D-R Curves for PM10 and cv mortality over the time with two-stage modeling - Basic and Stratification Models
Anna Alari
5/02/2021
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 | 1.001 | 0.999 | 1.002 | 1.002 | 1.000 | 1.003 | 1.001 | 1.000 | 1.003 |
| clermont | 1.000 | 0.998 | 1.002 | 1.001 | 0.999 | 1.003 | 1.001 | 0.999 | 1.003 |
| grenoble | 1.000 | 0.998 | 1.001 | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 |
| lehavre | 1.002 | 1.000 | 1.004 | 1.002 | 1.000 | 1.004 | 1.002 | 1.000 | 1.003 |
| lille | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 |
| lyon | 1.000 | 0.998 | 1.001 | 0.999 | 0.998 | 1.001 | 1.001 | 0.999 | 1.002 |
| marseille | 1.000 | 0.999 | 1.001 | 0.999 | 0.998 | 1.000 | 1.000 | 0.999 | 1.001 |
| montpellier | 1.002 | 1.000 | 1.004 | 1.001 | 0.999 | 1.003 | 1.001 | 0.999 | 1.003 |
| nancy | 1.000 | 0.998 | 1.002 | 0.999 | 0.996 | 1.001 | 0.999 | 0.997 | 1.001 |
| nantes | 1.000 | 0.998 | 1.002 | 1.000 | 0.998 | 1.001 | 1.000 | 0.999 | 1.002 |
| nice | 1.003 | 1.001 | 1.005 | 1.002 | 1.000 | 1.005 | 1.001 | 0.999 | 1.003 |
| paris | 0.999 | 0.999 | 1.000 | 1.000 | 0.999 | 1.000 | 0.999 | 0.999 | 1.000 |
| rennes | 1.000 | 0.998 | 1.003 | 1.001 | 0.998 | 1.003 | 1.000 | 0.997 | 1.003 |
| rouen | 0.999 | 0.998 | 1.001 | 1.000 | 0.999 | 1.002 | 1.000 | 0.998 | 1.001 |
| strasbourg | 1.000 | 0.999 | 1.002 | 1.000 | 0.999 | 1.002 | 1.000 | 0.999 | 1.002 |
| toulouse | 1.000 | 0.999 | 1.002 | 1.001 | 0.999 | 1.002 | 1.001 | 0.999 | 1.003 |
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) 0.0003 0.0002 1.3055 0.1917 -0.0002 0.0008
## ---
## 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 = 27.1392 (df = 15), p-value = 0.0276
## I-square statistic = 44.7%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 88.3677 -172.7354 -171.1902## 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 1.6366 0.1017 -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
## 0.0006
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 30.2930 (df = 15), p-value = 0.0109
## I-square statistic = 50.5%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 87.3969 -170.7939 -169.2487## 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 0.0002 1.5413 0.1233 -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
## 0.0004
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 18.9320 (df = 15), p-value = 0.2168
## I-square statistic = 20.8%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 92.2195 -180.4390 -178.8938Pooled Dose-Responses Curves For Basic Model
3 Periods Stratified analysis
For Lag0, Lag1 and Lag2 exposure
Meta-Analysis results
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 0.0003 0.6033 0.5463 -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 = 8.6322 (df = 12), p-value = 0.7340
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 67.1544 -130.3089 -129.1790Second 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 0.0003 0.5140 0.6072 -0.0004 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 = 13.6437 (df = 15), p-value = 0.5527
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 84.7671 -165.5341 -163.9890Third 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 0.0003 -1.5051 0.1323 -0.0010 0.0001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Between-study random-effects (co)variance components
