SWAN
SBP Average
Call:
geeglm(formula = sbp_formula1, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 62.4772 4.4140 200.35 < 2e-16 ***
AGE0 0.7708 0.0863 79.72 < 2e-16 ***
bmi 0.6389 0.0397 259.32 < 2e-16 ***
cum_avg_unfair -0.0265 0.0737 0.13 0.719
diabete -1.6491 0.8328 3.92 0.048 *
edu_g -2.0721 0.5048 16.85 4.0e-05 ***
ETHNICBLACK 3.7318 2.5400 2.16 0.142
ETHNICCHINE 3.7244 3.4264 1.18 0.277
ETHNICHISPA -2.0982 3.2284 0.42 0.516
ETHNICJAPAN 2.6265 2.8673 0.84 0.360
heartat_stroke -1.3349 0.9818 1.85 0.174
med_bp -4.9256 0.5372 84.07 < 2e-16 ***
med_other -0.0790 0.3242 0.06 0.807
SITE12 0.4558 0.9427 0.23 0.629
SITE13 5.8809 1.0792 29.69 5.1e-08 ***
SITE14 -2.1711 1.1063 3.85 0.050 *
SITE15 -2.0247 1.0959 3.41 0.065 .
SITE16 7.8588 1.2079 42.33 7.7e-11 ***
SITE17 -1.4335 0.9762 2.16 0.142
statusx 0.1313 0.0990 1.76 0.185
smoker 1.3704 0.6228 4.84 0.028 *
visit 0.6194 0.0492 158.25 < 2e-16 ***
cum_avg_unfair:ETHNICBLACK 0.2072 0.1374 2.27 0.132
cum_avg_unfair:ETHNICCHINE -0.0466 0.1806 0.07 0.797
cum_avg_unfair:ETHNICHISPA 0.2762 0.2399 1.33 0.249
cum_avg_unfair:ETHNICJAPAN 0.1005 0.1639 0.38 0.540
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 189 6.11
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.851 0.00714
Number of clusters: 2200 Maximum cluster size: 12
Call:
geeglm(formula = sbp_formula2, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 62.3198 4.3387 206.31 < 2e-16 ***
AGE0 0.7680 0.0860 79.70 < 2e-16 ***
bmi 0.6418 0.0398 260.56 < 2e-16 ***
chronicity -0.4152 0.8175 0.26 0.612
diabete -1.6398 0.8328 3.88 0.049 *
edu_g -2.1373 0.5042 17.97 2.2e-05 ***
ETHNICBLACK 8.7120 1.2630 47.58 5.3e-12 ***
ETHNICCHINE 2.8422 1.6081 3.12 0.077 .
ETHNICHISPA 0.8051 1.3860 0.34 0.561
ETHNICJAPAN 2.9082 1.3256 4.81 0.028 *
heartat_stroke -1.3188 0.9824 1.80 0.179
med_bp -4.9298 0.5380 83.96 < 2e-16 ***
med_other -0.0755 0.3242 0.05 0.816
SITE12 0.6502 0.9396 0.48 0.489
SITE13 5.8067 1.0697 29.46 5.7e-08 ***
SITE14 -2.0966 1.1053 3.60 0.058 .
SITE15 -1.9740 1.0926 3.26 0.071 .
SITE16 7.9176 1.2004 43.50 4.2e-11 ***
SITE17 -1.4046 0.9713 2.09 0.148
statusx 0.1292 0.0989 1.71 0.191
smoker 1.3818 0.6214 4.95 0.026 *
visit 0.6171 0.0490 158.37 < 2e-16 ***
chronicity:ETHNICBLACK -1.8379 1.8425 0.99 0.319
chronicity:ETHNICCHINE 0.0504 2.2096 0.00 0.982
chronicity:ETHNICHISPA 2.2686 2.6580 0.73 0.393
chronicity:ETHNICJAPAN 4.0003 2.5434 2.47 0.116
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 189 6.07
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.85 0.00719
Number of clusters: 2200 Maximum cluster size: 12
DBP Average
Call:
geeglm(formula = dbp_formula1, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 52.00597 2.78242 349.35 < 2e-16 ***
AGE0 0.19690 0.05356 13.52 0.00024 ***
bmi 0.35708 0.02495 204.87 < 2e-16 ***
cum_avg_unfair -0.00896 0.04746 0.04 0.85026
diabete -1.26382 0.52742 5.74 0.01656 *
edu_g -0.81378 0.31040 6.87 0.00875 **
ETHNICBLACK -0.05743 1.57887 0.00 0.97098
ETHNICCHINE 4.07241 2.28114 3.19 0.07422 .
