SWAN
SBP Average
Call:
geeglm(formula = sbp_formula1, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 56.7325 5.9844 89.87 < 2e-16 ***
AGE0 0.7742 0.0865 80.10 < 2e-16 ***
bmi 0.6402 0.0397 259.50 < 2e-16 ***
cum_avg_unfair 0.2749 0.2555 1.16 0.2819
diabete -1.6549 0.8327 3.95 0.0469 *
edu_g 1.3660 2.4733 0.31 0.5807
ETHNICBLACK 21.0900 7.8942 7.14 0.0075 **
ETHNICCHINE -8.1187 10.5920 0.59 0.4434
ETHNICHISPA -7.5398 15.8386 0.23 0.6340
ETHNICJAPAN 2.8072 9.0215 0.10 0.7557
heartat_stroke -1.3248 0.9820 1.82 0.1773
med_bp -4.9284 0.5370 84.23 < 2e-16 ***
med_other -0.0762 0.3236 0.06 0.8138
SITE12 0.5561 0.9521 0.34 0.5592
SITE13 6.1878 1.0898 32.24 1.4e-08 ***
SITE14 -2.0014 1.1076 3.27 0.0708 .
SITE15 -1.9174 1.0994 3.04 0.0811 .
SITE16 8.0823 1.2162 44.16 3.0e-11 ***
SITE17 -1.2582 0.9868 1.63 0.2023
statusx 0.1312 0.0989 1.76 0.1847
smoker 1.3833 0.6239 4.92 0.0266 *
visit 0.6189 0.0492 158.35 < 2e-16 ***
cum_avg_unfair:edu_g -0.1914 0.1493 1.64 0.1998
cum_avg_unfair:ETHNICBLACK -0.6202 0.4315 2.07 0.1506
cum_avg_unfair:ETHNICCHINE 0.4943 0.5759 0.74 0.3907
cum_avg_unfair:ETHNICHISPA 0.5920 1.2767 0.22 0.6429
cum_avg_unfair:ETHNICJAPAN 0.1573 0.5390 0.09 0.7705
edu_g:ETHNICBLACK -12.0316 5.1765 5.40 0.0201 *
edu_g:ETHNICCHINE 8.2043 6.7467 1.48 0.2240
edu_g:ETHNICHISPA 6.5357 14.5822 0.20 0.6540
edu_g:ETHNICJAPAN 0.1599 5.5551 0.00 0.9770
cum_avg_unfair:edu_g:ETHNICBLACK 0.5704 0.2845 4.02 0.0449 *
cum_avg_unfair:edu_g:ETHNICCHINE -0.3771 0.3664 1.06 0.3034
cum_avg_unfair:edu_g:ETHNICHISPA -0.3819 1.1926 0.10 0.7488
cum_avg_unfair:edu_g:ETHNICJAPAN -0.0495 0.3282 0.02 0.8802
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 189 6.09
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.851 0.00704
Number of clusters: 2200 Maximum cluster size: 12
Call:
geeglm(formula = sbp_formula2, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 60.2148 4.5012 178.96 < 2e-16 ***
AGE0 0.7747 0.0862 80.72 < 2e-16 ***
bmi 0.6448 0.0398 262.07 < 2e-16 ***
chronicity 2.0954 2.9210 0.51 0.473
diabete -1.6520 0.8326 3.94 0.047 *
edu_g -1.1434 0.8874 1.66 0.198
ETHNICBLACK 9.8923 3.8902 6.47 0.011 *
ETHNICCHINE -0.8962 4.9027 0.03 0.855
ETHNICHISPA -0.8873 3.9095 0.05 0.820
ETHNICJAPAN 6.9570 3.7787 3.39 0.066 .
heartat_stroke -1.3170 0.9817 1.80 0.180
med_bp -4.9214 0.5377 83.78 < 2e-16 ***
med_other -0.0757 0.3241 0.05 0.815
SITE12 0.8258 0.9415 0.77 0.380
SITE13 5.9875 1.0794 30.77 2.9e-08 ***
SITE14 -1.9724 1.1038 3.19 0.074 .
SITE15 -1.8863 1.0953 2.97 0.085 .
