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Time Series Study of Air Pollution Health Effects in COPSAC Children
4 Results
4.1 Population 1 - Inner City Copenhagen
4.2 Population 2 - Copenhagen Suburbs
4.3 Population 3 - Rest Of Zealand
Results are presented separately for three study populations defined in Section 2.1. Each table presents estimated effect of a unit increase in a pollutant using the GAM model (Section 3.1) with three lag
modeling methods: single day exposure lag model, the unconstrained distributed lag model, and the 6-day moving average (Section 3.2). Each table presents results for a single pollutant, from all measuring
stations available for that pollutant. Effect of confounders, temperature and time, are presented elsewhere (Appendix D).
4.1 Population 1 – Inner City Copenhagen
From Table 4.1.1, 6-day moving average results indicate that Copenhagen city background levels of CO are positively but not significantly associated with development of respiratory symptoms in
COPSAC children living in inner city, while street level concentrations of CO are significantly positively associated with the outcome. An increase in 1 ppm in 6-day average CO measured at street level
(Jagtvej and HCAB) is associated with 1.89 fold and 3.11 fold (with wide confidence intervals) increase respectively in in new cases of respiratory children living in Copenhagen inner city the following
Table 4.1.1: CO (ppm) effect in Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.699 |
1.626 |
| Lag 0 |
1.410 |
0.344 (0.58) |
0.56 |
1.810 |
0.593 (0.680) |
0.38 |
| Lag 1 |
0.773 |
-0.257 (0.60) |
0.67 |
0.435 |
-0.831 (0.775) |
0.28 |
| Lag 2 |
2.053 |
0.719 (0.56) |
0.20 |
1.516 |
0.416 (0.725) |
0.56 |
| Lag 3 |
3.455 |
1.240 (0.54) |
0.02 |
2.944 |
1.080 (0.710) |
0.13 |
| Lag 4 |
1.991 |
0.689 (0.57) |
0.23 |
1.171 |
0.158 (0.745) |
0.83 |
| Lag 5 |
1.701 |
0.531 (0.58) |
0.35 |
1.061 |
0.059 (0.682) |
0.93 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
4.898 |
1.589 (0.95) |
0.10 |
| Jagtvej (Street Level) |
| n |
1.699 |
1.573 |
| Lag 0 |
1.112 |
0.106 (0.15) |
0.48 |
0.987 |
-0.013 (0.312) |
0.94 |
| Lag 1 |
1.086 |
0.082 (0.15) |
0.57 |
0.851 |
-0.161 (0.180) |
0.37 |
| Lag 2 |
1.396 |
0.334 (0.15) |
0.02 |
1.365 |
0.311 (0.172) |
0.07 |
| Lag 3 |
1.492 |
0.400 (0.15) |
0.01 |
1.211 |
0.191 (0.176) |
0.28 |
| Lag 4 |
1.314 |
0.273 (0.15) |
0.07 |
1.096 |
0.092 (0.176) |
0.60 |
| Lag 5 |
1.293 |
0.257 (0.15) |
0.08 |
1.228 |
0.206 (0.166) |
0.22 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.894 |
0.638 (0.27) |
0.02 |
| HCAB (Street Level) |
| n |
775 |
735 |
| Lag 0 |
1.305 |
0.266 (0.24) |
0.26 |
1.067 |
0.065 (0.269) |
0.80 |
| Lag 1 |
1.428 |
0.356 (0.24) |
0.13 |
1.195 |
0.178 (0.280) |
0.52 |
| Lag 2 |
1.504 |
0.408 (0.24) |
0.08 |
1.282 |
0.249 (0.279) |
0.37 |
| Lag 3 |
1.427 |
0.356 (0.24) |
0.13 |
1.224 |
0.203 (0.284) |
0.48 |
| Lag 4 |
1.498 |
0.404 (0.24) |
0.09 |
1.317 |
0.275 (0.285) |
0.33 |
| Lag 5 |
1.188 |
0.172 (0.24) |
0.48 |
1.144 |
0.135 (0.268) |
0.62 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
3.110 |
1.134 (0.465) |
0.01 |
days. Estimates from a single day exposure lag models and unconstrained distributed lag model, indicate for all, background and street levels, that concurrent day pollution has weak effect on the
development of the symptoms on the same day, but that effect increases and lasts over several days, peaking at around 2 (street levels) or 3 (background levels) days delay.
Table 4.1.2: NOx (ppb) effect on Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.621 |
1.533 |
| Lag 0 |
1.000 |
-0.000 (0.01) |
0.98 |
1.000 |
0.000 (0.01) |
0.97 |
| Lag 1 |
1.001 |
0.001 (0.01) |
0.84 |
0.997 |
-0.003 (0.01) |
0.72 |
| Lag 2 |
1.012 |
0.012 (0.01) |
0.05 |
1.009 |
0.009 (0.01) |
0.23 |
| Lag 3 |
1.013 |
0.013 (0.01) |
0.02 |
1.010 |
0.010 (0.01) |
0.16 |
| Lag 4 |
1.007 |
0.007 (0.01) |
0.26 |
1.002 |
0.002 (0.01) |
0.79 |
| Lag 5 |
1.003 |
0.003 (0.01) |
0.61 |
0.998 |
-0.007 (0.01) |
0.81 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.019 |
0.019 (0.01) |
0.07 |
| Jagtvej (Street Level) |
| n |
1.668 |
1.572 |
| Lag 0 |
1.000 |
0.000 (0.00) |
0.98 |
0.999 |
-0.001 (0.00) |
0.59 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.78 |
0.998 |
-0.002 (0.00) |
0.37 |
| Lag 2 |
1.005 |
0.005 (0.00) |
0.01 |
1.005 |
0.005 (0.00) |
0.02 |
| Lag 3 |
1.004 |
0.004 (0.00) |
0.04 |
1.001 |
0.001 (0.00) |
0.58 |
| Lag 4 |
1.002 |
0.002 (0.00) |
0.18 |
1.001 |
0.001 (0.00) |
0.70 |
| Lag 5 |
1.002 |
0.002 (0.00) |
0.19 |
1.002 |
0.002 (0.00) |
0.40 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.006 |
0.006 (0.00) |
0.05 |
| HCAB (Street Level) |
| n |
767 |
731 |
| Lag 0 |
1.002 |
0.002 (0.00) |
0.28 |
1.001 |
0.001 (0.00) |
0.61 |
| Lag 1 |
1.002 |
0.002 (0.00) |
0.19 |
1.002 |
0.002 (0.00) |
0.35 |
| Lag 2 |
1.005 |
0.005 (0.00) |
0.00 |
1.004 |
0.004 (0.00) |
0.03 |
| Lag 3 |
1.003 |
0.003 (0.00) |
0.06 |
1.002 |
0.002 (0.00) |
0.36 |
| Lag 4 |
1.004 |
0.004 (0.00) |
0.05 |
1.002 |
0.002 (0.00) |
0.29 |
| Lag 5 |
1.002 |
0.002 (0.00) |
0.30 |
1.001 |
0.001 (0.00) |
0.68 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.013 |
0.013 (0.00) |
0.00 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
In Table 4.1.2, 6-day average results indicate that Copenhagen city background levels of NOx are positively and borderline significantly associated with development of respiratory symptoms in COPSAC
children living in inner city, whereas the association is significant in one street station. A unit increase in 6-day average city background NOx pollution levels results in 1.9 % increase in new respiratory cases
the next day, while a unit increase in 6-day average street level NOx pollution at Jagtvej and HCAB results in 0.6% and 1.3% increase in new respiratory cases the next day respectively. Looking at
estimates from a single day exposure lag model and unconstrained distributed lag models, for all, background and street levels, it can be seen that concurrent day pollution has weak or no effect on the
development of the new respiratory symptoms. This effect is increasing with a few days' delay, that seems to be strongest with a 3-day delay at city background levels, and 2-day delay at street levels.
In Table 4.1.3, 6-day average results indicate that there is positive but no significant association between Copenhagen city and street level NO2 pollution levels and development of respiratory symptoms in
COPSAC children living in inner city. Looking at estimates from a single day exposure lag model and unconstrained distributed lag models, for background and street NO2 levels, it can be seen that
concurrent day pollution has weak or no effect on the development of the new respiratory symptoms, but as seen in NOx, this effect is increasing with a delay, that seems to be strongest with a 2-day delay.
