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 | 0.99743 | 0.99651 | 0.99834 | 0.99837 | 0.99746 | 0.99928 | 0.99897 | 0.99807 | 0.99987 |
| clermont | 0.99940 | 0.99815 | 1.00064 | 0.99921 | 0.99796 | 1.00047 | 1.00011 | 0.99887 | 1.00136 |
| grenoble | 0.99982 | 0.99885 | 1.00078 | 1.00009 | 0.99912 | 1.00107 | 1.00044 | 0.99947 | 1.00141 |
| lehavre | 0.99982 | 0.99848 | 1.00116 | 1.00003 | 0.99870 | 1.00136 | 0.99942 | 0.99812 | 1.00071 |
| lille | 1.00018 | 0.99952 | 1.00084 | 1.00028 | 0.99963 | 1.00094 | 0.99991 | 0.99927 | 1.00056 |
| lyon | 1.00055 | 0.99986 | 1.00124 | 1.00095 | 1.00026 | 1.00163 | 1.00121 | 1.00052 | 1.00189 |
| marseille | 1.00066 | 1.00000 | 1.00133 | 1.00075 | 1.00009 | 1.00141 | 1.00062 | 0.99996 | 1.00129 |
| montpellier | 1.00093 | 0.99974 | 1.00212 | 1.00176 | 1.00056 | 1.00295 | 1.00082 | 0.99962 | 1.00202 |
| nancy | 0.99951 | 0.99838 | 1.00064 | 1.00010 | 0.99897 | 1.00124 | 0.99992 | 0.99878 | 1.00106 |
| nantes | 0.99925 | 0.99826 | 1.00024 | 1.00007 | 0.99908 | 1.00106 | 0.99954 | 0.99856 | 1.00051 |
| nice | 1.00180 | 1.00090 | 1.00270 | 1.00197 | 1.00107 | 1.00287 | 1.00186 | 1.00096 | 1.00277 |
| paris | 1.00005 | 0.99966 | 1.00044 | 1.00022 | 0.99983 | 1.00060 | 1.00000 | 0.99962 | 1.00038 |
| rennes | 0.99989 | 0.99834 | 1.00144 | 0.99872 | 0.99716 | 1.00028 | 0.99825 | 0.99672 | 0.99978 |
| rouen | 0.99995 | 0.99893 | 1.00096 | 1.00024 | 0.99924 | 1.00125 | 1.00042 | 0.99944 | 1.00141 |
| strasbourg | 0.99975 | 0.99884 | 1.00066 | 1.00000 | 0.99910 | 1.00090 | 1.00004 | 0.99914 | 1.00095 |
| toulouse | 1.00039 | 0.99937 | 1.00141 | 1.00093 | 0.99993 | 1.00193 | 1.00113 | 1.00012 | 1.00213 |
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) -0.0000 0.0002 -0.0823 0.9344 -0.0005 0.0004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0008
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 59.2263 (df = 15), p-value = 0.0000
## I-square statistic = 74.7%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 89.2319 -174.4638 -172.9186## 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) 0.0003 0.0002 1.2916 0.1965 -0.0001 0.0007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0007
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 50.8945 (df = 15), p-value = 0.0000
## I-square statistic = 70.5%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 89.6090 -175.2180 -173.6729## 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) 0.0002 0.0002 1.1519 0.2494 -0.0002 0.0006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0006
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 46.3598 (df = 15), p-value = 0.0000
## I-square statistic = 67.6%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 90.8851 -177.7701 -176.2249Pooled 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) -0.0001 0.0002 -0.3391 0.7345 -0.0004 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 = 11.0857 (df = 12), p-value = 0.5216
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 74.5670 -145.1340 -144.0041Second 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) 0.0002 0.0002 1.0016 0.3166 -0.0002 0.0006
## ---
## 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 = 14.7259 (df = 15), p-value = 0.4713
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 89.9780 -175.9561 -174.4109Third 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 0.0002 1.2342 0.2171 -0.0001 0.0006
