. 2021 Jan 14;18(2):658. doi: 10.3390/ijerph18020658

Table A2.

Output of all fitted models. Posterior probabilities (PPs) can be seen as “p-values” complementary to 1. The first column is the mean of the marginal posterior distributions (MPDs) of a parameter (OR) and the second column is with the standard deviation of that parameter. The following 5 columns are the percentiles (perc.) of MPD. Finally, the last two columns show the posterior probability (PP) that the estimated parameters are more, $p (\hat{β_{p}} > 1)$ , or less, $p (\hat{β_{p}} < 1)$ , than 1. A predictor was defined as a risk factor if it has a $p (\hat{β_{p}} > 1)$ close to 1; conversely, a parameter associated with a protective factor will have $p (\hat{β_{p}} < 1)$ close to 1.

(a) Model 1
					A
Covariates	$\hat{β_{p}}$	$S D (\hat{β_{p}})$	2.5th perc.	25th perc.	Median	75th perc.	97.5th perc.	$p (\hat{β_{p}} > 1)$	$p (\hat{β_{p}} < 1)$
(Intercept)	−0.0207	0.0117	−0.0437	−0.0285	−0.0208	−0.0128	0.00221	0.0393	0.961
PM2.5 in μg/m³	0.0731	0.0227	0.0291	0.0577	0.0728	0.0882	0.118	0.999	0.00065
DEGURBA index rural vs urban	0.0313	0.0180	−0.00422	0.0194	0.0313	0.0436	0.0662	0.959	0.0413
					B
(Intercept)	−0.0264	0.0175	−0.0609	−0.0383	−0.0263	−0.0145	0.00776	0.0644	0.936
PM2.5 in μg/m³	0.0747	0.0233	0.0289	0.0593	0.0746	0.0902	0.121	1.00	0.000500
DEGURBA index rural vs urban	0.0316	0.0182	−0.00454	0.0193	0.0318	0.0439	0.0670	0.959	0.0414
Calendar year	0.00210	0.00488	−0.00754	−0.00117	0.00209	0.00538	0.0117	0.671	0.329
(b) Model 2
					A
Covariates	$\hat{β_{p}}$	$S D (\hat{β_{p}})$	2.5th perc.	25th perc.	Median	75th perc.	97.5th perc.	$p (\hat{β_{p}} > 1)$	$p (\hat{β_{p}} < 1)$
Intercept	−0.0228	0.0102	−0.0426	−0.0297	−0.0228	−0.0159	−0.0026	0.0138	0.9860
PM2.5 in μg/m³	0.0726	0.0193	0.0341	0.0596	0.0726	0.0855	0.1100	1.0000	0.00005
DEGURBA index rural vs urban	0.0217	0.0142	−0.0063	0.0120	0.0217	0.0313	0.0494	0.9370	0.0628
					B
(Intercept)	−0.0297	0.0140	−0.0573	−0.0391	−0.0298	−0.0203	−0.0023	0.0168	0.9830
PM2.5 in μg/m³	0.0749	0.0193	0.0368	0.0617	0.0749	0.0880	0.1130	1.0000	0.0001
DEGURBA index rural vs urban	0.0226	0.0151	−0.0069	0.0123	0.0225	0.0329	0.0517	0.9310	0.0687
Calendar year	0.0018	0.0033	−0.0046	−0.0004	0.0018	0.0040	0.0083	0.7120	0.2880
(c) Model 3
					A
Covariates	$\hat{β_{p}}$	$S D (\hat{β_{p}})$	2.5th perc.	25th perc.	Median	75th perc.	97.5th perc.	$p (\hat{β_{p}} > 1)$	$p (\hat{β_{p}} < 1)$
(Intercept)	−0.0236	0.0083	−0.0399	−0.0291	−0.0236	−0.0181	−0.0073	0.0027	0.9970
PM2.5 in μg/m³	0.0678	0.0201	0.0287	0.0544	0.0676	0.0814	0.1070	1.0000	0.0003
DEGURBA index rural vs urban	0.0284	0.0122	0.0046	0.0203	0.0285	0.0366	0.0522	0.9900	0.0102
					B
(Intercept)	−0.0290	0.0122	−0.0529	−0.0373	−0.0289	−0.0207	−0.0050	0.0089	0.9910
PM2.5 in μg/m³	0.0693	0.0205	0.02910	0.0556	0.0693	0.0829	0.1090	1.0000	0.0004
DEGURBA index rural vs urban	0.0297	0.0127	0.0046	0.0211	0.0296	0.0382	0.0547	0.9890	0.0109
Calendar year	0.00144	0.00425	−0.00700	−0.00142	0.00145	0.00432	0.00974	0.634	0.366
(d) Model 4
					A
Covariates	$\hat{β_{p}}$	$S D (\hat{β_{p}})$	2.5th perc.	25th perc.	Median	75th perc.	97.5th perc.	$p (\hat{β_{p}} > 1)$	$p (\hat{β_{p}} < 1)$
(Intercept)	−0.0235	0.0103	−0.0436	−0.0304	−0.0235	−0.0166	−0.0031	0.0109	0.9890
PM2.5 in μg/m³	0.0768	0.0207	0.0360	0.0628	0.0769	0.0906	0.1180	1.0000	0.0002
DEGURBA index rural vs urban	0.0209	0.0140	−0.0067	0.0113	0.0207	0.0304	0.0483	0.9320	0.0680
					B
(Intercept)	−0.0300	0.0141	−0.0577	−0.0395	−0.0301	−0.0206	−0.0021	0.0182	0.9820
PM2.5 in μg/m³	0.0792	0.0212	0.0380	0.0649	0.0793	0.09330	0.1200	1.0000	0.00005
DEGURBA index rural vs urban	0.0229	0.0150	−0.0066	0.0127	0.0229	0.03280	0.0524	0.9370	0.0632
Calendar year	0.0001	0.0044	−0.0078	−0.0020	0.0010	0.0040	0.0098	0.5880	0.4120