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Table 1. Summary estimates for studies of PM10 and daily mortality by GAM or non-GAM statistical model and by single-city or multicity study design.

Studies No. of estimates Summary
Estimate
95% CI
1 No numerical estimate for 95% CI given. Graphical representation of marginal posterior distribution for PM10 indicated that effect was very unlikely to be due to chance. Note that in the paper by Dominici et al. (2002), the pooled estimate using default convergence criteria is 0.41 (posterior standard error 0.05). We have chosen the estimate given in the earlier published report (Samet et al., 2000)
GAM      
All studies 172 0.60 (0.52, 0.68)
NMMAPS 90 0.5 No numerical estimate1
APHEA 2 21 0.6 (0.4, 0.8)
Single city studies 61 0.68 (0.57, 0.79)
Single city studies (adjusted for publication bias)   0.6 (0.5, 0.8)
Non-GAM      
Single city studies 26 0.55 (0.38, 0.73)
Single city studies (adjusted for publication bias)   0.4 (0.2, 0.6)
All Studies (GAM and Non-GAM) 198 0.59 (0.52, 0.66)

% change in mortality per 10 µg/m3 increase in PM10

Source: WHO Regional Office for Europe  Health Aspects of Air Pollution - answers to follow-up questions from CAFE (2004), Section 5.5

Related publication:
Particulate Matter homeAir Pollution Particulate Matter
Other Figures & Tables on this publication:

Table 1. Estimated effects of air pollution on daily mortality and hospital admissions from APHEA2 and NMMAPS studies

Table 2. Summary of time series relating coarse particulate matter to mortality

Figure 1. Direct Release of Particles

Figure 2. Indirect Formation of Particles

Figure 4. Modelled deposition of particles in the human respiratory tract using the MPPD (Price et al., 2002) model

Fig. 1: Funnel plot of black smoke and "daily all cause mortality" in 47 studies.

Table 1. Summary estimates for studies of PM10 and daily mortality by GAM or non-GAM statistical model and by single-city or multicity study design.