## ---
## 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 = 14.1297 (df = 15), p-value = 0.5157
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 89.9178 -175.8357 -174.2905Results 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) 0.0004 0.0002 2.1899 0.0285 0.0000 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 = 10.1559 (df = 12), p-value = 0.6023
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 75.0786 -146.1573 -145.0274Second 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) 0.0005 0.0002 2.4698 0.0135 0.0001 0.0009 *
## ---
## 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 = 18.8540 (df = 15), p-value = 0.2204
## I-square statistic = 20.4%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 88.2454 -172.4909 -170.9457Third 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) 0.0004 0.0002 1.5280 0.1265 -0.0001 0.0008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0004
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 21.0928 (df = 15), p-value = 0.1339
## I-square statistic = 28.9%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 86.6397 -169.2795 -167.7343Results 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) 0.0002 0.0002 1.3517 0.1765 -0.0001 0.0006
## ---
## 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 = 9.8436 (df = 12), p-value = 0.6297
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 75.4312 -146.8624 -145.7326Second 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) 0.0004 0.0002 2.0265 0.0427 0.0000 0.0007 *
## ---
## 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 = 10.6282 (df = 15), p-value = 0.7785
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 92.3361 -180.6722 -179.1270Third 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) 0.0004 0.0003 1.3953 0.1629 -0.0002 0.0010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0007
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 26.9777 (df = 15), p-value = 0.0289
## I-square statistic = 44.4%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 84.7155 -165.4310 -163.8859Table 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 | 0.988 | 0.972 | 1.005 | 0.990 | 0.971 | 1.008 | 0.988 | 0.970 | 1.006 | 0.198 | -0.198 |
| clermont | 0.990 | 0.968 | 1.012 | 0.995 | 0.970 | 1.020 | 0.986 | 0.964 | 1.009 | 0.496 | -0.891 |
| grenoble | 1.007 | 0.990 | 1.024 | 0.999 | 0.981 | 1.017 | 0.990 | 0.970 | 1.009 | -0.802 | -0.895 |
| lehavre | 0.990 | 0.966 | 1.014 | 0.999 | 0.973 | 1.025 | 1.009 | 0.983 | 1.035 | 0.895 | 1.004 |
| lille | 1.006 | 0.994 | 1.018 | 1.004 | 0.991 | 1.017 | 1.002 | 0.988 | 1.015 | -0.201 | -0.201 |
| lyon | NA | NA | NA | 1.004 | 0.991 | 1.018 | 1.007 | 0.994 | 1.021 | NA | 0.302 |
| marseille | 1.002 | 0.991 | 1.013 | 1.006 | 0.994 | 1.019 | 1.002 | 0.989 | 1.016 | 0.402 | -0.402 |
| montpellier | 1.013 | 0.993 | 1.033 | 1.011 | 0.989 | 1.035 | 0.997 | 0.972 | 1.022 | -0.202 | -1.406 |
| nancy | NA | NA | NA | 0.989 | 0.968 | 1.011 | 0.989 | 0.967 | 1.012 | NA | 0.000 |
| nantes | 0.994 | 0.976 | 1.013 | 1.011 | 0.991 | 1.031 | 0.989 | 0.970 | 1.008 | 1.704 | -2.200 |
| nice | NA | NA | NA | 1.018 | 1.001 | 1.037 | 1.002 | 0.982 | 1.022 | NA | -1.616 |
| paris | 1.000 | 0.994 | 1.005 | 1.001 | 0.995 | 1.008 | 1.006 | 1.000 | 1.013 | 0.100 | 0.502 |
| rennes | 0.987 | 0.959 | 1.015 | 0.993 | 0.962 | 1.024 | 1.020 | 0.991 | 1.050 | 0.594 | 2.718 |
| rouen | 0.987 | 0.969 | 1.005 | 1.016 | 0.996 | 1.037 | 1.006 | 0.987 | 1.025 | 2.904 | -1.011 |
| strasbourg | 0.993 | 0.976 | 1.010 | 0.987 | 0.970 | 1.004 | 1.003 | 0.985 | 1.021 | -0.594 | 1.592 |