Table 4.1.3: NO2 (ppb) effect on Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.621 |
1.533 |
| Lag 0 |
0.991 |
-0.009 (0.01) |
0.46 |
0.995 |
-0.004 (0.01) |
0.74 |
| Lag 1 |
1.000 |
-0.000 (0.01) |
0.97 |
0.989 |
-0.011 (0.01) |
0.50 |
| Lag 2 |
1.025 |
0.025 (0.01) |
0.03 |
1.024 |
0.024 (0.01) |
0.11 |
| Lag 3 |
1.029 |
0.029 (0.01) |
0.01 |
1.011 |
0.011 (0.01) |
0.47 |
| Lag 4 |
1.016 |
0.016 (0.01) |
0.17 |
1.004 |
0.005 (0.01) |
0.79 |
| Lag 5 |
1.015 |
0.015 (0.01) |
0.20 |
1.003 |
0.003 (0.01) |
0.83 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.032 |
0.031 (0.02) |
0.10 |
| Jagtvej (Street Level) |
| n |
1.668 |
1.572 |
| Lag 0 |
0.994 |
-0.006 (0.01) |
0.41 |
0.992 |
-0.008 (0.01) |
0.36 |
| Lag 1 |
0.996 |
-0.004 (0.01) |
0.57 |
0.990 |
-0.010 (0.01) |
0.27 |
| Lag 2 |
1.011 |
0.011 (0.01) |
0.10 |
1.019 |
0.019 (0.01) |
0.04 |
| Lag 3 |
1.007 |
0.007 (0.01) |
0.32 |
0.999 |
-0.000 (0.01) |
0.95 |
| Lag 4 |
1.005 |
0.005 (0.01) |
0.47 |
1.003 |
0.003 (0.01) |
0.77 |
| Lag 5 |
1.006 |
0.006 (0.01) |
0.39 |
1.004 |
0.004 (0.01) |
0.60 |
| Moving Average Lag Model – Mean(Lag0 - 5) |
1.009 |
0.009 (0.01) |
0.46 |
| HCAB (Street Level) |
| n |
767 |
731 |
| Lag 0 |
1.000 |
-0.000 (0.01) |
0.99 |
0.996 |
-0.004 (0.01) |
0.66 |
| Lag 1 |
1.011 |
0.011 (0.01) |
0.24 |
0.997 |
-0.003 (0.01) |
0.77 |
| Lag 2 |
1.026 |
0.026 (0.01) |
0.01 |
1.029 |
0.029 (0.01) |
0.01 |
| Lag 3 |
1.008 |
0.008 (0.01) |
0.35 |
0.993 |
-0.007 (0.01) |
0.55 |
| Lag 4 |
1.012 |
0.012 (0.01) |
0.19 |
1.006 |
0.006 (0.01) |
0.58 |
| Lag 5 |
1.006 |
0.006 (0.01) |
0.54 |
0.997 |
-0.003 (0.01) |
0.76 |
| Moving Average Lag Model – Mean(Lag0 - 5) |
1.025 |
0.025 (0.02) |
0.11 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
In Table 4.1.4 can be seen that Copenhagen city background O3 is positively but not significantly associated with development of respiratory symptoms in COPSAC children living in inner city. This effect
seems to be strongest after 2-day lag. However, Copenhagen city street level O3 is negatively associated with the outcome, with this protective effect being more or less constant over the 5 days. Thus, a 1
ppb increase in 6-day average Jagtvej and HCAB O3 levels is associated with a 2% and 4.5% decrease in new respiratory cases in children the following days.
Table 4.1.5 shows that the incidence of new respiratory symptoms is positively and borderline significant with respect to the 6-day moving average model associated with the Copenhagen city background
levels of PM10. The estimate indicate that a 1 /m³ increase in 6-day average PM10 Copenhagen city background levels results in 1% increase in development of new respiratory symptoms in children living
in inner city the next 5 days. Estimates from a single day exposure lag model and unconstrained distributed lag models indicate that the effect is delayed with the strongest effect after 3 or 4 days.
Table 4.1.4: O3 (ppb) effect on Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
324 |
309 |
| Lag 0 |
1.012 |
0.012 (0.05) |
0.81 |
1.034 |
0.034 (0.06) |
0.60 |
| Lag 1 |
1.011 |
0.011 (0.05) |
0.82 |
0.897 |
-0.109 (0.08) |
0.15 |
| Lag 2 |
1.168 |
0.155 (0.06) |
0.01 |
1.282 |
0.248 (0.08) |
0.00 |
| Lag 3 |
0.966 |
-0.035 (0.05) |
0.49 |
0.890 |
-0.116 (0.06) |
0.07 |
| Lag 4 |
0.971 |
-0.029 (0.05) |
0.56 |
0.992 |
-0.008 (0.07) |
0.91 |
| Lag 5 |
0.970 |
-0.031 (0.05) |
0.54 |
0.956 |
-0.046 (0.06) |
0.48 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.022 |
0.022 (0.08) |
0.77 |
| Jagtvej (Street Level) |
| n |
1.680 |
1.599 |
| Lag 0 |
0.986 |
-0.014 (0.01) |
0.06 |
0.990 |
-0.010 (0.01) |
0.36 |
| Lag 1 |
0.991 |
-0.009 (0.01) |
0.25 |
1.020 |
0.020 (0.01) |
0.10 |
| Lag 2 |
0.984 |
-0.016 (0.01) |
0.04 |
0.991 |
-0.009 (0.01) |
0.46 |
| Lag 3 |
0.978 |
-0.023 (0.01) |
0.00 |
0.981 |
-0.018 (0.01) |
0.14 |
| Lag 4 |
0.989 |
-0.011 (0.01) |
0.16 |
1.013 |
-0.013 (0.01) |
0.28 |
| Lag 5 |
0.984 |
-0.011 (0.01) |
0.03 |
0.982 |
-0.018 (0.01) |
0.09 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.980 |
-0.020 (0.01) |
0.05 |
| HCAB (Street Level) |
| n |
683 |
655 |
| Lag 0 |
0.993 |
-0.007 (0.01) |
0.50 |
0.991 |
-0.009 (0.01) |
0.47 |
| Lag 1 |
0.993 |
-0.007 (0.01) |
0.52 |
1.000 |
0.000 (0.01) |
0.98 |
| Lag 2 |
0.982 |
-0.018 (0.01) |
0.08 |
0.988 |
-0.012 (0.01) |
0.42 |
| Lag 3 |
0.983 |
-0.017 (0.01) |
0.11 |
0.987 |
-0.013 (0.01) |
0.35 |
| Lag 4 |
0.990 |
-0.010 (0.01) |
0.36 |
1.001 |
0.001 (0.01) |
0.92 |
| Lag 5 |
0.985 |
-0.015 (0.01) |
0.14 |
0.986 |
-0.014 (0.01) |
0.28 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.955 |
-0.046 (0.02) |
0.00 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.1.5: PM10 (µg/m³)(combined with HCØ measurements and extrapolated values from Jagtvej) effect on Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.274 |
1.060 |
| Lag 0 |
1.000 |
-0.000 (0.00) |
0.93 |
1.002 |
0.002 (0.01) |
0.77 |
| Lag 1 |
0.998 |
-0.002 (0.00) |
0.71 |
0.995 |
-0.005 (0.01) |
0.49 |
| Lag 2 |
1.002 |
0.002 (0.00) |
0.64 |
1.000 |
0.000 (0.01) |
0.95 |
| Lag 3 |
1.008 |
0.008 (0.00) |
0.03 |
1.001 |
0.001 (0.01) |
0.83 |
| Lag 4 |
1.007 |
0.007 (0.00) |
0.04 |
1.009 |
0.009 (0.01) |
0.18 |
| Lag 5 |
1.003 |
0.004 (0.00) |
0.42 |
0.996 |
-0.004 (0.01) |
0.49 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.010 |
0.010 (0.01) |
0.07 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.1.6 shows that there is a positive but non-significant association between both, street level (Jagtvej) and rural level (Valby) PM10 and incidence of respiratory illness in small children living in inner
city. The distributed lag models indicate, in agreement with city background PM10 results, that this association is strongest after 3 to 4 day lag.
