D-R Curves for PM10 and nocc 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 | 1.000 | 0.999 | 1.001 | 1.001 | 1.000 | 1.001 | 1.001 | 1.000 | 1.001 |
| clermont | 1.000 | 0.999 | 1.001 | 1.000 | 0.999 | 1.002 | 1.000 | 0.999 | 1.001 |
| grenoble | 1.000 | 0.999 | 1.000 | 1.000 | 0.999 | 1.001 | 1.000 | 1.000 | 1.001 |
| lehavre | 1.000 | 0.999 | 1.001 | 1.000 | 0.999 | 1.001 | 1.000 | 0.999 | 1.001 |
| lille | 1.000 | 0.999 | 1.000 | 1.000 | 0.999 | 1.000 | 1.000 | 0.999 | 1.000 |
| lyon | 1.000 | 0.999 | 1.000 | 1.000 | 0.999 | 1.001 | 1.000 | 0.999 | 1.001 |
| marseille | 1.000 | 1.000 | 1.001 | 1.000 | 0.999 | 1.000 | 1.000 | 0.999 | 1.000 |
| montpellier | 1.000 | 0.999 | 1.001 | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 |
| nancy | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 |
| nantes | 1.000 | 0.999 | 1.001 | 1.000 | 0.999 | 1.001 | 1.001 | 1.000 | 1.002 |
| nice | 1.001 | 1.000 | 1.002 | 1.001 | 0.999 | 1.002 | 1.000 | 0.999 | 1.001 |
| paris | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.001 | 1.000 | 1.000 | 1.001 |
| rennes | 1.000 | 0.999 | 1.002 | 1.001 | 1.000 | 1.002 | 1.001 | 1.000 | 1.002 |
| rouen | 1.000 | 0.999 | 1.001 | 1.000 | 0.999 | 1.000 | 0.999 | 0.999 | 1.000 |
| strasbourg | 1.001 | 1.000 | 1.002 | 1.002 | 1.001 | 1.002 | 1.001 | 1.000 | 1.002 |
| toulouse | 1.000 | 0.999 | 1.000 | 1.000 | 0.999 | 1.001 | 0.999 | 0.998 | 1.000 |
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 0.0001 0.6669 0.5049 -0.0001 0.0002
## ---
## 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 = 18.5433 (df = 15), p-value = 0.2352
## I-square statistic = 19.1%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 100.5825 -197.1651 -195.6199## 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 0.0001 2.1341 0.0328 0.0000 0.0005 *
## ---
## 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 = 24.5628 (df = 15), p-value = 0.0561
## I-square statistic = 38.9%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 98.3791 -192.7582 -191.2131## 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 0.0001 1.6532 0.0983 -0.0000 0.0005 .
## ---
## 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 = 31.2671 (df = 15), p-value = 0.0081
## I-square statistic = 52.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 96.2201 -188.4403 -186.8951Pooled 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) -0.0000 0.0002 -0.1020 0.9187 -0.0004 0.0004
## ---
## 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 = 9.3846 (df = 12), p-value = 0.6698
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 74.3822 -144.7644 -143.6345Second 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) 0.0000 0.0001 0.2566 0.7975 -0.0002 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 = 8.0656 (df = 15), p-value = 0.9211
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 97.5044 -191.0088 -189.4636Third 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) -0.0001 0.0001 -0.6974 0.4855 -0.0004 0.0002
## ---
## 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 = 14.1555 (df = 15), p-value = 0.5138
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 94.0817 -184.1635 -182.6183Results 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) 0.0000 0.0002 0.0744 0.9407 -0.0004 0.0004