ETHNICHISPA -1.86064 2.40670 0.60 0.43946
ETHNICJAPAN 1.35187 1.93546 0.49 0.48488
heartat_stroke -0.30709 0.66505 0.21 0.64426
med_bp -2.96066 0.34160 75.12 < 2e-16 ***
med_other 0.01465 0.22876 0.00 0.94894
SITE12 7.42388 0.54522 185.40 < 2e-16 ***
SITE13 7.81289 0.62579 155.87 < 2e-16 ***
SITE14 1.66965 0.65848 6.43 0.01123 *
SITE15 3.84080 0.65923 33.94 5.7e-09 ***
SITE16 8.79983 0.79938 121.18 < 2e-16 ***
SITE17 2.86009 0.55480 26.58 2.5e-07 ***
statusx -0.00587 0.06959 0.01 0.93281
smoker 0.11891 0.40902 0.08 0.77127
visit 0.08141 0.03251 6.27 0.01228 *
cum_avg_unfair:ETHNICBLACK 0.16974 0.08555 3.94 0.04724 *
cum_avg_unfair:ETHNICCHINE -0.09329 0.11959 0.61 0.43537
cum_avg_unfair:ETHNICHISPA 0.26817 0.18452 2.11 0.14613
cum_avg_unfair:ETHNICJAPAN 0.12774 0.11418 1.25 0.26325
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 80.5 2.12
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.82 0.00801
Number of clusters: 2200 Maximum cluster size: 12
Call:
geeglm(formula = dbp_formula2, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 51.96115 2.72824 362.74 < 2e-16 ***
AGE0 0.19626 0.05364 13.39 0.00025 ***
bmi 0.35810 0.02502 204.92 < 2e-16 ***
chronicity -0.15165 0.51384 0.09 0.76790
diabete -1.26366 0.52672 5.76 0.01643 *
edu_g -0.82999 0.31118 7.11 0.00765 **
ETHNICBLACK 2.49188 0.73898 11.37 0.00075 ***
ETHNICCHINE 2.88415 1.07002 7.27 0.00703 **
ETHNICHISPA 0.99654 0.94898 1.10 0.29366
ETHNICJAPAN 2.70200 0.83746 10.41 0.00125 **
heartat_stroke -0.30314 0.66586 0.21 0.64892
med_bp -2.96781 0.34174 75.42 < 2e-16 ***
med_other 0.01547 0.22878 0.00 0.94611
SITE12 7.45160 0.54501 186.94 < 2e-16 ***
SITE13 7.76984 0.62503 154.53 < 2e-16 ***
SITE14 1.67402 0.65880 6.46 0.01105 *
SITE15 3.83738 0.65874 33.93 5.7e-09 ***
SITE16 8.79880 0.79382 122.86 < 2e-16 ***
SITE17 2.85419 0.55546 26.40 2.8e-07 ***
statusx -0.00648 0.06958 0.01 0.92576
smoker 0.11945 0.40876 0.09 0.77011
visit 0.07975 0.03232 6.09 0.01361 *
chronicity:ETHNICBLACK 1.12487 1.09548 1.05 0.30450
chronicity:ETHNICCHINE -0.96373 1.44834 0.44 0.50579
chronicity:ETHNICHISPA 2.07573 1.85226 1.26 0.26244
chronicity:ETHNICJAPAN 2.06311 1.62084 1.62 0.20307
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 80.5 2.12
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.821 0.00802
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) -6.694038 0.539338 154.05 < 2e-16 ***
AGE0 0.066472 0.010209 42.39 7.5e-11 ***
bmi 0.035219 0.003970 78.69 < 2e-16 ***
cum_avg_unfair 0.010078 0.012439 0.66 0.4178
diabete 0.080672 0.065986 1.49 0.2215
edu_g -0.108012 0.060149 3.22 0.0725 .
ETHNICBLACK 0.574904 0.305374 3.54 0.0598 .
ETHNICCHINE -0.077641 0.576075 0.02 0.8928
ETHNICHISPA -0.851822 0.469603 3.29 0.0697 .
ETHNICJAPAN 0.109704 0.526130 0.04 0.8348
heartat_stroke 0.029408 0.099929 0.09 0.7685
med_other 0.103519 0.038535 7.22 0.0072 **
SITE12 0.016498 0.090114 0.03 0.8547
SITE13 0.185830 0.091848 4.09 0.0430 *
SITE14 -0.427382 0.172849 6.11 0.0134 *
SITE15 -0.172435 0.153476 1.26 0.2612
SITE16 0.353091 0.201951 3.06 0.0804 .