Table 4.1.6: PM10 (µg/m³) (measured by SM200 gravimetric method) effect on Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| Jagtvej (Street Level) |
| N |
784 |
663 |
| Lag 0 |
1.000 |
0.000 (0.00) |
0.96 |
1.005 |
0.005 (0.01) |
0.45 |
| Lag 1 |
0.999 |
-0.000 (0.01) |
0.91 |
0.989 |
-0.011 (0.01) |
0.16 |
| Lag 2 |
1.004 |
0.004 (0.01) |
0.35 |
1.004 |
0.004 (0.01) |
0.60 |
| Lag 3 |
1.008 |
0.008 (0.01) |
0.04 |
1.006 |
0.006(0.01) |
0.41 |
| Lag 4 |
1.008 |
0.008 (0.01) |
0.04 |
1.001 |
0.001 (0.01) |
0.89 |
| Lag 5 |
1.003 |
0.003 (0.01) |
0.40 |
0.998 |
-0.002 (0.01) |
0.76 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.004 |
0.004 (0.01) |
0.54 |
| Lille Valby (Rural Level) |
| n |
723 |
607 |
| Lag 0 |
1.003 |
0.003 (0.01) |
0.62 |
1.002 |
0.002 (0.01) |
0.81 |
| Lag 1 |
1.002 |
0.002 (0.01) |
0.67 |
0.995 |
-0.005 (0.01) |
0.63 |
| Lag 2 |
1.006 |
0.006 (0.01) |
0.245 |
1.001 |
0.001 (0.01) |
0.92 |
| Lag 3 |
1.009 |
0.009 (0.01) |
0.08 |
1.006 |
0.006 (0.01) |
0.53 |
| Lag 4 |
1.008 |
0.008 (0.01) |
0.11 |
1.005 |
0.005 (0.01) |
0.62 |
| Lag 5 |
1.000 |
0.000 (0.01) |
0.94 |
0.990 |
-0.009 (0.01) |
0.26 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.001 |
0.001 (0.01) |
0.86 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.1.7 shows that, according to a 6-day moving average model, there is negative and no significant association between HCAB street level ultrafine particles PM2,5 and respiratory disease incidence in
small children living in inner city of Copenhagen. However, single day exposure lag model points at strong positive association with some delay of from 2 to 4 days. Unconstrained distributed lag model points
at positive association at 2-day lag. Note that this analysis is limited by a small amount of PM2.5 data.
Table 4.1.7: PM2.5 (µg/m³) (measured by SM200 gravimetric method) effect on Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCAB (Street Level) |
| n |
392 |
355 |
| Lag 0 |
0.992 |
-0.008 (0.02) |
0.61 |
0.992 |
-0.001 (0.02) |
0.65 |
| Lag 1 |
0.991 |
-0.009 (0.02) |
0.57 |
0.980 |
-0.020 (0.02) |
0.35 |
| Lag 2 |
1.018 |
0.018 (0.01) |
0.13 |
1.029 |
0.028 (0.02) |
0.06 |
| Lag 3 |
1.005 |
0.005 (0.01) |
0.68 |
0.981 |
-0.019 (0.02) |
0.34 |
| Lag 4 |
1.012 |
0.012 (0.01) |
0.35 |
1.001 |
0.009 (0.02) |
0.61 |
| Lag 5 |
1.006 |
0.006 (0.01) |
0.67 |
0.994 |
-0.006 (0.02) |
0.73 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.992 |
-0.008 (0.02) |
0.69 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.1.8 shows that there is no significant effect of neither, Copenhagen city background or street TON (part. /m³) levels on the incidence of respiratory disease in small children living in Copenhagen
inner city. The estimates presented in the table are multiplied by 100. The 6-day average moving average model indicate no or a negative association between TON city background levels and the
development of respiratory symptoms, but results from single day exposure lag model and unconstrained distributed lag model indicate positive association at 4-day lag. The street TON levels show a
positive non-significant association to the outcome with a 3-day lag for Jagtvej. Note that analyses with TON are based on a limited amount of available data.
Table 4.1.8: TON (part./m³) (x100) effect on Population 1
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR×100 |
β (se)×100 |
p |
RR×100 |
β (se)×100 |
p |
| HCØ (City Background) |
| n |
415 |
315 |
| Lag 0 |
0.997 |
-0.003 (0.00) |
0.26 |
0.999 |
-0.001 (0.00) |
0.79 |
| Lag 1 |
0.995 |
-0.005 (0.00) |
0.09 |
0.997 |
-0.003 (0.00) |
0.39 |
| Lag 2 |
0.999 |
-0.001 (0.00) |
0.78 |
0.999 |
-0.001 (0.00) |
0.77 |
| Lag 3 |
1.000 |
-0.000 (0.00) |
0.98 |
0.998 |
-0.002 (0.00) |
0.54 |
| Lag 4 |
1.005 |
0.005 (0.00) |
0.05 |
1.005 |
0.005 (0.00) |
0.17 |
| Lag 5 |
1.000 |
0.000 (0.00) |
0.88 |
1.001 |
0.001 (0.00) |
0.84 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.998 |
-0.002 (0.00) |
0.71 |
| Jagtvej (Street Level) |
| n |
175 |
142 |
| Lag 0 |
1.000 |
-0.000 (0.00) |
0.90 |
0.998 |
-0.002 (0.00) |
0.28 |
| Lag 1 |
1.002 |
0.002 (0.00) |
0.07 |
1.004 |
0.004 (0.00) |
0.03 |
| Lag 2 |
1.001 |
0.001 (0.00) |
0.60 |
0.998 |
-0.002 (0.00) |
0.26 |
| Lag 3 |
1.003 |
0.003 (0.00) |
0.01 |
1.003 |
0.003 (0.00) |
0.06 |
| Lag 4 |
1.001 |
0.001 (0.00) |
0.32 |
1.001 |
0.001 (0.00) |
0.63 |
| Lag 5 |
1.000 |
0.000 (0.00) |
0.68 |
0.999 |
-0.001 (0.00) |
0.43 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.003 |
0.003 (0.00) |
0.15 |
| HCAB (Street Level) |
| n |
307 |
255 |
| Lag 0 |
1.001 |
0.001 (0.00) |
0.24 |
1.000 |
0.000 (0.00) |
0.90 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.20 |
0.999 |
-0.000 (0.00) |
0.59 |
| Lag 2 |
1.000 |
-0.000 (0.00) |
0.99 |
0.999 |
-0.001 (0.00) |
0.38 |
| Lag 3 |
1.001 |
0.001 (0.00) |
0.29 |
1.005 |
0.000 (0.00) |
0.53 |
| Lag 4 |
1.000 |
0.000 (0.00) |
0.34 |
1.003 |
0.000 (0.00) |
0.67 |
| Lag 5 |
1.000 |
-0.000 (0.00) |
0.57 |
0.998 |
-0.002 (0.00) |
0.03 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.001 |
0.001 (0.00) |
0.40 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient, p - p value
4.2 Population 2 – Copenhagen Suburbs
From Table 4.2.1, 6-day moving average results indicate that Copenhagen city background levels of CO are not or if anything negatively associated with development of respiratory symptoms in COPSAC
children living in Copenhagen suburbs, while street level concentrations of CO are positively but far from significantly associated with the outcome. Estimates from a single day exposure lag model and
unconstrained distributed lag models, indicate that, consistently for all, background and street levels, positive association with the outcome may appear with 4-day lag.
