D-R Curves for Ozone and cv mortality over the time with two-stage modeling - Basic and Stratification Models
Anna Alari
25 février, 2021
Descriptive Statistics for O3
| Ville | Minimum | X1st.Qu | Median | Mean | X3rd.Qu | Max | NA.s |
|---|---|---|---|---|---|---|---|
| bordeaux | 1.9154062 | 49.55208 | 69.45833 | 69.41858 | 88.12500 | 169.5000 | NA |
| clermont | 1.7321429 | 54.31250 | 72.48661 | 72.01823 | 89.87500 | 178.5625 | 144 |
| grenoble | 0.4375000 | 35.29129 | 65.78125 | 65.64074 | 91.66964 | 185.8750 | NA |
| lehavre | 2.1250000 | 56.68750 | 69.87500 | 69.16447 | 81.75000 | 184.5000 | 76 |
| lille | 0.1666667 | 41.00000 | 58.75000 | 58.65181 | 74.21250 | 207.1250 | 285 |
| lyon | 0.4375000 | 39.43799 | 64.94705 | 65.15421 | 89.04688 | 229.3750 | 8 |
| marseille | 3.1250000 | 54.31250 | 79.00000 | 77.03170 | 99.10938 | 195.0625 | 16 |
| montpellier | 7.5458333 | 61.66667 | 81.16250 | 81.22084 | 100.05417 | 200.8083 | 9 |
| nancy | 0.5000000 | 52.00000 | 68.25000 | 70.65748 | 87.37500 | 221.0000 | 170 |
| nantes | 1.1250000 | 52.93750 | 68.75000 | 70.20679 | 86.12500 | 216.5000 | 75 |
| nice | 14.5625000 | 60.50000 | 89.25000 | 87.41249 | 110.50000 | 206.3750 | 60 |
| paris | 0.8592206 | 36.77541 | 56.96864 | 58.49854 | 76.43874 | 216.3857 | NA |
| rennes | 1.5625000 | 50.75000 | 64.87500 | 65.52426 | 79.50000 | 213.5625 | 18 |
| rouen | 1.2972346 | 47.00000 | 63.15625 | 63.59236 | 78.75000 | 178.6607 | 16 |
| strasbourg | 0.0000000 | 36.62500 | 61.75000 | 63.87351 | 87.25000 | 206.5000 | 113 |
| toulouse | 1.6250000 | 56.33750 | 75.54063 | 75.36355 | 93.80938 | 180.9594 | NA |
Dose-Response Curves for o3 and Non-accidental Mortality, Basic Model
For Lag0, Lag1 and Lag2 exposure
| Ville | Coefficient_L0 | L0_CI95_Low | L0_CI95_High | Coefficient_L1 | L1_CI95_Low | L1_CI95_High | Coefficient_L2 | L2_CI95_Low | L2_CI95_High |
|---|---|---|---|---|---|---|---|---|---|
| bordeaux | 0.99974 | 0.99786 | 1.00162 | 1.00011 | 0.99825 | 1.00198 | 0.99974 | 0.99790 | 1.00158 |
| clermont | 1.00158 | 0.99885 | 1.00431 | 1.00186 | 0.99910 | 1.00462 | 0.99946 | 0.99673 | 1.00220 |
| grenoble | 1.00117 | 0.99902 | 1.00333 | 0.99986 | 0.99769 | 1.00203 | 1.00022 | 0.99808 | 1.00237 |
| lehavre | 0.99887 | 0.99606 | 1.00168 | 1.00031 | 0.99752 | 1.00311 | 1.00113 | 0.99842 | 1.00385 |
| lille | 1.00085 | 0.99960 | 1.00209 | 1.00058 | 0.99935 | 1.00182 | 1.00059 | 0.99938 | 1.00181 |
| lyon | 1.00134 | 0.99992 | 1.00277 | 1.00050 | 0.99909 | 1.00192 | 1.00096 | 0.99956 | 1.00237 |
| marseille | 1.00107 | 0.99972 | 1.00242 | 1.00087 | 0.99954 | 1.00221 | 1.00012 | 0.99878 | 1.00147 |
| montpellier | 0.99973 | 0.99719 | 1.00227 | 1.00149 | 0.99894 | 1.00404 | 0.99992 | 0.99737 | 1.00248 |
| nancy | 1.00063 | 0.99847 | 1.00281 | 1.00147 | 0.99928 | 1.00366 | 1.00375 | 1.00156 | 1.00594 |
| nantes | 1.00376 | 1.00165 | 1.00587 | 1.00450 | 1.00240 | 1.00661 | 1.00258 | 1.00051 | 1.00465 |
| nice | 0.99948 | 0.99764 | 1.00133 | 0.99877 | 0.99691 | 1.00062 | 0.99835 | 0.99649 | 1.00022 |