## Std. Dev
## 0.0002
##
## Univariate Cochran Q-test for heterogeneity:
## Q = 12.4289 (df = 15), p-value = 0.6463
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 84.0753 -164.1506 -162.6054Results 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) 0.0002 0.0003 0.6111 0.5411 -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 = 7.4933 (df = 12), p-value = 0.8234
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 67.5999 -131.1999 -130.0700Second 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 0.0003 -0.1745 0.8615 -0.0007 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 = 19.2511 (df = 15), p-value = 0.2026
## I-square statistic = 22.1%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 82.1195 -160.2390 -158.6939Third 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) 0.0001 0.0003 0.2219 0.8244 -0.0005 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 = 11.4157 (df = 15), p-value = 0.7226
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 84.5292 -165.0584 -163.5133Results 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) 0.0001 0.0003 0.1529 0.8785 -0.0006 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 = 6.3634 (df = 12), p-value = 0.8967
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 68.2402 -132.4803 -131.3504Second 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 0.0003 -0.2483 0.8039 -0.0007 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 = 21.0612 (df = 15), p-value = 0.1349
## I-square statistic = 28.8%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 81.8479 -159.6959 -158.1507Third 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 0.0003 0.1229 0.9022 -0.0005 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 = 7.8748 (df = 15), p-value = 0.9287
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 86.4956 -168.9912 -167.4460Table 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 | 1.012 | 0.979 | 1.047 | 0.995 | 0.968 | 1.024 | 0.998 | 0.972 | 1.024 | -1.706 | 0.299 |
| clermont | 1.004 | 0.962 | 1.047 | 1.008 | 0.971 | 1.045 | 0.989 | 0.951 | 1.027 | 0.402 | -1.897 |
| grenoble | 0.982 | 0.946 | 1.018 | 0.997 | 0.973 | 1.021 | 1.007 | 0.976 | 1.038 | 1.484 | 1.002 |
| lehavre | 1.001 | 0.957 | 1.048 | 1.034 | 0.999 | 1.068 | 1.007 | 0.971 | 1.044 | 3.255 | -2.653 |
| lille | 1.019 | 0.995 | 1.043 | 1.002 | 0.987 | 1.017 | 0.994 | 0.975 | 1.013 | -1.718 | -0.798 |
| lyon | NA | NA | NA | 0.996 | 0.977 | 1.015 | 0.977 | 0.958 | 0.997 | NA | -1.875 |
| marseille | 1.000 | 0.980 | 1.020 | 0.991 | 0.973 | 1.008 | 1.001 | 0.983 | 1.020 | -0.896 | 0.996 |
| montpellier | 1.000 | 0.967 | 1.035 | 1.037 | 1.001 | 1.074 | 1.013 | 0.980 | 1.046 | 3.666 | -2.357 |
| nancy | NA | NA | NA | 1.003 | 0.972 | 1.035 | 1.010 | 0.971 | 1.049 | NA | 0.705 |
| nantes | 0.989 | 0.945 | 1.035 | 1.006 | 0.977 | 1.036 | 1.022 | 0.990 | 1.055 | 1.696 | 1.623 |
| nice | NA | NA | NA | 1.024 | 0.993 | 1.058 | 1.007 | 0.974 | 1.041 | NA | -1.727 |
| paris | 0.998 | 0.989 | 1.008 | 1.002 | 0.994 | 1.010 | 0.990 | 0.981 | 0.999 | 0.400 | -1.195 |
| rennes | 1.052 | 0.982 | 1.126 | 0.994 | 0.947 | 1.043 | 1.016 | 0.964 | 1.073 | -5.830 | 2.211 |
| rouen | 0.991 | 0.956 | 1.026 | 0.994 | 0.969 | 1.020 | 0.987 | 0.960 | 1.015 | 0.298 | -0.693 |
| strasbourg | 1.016 | 0.986 | 1.047 | 0.996 | 0.969 | 1.023 | 1.004 | 0.976 | 1.033 | -2.012 | 0.800 |
| toulouse | 1.018 | 0.987 | 1.051 | 0.981 | 0.951 | 1.012 | 0.998 | 0.967 | 1.030 | -3.698 | 1.682 |
