D-R Curves for PM10 and respi mortality over the time with two-stage modeling - Basic and Stratification Models
Anna Alari
8/02/2021
Descriptive Statistics for PM10
| Ville | Minimum | X1st.Qu | Median | Mean | X3rd.Qu | Max | NA.s |
|---|---|---|---|---|---|---|---|
| bordeaux | 0.0000000 | 17.00000 | 22.33333 | 24.68781 | 29.49141 | 101.66667 | 859 |
| clermont | 2.0000000 | 12.66667 | 17.50000 | 19.80135 | 24.00000 | 105.75000 | 372 |
| grenoble | 3.0000000 | 17.00000 | 24.00000 | 27.29601 | 34.00000 | 110.50000 | 821 |
| lehavre | 5.0416667 | 16.79354 | 21.66667 | 24.75754 | 29.33333 | 123.66667 | 415 |
| lille | 0.0000000 | 16.50000 | 22.00000 | 25.53733 | 30.50000 | 131.00000 | 863 |
| lyon | 3.0729167 | 16.11979 | 22.69792 | 26.41374 | 32.00000 | 167.00000 | 2648 |
| marseille | 4.8229167 | 22.66667 | 30.33333 | 31.90142 | 39.00000 | 120.00000 | 432 |
| montpellier | 1.0000000 | 13.67437 | 21.00000 | 22.40362 | 29.00000 | 131.41667 | 751 |
| nancy | 0.0000000 | 14.00000 | 19.71875 | 22.13904 | 27.00000 | 178.00000 | 1743 |
| nantes | 4.5000000 | 14.72830 | 19.00000 | 21.44513 | 25.33333 | 112.00000 | 423 |
| nice | -1.0000000 | 22.91667 | 28.45312 | 29.39850 | 34.85417 | 127.58333 | 2401 |
| paris | 7.0000000 | 22.50000 | 28.80000 | 31.49370 | 37.32765 | 153.50000 | 395 |
| rennes | 0.0000000 | 12.50000 | 17.00000 | 19.34861 | 23.00000 | 101.50000 | 798 |
| rouen | 0.3958333 | 17.67969 | 22.50000 | 25.34694 | 29.50000 | 116.50000 | 407 |
| strasbourg | 2.0000000 | 16.25000 | 22.66667 | 25.45405 | 31.33333 | 167.00000 | 474 |
| toulouse | 3.2000000 | 14.44066 | 19.60000 | 21.15854 | 26.00000 | 95.33333 | 500 |
Descriptive Statistics for pm10 by period (to complete….)
Dose-Response Curves for PM10 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 | 0.999 | 0.996 | 1.002 | 1.001 | 0.998 | 1.004 | 1.001 | 0.998 | 1.004 |
| clermont | 0.997 | 0.993 | 1.002 | 0.996 | 0.991 | 1.000 | 0.997 | 0.993 | 1.002 |
| grenoble | 0.995 | 0.992 | 0.998 | 0.997 | 0.994 | 1.000 | 0.997 | 0.994 | 1.000 |
| lehavre | 1.000 | 0.996 | 1.004 | 1.000 | 0.996 | 1.004 | 0.999 | 0.995 | 1.003 |
| lille | 0.999 | 0.997 | 1.001 | 0.998 | 0.997 | 1.000 | 0.999 | 0.997 | 1.001 |
| lyon | 0.998 | 0.996 | 1.000 | 0.998 | 0.996 | 1.001 | 0.998 | 0.996 | 1.000 |
| marseille | 1.001 | 0.999 | 1.003 | 1.000 | 0.998 | 1.002 | 1.000 | 0.999 | 1.002 |
| montpellier | 1.000 | 0.997 | 1.004 | 1.000 | 0.996 | 1.003 | 0.999 | 0.995 | 1.003 |
| nancy | 1.001 | 0.997 | 1.005 | 1.000 | 0.996 | 1.004 | 1.000 | 0.997 | 1.004 |
| nantes | 0.999 | 0.995 | 1.002 | 1.001 | 0.997 | 1.004 | 0.999 | 0.995 | 1.003 |
| nice | 0.999 | 0.995 | 1.003 | 1.000 | 0.996 | 1.004 | 1.002 | 0.998 | 1.006 |
| paris | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 | 1.001 | 1.001 | 1.002 |
| rennes | 1.000 | 0.995 | 1.005 | 1.002 | 0.997 | 1.007 | 1.000 | 0.995 | 1.005 |
| rouen | 1.004 | 1.001 | 1.007 | 1.001 | 0.998 | 1.004 | 0.997 | 0.994 | 1.001 |
| strasbourg | 1.004 | 1.001 | 1.007 | 1.004 | 1.001 | 1.007 | 1.003 | 1.000 | 1.006 |