Table 4.2.1: CO (ppm)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| N |
1.647 |
1.574 |
| Lag 0 |
0.473 |
-0.749 (0.37) |
0.08 |
0.567 |
-0.567 (0.51) |
0.27 |
| Lag 1 |
0.584 |
-0.537 (0.38) |
0.21 |
0.647 |
-0.436 (0.57) |
0.43 |
| Lag 2 |
1.258 |
0.229 (0.39) |
0.57 |
1.614 |
0.479 (0.53) |
0.37 |
| Lag 3 |
1.247 |
0.221 (0.35) |
0.59 |
1.257 |
0.229 (0.43) |
0.67 |
| Lag 4 |
1.156 |
0.145 (0.35) |
0.73 |
1.281 |
0.248 (0.46) |
0.65 |
| Lag 5 |
1.156 |
-0.145 (0.36) |
0.73 |
0.781 |
-0.247 (0.50) |
0.62 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.930 |
-0.073 (0.67) |
0.91 |
| Jagtvej (Street Level) |
| N |
1.617 |
1.521 |
| Lag 0 |
0.873 |
-0.135 (0.11) |
0.21 |
0.849 |
-0.164 (0.12) |
0.18 |
| Lag 1 |
0.964 |
-0.036 (0.11) |
0.73 |
1.079 |
0.076 (0.13) |
0.54 |
| Lag 2 |
0.959 |
-0.042 (0.11) |
0.73 |
0.949 |
-0.052 (0.13) |
0.68 |
| Lag 3 |
0.996 |
-0.004 (0.11) |
0.97 |
0.936 |
-0.062 (0.13) |
0.62 |
| Lag 4 |
1.184 |
0.169 (0.11) |
0.11 |
1.200 |
0.183 (0.12) |
0.14 |
| Lag 5 |
1.096 |
0.092 (0.11) |
0.38 |
1.057 |
0.056 (0.12) |
0.64 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.052 |
0.051 (0.18) |
0.78 |
| HCAB (Street Level) |
| N |
774 |
734 |
| Lag 0 |
1.020 |
0.020 (0.17) |
0.91 |
0.962 |
-0.038 (0.19) |
0.84 |
| Lag 1 |
1.016 |
0.015 (0.17) |
0.93 |
0.995 |
-0.005 (0.20) |
0.98 |
| Lag 2 |
1.169 |
0.156 (0.17) |
0.35 |
1.135 |
0.127 (0.20) |
0.53 |
| Lag 3 |
1.086 |
0.083 (0.17) |
0.63 |
0.984 |
-0.016 (0.21) |
0.94 |
| Lag 4 |
1.375 |
0.319 (0.17) |
0.06 |
1.409 |
0.343 (0.20) |
0.08 |
| Lag 5 |
1.080 |
0.077 (0.17) |
0.65 |
0.947 |
-0.054 (0.19) |
0.78 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.263 |
0.233 (0.32) |
0.47 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
In Table 4.2.2, 6-day moving average results indicate that Copenhagen city background levels of NOx are not associated with development of respiratory symptoms in COPSAC children living in
Copenhagen suburbs, while street level concentrations of NOx are positively but not significantly associated with the outcome. Estimates from a single day exposure lag model and unconstrained distributed
lag models, indicate that consistently for all, background and street levels, positive effect on the outcome is strongest at 4-day lag.
Table 4.2.2: NOx (ppb)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.569 |
1.481 |
| Lag 0 |
0.993 |
-0.007 (0.00) |
0.18 |
0.996 |
-0.004 (0.01) |
0.51 |
| Lag 1 |
0.994 |
-0.006 (0.00) |
0.19 |
0.994 |
-0.006 (0.01) |
0.33 |
| Lag 2 |
1.006 |
0.006 (0.00) |
0.20 |
1.009 |
0.009 (0.01) |
0.09 |
| Lag 3 |
1.001 |
0.001 (0.00) |
0.75 |
0.999 |
-0.001 (0.01) |
0.85 |
| Lag 4 |
1.002 |
0.002 (0.00) |
0.60 |
1.004 |
0.004 (0.01) |
0.41 |
| Lag 5 |
1.000 |
-0.000 (0.00) |
0.94 |
0.999 |
-0.001 (0.01) |
0.87 |
| Moving Average Lag Model – Mean(Lag0 - 5) |
1.004 |
0.004 (0.01) |
0.62 |
| Jagtvej (Street Level) |
| n |
1.616 |
1.520 |
| Lag 0 |
0.999 |
-0.001 (0.00) |
0.45 |
0.999 |
-0.001 (0.00) |
0.49 |
| Lag 1 |
1.000 |
0.000 (0.00) |
0.81 |
1.000 |
0.001 (0.00) |
0.74 |
| Lag 2 |
1.000 |
-0.002 (0.00) |
0.95 |
1.000 |
0.000 (0.00) |
0.95 |
| Lag 3 |
0.999 |
-0.001 (0.00) |
0.71 |
0.999 |
-0.001 (0.00) |
0.39 |
| Lag 4 |
1.002 |
0.002 (0.00) |
0.14 |
1.002 |
0.002 (0.00) |
0.17 |
| Lag 5 |
1.001 |
0.001 (0.00) |
0.55 |
1.000 |
0.000 (0.00) |
0.81 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.001 |
0.001 (0.00) |
0.73 |
| HCAB (Street Level) |
| n |
766 |
730 |
| Lag 0 |
1.000 |
0.000 (0.00) |
0.83 |
1.000 |
-0.000 (0.00) |
0.74 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.31 |
1.001 |
0.001 (0.00) |
0.63 |
| Lag 2 |
1.002 |
0.002 (0.00) |
0.16 |
1.001 |
0.001 (0.00) |
0.30 |
| Lag 3 |
1.001 |
0.001 (0.00) |
0.60 |
0.999 |
-0.001 (0.00) |
0.71 |
| Lag 4 |
1.002 |
0.002 (0.00) |
0.05 |
1.002 |
0.002 (0.00) |
0.09 |
| Lag 5 |
1.000 |
0.000 (0.00) |
0.73 |
0.999 |
-0.000 (0.00) |
0.74 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.004 |
0.004 (0.00) |
0.13 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.2.3 show that the NO2 levels Copenhagen city background and streets are only weakly and far from significantly with respect to 6-day average associated with development of respiratory
symptoms in COPSAC children living in Copenhagen suburbs. Estimates from a single day exposure lag model and unconstrained distributed lag models points to possible positive associations with 3-4 days
lag.
Table 4.2.3: NO2 (ppb)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.569 |
1.481 |
| Lag 0 |
0.992 |
-0.008 (0.00) |
0.36 |
0.994 |
-0.005 (0.01) |
0.58 |
| Lag 1 |
0.996 |
-0.004 (0.00) |
0.62 |
1.000 |
-0.000 (0.01) |
0.99 |
| Lag 2 |
1.008 |
0.008 (0.00) |
0.31 |
1.006 |
0.006 (0.01) |
0.57 |
| Lag 3 |
1.011 |
0.011 (0.00) |
0.17 |
1.010 |
0.010 (0.01) |
0.37 |
| Lag 4 |
1.003 |
0.003 (0.00) |
0.73 |
1.002 |
0.002 (0.01) |
0.86 |
| Lag 5 |
0.997 |
-0.003 (0.00) |
0.77 |
0.998 |
-0.002 (0.01) |
0.85 |
| Moving Average Lag Model – Mean(Lag0 - 5) |
1.014 |
0.0135 (0.01) |
0.36 |
| Jagtvej (Street Level) |
| n |
1.616 |
1.520 |
| Lag 0 |
0.997 |
-0.003 (0.00) |
0.61 |
0.996 |
-0.004 (0.01) |
0.54 |
| Lag 1 |
1.000 |
-0.000 (0.00) |
0.96 |
1.003 |
0.003 (0.01) |
0.67 |
| Lag 2 |
1.001 |
0.001 (0.00) |
0.85 |
0.999 |
-0.001 (0.01) |
0.88 |
| Lag 3 |
1.004 |
0.004 (0.00) |
0481 |
1.001 |
0.001 (0.01) |
0.92 |
| Lag 4 |
1.008 |
0.008 (0.00) |
0.13 |
1.009 |
0.008 (0.01) |
0.18 |
| Lag 5 |
1.000 |
-0.000 (0.00) |
0.99 |
0.997 |
-0.003 (0.01) |
0.60 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.005 |
0.005 (0.01) |
0.51 |
| HCAB (Street Level) |
| n |
766 |
730 |
| Lag 0 |
0.999 |
-0.001 (0.01) |
0.93 |
0.995 |
-0.005 (0.01) |
0.51 |
| Lag 1 |
1.006 |
0.006 (0.01) |
0.34 |
1.005 |
0.005 (0.01) |
0.55 |
| Lag 2 |
1.002 |
0.002 (0.01) |
0.71 |
0.998 |
-0.002 (0.01) |
0.79 |
| Lag 3 |
1.006 |
0.006 (0.01) |
0.35 |
1.001 |
0.001 (0.01) |
0.94 |
| Lag 4 |
1.012 |
0.011 (0.01) |
0.05 |
1.017 |
0.017 (0.01) |
0.03 |
| Lag 5 |
0.995 |
-0.005 (0.01) |
0.43 |
0.986 |
-0.014 (0.01) |
0.05 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.005 |
0.005 (0.01) |
0.63 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
In Table 4.2.4 it can be seen that 6-day average of all, Copenhagen city background and street O3 levels are mainly negatively but far from significantly associated with development of respiratory symptoms
in COPSAC children living in Copenhagen suburbs. Estimates from a single day exposure lag model and unconstrained distributed lag models confirm the weak associations and show no obvious lag
patterns.
