SWAN
SBP Average
Call:
geeglm(formula = sbp_formula1, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 62.27618 5.48688 128.823 < 2e-16 ***
AGE0 0.75332 0.08943 70.952 < 2e-16 ***
bmi 0.64967 0.04015 261.827 < 2e-16 ***
cum_avg_unfair 0.31290 0.16734 3.496 0.06150 .
diabete -1.60113 0.83554 3.672 0.05533 .
edu_g -0.88023 1.83918 0.229 0.63223
heartat_stroke -1.34761 0.98359 1.877 0.17066
med_bp -4.91736 0.53947 83.088 < 2e-16 ***
med_other -0.09668 0.32502 0.088 0.76612
SITE12 -0.43867 1.00349 0.191 0.66200
SITE13 5.64384 1.13009 24.942 5.91e-07 ***
SITE14 -4.19106 1.01899 16.916 3.91e-05 ***
SITE15 -3.08560 1.02842 9.002 0.00270 **
SITE16 5.38211 1.06197 25.685 4.02e-07 ***
SITE17 -2.94452 1.03584 8.081 0.00447 **
statusx 0.12649 0.09931 1.623 0.20274
smoker 1.38332 0.63309 4.774 0.02889 *
visit 0.62419 0.04974 157.467 < 2e-16 ***
cum_avg_unfair:edu_g -0.12500 0.10680 1.370 0.24182
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 196.7 6.32
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.8592 0.006294
Number of clusters: 2200 Maximum cluster size: 12
Call:
geeglm(formula = sbp_formula2, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 65.8911 4.6284 202.67 < 2e-16 ***
AGE0 0.7530 0.0894 70.99 < 2e-16 ***
bmi 0.6527 0.0403 262.23 < 2e-16 ***
chronicity 3.7865 2.1640 3.06 0.0802 .
diabete -1.5873 0.8364 3.60 0.0577 .
edu_g -2.0601 0.7716 7.13 0.0076 **
heartat_stroke -1.3396 0.9839 1.85 0.1733
med_bp -4.9250 0.5395 83.34 < 2e-16 ***
med_other -0.0988 0.3250 0.09 0.7613
SITE12 -0.5246 1.0010 0.27 0.6002
SITE13 5.4858 1.1294 23.59 1.2e-06 ***
SITE14 -4.3038 1.0147 17.99 2.2e-05 ***
SITE15 -3.2540 1.0285 10.01 0.0016 **
SITE16 4.9304 1.0289 22.96 1.7e-06 ***
SITE17 -3.0539 1.0341 8.72 0.0031 **
statusx 0.1265 0.0993 1.63 0.2024
smoker 1.3839 0.6326 4.79 0.0287 *
visit 0.6186 0.0497 155.13 < 2e-16 ***
chronicity:edu_g -2.0121 1.3212 2.32 0.1278
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 197 6.35
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.86 0.00632
Number of clusters: 2200 Maximum cluster size: 12
DBP Average
Call:
geeglm(formula = dbp_formula1, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 50.78944 3.41791 220.81 < 2e-16 ***
AGE0 0.19323 0.05478 12.44 0.00042 ***
bmi 0.35799 0.02525 200.95 < 2e-16 ***
cum_avg_unfair 0.19664 0.10813 3.31 0.06898 .