| toulouse | 1.007 | 0.990 | 1.025 | 0.995 | 0.976 | 1.014 | 1.013 | 0.992 | 1.034 | -1.201 | 1.807 |
| Period1 | Period2 | Period3 | change12 | change23 |
|---|---|---|---|---|
| 0.9999365 | 1.000192 | 1.00021 | 0.2558002 | 0.0184794 |
####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 | 1.007 | 0.991 | 1.024 | 0.996 | 0.978 | 1.014 | 0.989 | 0.971 | 1.007 | -1.102 | -0.695 |
| clermont | 0.990 | 0.968 | 1.012 | 0.980 | 0.956 | 1.004 | 0.996 | 0.973 | 1.020 | -0.985 | 1.581 |
| grenoble | 1.003 | 0.986 | 1.020 | 1.011 | 0.994 | 1.029 | 0.992 | 0.972 | 1.011 | 0.806 | -1.903 |
| lehavre | 1.002 | 0.977 | 1.027 | 0.991 | 0.967 | 1.017 | 1.014 | 0.988 | 1.040 | -1.096 | 2.306 |
| lille | 1.007 | 0.995 | 1.019 | 1.000 | 0.987 | 1.012 | 0.996 | 0.982 | 1.009 | -0.702 | -0.399 |
| lyon | NA | NA | NA | 1.010 | 0.997 | 1.023 | 1.012 | 0.999 | 1.026 | NA | 0.202 |
| marseille | 1.003 | 0.992 | 1.014 | 1.013 | 1.001 | 1.026 | 0.993 | 0.979 | 1.006 | 1.008 | -2.006 |
| montpellier | 1.011 | 0.992 | 1.031 | 1.011 | 0.989 | 1.035 | 1.027 | 1.002 | 1.053 | 0.000 | 1.631 |
| nancy | NA | NA | NA | 1.009 | 0.987 | 1.033 | 0.992 | 0.969 | 1.015 | NA | -1.701 |
| nantes | 0.997 | 0.978 | 1.016 | 1.019 | 0.999 | 1.039 | 1.008 | 0.988 | 1.027 | 2.218 | -1.115 |
| nice | NA | NA | NA | 1.006 | 0.988 | 1.024 | 1.008 | 0.988 | 1.028 | NA | 0.201 |
| paris | 1.003 | 0.998 | 1.009 | 1.009 | 1.003 | 1.015 | 1.007 | 1.000 | 1.013 | 0.604 | -0.202 |
| rennes | 0.984 | 0.957 | 1.012 | 0.991 | 0.961 | 1.022 | 0.985 | 0.956 | 1.014 | 0.691 | -0.593 |
| rouen | 1.010 | 0.991 | 1.028 | 1.003 | 0.984 | 1.022 | 1.013 | 0.994 | 1.031 | -0.705 | 1.008 |
| strasbourg | 1.001 | 0.984 | 1.017 | 0.987 | 0.971 | 1.004 | 1.006 | 0.988 | 1.023 | -1.392 | 1.893 |
| toulouse | 1.023 | 1.006 | 1.041 | 1.001 | 0.983 | 1.019 | 1.020 | 1.000 | 1.041 | -2.227 | 1.920 |
####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 | 1.005 | 0.989 | 1.021 | 1.004 | 0.986 | 1.022 | 1.009 | 0.992 | 1.027 | -0.100 | 0.503 |
| clermont | 1.010 | 0.988 | 1.034 | 1.002 | 0.978 | 1.027 | 0.984 | 0.962 | 1.007 | -0.805 | -1.787 |
| grenoble | 1.008 | 0.991 | 1.025 | 1.000 | 0.982 | 1.018 | 1.010 | 0.991 | 1.030 | -0.803 | 1.005 |
| lehavre | 1.005 | 0.981 | 1.028 | 0.988 | 0.964 | 1.014 | 0.998 | 0.973 | 1.022 | -1.694 | 0.993 |
| lille | 0.993 | 0.982 | 1.005 | 1.001 | 0.988 | 1.013 | 1.000 | 0.987 | 1.014 | 0.798 | -0.100 |
| lyon | NA | NA | NA | 1.004 | 0.991 | 1.017 | 1.024 | 1.010 | 1.038 | NA | 2.028 |
| marseille | 1.009 | 0.998 | 1.020 | 1.003 | 0.991 | 1.016 | 0.989 | 0.976 | 1.003 | -0.604 | -1.394 |
| montpellier | 1.008 | 0.988 | 1.028 | 1.001 | 0.978 | 1.024 | 1.013 | 0.988 | 1.039 | -0.703 | 1.208 |
| nancy | NA | NA | NA | 1.000 | 0.978 | 1.022 | 1.002 | 0.979 | 1.025 | NA | 0.200 |
| nantes | 1.006 | 0.987 | 1.024 | 1.006 | 0.987 | 1.026 | 0.995 | 0.977 | 1.014 | 0.000 | -1.101 |
| nice | NA | NA | NA | 1.000 | 0.983 | 1.018 | 1.005 | 0.985 | 1.025 | NA | 0.501 |
| paris | 1.002 | 0.996 | 1.007 | 1.005 | 0.999 | 1.012 | 1.005 | 0.999 | 1.012 | 0.301 | 0.000 |
| rennes | 0.981 | 0.954 | 1.009 | 0.986 | 0.957 | 1.016 | 0.971 | 0.945 | 1.000 | 0.492 | -1.468 |
| rouen | 0.993 | 0.975 | 1.010 | 1.024 | 1.005 | 1.044 | 1.015 | 0.996 | 1.034 | 3.127 | -0.918 |
| strasbourg | 1.001 | 0.984 | 1.017 | 0.995 | 0.978 | 1.012 | 1.002 | 0.984 | 1.020 | -0.599 | 0.699 |
| toulouse | 1.012 | 0.994 | 1.029 | 1.016 | 0.997 | 1.035 | 1.020 | 1.000 | 1.041 | 0.406 | 0.407 |
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 