Table 4.2.5 shows that there is negative although not significant associations between Copenhagen city background PM10 levels and increase in development of new respiratory symptoms in children living in
Copenhagen suburbs with the 6-day moving average model. This weak association holds for the same day and delayed effects, without obvious lag patterns.
Table 4.2.4: O3 (ppb)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
273 |
258 |
| Lag 0 |
1.067 |
0.065 (0.07) |
0.33 |
1.080 |
0.077 (0.08) |
0.35 |
| Lag 1 |
1.048 |
0.047 (0.07) |
0.48 |
1.069 |
0.067 (0.08) |
0.43 |
| Lag 2 |
0.944 |
-0.057 (0.07) |
0.44 |
0.937 |
-0.065 (0.08) |
0.41 |
| Lag 3 |
0.944 |
-0.057 (0.07) |
0.44 |
0.983 |
-0.017 (0.08) |
0.86 |
| Lag 4 |
0.912 |
-0.092 (0.07) |
0.24 |
0.883 |
-0.125 (0.08) |
0.24 |
| Lag 5 |
0.990 |
-0.010 (0.07) |
0.88 |
1.028 |
0.027 (0.08) |
0.75 |
| Moving Average Lag Model – Mean(Lag0 - 5) |
0.964 |
-0.036 (0.08) |
0.74 |
| Jagtvej (Street Level) |
| n |
1.628 |
1.547 |
| Lag 0 |
1.005 |
0.005 (0.01) |
0.39 |
1.007 |
0.007 (0.01) |
0.35 |
| Lag 1 |
1.001 |
0.001 (0.01) |
0.93 |
0.995 |
-0.005 (0.01) |
0.57 |
| Lag 2 |
1.000 |
-0.002 (0.01) |
0.98 |
1.005 |
0.005 (0.01) |
0.55 |
| Lag 3 |
0.994 |
-0.006 (0.01) |
0.32 |
0.992 |
-0.008 (0.01) |
0.36 |
| Lag 4 |
0.997 |
-0.003 (0.01) |
0.55 |
0.998 |
-0.002 (0.01) |
0.85 |
| Lag 5 |
1.000 |
0.000 (0.01) |
0.96 |
1.001 |
0.001 (0.01) |
0.86 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.998 |
-0.002 (0.01) |
0.79 |
| HCAB (Street Level) |
| n |
682 |
654 |
| Lag 0 |
0.995 |
-0.005 (0.01) |
0.44 |
1.003 |
0.003 (0.01) |
0.75 |
| Lag 1 |
0.993 |
-0.007 (0.01) |
0.28 |
0.993 |
-0.007 (0.01) |
0.51 |
| Lag 2 |
0.994 |
-0.006 (0.01) |
0.33 |
1.000 |
0.000 (0.01) |
0.99 |
| Lag 3 |
0.993 |
-0.007 (0.01) |
0.29 |
0.992 |
-0.008 (0.01) |
0.44 |
| Lag 4 |
0.995 |
-0.004 (0.01) |
0.51 |
0.998 |
-0.002 (0.01) |
0.82 |
| Lag 5 |
1.002 |
0.002 (0.01) |
0.80 |
1.009 |
0.009 (0.01) |
0.31 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.994 |
0.994 (0.01) |
0.50 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.2.5: PM10 (µg/m³), combined with HCØ measurements and extrapolated values from Jagtvej
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.222 |
1.010 |
| Lag 0 |
0.995 |
-0.005 (0.00) |
0.11 |
0.998 |
-0.002 (0.00) |
0.68 |
| Lag 1 |
0.997 |
-0.003 (0.00) |
0.28 |
1.000 |
0.000 (0.00) |
0.97 |
| Lag 2 |
0.996 |
-0.004 (0.00) |
0.19 |
0.998 |
-0.002 (0.00) |
0.70 |
| Lag 3 |
0.997 |
-0.003 (0.00) |
0.33 |
1.005 |
0.006 (0.00) |
0.25 |
| Lag 4 |
0.996 |
-0.004 (0.00) |
0.23 |
0.993 |
-0.007 (0.00) |
0.19 |
| Lag 5 |
0.996 |
-0.004 (0.00) |
0.17 |
0.998 |
-0.002 (0.00) |
0.70 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.996 |
-0.004 (0.00) |
0.30 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.2.6: PM10 (g/m³) measured by SM200 gravimetric method
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| Jagtvej (Street Level) |
| n |
778 |
662 |
| Lag 0 |
0.995 |
-0.005 (0.00) |
0.13 |
0.995 |
-0.005 (0.00) |
0.24 |
| Lag 1 |
0.998 |
-0.002 (0.00) |
0.55 |
1.003 |
0.003 (0.00) |
0.54 |
| Lag 2 |
0.998 |
-0.002 (0.00) |
0.45 |
1.002 |
0.002 (0.00) |
0.77 |
| Lag 3 |
0.996 |
-0.004(0.00) |
0.21 |
0.999 |
-0.001 (0.00) |
0.84 |
| Lag 4 |
0.995 |
-0.005 (0.00) |
0.14 |
0.998 |
-0.002 (0.00) |
0.72 |
| Lag 5 |
0.994 |
-0.006(0.00) |
0.06 |
0.998 |
-0.002 (0.00) |
0.59 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.995 |
-0.005 (0.01) |
0.33 |
| Lille Valby (Rural Level) |
| n |
723 |
607 |
| Lag 0 |
0.992 |
-0.008 (0.00) |
0.07 |
0.995 |
-0.005 (0.01) |
0.43 |
| Lag 1 |
0.992 |
-0.008 (0.00) |
0.05 |
1.001 |
0.001 (0.01) |
0.90 |
| Lag 2 |
0.993 |
-0.007 (0.00) |
0.07 |
0.988 |
-0.012 (0.01) |
0.14 |
| Lag 3 |
0.999 |
-0.001 (0.00) |
0.80 |
1.016 |
0.015 (0.01) |
0.03 |
| Lag 4 |
0.994 |
-0.006 (0.00) |
0.13 |
0.989 |
-0.011 (0.01) |
0.13 |
| Lag 5 |
0.994 |
-0.005 (0.00) |
0.19 |
1.001 |
0.001 (0.01) |
0.93 |
| Moving Average Lag Model – Mean(Lag0 - 5) |
0.990 |
-0.019 (0.01) |
0.10 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
In Table 4.2.6, similarly to results for city background levels of PM10 (Table 4.2.5), it can be seen that there is no significant effect of Copenhagen street (Jagtvej) or rural (Lille Valby) PM10 levels on the
respiratory disease incidence in small children living in Copenhagen suburbs, with non-significant negative association and no obvious lag patterns
Table 4.2.7: PM2,5 (µg/m³) effects on Population 2
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR×100 |
β (se)×100 |
p |
RR×100 |
β (se)×100 |
p |
| HCAB (Street Level) |
| n |
391 |
354 |
| Lag 0 |
1.003 |
0.003 (0.01) |
0.72 |
1.007 |
0.007 (0.01) |
0.48 |
| Lag 1 |
0.996 |
-0.003 (0.01) |
0.69 |
0.999 |
-0.001 (0.01) |
0.94 |
| Lag 2 |
0.993 |
-0.007 (0.01) |
0.41 |
0.988 |
-0.012 (0.01) |
0.36 |
| Lag 3 |
0.996 |
-0.004 (0.01) |
0.67 |
1.008 |
0.008 (0.01) |
0.48 |
| Lag 4 |
0.989 |
-0.011 (0.01) |
0.24 |
0.990 |
-0.010 (0.01) |
0.45 |
| Lag 5 |
0.989 |
-0.011 (0.01) |
0.25 |
0.995 |
-0.005 (0.01) |
0.64 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.988 |
-0.012 (0.01) |
0.36 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
There is no significant association between Copenhagen street level (HCAB) ultra fine particle level PM2,5 and respiratory disease incidence in small children living in Copenhagen suburbs as it can be seen
in Table 4.2.7.