diabete -1.21344 0.53019 5.24 0.02210 *
edu_g -0.08266 1.18953 0.00 0.94460
heartat_stroke -0.31530 0.66710 0.22 0.63647
med_bp -2.96794 0.34384 74.51 < 2e-16 ***
med_other -0.00275 0.22967 0.00 0.99045
SITE12 7.15347 0.57049 157.23 < 2e-16 ***
SITE13 7.75121 0.64117 146.15 < 2e-16 ***
SITE14 1.54714 0.58652 6.96 0.00834 **
SITE15 4.42618 0.59626 55.11 1.1e-13 ***
SITE16 8.56510 0.63931 179.49 < 2e-16 ***
SITE17 2.28265 0.58051 15.46 8.4e-05 ***
statusx -0.00840 0.06991 0.01 0.90438
smoker 0.11337 0.41434 0.07 0.78438
visit 0.08547 0.03287 6.76 0.00932 **
cum_avg_unfair:edu_g -0.07140 0.06865 1.08 0.29830
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 82.9 2.16
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.83 0.00721
Number of clusters: 2200 Maximum cluster size: 12
Call:
geeglm(formula = dbp_formula2, data = gee_data, id = gee_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 53.21113 2.86045 346.05 < 2e-16 ***
AGE0 0.19346 0.05468 12.52 0.00040 ***
bmi 0.35958 0.02535 201.25 < 2e-16 ***
chronicity 2.00551 1.31847 2.31 0.12824
diabete -1.20310 0.53011 5.15 0.02324 *
edu_g -0.87680 0.47485 3.41 0.06482 .
heartat_stroke -0.31025 0.66740 0.22 0.64203
med_bp -2.97223 0.34376 74.76 < 2e-16 ***
med_other -0.00405 0.22972 0.00 0.98594
SITE12 7.08915 0.56919 155.12 < 2e-16 ***
SITE13 7.66274 0.64019 143.27 < 2e-16 ***
SITE14 1.47374 0.58398 6.37 0.01162 *
SITE15 4.33345 0.59542 52.97 3.4e-13 ***
SITE16 8.26464 0.61069 183.15 < 2e-16 ***
SITE17 2.21834 0.57931 14.66 0.00013 ***
statusx -0.00851 0.06993 0.01 0.90318
smoker 0.11324 0.41454 0.07 0.78473
visit 0.08140 0.03267 6.21 0.01273 *
chronicity:edu_g -0.86454 0.81622 1.12 0.28951
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 82.9 2.18
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.831 0.00726
Number of clusters: 2200 Maximum cluster size: 12
HTN
Call:
geeglm(formula = htn_formula1, family = poisson(link = "log"),
data = gee_data, id = gee_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -6.32966 0.67229 88.64 < 2e-16 ***
AGE0 0.06265 0.01038 36.45 1.6e-09 ***
bmi 0.03512 0.00388 81.74 < 2e-16 ***
cum_avg_unfair 0.02365 0.02241 1.11 0.2913
diabete 0.08580 0.06764 1.61 0.2047
edu_g -0.22992 0.26627 0.75 0.3879
heartat_stroke 0.02750 0.10081 0.07 0.7850
med_other 0.10232 0.03829 7.14 0.0075 **
SITE12 -0.03536 0.09383 0.14 0.7063
SITE13 0.18798 0.09395 4.00 0.0454 *
SITE14 -0.48414 0.11942 16.44 5.0e-05 ***
SITE15 -0.29067 0.11167 6.78 0.0092 **
SITE16 0.20289 0.12367 2.69 0.1009
SITE17 -0.22449 0.10218 4.83 0.0280 *
statusx 0.00927 0.01209 0.59 0.4433
smoker -0.06829 0.06867 0.99 0.3200
visit 0.10834 0.00585 343.46 < 2e-16 ***
cum_avg_unfair:edu_g 0.00260 0.01475 0.03 0.8599
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.73 0.0609
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.841 0.0213
Number of clusters: 2200 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.065 36.446 0.000
bmi 1.036 81.742 0.000
cum_avg_unfair 1.024 1.114 0.291
diabete 1.090 1.609 0.205
edu_g 0.795 0.746 0.388
heartat_stroke 1.028 0.074 0.785
med_other 1.108 7.140 0.008
SITE12 0.965 0.142 0.706
SITE13 1.207 4.003 0.045
SITE14 0.616 16.436 0.000
SITE15 0.748 6.776 0.009
SITE16 1.225 2.692 0.101
SITE17 0.799 4.827 0.028
statusx 1.009 0.588 0.443
smoker 0.934 0.989 0.320
visit 1.114 343.456 0.000
cum_avg_unfair:edu_g 1.003 0.031 0.860
Call:
geeglm(formula = htn_formula2, family = poisson(link = "log"),
data = gee_data, id = gee_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -5.98062 0.53519 124.88 < 2e-16 ***
AGE0 0.06274 0.01034 36.81 1.3e-09 ***
bmi 0.03545 0.00388 83.56 < 2e-16 ***
chronicity 0.13910 0.24029 0.34 0.5627
diabete 0.08906 0.06811 1.71 0.1910
edu_g -0.18774 0.10044 3.49 0.0616 .