SITE16 8.0945 1.2044 45.17 1.8e-11 ***
SITE17 -1.2450 0.9787 1.62 0.203
statusx 0.1300 0.0989 1.73 0.189
smoker 1.3711 0.6208 4.88 0.027 *
visit 0.6164 0.0490 158.40 < 2e-16 ***
chronicity:edu_g -1.5581 1.6817 0.86 0.354
chronicity:ETHNICBLACK -0.0526 5.8871 0.00 0.993
chronicity:ETHNICCHINE 3.5295 7.1559 0.24 0.622
chronicity:ETHNICHISPA 0.7161 8.5634 0.01 0.933
chronicity:ETHNICJAPAN -4.5419 8.6633 0.27 0.600
edu_g:ETHNICBLACK -0.7000 2.4678 0.08 0.777
edu_g:ETHNICCHINE 2.3024 2.8838 0.64 0.425
edu_g:ETHNICHISPA 2.0118 3.1625 0.40 0.525
edu_g:ETHNICJAPAN -2.6394 2.2841 1.34 0.248
chronicity:edu_g:ETHNICBLACK -1.5694 3.7588 0.17 0.676
chronicity:edu_g:ETHNICCHINE -2.1019 4.4538 0.22 0.637
chronicity:edu_g:ETHNICHISPA 0.5500 6.7784 0.01 0.935
chronicity:edu_g:ETHNICJAPAN 5.5561 5.1418 1.17 0.280
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 188 6.07
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.85 0.00724
Number of clusters: 2200 Maximum cluster size: 12
DBP Average
Call:
geeglm(formula = dbp_formula1, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 48.02252 3.80165 159.57 < 2e-16 ***
AGE0 0.19871 0.05373 13.68 0.00022 ***
bmi 0.35875 0.02492 207.29 < 2e-16 ***
cum_avg_unfair 0.18807 0.16206 1.35 0.24584
diabete -1.27248 0.52709 5.83 0.01577 *
edu_g 1.55776 1.60152 0.95 0.33072
ETHNICBLACK 10.02014 4.92485 4.14 0.04189 *
ETHNICCHINE -1.24420 7.12099 0.03 0.86130
ETHNICHISPA 8.05469 8.79663 0.84 0.35985
ETHNICJAPAN 4.60653 5.80566 0.63 0.42751
heartat_stroke -0.30425 0.66493 0.21 0.64726
med_bp -2.95777 0.34142 75.05 < 2e-16 ***
med_other 0.01528 0.22880 0.00 0.94675
SITE12 7.52824 0.54662 189.68 < 2e-16 ***
SITE13 8.05977 0.62485 166.38 < 2e-16 ***
SITE14 1.80437 0.65998 7.47 0.00626 **
SITE15 3.94374 0.65738 35.99 0.000000002 ***
SITE16 8.99414 0.79954 126.54 < 2e-16 ***
SITE17 3.01979 0.55605 29.49 0.000000056 ***
statusx -0.00600 0.06957 0.01 0.93121
smoker 0.12278 0.40946 0.09 0.76428
visit 0.08068 0.03246 6.18 0.01293 *
cum_avg_unfair:edu_g -0.12526 0.09551 1.72 0.18971
cum_avg_unfair:ETHNICBLACK -0.26961 0.27022 1.00 0.31840
cum_avg_unfair:ETHNICCHINE 0.09315 0.38310 0.06 0.80789
cum_avg_unfair:ETHNICHISPA -0.46983 0.72547 0.42 0.51722
cum_avg_unfair:ETHNICJAPAN 0.00489 0.35257 0.00 0.98894
edu_g:ETHNICBLACK -6.91679 3.18320 4.72 0.02979 *
edu_g:ETHNICCHINE 3.72582 4.40658 0.71 0.39782
edu_g:ETHNICHISPA -8.20439 7.57741 1.17 0.27892
edu_g:ETHNICJAPAN -2.06391 3.83343 0.29 0.59030
cum_avg_unfair:edu_g:ETHNICBLACK 0.29886 0.17540 2.90 0.08841 .
