Comparison of different methods in analyzing short-term air pollution effects in a cohort study of susceptible individuals

Table 2 Comparison of the regression coefficients (beta) and standard errors (se) in the analyses of NO₂ concentrations (average of current and previous day) and any cardiac readmission applying different statistical models and assessing the sensitivity of the results for confounder selection as listed in table 3.

	Augsburg		Barcelona		Helsinki		Rome		Stockholm		pooled		Heterogeneity test
Poisson Regression Analyses	beta	se	beta	se	beta	se	beta	se	beta	se	beta	se	p-value
Final Model	0.0119	0.0044	0.0068	0.0044	0.0015	0.0026	0.0041	0.0022	0.0013	0.0032	0.0039	0.0013	0.26
natural splines^a	0.0119	0.0044	0.0066	0.0044	0.0012	0.0026	0.0037	0.0022	0.0012	0.0032	0.0036	0.0013	0.25
loess^b	0.0119	0.0044	0.0065	0.0042	0.0015	0.0026	0.0040	0.0021	0.0014	0.0031	0.0038	0.0013	0.27
Removing Confounders	0.0117	0.0041	0.0078	0.0042	0.0019	0.0025	0.0049	0.0022	0.0011	0.0030	0.0044	0.0013	0.19
Adding Confounders	0.0122	0.0045	0.0051	0.0046	0.0024	0.0026	0.0043	0.0022	0.0018	0.0032	0.0041	0.0013	0.37
Apparent Temperature^c	0.0115	0.0044	0.0067	0.0044	0.0012	0.0026	0.0039	0.0022	0.0013	0.0031	0.0037	0.0013	0.28
Case-crossover Analyses
Stratified control selection	0.0144	0.0060	-0.0024	0.0065	0.0031	0.0031	0.0024	0.0026	0.0008	0.0036	0.0029	0.0016	0.32
Cox-Extended Regression
Time-varying confounders	0.0123	0.0043	0.0046	0.0041	0.0013	0.0025	0.0034	0.0021	0.0017	0.0031	0.0035	0.0013	0.24
+ individual confounders	0.0123	0.0043	0.0047	0.0041	0.0011	0.0025	0.0034	0.0021	0.0016	0.0031	0.0034	0.0013	0.23

a) Model as described in table 3, p-splines replaced by natural splines
b) Model as described in table 3, p-splines replaced by loess
c) Final models described in table 3 with p-splines, apparent temperature replaces temperature