heartat_stroke 0.02958 0.10185 0.08 0.7715
med_other 0.10292 0.03848 7.16 0.0075 **
SITE12 -0.06035 0.09340 0.42 0.5182
SITE13 0.16497 0.09295 3.15 0.0759 .
SITE14 -0.50539 0.11806 18.33 1.9e-05 ***
SITE15 -0.32833 0.10957 8.98 0.0027 **
SITE16 0.10027 0.11504 0.76 0.3834
SITE17 -0.25253 0.10125 6.22 0.0126 *
statusx 0.00910 0.01212 0.56 0.4531
smoker -0.06629 0.06785 0.95 0.3286
visit 0.10731 0.00583 339.08 < 2e-16 ***
chronicity:edu_g 0.01823 0.15636 0.01 0.9072
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.728 0.0585
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.842 0.0207
Number of clusters: 2200 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.065 36.806 0.000
bmi 1.036 83.559 0.000
chronicity 1.149 0.335 0.563
diabete 1.093 1.710 0.191
edu_g 0.829 3.494 0.062
heartat_stroke 1.030 0.084 0.772
med_other 1.108 7.156 0.007
SITE12 0.941 0.417 0.518
SITE13 1.179 3.150 0.076
SITE14 0.603 18.326 0.000
SITE15 0.720 8.979 0.003
SITE16 1.105 0.760 0.383
SITE17 0.777 6.221 0.013
statusx 1.009 0.563 0.453
smoker 0.936 0.954 0.329
visit 1.113 339.084 0.000
chronicity:edu_g 1.018 0.014 0.907
SBP Average Caucasian
Call:
geeglm(formula = sbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 61.8993 7.1732 74.46 < 2e-16 ***
AGE0 0.6207 0.1143 29.51 5.6e-08 ***
bmi 0.7030 0.0493 203.64 < 2e-16 ***
cum_avg_unfair 0.2906 0.2695 1.16 0.281
diabete -0.8318 1.3550 0.38 0.539
edu_g 1.9682 2.6211 0.56 0.453
heartat_stroke -2.0641 1.2518 2.72 0.099 .
med_bp -4.1954 0.7522 31.11 2.4e-08 ***
med_other -0.1636 0.3774 0.19 0.665
statusx -0.0282 0.1204 0.05 0.815
smoker 0.5851 0.6809 0.74 0.390
visit 0.5855 0.0612 91.50 < 2e-16 ***
cum_avg_unfair:edu_g -0.2140 0.1573 1.85 0.174
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 165 8.99
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.871 0.0124
Number of clusters: 1130 Maximum cluster size: 12
Call:
geeglm(formula = sbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 65.7761 5.7694 129.98 < 2e-16 ***
AGE0 0.6192 0.1144 29.29 6.2e-08 ***
bmi 0.7038 0.0494 203.33 < 2e-16 ***
chronicity 2.2498 3.0491 0.54 0.461
diabete -0.8366 1.3553 0.38 0.537
edu_g -0.7871 0.9359 0.71 0.400
heartat_stroke -2.0700 1.2517 2.74 0.098 .