## ---
## 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 = 12.6021 (df = 12), p-value = 0.3986
## I-square statistic = 4.8%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 72.6693 -141.3385 -140.2087Second 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) 0.0003 0.0001 2.4275 0.0152 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 = 6.5618 (df = 15), p-value = 0.9687
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 98.2878 -192.5756 -191.0304Third 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 0.0001 0.8352 0.4036 -0.0002 0.0004
## ---
## 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 = 13.3012 (df = 15), p-value = 0.5790
## I-square statistic = 1.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 94.4410 -184.8820 -183.3369Results 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 -0.8639 0.3876 -0.0005 0.0002
## ---
## 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 = 8.7591 (df = 12), p-value = 0.7234
## I-square statistic = 1.0%
##
## 13 studies, 13 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 74.6875 -145.3751 -144.2452Second 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 0.0002 0.4257 0.6703 -0.0003 0.0005
## ---
## 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 = 26.0825 (df = 15), p-value = 0.0372
## I-square statistic = 42.5%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 90.4754 -176.9508 -175.4057Third 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) 0.0001 0.0001 0.5103 0.6098 -0.0002 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 = 15.6170 (df = 15), p-value = 0.4079
## I-square statistic = 4.0%
##
## 16 studies, 16 observations, 1 fixed and 1 random-effects parameters
## logLik AIC BIC
## 93.5139 -183.0279 -181.4827Table 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 | 1.010 | 0.991 | 1.028 | 0.997 | 0.982 | 1.012 | 0.997 | 0.984 | 1.009 | -1.305 | 0.000 |
| clermont | 1.001 | 0.977 | 1.025 | 1.005 | 0.986 | 1.025 | 0.989 | 0.969 | 1.009 | 0.401 | -1.595 |
| grenoble | 0.994 | 0.974 | 1.015 | 0.994 | 0.980 | 1.007 | 1.000 | 0.984 | 1.016 | 0.000 | 0.598 |
| lehavre | 0.995 | 0.972 | 1.019 | 1.004 | 0.986 | 1.022 | 0.999 | 0.980 | 1.017 | 0.900 | -0.501 |
| lille | 0.995 | 0.982 | 1.007 | 1.003 | 0.995 | 1.011 | 0.995 | 0.985 | 1.004 | 0.799 | -0.799 |
| lyon | NA | NA | NA | 1.001 | 0.991 | 1.011 | 0.997 | 0.987 | 1.007 | NA | -0.400 |
| marseille | 0.998 | 0.986 | 1.009 | 0.999 | 0.990 | 1.008 | 1.005 | 0.995 | 1.014 | 0.100 | 0.601 |
| montpellier | 1.005 | 0.985 | 1.024 | 0.999 | 0.979 | 1.018 | 1.010 | 0.994 | 1.026 | -0.601 | 1.105 |
| nancy | NA | NA | NA | 1.005 | 0.989 | 1.021 | 1.018 | 1.000 | 1.038 | NA | 1.315 |
| nantes | 0.981 | 0.958 | 1.005 | 1.000 | 0.984 | 1.016 | 1.003 | 0.987 | 1.019 | 1.882 | 0.300 |
| nice | NA | NA | NA | 1.014 | 0.997 | 1.033 | 1.004 | 0.987 | 1.021 | NA | -1.009 |
| paris | 1.002 | 0.997 | 1.007 | 0.999 | 0.995 | 1.003 | 0.997 | 0.992 | 1.001 | -0.300 | -0.200 |
| rennes | 0.981 | 0.944 | 1.020 | 1.000 | 0.975 | 1.026 | 0.998 | 0.970 | 1.026 | 1.882 | -0.200 |
| rouen | 0.993 | 0.974 | 1.013 | 1.001 | 0.987 | 1.015 | 0.996 | 0.982 | 1.010 | 0.798 | -0.499 |
| strasbourg | 1.009 | 0.992 | 1.026 | 1.009 | 0.995 | 1.024 | 0.996 | 0.982 | 1.010 | 0.000 | -1.303 |
| toulouse | 0.989 | 0.970 | 1.008 | 0.991 | 0.974 | 1.008 | 1.013 | 0.996 | 1.029 | 0.198 | 2.204 |
| Period1 | Period2 | Period3 | change12 | change23 |