Table 4.2.8: TON (part./m³) (x100)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR×100 |
β (se)×100 |
p |
RR×100 |
β (se)×100 |
p |
| HCØ (City Background) |
| n |
415 |
315 |
| Lag 0 |
0.998 |
-0.002 (0.00) |
0.39 |
0.999 |
-0.001 (0.00) |
0.62 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.61 |
0.999 |
-0.001 (0.00) |
0.84 |
| Lag 2 |
1.000 |
-0.000 (0.00) |
0.92 |
1.000 |
0.000 (0.00) |
0.96 |
| Lag 3 |
0.998 |
-0.001 (0.00) |
0.43 |
0.997 |
-0.003 (0.00) |
0.27 |
| Lag 4 |
0.999 |
-0.001 (0.00) |
0.69 |
1.002 |
0.002 (0.00) |
0.49 |
| Lag 5 |
0.997 |
-0.002 (0.00) |
0.21 |
0.996 |
-0.004 (0.00) |
0.12 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.994 |
-0.006 (0.00) |
0.07 |
| Jagtvej (Street Level) |
| n |
175 |
142 |
| Lag 0 |
0.999 |
-0.001 (0.00) |
0.55 |
1.001 |
0.001 (0.00) |
0.49 |
| Lag 1 |
0.999 |
-0.001 (0.00) |
0.51 |
1.001 |
0.001 (0.00) |
0.70 |
| Lag 2 |
1.000 |
0.000 (0.00) |
0.78 |
0.999 |
-0.001 (0.00) |
0.63 |
| Lag 3 |
1.000 |
0.000 (0.00) |
0.61 |
1.001 |
0.001 (0.00) |
0.72 |
| Lag 4 |
1.000 |
0.000 (0.00) |
0.59 |
1.000 |
-0.000 (0.00) |
0.89 |
| Lag 5 |
0.999 |
-0.001 (0.00) |
0.40 |
0.999 |
-0.001 (0.00) |
0.32 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.999 |
-0.001 (0.00) |
0.50 |
| HCAB (Street Level) |
| n |
306 |
254 |
| Lag 0 |
1.000 |
-0.000 (0.00) |
0.60 |
1.000 |
-0.000 (0.00) |
0.83 |
| Lag 1 |
1.000 |
0.000 (0.00) |
0.97 |
0.999 |
-0.001 (0.00) |
0.52 |
| Lag 2 |
1.000 |
-0.000 (0.00) |
0.44 |
1.000 |
-0.00 (0.00) |
0.93 |
| Lag 3 |
1.000 |
-0.000 (0.00) |
0.57 |
0.999 |
-0.001 (0.00) |
0.34 |
| Lag 4 |
1.000 |
0.000 (0.00) |
0.47 |
1.000 |
-0.000 (0.00) |
0.68 |
| Lag 5 |
1.000 |
0.000 (0.00) |
0.90 |
1.000 |
-0.000 (0.00) |
0.57 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.999 |
-0.001 (0.00) |
0.20 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.2.8 shows that there is no significant association between neither Copenhagen city background nor street TON (part./m³) levels and the incidence of respiratory disease in small children living in
Copenhagen suburbs. The estimates presented in the table are multiplied by 100. An association may be negative, in particularly, with respect to the city background levels.
4.3 Population 3 – Rest Of Zealand
From Table 4.3.1, 6-day moving average results indicate that Copenhagen city background and street levels of CO are positively but not significantly associated with development of respiratory symptoms in
COPSAC children living in the rest of Sealand. Looking at estimates from a single day exposure lag model and unconstrained distributed lag models, it can be seen that at all, background and street levels,
positive effect on the outcome is strongest at 2-day lag.
In Table 4.3.2, 6-day moving average results indicate that Copenhagen city background and street levels of NOx are positively non-significantly associated with development of respiratory symptoms in
COPSAC children living in the rest of Zealand. Looking at estimates from a single day exposure lag model and unconstrained distributed lag models, it can be seen that, positive associations may be present
and strongest at around 2-day lag.
Table 4.3.3 shows that according to 6-day average results there is positive but nonsignificant association between Copenhagen city background and street levels (HCAB) of NO2 and development of
respiratory symptoms in COPSAC children living in the rest of Sealand, while this association is positive at city street levels from Jagtvej (increase in 1 ppb of 6-day average NO2 levels resulting in 1.7%
increase in new respiratory symptoms cases the following days). Estimates from a single day exposure lag model and unconstrained distributed lag models, indicate that for all, background and street levels of
NO2, positive the association with the outcome is strongest at a 2-day lag.
Table 4.3.1: CO (ppm)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.706 |
1.628 |
| Lag 0 |
1.089 |
0.085 (0.36) |
0.81 |
0.854 |
-0.157 (0.42) |
0.71 |
| Lag 1 |
1.877 |
0.629 (0.35) |
0.07 |
1.269 |
0.238 (0.45) |
0.60 |
| Lag 2 |
2.834 |
1.042 (0.33) |
0.00 |
2.656 |
0.977 (0.45) |
0.03 |
| Lag 3 |
1.431 |
0.358 (0.35) |
0.30 |
0.912 |
-0.092 (0.45) |
0.84 |
| Lag 4 |
1.140 |
0.131 (0.36) |
0.71 |
0.909 |
-0.095 (0.47) |
0.84 |
| Lag 5 |
1.019 |
0.019 (0.36) |
0.96 |
0.944 |
-0.057 (0.47) |
0.89 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
2.670 |
0.982 (0.57) |
0.09 |
| Jagtvej (Street Level) |
| n |
1.676 |
1.575 |
| Lag 0 |
1.055 |
0.054 (0.09) |
0.55 |
0.996 |
-0.004 (0.10) |
0.97 |
| Lag 1 |
1.159 |
0.148 (0.09) |
0.09 |
1.059 |
0.058 (0.11) |
0.59 |
| Lag 2 |
1.310 |
0.270 (0.09) |
0.00 |
1.314 |
0.273 (0.11) |
0.00 |
| Lag 3 |
1.097 |
0.093 (0.09) |
0.29 |
0.995 |
-0.050 (0.11) |
0.64 |
| Lag 4 |
0.993 |
-0.007 (0.09) |
0.94 |
0.996 |
-0.039 (0.11) |
0.72 |
| Lag 5 |
0.976 |
-0.024 (0.09) |
0.93 |
0.996 |
-0.038 (0.11) |
0.71 |
| Moving Average Lag Model – Mean(Lag0 – 5) |
1.260 |
0.231 (0.16) |
0.14 |
| HCAB (Street Level) |
| n |
768 |
728 |
| Lag 0 |
0.799 |
-0.225 (0.17) |
0.17 |
0.709 |
-0.343 (0.19) |
0.07 |
| Lag 1 |
1.073 |
0.071 (0.16) |
0.66 |
1.193 |
0.177 (0.19) |
0.36 |
| Lag 2 |
1.441 |
0.365 (0.15) |
0.02 |
1.398 |
0.335 (0.19) |
0.08 |
| Lag 3 |
1.014 |
0.014 (0.16) |
0.93 |
0.752 |
-0.285 (0.20) |
0.16 |
| Lag 4 |
1.253 |
0.226 (0.16) |
0.15 |
1.417 |
0.348 (0.19) |
0.07 |
| Lag 5 |
0.987 |
-0.013 (0.16) |
0.94 |
0.845 |
-0.168(0.19) |
0.37 |
| Moving Average Lag Model – Mean(Lag0 – 5) |
1.221 |
0.199 (0.31) |
0.52 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
In Table 4.3.4 can be seen that Copenhagen city background O3 is negatively but far from significantly associated with development of respiratory symptoms in COPSAC children living in the rest of
Zealand. However, Copenhagen city levels of O3 are significantly negatively associated with the outcome. Thus, a 1 ppb increase in 6-day average Jagtvej and HCAB O3 levels is associated with a 2.6%
and 1.5% decrease in new respiratory symptoms in children the following days.
Table 4.3.5 shows that there is no effect of Copenhagen city background levels of PM10 and development of new respiratory symptoms in children living in the rest of Zealand.