SITE17 -0.126770 0.098553 1.65 0.1983
statusx 0.010455 0.012514 0.70 0.4035
smoker -0.066211 0.068248 0.94 0.3320
visit 0.108039 0.005808 345.98 < 2e-16 ***
cum_avg_unfair:ETHNICBLACK -0.000694 0.016675 0.00 0.9668
cum_avg_unfair:ETHNICCHINE 0.027792 0.028970 0.92 0.3374
cum_avg_unfair:ETHNICHISPA 0.079814 0.030070 7.05 0.0079 **
cum_avg_unfair:ETHNICJAPAN 0.011330 0.030940 0.13 0.7142
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.708 0.0599
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.835 0.0218
Number of clusters: 2200 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.069 42.393 0.000
bmi 1.036 78.691 0.000
cum_avg_unfair 1.010 0.656 0.418
diabete 1.084 1.495 0.221
edu_g 0.898 3.225 0.073
ETHNICBLACK 1.777 3.544 0.060
ETHNICCHINE 0.925 0.018 0.893
ETHNICHISPA 0.427 3.290 0.070
ETHNICJAPAN 1.116 0.043 0.835
heartat_stroke 1.030 0.087 0.769
med_other 1.109 7.217 0.007
SITE12 1.017 0.034 0.855
SITE13 1.204 4.093 0.043
SITE14 0.652 6.114 0.013
SITE15 0.842 1.262 0.261
SITE16 1.423 3.057 0.080
SITE17 0.881 1.655 0.198
statusx 1.011 0.698 0.403
smoker 0.936 0.941 0.332
visit 1.114 345.982 0.000
cum_avg_unfair:ETHNICBLACK 0.999 0.002 0.967
cum_avg_unfair:ETHNICCHINE 1.028 0.920 0.337
cum_avg_unfair:ETHNICHISPA 1.083 7.045 0.008
cum_avg_unfair:ETHNICJAPAN 1.011 0.134 0.714
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) -6.57179 0.50832 167.14 < 2e-16 ***
AGE0 0.06613 0.01016 42.34 7.7e-11 ***
bmi 0.03578 0.00394 82.44 < 2e-16 ***
chronicity 0.10914 0.12411 0.77 0.3792
diabete 0.08231 0.06654 1.53 0.2161
edu_g -0.11103 0.05970 3.46 0.0629 .
ETHNICBLACK 0.69511 0.11312 37.76 8.0e-10 ***
ETHNICCHINE 0.44971 0.27562 2.66 0.1028
ETHNICHISPA 0.01917 0.24907 0.01 0.9387
ETHNICJAPAN 0.24829 0.20504 1.47 0.2259
heartat_stroke 0.02901 0.10116 0.08 0.7743
med_other 0.10626 0.03881 7.50 0.0062 **
SITE12 0.02399 0.09021 0.07 0.7903
SITE13 0.18394 0.09075 4.11 0.0427 *
SITE14 -0.42479 0.17293 6.03 0.0140 *
SITE15 -0.16761 0.15308 1.20 0.2735
SITE16 0.34071 0.19870 2.94 0.0864 .
SITE17 -0.12749 0.09792 1.70 0.1929
statusx 0.00985 0.01255 0.62 0.4327
smoker -0.06306 0.06759 0.87 0.3508
visit 0.10750 0.00579 345.04 < 2e-16 ***
chronicity:ETHNICBLACK -0.21611 0.16943 1.63 0.2021
chronicity:ETHNICCHINE -0.01867 0.34972 0.00 0.9574
chronicity:ETHNICHISPA 0.54525 0.36412 2.24 0.1343
chronicity:ETHNICJAPAN 0.13441 0.32258 0.17 0.6769
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.704 0.0563
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.834 0.0211
Number of clusters: 2200 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.068 42.340 0.000
bmi 1.036 82.439 0.000
chronicity 1.115 0.773 0.379
diabete 1.086 1.530 0.216
edu_g 0.895 3.459 0.063
ETHNICBLACK 2.004 37.758 0.000
ETHNICCHINE 1.568 2.662 0.103
ETHNICHISPA 1.019 0.006 0.939
ETHNICJAPAN 1.282 1.466 0.226
heartat_stroke 1.029 0.082 0.774
med_other 1.112 7.498 0.006
SITE12 1.024 0.071 0.790
SITE13 1.202 4.109 0.043
SITE14 0.654 6.034 0.014
SITE15 0.846 1.199 0.274
SITE16 1.406 2.940 0.086
SITE17 0.880 1.695 0.193
statusx 1.010 0.616 0.433
smoker 0.939 0.870 0.351
visit 1.113 345.037 0.000
chronicity:ETHNICBLACK 0.806 1.627 0.202
chronicity:ETHNICCHINE 0.982 0.003 0.957
chronicity:ETHNICHISPA 1.725 2.242 0.134
chronicity:ETHNICJAPAN 1.144 0.174 0.677