| paris | 1.00009 | 0.99940 | 1.00078 | 1.00010 | 0.99942 | 1.00078 | 0.99952 | 0.99884 | 1.00019 |
| rennes | 0.99892 | 0.99589 | 1.00196 | 0.99779 | 0.99476 | 1.00083 | 0.99997 | 0.99697 | 1.00297 |
| rouen | 0.99930 | 0.99714 | 1.00147 | 0.99964 | 0.99750 | 1.00180 | 0.99942 | 0.99733 | 1.00152 |
| strasbourg | 1.00006 | 0.99810 | 1.00202 | 0.99790 | 0.99597 | 0.99983 | 0.99894 | 0.99701 | 1.00088 |
| toulouse | 1.00119 | 0.99903 | 1.00335 | 1.00104 | 0.99894 | 1.00315 | 1.00315 | 1.00102 | 1.00528 |
Meta-Analysis results
## Call: mvmeta(formula = y ~ 1, S = S, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0005 0.0002 2.2900 0.0220 0.0001 0.0010 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0003
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 20.1426 (df = 15), p-value = 0.1665
## I-square statistic = 25.5%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 86.2777 -168.5554 -167.0103## Call: mvmeta(formula = y_L1 ~ 1, S = S_L1, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0004 0.0003 1.2443 0.2134 -0.0002 0.0011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0009
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 32.0141 (df = 15), p-value = 0.0064
## I-square statistic = 53.1%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 81.5808 -159.1617 -157.6165## Call: mvmeta(formula = y_L2 ~ 1, S = S_L2, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0004 0.0003 1.2387 0.2154 -0.0002 0.0011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0010
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 35.0240 (df = 15), p-value = 0.0024
## I-square statistic = 57.2%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 81.9869 -159.9737 -158.4285Pooled Dose-Responses Curves For Basic Model
3 Periods Stratified analysis
For Lag0, Lag1 and Lag2 exposure
Meta-Analysis results
For Lag0
First Period:
## Call: mvmeta(formula = y0105 ~ 1, S = S0105, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0002 0.0004 0.5466 0.5846 -0.0006 0.0010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0000
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 13.7687 (df = 12), p-value = 0.3157
## I-square statistic = 12.8%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 63.0665 -122.1330 -121.0031Second Period: 
## Call: mvmeta(formula = y0610 ~ 1, S = S0610, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0001 0.0004 0.3648 0.7152 -0.0006 0.0009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0000
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 7.7568 (df = 15), p-value = 0.9332
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 81.1900 -158.3800 -156.8348Third Period: 
## Call: mvmeta(formula = y1115 ~ 1, S = S1115, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0002 0.0004 0.4839 0.6285 -0.0005 0.0009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0000
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 19.6779 (df = 15), p-value = 0.1846