| toulouse | 1.000 | 0.997 | 1.004 | 1.001 | 0.997 | 1.004 | 0.998 | 0.995 | 1.002 |
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) -0.0001 0.0006 -0.1752 0.8609 -0.0012 0.0010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0016
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 34.2692 (df = 15), p-value = 0.0031
## I-square statistic = 56.2%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 74.8743 -145.7487 -144.2035## 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) 0.0000 0.0005 0.0645 0.9485 -0.0009 0.0010
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0012
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 28.9041 (df = 15), p-value = 0.0165
## I-square statistic = 48.1%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 77.1258 -150.2516 -148.7065## 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) -0.0003 0.0005 -0.5966 0.5507 -0.0012 0.0006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0012
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 29.4216 (df = 15), p-value = 0.0142
## I-square statistic = 49.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 77.9122 -151.8245 -150.2793Pooled 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) -0.0015 0.0013 -1.1479 0.2510 -0.0041 0.0011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0032
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 25.1285 (df = 12), p-value = 0.0142
## I-square statistic = 52.2%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 49.9554 -95.9107 -94.7808Second 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) 0.0006 0.0005 1.1589 0.2465 -0.0004 0.0016
## ---
## 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 = 11.5801 (df = 15), p-value = 0.7105
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 74.1314 -144.2627 -142.7175Third 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) 0.0004 0.0005 0.7794 0.4358 -0.0006 0.0014
## ---
## 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 = 17.0436 (df = 15), p-value = 0.3163
## I-square statistic = 12.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 72.0013 -140.0026 -138.4575Results 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) -0.0010 0.0013 -0.7731 0.4395 -0.0036 0.0016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0031
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 26.4126 (df = 12), p-value = 0.0094
## I-square statistic = 54.6%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 48.7583 -93.5166 -92.3867Second 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) 0.0006 0.0005 1.1140 0.2653 -0.0005 0.0016
## ---
## 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 = 14.0392 (df = 15), p-value = 0.5226
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 72.8452 -141.6904 -140.1453Third 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) 0.0007 0.0005 1.4733 0.1407 -0.0002 0.0017