cum_avg_unfair:edu_g:ETHNICCHINE -0.13394 0.23769 0.32 0.57308
cum_avg_unfair:edu_g:ETHNICHISPA 0.62975 0.64027 0.97 0.32533
cum_avg_unfair:edu_g:ETHNICJAPAN 0.07831 0.23045 0.12 0.73399
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 80.3 2.1
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.82 0.00795
Number of clusters: 2200 Maximum cluster size: 12
Call:
geeglm(formula = dbp_formula2, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 50.7132 2.8526 316.06 < 2e-16 ***
AGE0 0.2014 0.0539 13.96 0.00019 ***
bmi 0.3602 0.0251 206.24 < 2e-16 ***
chronicity 0.6452 1.7655 0.13 0.71477
diabete -1.2766 0.5263 5.88 0.01529 *
edu_g -0.2960 0.5571 0.28 0.59513
ETHNICBLACK 3.9533 2.2693 3.03 0.08149 .
ETHNICCHINE 0.1295 3.5600 0.00 0.97097
ETHNICHISPA -0.8576 2.4177 0.13 0.72281
ETHNICJAPAN 5.8596 2.2950 6.52 0.01067 *
heartat_stroke -0.3044 0.6654 0.21 0.64735
med_bp -2.9643 0.3415 75.35 < 2e-16 ***
med_other 0.0160 0.2287 0.00 0.94419
SITE12 7.5751 0.5435 194.22 < 2e-16 ***
SITE13 7.9438 0.6221 163.03 < 2e-16 ***
SITE14 1.7619 0.6572 7.19 0.00734 **
SITE15 3.9336 0.6561 35.95 0.000000002 ***
SITE16 8.9512 0.7894 128.58 < 2e-16 ***
SITE17 2.9907 0.5540 29.14 0.000000067 ***
statusx -0.0059 0.0696 0.01 0.93244
smoker 0.1173 0.4084 0.08 0.77391
visit 0.0793 0.0323 6.03 0.01405 *
chronicity:edu_g -0.4896 1.0408 0.22 0.63807
chronicity:ETHNICBLACK 2.2138 3.4761 0.41 0.52422
chronicity:ETHNICCHINE 1.3519 4.9924 0.07 0.78655
chronicity:ETHNICHISPA 8.6289 5.4930 2.47 0.11621
chronicity:ETHNICJAPAN -3.9971 5.1400 0.60 0.43678
edu_g:ETHNICBLACK -0.9529 1.4490 0.43 0.51077
edu_g:ETHNICCHINE 1.6997 2.0268 0.70 0.40167
edu_g:ETHNICHISPA 1.9436 1.9035 1.04 0.30723
edu_g:ETHNICJAPAN -2.0995 1.4371 2.13 0.14405
chronicity:edu_g:ETHNICBLACK -0.9141 2.2353 0.17 0.68260
chronicity:edu_g:ETHNICCHINE -1.3259 2.9881 0.20 0.65725
chronicity:edu_g:ETHNICHISPA -6.2694 4.1589 2.27 0.13170
chronicity:edu_g:ETHNICJAPAN 3.9900 3.2229 1.53 0.21570
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 80.4 2.12
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.82 0.00809
Number of clusters: 2200 Maximum cluster size: 12
HTN
Call:
geeglm(formula = htn_formula1, family = poisson(link = "log"),
data = gee_data, id = gee_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -7.54378 0.83934 80.78 < 2e-16 ***
AGE0 0.06513 0.01027 40.19 2.3e-10 ***
bmi 0.03563 0.00398 80.34 < 2e-16 ***
cum_avg_unfair 0.05825 0.03796 2.35 0.12494
diabete 0.08038 0.06610 1.48 0.22395
edu_g 0.48311 0.41365 1.36 0.24284
ETHNICBLACK 2.22434 0.93540 5.65 0.01741 *
ETHNICCHINE 1.57025 1.64217 0.91 0.33897
ETHNICHISPA 3.46895 1.73581 3.99 0.04567 *
ETHNICJAPAN 0.63416 1.54420 0.17 0.68131
heartat_stroke 0.02932 0.09988 0.09 0.76910
med_other 0.10363 0.03852 7.24 0.00714 **
SITE12 0.01102 0.09071 0.01 0.90335
SITE13 0.18471 0.09224 4.01 0.04524 *
SITE14 -0.41222 0.17453 5.58 0.01818 *
SITE15 -0.17836 0.15290 1.36 0.24341
SITE16 0.36972 0.20037 3.40 0.06502 .