## I-square statistic = 23.8%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 76.7643 -149.5285 -147.9833Results for Lag1
First Period: 
## Call: mvmeta(formula = y0105_L1 ~ 1, S = S0105_L1, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) -0.0003 0.0004 -0.6470 0.5177 -0.0010 0.0005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0000
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 8.7308 (df = 12), p-value = 0.7257
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 65.6278 -127.2556 -126.1257Second Period: 
## Call: mvmeta(formula = y0610_L1 ~ 1, S = S0610_L1, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0001 0.0004 0.3145 0.7531 -0.0007 0.0009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0001
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 12.5025 (df = 15), p-value = 0.6407
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 79.1123 -154.2245 -152.6794Third Period: 
## Call: mvmeta(formula = y1115_L1 ~ 1, S = S1115_L1, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0003 0.0004 0.7910 0.4289 -0.0004 0.0010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0000
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 7.4345 (df = 15), p-value = 0.9445
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 82.9610 -161.9220 -160.3768Results for Lag2
First Period: 
## Call: mvmeta(formula = y0105_L2 ~ 1, S = S0105_L2, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0000 0.0004 0.0538 0.9571 -0.0007 0.0008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0000
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 11.5032 (df = 12), p-value = 0.4864
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 64.4588 -124.9177 -123.7878Second Period: 
## Call: mvmeta(formula = y0610_L2 ~ 1, S = S0610_L2, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) -0.0005 0.0004 -1.3230 0.1858 -0.0013 0.0003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0000
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 15.6452 (df = 15), p-value = 0.4060
## I-square statistic = 4.1%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 77.6320 -151.2640 -149.7188Third Period: 
## Call: mvmeta(formula = y1115_L2 ~ 1, S = S1115_L2, method = "ml")
##
## Univariate random-effects meta-analysis
## Dimension: 1
## Estimation method: ML
##
## Fixed-effects coefficients
## Estimate Std. Error z Pr(>|z|) 95%ci.lb 95%ci.ub
## (Intercept) 0.0003 0.0004 0.7066 0.4798 -0.0005 0.0011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0005
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 17.8383 (df = 15), p-value = 0.2713
## I-square statistic = 15.9%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 77.8720 -151.7441 -150.1989Table of results: change in mortality risk per 10 μg/m3 increase in PM10 for 3 time periods, with Temporal Change expressed in percentage points
####Results for Lag0
