.2396
rennes	1.4808	0.9427	2.2680	1.0305	0.9502	1.1274	0.0872	1.5228	0.9633	2.3493
rouen	1.2178	0.9138	1.5689	1.0759	1.0054	1.1555	0.2987	1.3085	0.9869	1.6925
strasbourg	1.4740	1.2141	1.7991	0.9863	0.9446	1.0291	-0.0455	1.4567	1.1987	1.7603
toulouse	1.2175	0.9823	1.4776	1.0596	1.0223	1.1008	0.2462	1.2935	1.0347	1.5645

Meta-Analysis and Forestplot

Natural Direct Effect

## 
## Random-Effects Model (k = 15; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0171 (SE = 0.0165)
## tau (square root of estimated tau^2 value):      0.1306
## I^2 (total heterogeneity / total variability):   40.41%
## H^2 (total variability / sampling variability):  1.68
## 
## Test for Heterogeneity:
## Q(df = 14) = 23.5839, p-val = 0.0514
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   1.3084  0.0553  23.6593  <.0001  1.2000  1.4168  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Natural Indirect Effect

## 
## Random-Effects Model (k = 15; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value):      0.0170
## I^2 (total heterogeneity / total variability):   56.78%
## H^2 (total variability / sampling variability):  2.31
## 
## Test for Heterogeneity:
## Q(df = 14) = 31.4751, p-val = 0.0048
## 
## Model Results:
## 
## estimate      se      zval    pval   ci.lb   ci.ub 
##   1.0206  0.0068  149.0116  <.0001  1.0072  1.0340  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Analysis On Respiratory Mortality

Results:

city	NDE	NDE_2.5	NDE_97.5	NIE	NIE_2.5	NIE_97.5	PMM	TE	TE_2.5	TE_97.5
bordeaux	2.0701	1.3912	3.0560	1.0687	0.9626	1.1876	0.1175	2.2136	1.5239	3.1739
clermont	1.1712	0.4799	2.5452	0.9489	0.8339	1.0802	-0.0574	1.1064	0.4475	2.4146
grenoble	1.4631	0.8836	2.2347	0.9541	0.8547	1.0514	-0.1572	1.3873	0.8533	2.1678
lehavre	2.4045	1.0285	4.8438	0.9202	0.7522	1.1232	-0.1600	2.2329	0.9713	4.1275
lyon	1.9310	1.4078	2.6515	1.0723	1.0162	1.1509	0.1346	2.0667	1.5161	2.9134
marseille	1.2442	0.9120	1.6542	1.0004	0.9898	1.0175	0.0014	1.2419	0.9126	1.6559
montpellier	1.1299	0.6024	1.8070	1.0628	1.0057	1.1494	0.1739	1.2058	0.6294	1.9442
nancy	1.6835	0.9571	2.8183	0.9509	0.8580	1.0381	-0.1314	1.6193	0.9122	2.6571
nantes	1.0418	0.6203	1.6001	1.2743	1.0770	1.5387	0.7115	1.3232	0.8232	1.9946
nice	1.2142	0.7882	1.8221	1.0068	0.9856	1.0413	0.0230	1.2253	0.7992	1.8317
paris	1.8864	1.4856	2.3918	1.0431	1.0109	1.0887	0.0847	1.9684	1.5523	2.4632
rennes	1.7232	0.8480	3.1175	1.1178	0.9382	1.3619	0.2120	1.9420	0.9232	3.4542
rouen	1.5397	0.8882	2.5089	1.0549	0.9070	1.2225	0.1258	1.6166	0.9575	2.5735
strasbourg	1.9418	1.3059	2.8571	1.0377	0.9350	1.1535	0.0722	2.0199	1.4062	2.9126
toulouse	1.4912	0.9608	2.2915	1.0002	0.9217	1.0903	0.0014	1.4938	0.9646	2.2367

Meta-analysis and Forestplot

Natural Direct Effect

## 
## Random-Effects Model (k = 15; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0396 (SE = 0.0543)
## tau (square root of estimated tau^2 value):      0.1990
## I^2 (total heterogeneity / total variability):   26.87%
## H^2 (total variability / sampling variability):  1.37
## 
## Test for Heterogeneity:
## Q(df = 14) = 16.9650, p-val = 0.2580
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   1.4987  0.1024  14.6329  <.0001  1.2980  1.6995  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Natural Indirect Effect

## 
## Random-Effects Model (k = 15; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value):      0.0181
## I^2 (total heterogeneity / total variability):   29.81%
## H^2 (total variability / sampling variability):  1.42
## 
## Test for Heterogeneity:
## Q(df = 14) = 22.3854, p-val = 0.0710
## 
## Model Results:
## 
## estimate      se     zval    pval   ci.lb   ci.ub 
##   1.0176  0.0102  99.8241  <.0001  0.9976  1.0376  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Heat wave and Ozone, Mediation analysis on mortality (Meteo France definition)

Anna Alari

10/12/2020

Temperature Data Management

Creating Database only with summer months

Creation of Heat Wave Variable

Descriptive Statistics

Temperatures Distribution

Ozone concentrations Distribution

Description of Mortality

Non accidental Mortality

Cardiovascular Mortality

Respiratory Mortality

Analysis On Non-Accidental Mortality

BOOTSTRAP METHOD

Meta-Analysis and Forestplot

Analysis On Cardiovascular Mortality

Meta-Analysis and Forestplot

Analysis On Respiratory Mortality

Meta-analysis and Forestplot