SITE17 -0.11811 0.09893 1.43 0.23253
statusx 0.01026 0.01254 0.67 0.41305
smoker -0.05969 0.06875 0.75 0.38527
visit 0.10785 0.00581 345.11 < 2e-16 ***
cum_avg_unfair:edu_g -0.03178 0.02419 1.73 0.18900
cum_avg_unfair:ETHNICBLACK -0.08801 0.05117 2.96 0.08547 .
cum_avg_unfair:ETHNICCHINE -0.05523 0.08587 0.41 0.52011
cum_avg_unfair:ETHNICHISPA -0.19699 0.08740 5.08 0.02420 *
cum_avg_unfair:ETHNICJAPAN 0.00361 0.09325 0.00 0.96910
edu_g:ETHNICBLACK -1.13344 0.59689 3.61 0.05758 .
edu_g:ETHNICCHINE -1.11279 1.12999 0.97 0.32473
edu_g:ETHNICHISPA -3.79980 1.51308 6.31 0.01203 *
edu_g:ETHNICJAPAN -0.33340 1.03988 0.10 0.74850
cum_avg_unfair:edu_g:ETHNICBLACK 0.06002 0.03272 3.36 0.06662 .
cum_avg_unfair:edu_g:ETHNICCHINE 0.05565 0.05863 0.90 0.34256
cum_avg_unfair:edu_g:ETHNICHISPA 0.24451 0.06677 13.41 0.00025 ***
cum_avg_unfair:edu_g:ETHNICJAPAN 0.00436 0.06264 0.00 0.94447
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.708 0.0614
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.835 0.0224
Number of clusters: 2200 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.067 40.189 0.000
bmi 1.036 80.337 0.000
cum_avg_unfair 1.060 2.354 0.125
diabete 1.084 1.479 0.224
edu_g 1.621 1.364 0.243
ETHNICBLACK 9.247 5.655 0.017
ETHNICCHINE 4.808 0.914 0.339
ETHNICHISPA 32.103 3.994 0.046
ETHNICJAPAN 1.885 0.169 0.681
heartat_stroke 1.030 0.086 0.769
med_other 1.109 7.238 0.007
SITE12 1.011 0.015 0.903
SITE13 1.203 4.010 0.045
SITE14 0.662 5.579 0.018
SITE15 0.837 1.361 0.243
SITE16 1.447 3.405 0.065
SITE17 0.889 1.425 0.233
statusx 1.010 0.670 0.413
smoker 0.942 0.754 0.385
visit 1.114 345.106 0.000
cum_avg_unfair:edu_g 0.969 1.725 0.189
cum_avg_unfair:ETHNICBLACK 0.916 2.958 0.085
cum_avg_unfair:ETHNICCHINE 0.946 0.414 0.520
cum_avg_unfair:ETHNICHISPA 0.821 5.080 0.024
cum_avg_unfair:ETHNICJAPAN 1.004 0.002 0.969
edu_g:ETHNICBLACK 0.322 3.606 0.058
edu_g:ETHNICCHINE 0.329 0.970 0.325
edu_g:ETHNICHISPA 0.022 6.307 0.012
edu_g:ETHNICJAPAN 0.716 0.103 0.749
cum_avg_unfair:edu_g:ETHNICBLACK 1.062 3.364 0.067
cum_avg_unfair:edu_g:ETHNICCHINE 1.057 0.901 0.343
cum_avg_unfair:edu_g:ETHNICHISPA 1.277 13.409 0.000
cum_avg_unfair:edu_g:ETHNICJAPAN 1.004 0.005 0.944
Call:
geeglm(formula = htn_formula2, family = poisson(link = "log"),
data = gee_data, id = gee_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -6.63882 0.55995 140.57 < 2e-16 ***
AGE0 0.06577 0.01020 41.55 1.2e-10 ***
bmi 0.03577 0.00397 81.25 < 2e-16 ***
chronicity 0.04450 0.41706 0.01 0.9150
diabete 0.08169 0.06659 1.50 0.2200
edu_g -0.05510 0.15014 0.13 0.7136
ETHNICBLACK 0.81492 0.34054 5.73 0.0167 *
ETHNICCHINE 1.01723 0.74338 1.87 0.1712
ETHNICHISPA -0.31190 0.69798 0.20 0.6550
ETHNICJAPAN 0.61164 0.52085 1.38 0.2403
heartat_stroke 0.02765 0.10080 0.08 0.7839
med_other 0.10685 0.03885 7.57 0.0059 **
SITE12 0.01654 0.09054 0.03 0.8550
SITE13 0.18126 0.09191 3.89 0.0486 *
SITE14 -0.43355 0.17250 6.32 0.0120 *
SITE15 -0.16955 0.15305 1.23 0.2679
SITE16 0.34212 0.19750 3.00 0.0832 .