| villes | Period1 | P1_CI95_Low | P1_CI95_High | Period2 | P2_CI95_Low | P2_CI95_High | Period3 | P3_CI95_Low | P3_CI95_High | Temp_Change1_2 | Temp_Change2_3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| bordeaux | 0.994 | 0.959 | 1.031 | 1.002 | 0.963 | 1.043 | 0.987 | 0.954 | 1.022 | 0.798 | -1.492 |
| clermont | 1.017 | 0.963 | 1.076 | 1.002 | 0.949 | 1.058 | 1.012 | 0.967 | 1.061 | -1.514 | 1.007 |
| grenoble | 0.969 | 0.931 | 1.007 | 0.997 | 0.955 | 1.040 | 1.042 | 1.003 | 1.083 | 2.850 | 4.485 |
| lehavre | 0.956 | 0.909 | 1.006 | 1.033 | 0.974 | 1.095 | 0.975 | 0.926 | 1.028 | 7.652 | -5.721 |
| lille | 0.998 | 0.973 | 1.022 | 0.996 | 0.971 | 1.022 | 1.024 | 1.001 | 1.048 | -0.199 | 2.828 |
| lyon | NA | NA | NA | 1.007 | 0.978 | 1.037 | 1.010 | 0.984 | 1.037 | NA | 0.303 |
| marseille | 1.016 | 0.992 | 1.040 | 1.018 | 0.991 | 1.046 | 0.985 | 0.961 | 1.009 | 0.203 | -3.305 |
| montpellier | 1.013 | 0.969 | 1.060 | 1.002 | 0.952 | 1.053 | 0.955 | 0.910 | 1.003 | -1.108 | -4.696 |
| nancy | NA | NA | NA | 1.010 | 0.968 | 1.055 | 0.990 | 0.951 | 1.030 | NA | -2.000 |
| nantes | 1.046 | 1.005 | 1.088 | 1.002 | 0.960 | 1.047 | 0.996 | 0.959 | 1.035 | -4.403 | -0.599 |
| nice | NA | NA | NA | 1.005 | 0.968 | 1.044 | 0.970 | 0.935 | 1.008 | NA | -3.457 |
| paris | 1.004 | 0.992 | 1.016 | 1.001 | 0.987 | 1.014 | 1.001 | 0.989 | 1.013 | -0.301 | 0.000 |
| rennes | 0.999 | 0.946 | 1.055 | 0.964 | 0.904 | 1.027 | 1.019 | 0.962 | 1.081 | -3.532 | 5.551 |
| rouen | 1.001 | 0.962 | 1.042 | 0.985 | 0.943 | 1.029 | 1.002 | 0.965 | 1.041 | -1.589 | 1.689 |
| strasbourg | 0.986 | 0.948 | 1.024 | 1.010 | 0.973 | 1.049 | 1.012 | 0.977 | 1.048 | 2.395 | 0.202 |
| toulouse | 0.991 | 0.951 | 1.033 | 0.969 | 0.930 | 1.009 | 1.013 | 0.974 | 1.054 | -2.253 | 4.458 |
| Period1 | Period2 | Period3 | change12 | change23 |
|---|---|---|---|---|
| 1.000187 | 1.000148 | 1.000143 | -0.0386372 | -0.0056923 |
####Results for Lag1
| villes | Period1 | P1_CI95_Low | P1_CI95_High | Period2 | P2_CI95_Low | P2_CI95_High | Period3 | P3_CI95_Low | P3_CI95_High | Temp_Change1_2 | Temp_Change2_3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| bordeaux | 0.978 | 0.944 | 1.014 | 1.015 | 0.976 | 1.055 | 1.002 | 0.969 | 1.037 | 3.687 | -1.311 |
| clermont | 0.983 | 0.930 | 1.041 | 1.028 | 0.973 | 1.085 | 1.014 | 0.969 | 1.063 | 4.525 | -1.430 |
| grenoble | 1.000 | 0.961 | 1.041 | 0.981 | 0.941 | 1.023 | 0.989 | 0.951 | 1.028 | -1.882 | 0.788 |
| lehavre | 0.992 | 0.943 | 1.044 | 1.016 | 0.960 | 1.075 | 0.999 | 0.948 | 1.052 | 2.410 | -1.713 |
| lille | 0.995 | 0.970 | 1.020 | 0.984 | 0.960 | 1.009 | 1.018 | 0.995 | 1.041 | -1.089 | 3.404 |
| lyon | NA | NA | NA | 0.997 | 0.969 | 1.025 | 1.004 | 0.978 | 1.030 | NA | 0.700 |
| marseille | 1.000 | 0.976 | 1.023 | 1.007 | 0.981 | 1.035 | 1.003 | 0.978 | 1.027 | 0.702 | -0.402 |
| montpellier | 1.018 | 0.973 | 1.065 | 1.031 | 0.980 | 1.085 | 1.003 | 0.956 | 1.053 | 1.332 | -2.848 |
| nancy | NA | NA | NA | 0.996 | 0.955 | 1.040 | 0.996 | 0.957 | 1.037 | NA | 0.000 |
| nantes | 1.044 | 1.003 | 1.085 | 1.004 | 0.961 | 1.049 | 1.008 | 0.970 | 1.047 | -3.993 | 0.402 |