## ---
## 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 = 7.5201 (df = 15), p-value = 0.9416
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 76.6982 -149.3963 -147.8511Results 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) -0.0006 0.0007 -0.7905 0.4293 -0.0020 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 = 7.8699 (df = 12), p-value = 0.7952
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 57.5897 -111.1794 -110.0495Second 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) 0.0001 0.0006 0.1381 0.8901 -0.0012 0.0014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0011
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 16.2683 (df = 15), p-value = 0.3644
## I-square statistic = 7.8%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 73.0060 -142.0120 -140.4668Third 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) 0.0000 0.0005 0.0560 0.9553 -0.0010 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 = 12.4608 (df = 15), p-value = 0.6439
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 74.3724 -144.7447 -143.1995Table 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 | 1.015 | 0.946 | 1.090 | 0.991 | 0.935 | 1.051 | 0.983 | 0.939 | 1.030 | -2.407 | -0.790 |
| clermont | 0.929 | 0.836 | 1.033 | 0.975 | 0.899 | 1.057 | 1.006 | 0.933 | 1.083 | 4.664 | 3.071 |
| grenoble | 0.856 | 0.788 | 0.929 | 0.973 | 0.920 | 1.029 | 1.028 | 0.971 | 1.089 | 11.780 | 5.503 |
| lehavre | 1.008 | 0.918 | 1.107 | 0.985 | 0.914 | 1.062 | 0.954 | 0.886 | 1.027 | -2.292 | -3.102 |
| lille | 0.933 | 0.888 | 0.981 | 0.993 | 0.964 | 1.023 | 1.018 | 0.987 | 1.050 | 5.970 | 2.514 |
| lyon | NA | NA | NA | 1.008 | 0.969 | 1.048 | 0.982 | 0.946 | 1.019 | NA | -2.587 |
| marseille | 0.999 | 0.959 | 1.041 | 1.019 | 0.983 | 1.057 | 1.018 | 0.985 | 1.052 | 2.018 | -0.102 |
| montpellier | 1.026 | 0.955 | 1.104 | 0.961 | 0.891 | 1.037 | 1.016 | 0.957 | 1.079 | -6.555 | 5.534 |
| nancy | NA | NA | NA | 0.985 | 0.931 | 1.042 | 1.035 | 0.969 | 1.104 | NA | 4.947 |
| nantes | 1.020 | 0.930 | 1.119 | 1.007 | 0.946 | 1.071 | 0.973 | 0.914 | 1.037 | -1.318 | -3.366 |
| nice | NA | NA | NA | 0.988 | 0.925 | 1.054 | 0.957 | 0.898 | 1.020 | NA | -3.112 |
| paris | 0.996 | 0.976 | 1.015 | 1.009 | 0.994 | 1.025 | 0.998 | 0.982 | 1.013 | 1.303 | -1.104 |
| rennes | 1.030 | 0.905 | 1.174 | 0.971 | 0.886 | 1.066 | 0.979 | 0.889 | 1.079 | -5.904 | 0.780 |
| rouen | 0.985 | 0.912 | 1.063 | 1.039 | 0.985 | 1.095 | 1.054 | 1.000 | 1.113 | 5.362 | 1.570 |
| strasbourg | 1.051 | 0.982 | 1.125 | 1.046 | 0.990 | 1.105 | 1.044 | 0.992 | 1.099 | -0.524 | -0.209 |
| toulouse | 0.969 | 0.900 | 1.042 | 1.048 | 0.983 | 1.117 | 1.023 | 0.965 | 1.085 | 7.962 | -2.486 |
| Period1 | Period2 | Period3 | change12 | change23 |
|---|---|---|---|---|
| 0.9984701 | 1.000597 | 1.000597 | 2.118354 | 0 |
####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 | 1.003 | 0.935 | 1.077 | 1.020 | 0.964 | 1.081 | 1.017 | 0.971 | 1.065 | 1.720 | -0.306 |
| clermont | 0.913 | 0.820 | 1.016 | 0.964 | 0.889 | 1.046 | 0.973 | 0.900 | 1.052 | 5.066 | 0.969 |