SITE17 -0.12486 0.09809 1.62 0.2030
statusx 0.00971 0.01257 0.60 0.4401
smoker -0.05945 0.06786 0.77 0.3810
visit 0.10758 0.00579 344.71 < 2e-16 ***
chronicity:edu_g 0.04046 0.24988 0.03 0.8714
chronicity:ETHNICBLACK -0.19361 0.54498 0.13 0.7224
chronicity:ETHNICCHINE -0.67708 1.09752 0.38 0.5373
chronicity:ETHNICHISPA 4.11994 2.14153 3.70 0.0544 .
chronicity:ETHNICJAPAN 0.14592 0.98127 0.02 0.8818
edu_g:ETHNICBLACK -0.08060 0.21809 0.14 0.7117
edu_g:ETHNICCHINE -0.36282 0.46560 0.61 0.4358
edu_g:ETHNICHISPA 0.34200 0.58770 0.34 0.5606
edu_g:ETHNICJAPAN -0.25958 0.34932 0.55 0.4574
chronicity:edu_g:ETHNICBLACK -0.00983 0.34486 0.00 0.9773
chronicity:edu_g:ETHNICCHINE 0.43670 0.69989 0.39 0.5327
chronicity:edu_g:ETHNICHISPA -3.39470 2.03223 2.79 0.0948 .
chronicity:edu_g:ETHNICJAPAN 0.01746 0.66438 0.00 0.9790
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.706 0.0585
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.834 0.0217
Number of clusters: 2200 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.068 41.548 0.000
bmi 1.036 81.254 0.000
chronicity 1.046 0.011 0.915
diabete 1.085 1.505 0.220
edu_g 0.946 0.135 0.714
ETHNICBLACK 2.259 5.726 0.017
ETHNICCHINE 2.766 1.872 0.171
ETHNICHISPA 0.732 0.200 0.655
ETHNICJAPAN 1.843 1.379 0.240
heartat_stroke 1.028 0.075 0.784
med_other 1.113 7.566 0.006
SITE12 1.017 0.033 0.855
SITE13 1.199 3.889 0.049
SITE14 0.648 6.317 0.012
SITE15 0.844 1.227 0.268
SITE16 1.408 3.000 0.083
SITE17 0.883 1.620 0.203
statusx 1.010 0.596 0.440
smoker 0.942 0.767 0.381
visit 1.114 344.705 0.000
chronicity:edu_g 1.041 0.026 0.871
chronicity:ETHNICBLACK 0.824 0.126 0.722
chronicity:ETHNICCHINE 0.508 0.381 0.537
chronicity:ETHNICHISPA 61.555 3.701 0.054
chronicity:ETHNICJAPAN 1.157 0.022 0.882
edu_g:ETHNICBLACK 0.923 0.137 0.712
edu_g:ETHNICCHINE 0.696 0.607 0.436
edu_g:ETHNICHISPA 1.408 0.339 0.561
edu_g:ETHNICJAPAN 0.771 0.552 0.457
chronicity:edu_g:ETHNICBLACK 0.990 0.001 0.977
chronicity:edu_g:ETHNICCHINE 1.548 0.389 0.533
chronicity:edu_g:ETHNICHISPA 0.034 2.790 0.095
chronicity:edu_g:ETHNICJAPAN 1.018 0.001 0.979