|---|---|---|---|---|
| 0.9999807 | 1.000035 | 1.000035 | 0.0538353 | 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 | 1.013 | 0.994 | 1.031 | 1.001 | 0.986 | 1.016 | 1.008 | 0.996 | 1.021 | -1.208 | 0.703 |
| clermont | 1.004 | 0.980 | 1.029 | 1.009 | 0.990 | 1.029 | 0.988 | 0.969 | 1.009 | 0.503 | -2.097 |
| grenoble | 1.000 | 0.979 | 1.021 | 1.003 | 0.990 | 1.016 | 1.000 | 0.985 | 1.016 | 0.300 | -0.300 |
| lehavre | 1.008 | 0.985 | 1.033 | 1.005 | 0.987 | 1.022 | 0.998 | 0.979 | 1.016 | -0.302 | -0.701 |
| lille | 0.991 | 0.978 | 1.003 | 1.001 | 0.993 | 1.009 | 0.998 | 0.989 | 1.008 | 0.996 | -0.300 |
| lyon | NA | NA | NA | 1.005 | 0.995 | 1.015 | 1.000 | 0.990 | 1.010 | NA | -0.501 |
| marseille | 0.993 | 0.981 | 1.005 | 0.996 | 0.986 | 1.005 | 1.000 | 0.991 | 1.010 | 0.298 | 0.399 |
| montpellier | 1.004 | 0.984 | 1.024 | 1.009 | 0.989 | 1.029 | 1.016 | 1.000 | 1.034 | 0.503 | 0.709 |
| nancy | NA | NA | NA | 1.005 | 0.989 | 1.021 | 1.010 | 0.991 | 1.028 | NA | 0.504 |
| nantes | 0.989 | 0.965 | 1.013 | 1.005 | 0.989 | 1.020 | 1.001 | 0.985 | 1.017 | 1.595 | -0.401 |
| nice | NA | NA | NA | 0.994 | 0.977 | 1.011 | 1.017 | 1.000 | 1.035 | NA | 2.313 |
| paris | 1.002 | 0.996 | 1.007 | 1.005 | 1.001 | 1.009 | 1.000 | 0.996 | 1.005 | 0.301 | -0.501 |
| rennes | 0.992 | 0.954 | 1.030 | 1.007 | 0.981 | 1.033 | 1.011 | 0.984 | 1.040 | 1.499 | 0.404 |
| rouen | 0.987 | 0.968 | 1.007 | 1.001 | 0.987 | 1.015 | 0.992 | 0.977 | 1.006 | 1.392 | -0.897 |
| strasbourg | 1.016 | 0.999 | 1.034 | 1.008 | 0.994 | 1.023 | 1.003 | 0.989 | 1.017 | -0.810 | -0.503 |
| toulouse | 0.996 | 0.977 | 1.015 | 0.999 | 0.982 | 1.016 | 1.002 | 0.985 | 1.018 | 0.299 | 0.300 |
####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 | 0.983 | 1.019 | 1.012 | 0.998 | 1.027 | 1.006 | 0.994 | 1.019 | 1.107 | -0.605 |
| clermont | 0.995 | 0.971 | 1.020 | 1.004 | 0.985 | 1.023 | 0.980 | 0.961 | 1.000 | 0.900 | -2.381 |
| grenoble | 1.008 | 0.987 | 1.028 | 1.000 | 0.987 | 1.013 | 1.004 | 0.989 | 1.019 | -0.803 | 0.401 |
| lehavre | 0.996 | 0.973 | 1.020 | 1.002 | 0.985 | 1.020 | 0.995 | 0.977 | 1.014 | 0.599 | -0.699 |
| lille | 0.991 | 0.978 | 1.004 | 0.999 | 0.990 | 1.007 | 0.999 | 0.990 | 1.009 | 0.796 | 0.000 |
| lyon | NA | NA | NA | 1.006 | 0.997 | 1.016 | 0.993 | 0.983 | 1.003 | NA | -1.299 |
| marseille | 1.000 | 0.988 | 1.012 | 0.987 | 0.977 | 0.996 | 1.002 | 0.992 | 1.011 | -1.292 | 1.492 |
| montpellier | 1.009 | 0.989 | 1.029 | 1.017 | 0.997 | 1.037 | 1.004 | 0.988 | 1.020 | 0.810 | -1.314 |
| nancy | NA | NA | NA | 1.008 | 0.993 | 1.022 | 1.015 | 0.996 | 1.034 | NA | 0.708 |
| nantes | 1.019 | 0.995 | 1.044 | 1.002 | 0.987 | 1.018 | 1.010 | 0.994 | 1.026 | -1.718 | 0.805 |
| nice | NA | NA | NA | 0.990 | 0.972 | 1.007 | 1.015 | 0.998 | 1.033 | NA | 2.506 |
| paris | 0.998 | 0.993 | 1.003 | 1.004 | 1.000 | 1.008 | 1.000 | 0.996 | 1.005 | 0.601 | -0.401 |
| rennes | 0.992 | 0.955 | 1.030 | 1.012 | 0.987 | 1.038 | 1.003 | 0.976 | 1.031 | 2.004 | -0.907 |
| rouen | 0.991 | 0.972 | 1.010 | 0.986 | 0.973 | 1.000 | 0.993 | 0.979 | 1.007 | -0.494 | 0.693 |
| strasbourg | 1.003 | 0.986 | 1.020 | 1.004 | 0.990 | 1.017 | 1.002 | 0.988 | 1.016 | 0.100 | -0.201 |
| toulouse | 0.986 | 0.969 | 1.005 | 0.997 | 0.981 | 1.013 | 1.004 | 0.988 | 1.020 | 1.091 | 0.700 |
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 