| grenoble | 0.899 | 0.826 | 0.980 | 0.977 | 0.924 | 1.033 | 1.034 | 0.976 | 1.094 | 7.784 | 5.629 |
| lehavre | 1.022 | 0.932 | 1.122 | 0.933 | 0.863 | 1.009 | 0.985 | 0.917 | 1.060 | -8.892 | 5.179 |
| lille | 0.933 | 0.887 | 0.982 | 0.993 | 0.964 | 1.023 | 1.001 | 0.969 | 1.033 | 5.970 | 0.798 |
| lyon | NA | NA | NA | 1.007 | 0.969 | 1.047 | 0.985 | 0.948 | 1.022 | NA | -2.191 |
| marseille | 0.985 | 0.946 | 1.027 | 1.013 | 0.976 | 1.051 | 0.997 | 0.965 | 1.031 | 2.797 | -1.608 |
| montpellier | 1.000 | 0.928 | 1.077 | 0.953 | 0.882 | 1.029 | 1.017 | 0.957 | 1.082 | -4.687 | 6.401 |
| nancy | NA | NA | NA | 0.970 | 0.917 | 1.026 | 1.018 | 0.953 | 1.089 | NA | 4.772 |
| nantes | 1.084 | 0.989 | 1.188 | 1.020 | 0.958 | 1.087 | 0.995 | 0.934 | 1.059 | -6.417 | -2.519 |
| nice | NA | NA | NA | 1.003 | 0.939 | 1.071 | 0.983 | 0.924 | 1.047 | NA | -1.986 |
| paris | 0.993 | 0.973 | 1.012 | 1.016 | 1.001 | 1.031 | 1.011 | 0.995 | 1.026 | 2.310 | -0.507 |
| rennes | 1.132 | 1.001 | 1.280 | 0.980 | 0.895 | 1.075 | 1.012 | 0.919 | 1.113 | -15.182 | 3.187 |
| rouen | 0.931 | 0.860 | 1.007 | 1.001 | 0.947 | 1.058 | 1.024 | 0.970 | 1.082 | 7.047 | 2.329 |
| strasbourg | 1.040 | 0.970 | 1.115 | 1.030 | 0.977 | 1.088 | 1.045 | 0.993 | 1.100 | -0.932 | 1.453 |
| toulouse | 1.024 | 0.954 | 1.100 | 1.033 | 0.969 | 1.102 | 1.011 | 0.952 | 1.074 | 0.823 | -2.146 |
####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 | 1.011 | 0.945 | 1.081 | 1.010 | 0.954 | 1.069 | 1.010 | 0.966 | 1.057 | -0.101 | 0.000 |
| clermont | 0.967 | 0.871 | 1.074 | 1.028 | 0.955 | 1.107 | 0.928 | 0.858 | 1.003 | 6.182 | -10.065 |
| grenoble | 0.974 | 0.898 | 1.058 | 0.984 | 0.933 | 1.038 | 1.005 | 0.951 | 1.062 | 0.979 | 2.089 |
| lehavre | 0.914 | 0.829 | 1.007 | 0.970 | 0.902 | 1.045 | 0.990 | 0.922 | 1.063 | 5.651 | 1.960 |
| lille | 0.991 | 0.944 | 1.041 | 0.985 | 0.956 | 1.014 | 0.996 | 0.965 | 1.027 | -0.593 | 1.090 |
| lyon | NA | NA | NA | 1.004 | 0.969 | 1.042 | 0.978 | 0.943 | 1.014 | NA | -2.577 |
| marseille | 1.018 | 0.977 | 1.060 | 1.006 | 0.970 | 1.043 | 1.001 | 0.969 | 1.035 | -1.214 | -0.502 |
| montpellier | 0.983 | 0.912 | 1.060 | 0.987 | 0.916 | 1.064 | 1.006 | 0.946 | 1.069 | 0.394 | 1.893 |
| nancy | NA | NA | NA | 0.963 | 0.912 | 1.017 | 1.035 | 0.970 | 1.104 | NA | 7.187 |
| nantes | 1.009 | 0.919 | 1.106 | 1.024 | 0.964 | 1.088 | 0.960 | 0.903 | 1.020 | 1.525 | -6.446 |
| nice | NA | NA | NA | 1.014 | 0.949 | 1.082 | 1.015 | 0.954 | 1.080 | NA | 0.101 |
| paris | 0.995 | 0.975 | 1.015 | 1.020 | 1.005 | 1.036 | 1.008 | 0.992 | 1.023 | 2.519 | -1.217 |
| rennes | 0.980 | 0.859 | 1.117 | 0.979 | 0.897 | 1.069 | 1.002 | 0.911 | 1.102 | -0.098 | 2.278 |
| rouen | 0.940 | 0.869 | 1.016 | 0.946 | 0.896 | 1.000 | 0.967 | 0.913 | 1.022 | 0.660 | 2.009 |
| strasbourg | 1.023 | 0.955 | 1.096 | 1.024 | 0.973 | 1.078 | 1.030 | 0.979 | 1.084 | 0.102 | 0.616 |
| toulouse | 0.998 | 0.930 | 1.071 | 0.990 | 0.929 | 1.054 | 0.983 | 0.926 | 1.043 | -0.795 | -0.691 |
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 