Table 4.3.2: NOx (ppb)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.628 |
1.535 |
| Lag 0 |
0.999 |
-0.001 (0.00) |
0.82 |
0.997 |
-0.003 (0.00) |
0.48 |
| Lag 1 |
1.004 |
0.005 (0.00) |
0.23 |
1.001 |
0.001 (0.00) |
0.80 |
| Lag 2 |
1.012 |
0.012 (0.00) |
0.00 |
1.014 |
0.014 (0.00) |
0.00 |
| Lag 3 |
1.001 |
0.001 (0.00) |
0.73 |
0.996 |
-0.004 (0.00) |
0.42 |
| Lag 4 |
1.001 |
0.001 (0.00) |
0.70 |
1.001 |
0.001 (0.00) |
0.79 |
| Lag 5 |
1.000 |
-0.000 (0.00) |
0.98 |
0.998 |
-0.002 (0.00) |
0.69 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.010 |
0.010 (0.01) |
0.15 |
| Jagtvej (Street Level) |
| n |
1.675 |
1.574 |
| Lag 0 |
1.000 |
0.000 (0.00) |
0.73 |
1.000 |
0.000 (0.00) |
0.93 |
| Lag 1 |
1.002 |
0.002 (0.00) |
0.14 |
1.001 |
0.001 (0.00) |
0.63 |
| Lag 2 |
1.004 |
0.004 (0.00) |
0.00 |
1.004 |
0.004 (0.00) |
0.00 |
| Lag 3 |
1.001 |
0.001 (0.00) |
0.42 |
0.999 |
-0.001 (0.00) |
0.34 |
| Lag 4 |
1.000 |
-0.000 (0.00) |
0.98 |
1.000 |
-0.000 (0.00) |
0.86 |
| Lag 5 |
1.000 |
-0.000 (0.00) |
0.76 |
1.000 |
-0.000 (0.00) |
0.81 |
| Moving Average Lag Model – Mean(Lag0 – 5) |
1.002 |
0.002 (0.00) |
0.28 |
| HCAB (Street Level) |
| n |
760 |
724 |
| Lag 0 |
0.999 |
-0.001 (0.00) |
0.48 |
0.998 |
-0.002 (0.00) |
0.13 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.26 |
1.002 |
0.002 (0.00) |
0.25 |
| Lag 2 |
1.002 |
0.002 (0.00) |
0.06 |
1.002 |
0.002 (0.00) |
0.17 |
| Lag 3 |
0.999 |
-0.000 (0.00) |
0.67 |
0.997 |
-0.002 (0.00) |
0.10 |
| Lag 4 |
1.002 |
0.002 (0.00) |
0.12 |
1.002 |
0.002 (0.00) |
0.10 |
| Lag 5 |
1.000 |
0.000 (0.00) |
0.88 |
0.999 |
-0.001 (0.00) |
0.64 |
| Moving Average Lag Model – Mean(Lag0 – 5) |
1.003 |
0.003 (0.00) |
0.29 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
In Table 4.3.6, similarly to results for Copenhagen city background levels of PM10 (Table 4.3.5), it can be seen that there is no significant effect of street level (Jagtvej) or rural levels (Lille Valby) PM10
levels on the respiratory disease incidence in small children living in the rest of Zealand, with negative association and no obvious lag patterns.
Table 4.2.8 shows that there is no significant effect of Copenhagen city background or street TON (part./m³) levels on the incidence of respiratory disease in small children living in the rest of Zealand. The
estimates presented in the Table 4.3.8 are multiplied by 100.
Table 4.3.3: NO2 (ppb)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.628 |
1.535 |
| Lag 0 |
1.003 |
0.003 (0.01) |
0.71 |
0.994 |
-0.006 (0.01) |
0.49 |
| Lag 1 |
1.013 |
0.013 (0.01) |
0.06 |
1.007 |
0.007 (0.01) |
0.44 |
| Lag 2 |
1.021 |
0.021 (0.01) |
0.00 |
1.017 |
0.017 (0.01) |
0.08 |
| Lag 3 |
1.010 |
0.010 (0.01) |
0.17 |
1.000 |
-0.000 (0.01) |
0.98 |
| Lag 4 |
1.004 |
0.004 (0.01) |
0.58 |
1.002 |
0.002 (0.01) |
0.81 |
| Lag 5 |
1.000 |
-0.000 (0.01) |
0.96 |
0.995 |
-0.005 (0.01) |
0.55 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.022 |
0.022 (0.01) |
0.07 |
| Jagtvej (Street Level) |
| n |
1.675 |
1.574 |
| Lag 0 |
1.003 |
0.003 (0.00) |
0.49 |
1.001 |
0.001 (0.01) |
0.89 |
| Lag 1 |
1.010 |
0.010 (0.00) |
0.03 |
1.004 |
0.004 (0.01) |
0.46 |
| Lag 2 |
1.015 |
0.015 (0.00) |
0.00 |
1.015 |
0.014 (0.01) |
0.01 |
| Lag 3 |
1.008 |
0.008 (0.00) |
0.06 |
0.998 |
-0.002 (0.01) |
0.69 |
| Lag 4 |
1.004 |
0.004 (0.00) |
0.43 |
1.000 |
-0.000 (0.01) |
0.99 |
| Lag 5 |
1.001 |
0.001 (0.01) |
0.78 |
0.998 |
-0.002 (0.01) |
0.75 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.017 |
0.017 (0.01) |
0.02 |
| HCAB (Street Level) |
| n |
760 |
724 |
| Lag 0 |
0.994 |
-0.006 (0.00) |
0.34 |
0.988 |
-0.012 (0.01) |
0.07 |
| Lag 1 |
1.005 |
0.005 (0.00) |
0.32 |
1.008 |
0.008 (0.01) |
0.30 |
| Lag 2 |
1.007 |
0.007 (0.00) |
0.22 |
1.008 |
0.008 (0.01) |
0.29 |
| Lag 3 |
0.995 |
-0.005 (0.00) |
0.39 |
0.987 |
-0.013 (0.01) |
0.08 |
| Lag 4 |
1.003 |
0.003 (0.00) |
0.55 |
1.010 |
0.010 (0.01) |
0.20 |
| Lag 5 |
0.996 |
-0.004 (0.00) |
0.46 |
0.993 |
-0.007 (0.01) |
0.29 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.995 |
-0.005 (0.01) |
0.63 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.3.4: O3 (ppb)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
338 |
318 |
| Lag 0 |
1.042 |
0.041 (0.02) |
0.06 |
1.055 |
0.054 (0.02) |
0.03 |
| Lag 1 |
0.995 |
-0.005 (0.02) |
0.81 |
0.987 |
-0.013 (0.03) |
0.62 |
| Lag 2 |
0.967 |
-0.034 (0.02) |
0.13 |
0.966 |
-0.034 (0.03) |
0.23 |
| Lag 3 |
0.984 |
-0.016 (0.02) |
0.46 |
0.993 |
-0.007 (0.03) |
0.81 |
| Lag 4 |
0.998 |
-0.002 (0.02) |
0.94 |
1.007 |
0.007 (0.03) |
0.81 |
| Lag 5 |
0.995 |
-0.005 (0.02) |
0.82 |
1.002 |
0.002 (0.03) |
0.93 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.996 |
-0.004 (0.01) |
0.90 |
| Jagtvej (Street Level) |
| n |
1.687 |
1.601 |
| Lag 0 |
1.001 |
0.001 (0.00) |
0.83 |
1.005 |
0.005 (0.01) |
0.45 |
| Lag 1 |
0.996 |
-0.004 (0.00) |
0.42 |
0.995 |
-0.004 (0.01) |
0.55 |
| Lag 2 |
0.989 |
-0.011 (0.00) |
0.03 |
0.991 |
-0.009 (0.01) |
0.24 |
| Lag 3 |
0.990 |
-0.010 (0.00) |
0.06 |
0.996 |
-0.004 (0.01) |
0.63 |
| Lag 4 |
0.997 |
-0.003 (0.00) |
0.53 |
0.998 |
-0.002 (0.01) |
0.77 |
| Lag 5 |
0.997 |
-0.003 (0.00) |
0.49 |
1.000 |
0.001 (0.01) |
0.93 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.974 |
-0.027 (0.01) |
0.02 |
| HCAB (Street Level) |
| n |
676 |
648 |
| Lag 0 |