| nice | NA | NA | NA | 0.992 | 0.955 | 1.030 | 0.974 | 0.939 | 1.012 | NA | -1.770 |
| paris | 0.998 | 0.986 | 1.009 | 1.009 | 0.997 | 1.022 | 1.007 | 0.996 | 1.019 | 1.104 | -0.202 |
| rennes | 0.990 | 0.937 | 1.046 | 0.952 | 0.895 | 1.014 | 0.997 | 0.940 | 1.057 | -3.787 | 4.482 |
| rouen | 0.993 | 0.955 | 1.034 | 0.990 | 0.948 | 1.033 | 0.995 | 0.958 | 1.034 | -0.297 | 0.496 |
| strasbourg | 0.976 | 0.940 | 1.013 | 0.976 | 0.941 | 1.012 | 0.984 | 0.951 | 1.018 | 0.000 | 0.784 |
| toulouse | 0.996 | 0.956 | 1.037 | 1.000 | 0.962 | 1.040 | 0.986 | 0.948 | 1.025 | 0.399 | -1.390 |
####Results for Lag2
| villes | Period1 | P1_CI95_Low | P1_CI95_High | Period2 | P2_CI95_Low | P2_CI95_High | Period3 | P3_CI95_Low | P3_CI95_High | Temp_Change1_2 | Temp_Change2_3 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| bordeaux | 1.002 | 0.968 | 1.038 | 0.976 | 0.939 | 1.014 | 1.013 | 0.979 | 1.048 | -2.572 | 3.680 |
| clermont | 0.926 | 0.875 | 0.980 | 0.997 | 0.945 | 1.051 | 1.011 | 0.966 | 1.059 | 7.111 | 1.406 |
| grenoble | 0.992 | 0.953 | 1.033 | 0.986 | 0.946 | 1.028 | 0.998 | 0.961 | 1.036 | -0.593 | 1.190 |
| lehavre | 1.018 | 0.969 | 1.068 | 1.025 | 0.969 | 1.083 | 0.990 | 0.942 | 1.042 | 0.715 | -3.527 |
| lille | 0.999 | 0.976 | 1.023 | 0.981 | 0.958 | 1.006 | 1.029 | 1.006 | 1.053 | -1.782 | 4.825 |
| lyon | NA | NA | NA | 0.981 | 0.954 | 1.009 | 1.014 | 0.989 | 1.041 | NA | 3.292 |
| marseille | 0.995 | 0.971 | 1.018 | 1.019 | 0.992 | 1.047 | 0.988 | 0.964 | 1.012 | 2.417 | -3.111 |
| montpellier | 1.013 | 0.968 | 1.060 | 1.010 | 0.960 | 1.062 | 0.987 | 0.941 | 1.037 | -0.303 | -2.297 |
| nancy | NA | NA | NA | 1.039 | 0.995 | 1.083 | 1.041 | 1.000 | 1.084 | NA | 0.208 |
| nantes | 1.028 | 0.988 | 1.069 | 0.992 | 0.950 | 1.037 | 0.988 | 0.952 | 1.026 | -3.636 | -0.396 |
| nice | NA | NA | NA | 0.985 | 0.948 | 1.023 | 0.985 | 0.949 | 1.023 | NA | 0.000 |
| paris | 1.000 | 0.989 | 1.011 | 0.990 | 0.978 | 1.003 | 1.003 | 0.992 | 1.015 | -0.995 | 1.295 |
| rennes | 1.020 | 0.967 | 1.077 | 1.028 | 0.967 | 1.093 | 0.973 | 0.919 | 1.030 | 0.819 | -5.503 |
| rouen | 0.991 | 0.955 | 1.028 | 0.981 | 0.940 | 1.023 | 0.990 | 0.953 | 1.028 | -0.986 | 0.887 |
| strasbourg | 1.002 | 0.965 | 1.041 | 0.996 | 0.960 | 1.034 | 0.973 | 0.941 | 1.007 | -0.599 | -2.265 |
| toulouse | 1.015 | 0.974 | 1.057 | 1.021 | 0.982 | 1.063 | 1.014 | 0.974 | 1.055 | 0.611 | -0.712 |
Metanalysis of the results
Pooled Dose-Responses Curves
L0
L1
L2
Doses-Response Curves by cities
Bordeaux
Lag 0 
Lag1 
Lag2 
Clermont-Ferrand
Lag 0 
Lag 1 
Lag 2 
Grenoble
Lag 0 
Lag 1 
Lag 2 
Le Havre
Lag 0 
Lag 1 
Lag 2 
Lille
Lag 0 
Lag 1 
Lag 2 
Lyon
Lag 0 
Lag 1 
Lag 2 
Marseille
Lag 0 
Lag 1 
Lag 2 
Montpellier
Lag 0 
Lag 1 
Lag 2 
Nancy
Lag 0 
Lag 1 
Lag 2 
Nantes
Lag 0 
Lag 1 
Lag 2 
Nice
Lag 0 
Lag 1 
Lag 2 
Paris
Lag 0 
Lag 1 
Lag 2 
Rennes
Lag 0 
Lag 1 
Lag 2 
Rouen
Lag 0 
Lag 1 
Lag 2 
Strasbourg
Lag 0 
Lag 1 
Lag 2 
Toulouse
Lag 0 
Lag 1 
Lag 2 