| Period1 | Period2 | Period3 | change12 | change23 |
|---|---|---|---|---|
| 1.000206 | 1.00013 | 1.00013 | -0.0753733 | 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 | 1.019 | 0.986 | 1.053 | 0.997 | 0.969 | 1.026 | 1.011 | 0.985 | 1.038 | -2.218 | 1.406 |
| clermont | 1.018 | 0.975 | 1.063 | 1.010 | 0.973 | 1.048 | 0.998 | 0.960 | 1.038 | -0.811 | -1.205 |
| grenoble | 1.003 | 0.966 | 1.041 | 1.014 | 0.991 | 1.039 | 1.004 | 0.973 | 1.036 | 1.109 | -1.009 |
| lehavre | 0.986 | 0.942 | 1.033 | 1.038 | 1.004 | 1.073 | 1.003 | 0.968 | 1.040 | 5.160 | -3.469 |
| lille | 1.003 | 0.978 | 1.027 | 1.007 | 0.992 | 1.022 | 1.007 | 0.988 | 1.026 | 0.402 | 0.000 |
| lyon | NA | NA | NA | 0.983 | 0.965 | 1.003 | 0.980 | 0.960 | 1.000 | NA | -0.295 |
| marseille | 0.996 | 0.976 | 1.016 | 0.976 | 0.960 | 0.994 | 0.991 | 0.973 | 1.010 | -1.972 | 1.475 |
| montpellier | 0.994 | 0.960 | 1.028 | 1.007 | 0.970 | 1.044 | 1.018 | 0.985 | 1.052 | 1.301 | 1.114 |
| nancy | NA | NA | NA | 0.992 | 0.962 | 1.023 | 1.011 | 0.972 | 1.050 | NA | 1.903 |
| nantes | 0.993 | 0.947 | 1.040 | 0.995 | 0.967 | 1.024 | 1.017 | 0.985 | 1.050 | 0.199 | 2.213 |
| nice | NA | NA | NA | 1.001 | 0.969 | 1.034 | 1.020 | 0.988 | 1.054 | NA | 1.920 |
| paris | 0.998 | 0.989 | 1.008 | 1.001 | 0.994 | 1.009 | 0.999 | 0.990 | 1.008 | 0.300 | -0.200 |
| rennes | 1.021 | 0.955 | 1.093 | 1.007 | 0.960 | 1.057 | 1.022 | 0.969 | 1.078 | -1.420 | 1.522 |
| rouen | 1.007 | 0.971 | 1.044 | 1.005 | 0.980 | 1.031 | 0.995 | 0.968 | 1.023 | -0.201 | -1.000 |
| strasbourg | 1.031 | 1.000 | 1.063 | 0.985 | 0.959 | 1.011 | 1.008 | 0.980 | 1.037 | -4.637 | 2.292 |
| toulouse | 1.012 | 0.981 | 1.045 | 1.007 | 0.976 | 1.039 | 0.990 | 0.958 | 1.022 | -0.505 | -1.697 |
####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 | 1.001 | 0.969 | 1.035 | 1.010 | 0.982 | 1.039 | 1.008 | 0.982 | 1.034 | 0.905 | -0.202 |
| clermont | 1.007 | 0.965 | 1.051 | 0.993 | 0.959 | 1.029 | 0.995 | 0.958 | 1.033 | -1.400 | 0.199 |
| grenoble | 0.991 | 0.956 | 1.028 | 1.019 | 0.996 | 1.042 | 0.992 | 0.963 | 1.022 | 2.814 | -2.715 |
| lehavre | 1.008 | 0.964 | 1.054 | 1.033 | 0.999 | 1.066 | 0.984 | 0.949 | 1.020 | 2.449 | -4.839 |
| lille | 0.993 | 0.969 | 1.018 | 1.005 | 0.990 | 1.020 | 0.999 | 0.980 | 1.018 | 1.199 | -0.601 |
| lyon | NA | NA | NA | 1.004 | 0.986 | 1.022 | 0.993 | 0.973 | 1.013 | NA | -1.098 |
| marseille | 1.015 | 0.995 | 1.036 | 0.977 | 0.960 | 0.994 | 1.007 | 0.988 | 1.025 | -3.785 | 2.976 |
| montpellier | 0.995 | 0.962 | 1.030 | 1.005 | 0.969 | 1.042 | 1.016 | 0.983 | 1.050 | 1.000 | 1.112 |
| nancy | NA | NA | NA | 1.002 | 0.972 | 1.031 | 0.994 | 0.956 | 1.033 | NA | -0.798 |
| nantes | 1.022 | 0.976 | 1.070 | 0.990 | 0.962 | 1.018 | 1.018 | 0.987 | 1.050 | -3.219 | 2.811 |
| nice | NA | NA | NA | 0.979 | 0.948 | 1.010 | 1.018 | 0.985 | 1.051 | NA | 3.894 |
| paris | 0.999 | 0.989 | 1.008 | 0.994 | 0.987 | 1.002 | 0.997 | 0.988 | 1.006 | -0.498 | 0.299 |
| rennes | 0.992 | 0.926 | 1.062 | 1.009 | 0.964 | 1.057 | 1.003 | 0.951 | 1.057 | 1.701 | -0.604 |
| rouen | 1.014 | 0.979 | 1.050 | 0.987 | 0.962 | 1.012 | 0.992 | 0.965 | 1.019 | -2.701 | 0.495 |
| strasbourg | 0.999 | 0.969 | 1.029 | 0.996 | 0.971 | 1.022 | 1.012 | 0.984 | 1.040 | -0.299 | 1.606 |
| toulouse | 0.979 | 0.948 | 1.011 | 1.020 | 0.991 | 1.051 | 1.011 | 0.979 | 1.044 | 4.098 | -0.914 |
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 