0.992 |
-0.008 (0.01) |
0.24 |
0.999 |
-0.001 (0.01) |
0.87 |
| Lag 1 |
0.986 |
-0.014 (0.01) |
0.04 |
0.994 |
-0.006 (0.01) |
0.53 |
| Lag 2 |
0.983 |
-0.017 (0.01) |
0.01 |
0.987 |
-0.013 (0.01) |
0.17 |
| Lag 3 |
0.991 |
-0.009 (0.01) |
0.20 |
1.008 |
0.008 (0.01) |
0.41 |
| Lag 4 |
0.985 |
-0.015 (0.01) |
0.03 |
0.982 |
-0.018 (0.01) |
0.05 |
| Lag 5 |
0.995 |
-0.005 (0.01) |
0.40 |
1.005 |
0.005 (0.01) |
0.53 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.985 |
-0.015 (0.01) |
0.05 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.3.5: PM10 (µg/m³) , combined with HCØ measurements and extrapolated values from Jagtvej
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCØ (City Background) |
| n |
1.277 |
1.055 |
| Lag 0 |
1.000 |
-0.000 (0.00) |
0.98 |
1.000 |
-0.000 (0.00) |
0.98 |
| Lag 1 |
1.003 |
0.003 (0.00) |
0.24 |
1.005 |
0.002 (0.00) |
0.66 |
| Lag 2 |
1.003 |
0.003 (0.00) |
0.19 |
1.005 |
0.005 (0.00) |
0.28 |
| Lag 3 |
0.999 |
-0.001 (0.00) |
0.74 |
0.997 |
-0.003 (0.00) |
0.53 |
| Lag 4 |
0.998 |
-0.002 (0.00) |
0.43 |
0.997 |
-0.003 (0.00) |
0.54 |
| Lag 5 |
0.999 |
-0.001 (0.00) |
0.58 |
0.999 |
-0.001 (0.01) |
0.81 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.000 |
0.000 (0.01) |
0.93 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.3.6: PM10 (µg/m³) measured by SM200 gravimetric method
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| Jagtvej (Street Level) |
| n |
778 |
662 |
| Lag 0 |
0.996 |
-0.004 (0.00) |
0.21 |
0.999 |
-0.001 (0.00) |
0.74 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.68 |
1.000 |
0.000 (0.00) |
0.93 |
| Lag 2 |
1.001 |
0.001 (0.00) |
0.59 |
1.009 |
0.009 (0.00) |
0.07 |
| Lag 3 |
0.999 |
-0.001 (0.00) |
0.78 |
0.996 |
-0.004 (0.00) |
0.37 |
| Lag 4 |
0.996 |
-0.004 (0.00) |
0.21 |
0.999 |
-0.001 (0.00) |
0.88 |
| Lag 5 |
0.995 |
-0.005 (0.00) |
0.12 |
0.995 |
-0.005 (0.00) |
0.24 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.999 |
-0.001 (0.00) |
0.90 |
| Lille Valby (Rural Level) |
| n |
723 |
526 |
| Lag 0 |
0.996 |
-0.004 (0.00) |
0.26 |
0.990 |
-0.010 (0.01) |
0.11 |
| Lag 1 |
0.999 |
-0.001 (0.00) |
0.88 |
1.010 |
0.010 (0.01) |
0.20 |
| Lag 2 |
0.999 |
-0.001 (0.00) |
0.80 |
0.996 |
-0.004 (0.01) |
0.64 |
| Lag 3 |
0.998 |
-0.001 (0.00) |
0.67 |
0.995 |
-0.005 (0.01) |
0.53 |
| Lag 4 |
0.996 |
-0.004 (0.00) |
0.33 |
1.008 |
0.008 (0.01) |
0.27 |
| Lag 5 |
0.994 |
-0.006 (0.00) |
0.14 |
0.995 |
-0.005 (0.01) |
0.31 |
| Moving Average Lag Model – Mean(Lag0 - 5) |
0.994 |
-0.006 (0.01) |
0.24 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.3.7: PM2,5 (µg/m³)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR |
β (se) |
p |
RR |
β (se) |
p |
| HCAB (Street Level) |
| n |
385 |
348 |
| Lag 0 |
1.002 |
0.002 (0.01) |
0.84 |
0.996 |
-0.004 (0.01) |
0.72 |
| Lag 1 |
1.008 |
0.008 (0.01) |
0.29 |
1.006 |
0.006 (0.01) |
0.59 |
| Lag 2 |
1.005 |
0.005 (0.01) |
0.47 |
1.014 |
0.014 (0.01) |
0.22 |
| Lag 3 |
0.986 |
-0.014 (0.01) |
0.16 |
0.976 |
-0.024 (0.01) |
0.09 |
| Lag 4 |
0.986 |
-0.014 (0.01) |
0.15 |
1.003 |
0.003 (0.01) |
0.81 |
| Lag 5 |
0.981 |
-0.020 (0.01) |
0.07 |
0.981 |
-0.019 (0.01) |
0.12 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.982 |
-0.018 (0.01) |
0.21 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
Table 4.3.8: TON (part./m³) (x100)
| |
Single Day Exposure Lag Model |
Unconstrained
Distributed Lag Model |
| RR×100 |
β (se)×100 |
p |
RR×100 |
β (se)×100 |
p |
| HCØ (City Background) |
| n |
472 |
315 |
| Lag 0 |
0.997 |
-0.003 (0.00) |
0.12 |
0.998 |
-0.002 (0.00) |
0.42 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.60 |
1.002 |
0.002 (0.00) |
0.34 |
| Lag 2 |
1.000 |
-0.000 (0.00) |
0.98 |
1.003 |
0.003 (0.00) |
0.21 |
| Lag 3 |
0.998 |
-0.002 (0.00) |
0.18 |
0.992 |
-0.008(0.00) |
0.01 |
| Lag 4 |
1.001 |
0.001 (0.00) |
0.65 |
1.006 |
0.006 (0.00) |
0.03 |
| Lag 5 |
0.998 |
-0.002 (0.00) |
0.19 |
0.994 |
-0.005 (0.00) |
0.03 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
0.999 |
-0.001 (0.00) |
0.64 |
| Jagtvej (Street Level) |
| n |
175 |
142 |
| Lag 0 |
1.000 |
-0.000 (0.00) |
0.68 |
0.999 |
-0.001 (0.00) |
0.35 |
| Lag 1 |
1.000 |
-0.000 (0.00) |
0.63 |
1.000 |
0.000 (0.00) |
0.85 |
| Lag 2 |
0.999 |
-0.001 (0.00) |
0.45 |
0.999 |
-0.001 (0.00) |
0.49 |
| Lag 3 |
1.001 |
0.001 (0.00) |
0.19 |
1.001 |
0.001 (0.00) |
0.30 |
| Lag 4 |
1.001 |
0.001 (0.00) |
0.30 |
1.001 |
0.001 (0.00) |
0.49 |
| Lag 5 |
0.999 |
-0.001 (0.00) |
0.38 |
0.999 |
-0.001 (0.00) |
0.26 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.000 |
-0.000 (0.00) |
0.86 |
| HCAB (Street Level) |
| n |
300 |
248 |
| Lag 0 |
1.000 |
-0.000 (0.00) |
0.38 |
0.999 |
-0.001 (0.00) |
0.21 |
| Lag 1 |
1.001 |
0.001 (0.00) |
0.19 |
1.000 |
0.000 (0.00) |
0.53 |
| Lag 2 |
1.000 |
-0.000 (0.00) |
0.29 |
1.000 |
0.000 (0.00) |
0.62 |
| Lag 3 |
1.000 |
-0.000 (0.00) |
0.56 |
0.999 |
-0.001 (0.00) |
0.21 |
| Lag 4 |
1.000 |
-0.000 (0.00) |
0.37 |
1.000 |
0.000 (0.00) |
0.43 |
| Lag 5 |
1.000 |
-0.000 (0.00) |
0.26 |
1.000 |
0.000 (0.00) |
0.80 |
| Moving Average Lag Model - Mean(Lag0 - 5) |
1.000 |
-0.000 (0.00) |
0.98 |
RR - relative risk; - regression coefficient; se - standard error of the regression coefficient; p – significance level of the regression coefficient
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