med_bp -4.2058 0.7527 31.23 2.3e-08 ***
med_other -0.1656 0.3775 0.19 0.661
statusx -0.0265 0.1205 0.05 0.826
smoker 0.5883 0.6809 0.75 0.388
visit 0.5880 0.0608 93.49 < 2e-16 ***
chronicity:edu_g -1.8470 1.7666 1.09 0.296
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 165 8.98
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.871 0.0124
Number of clusters: 1130 Maximum cluster size: 12
DBP Average Caucasian
Call:
geeglm(formula = dbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 52.0390 4.7513 119.96 < 2e-16 ***
AGE0 0.1840 0.0764 5.80 0.016 *
bmi 0.3943 0.0344 131.23 < 2e-16 ***
cum_avg_unfair 0.1709 0.1820 0.88 0.348
diabete -1.7735 0.7924 5.01 0.025 *
edu_g 2.3786 1.7991 1.75 0.186
heartat_stroke -0.2529 0.8393 0.09 0.763
med_bp -2.9479 0.5234 31.72 1.8e-08 ***
med_other -0.1048 0.2756 0.14 0.704
statusx -0.0384 0.0907 0.18 0.672
smoker -0.4718 0.5083 0.86 0.353
visit 0.0953 0.0416 5.25 0.022 *
cum_avg_unfair:edu_g -0.1331 0.1071 1.54 0.214
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 78.9 3.56
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.854 0.0121
Number of clusters: 1130 Maximum cluster size: 12
Call:
geeglm(formula = dbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 54.6117 3.8896 197.13 < 2e-16 ***
AGE0 0.1832 0.0766 5.72 0.017 *
bmi 0.3951 0.0345 130.79 < 2e-16 ***
chronicity 0.5802 1.9592 0.09 0.767
diabete -1.7743 0.7926 5.01 0.025 *
edu_g 0.4823 0.6212 0.60 0.438
heartat_stroke -0.2576 0.8390 0.09 0.759
med_bp -2.9561 0.5238 31.85 1.7e-08 ***
med_other -0.1059 0.2757 0.15 0.701
statusx -0.0372 0.0907 0.17 0.682
smoker -0.4684 0.5091 0.85 0.358
visit 0.0974 0.0412 5.59 0.018 *
chronicity:edu_g -0.7129 1.1659 0.37 0.541
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 79 3.56
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.855 0.012
Number of clusters: 1130 Maximum cluster size: 12
HTN Caucasian
Call:
geeglm(formula = htn_formula1, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -8.25223 1.10470 55.80 8e-14 ***
AGE0 0.05420 0.01755 9.53 0.002 **
bmi 0.05361 0.00536 100.12 <2e-16 ***
cum_avg_unfair 0.07522 0.03922 3.68 0.055 .
diabete 0.18834 0.13309 2.00 0.157
edu_g 0.74524 0.42991 3.01 0.083 .
heartat_stroke 0.04600 0.22036 0.04 0.835
med_other 0.14618 0.06465 5.11 0.024 *
statusx -0.01510 0.01920 0.62 0.432
smoker -0.14045 0.16714 0.71 0.401
visit 0.12499 0.01087 132.16 <2e-16 ***
cum_avg_unfair:edu_g -0.04402 0.02489 3.13 0.077 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.774 0.138
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.823 0.0476
Number of clusters: 1130 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.056 9.534 0.002
bmi 1.055 100.118 0.000
cum_avg_unfair 1.078 3.678 0.055
diabete 1.207 2.003 0.157
edu_g 2.107 3.005 0.083
heartat_stroke 1.047 0.044 0.835
med_other 1.157 5.113 0.024
statusx 0.985 0.619 0.432
smoker 0.869 0.706 0.401
visit 1.133 132.157 0.000
cum_avg_unfair:edu_g 0.957 3.129 0.077
Call:
geeglm(formula = htn_formula2, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -6.96348 0.89367 60.72 6.6e-15 ***
AGE0 0.05264 0.01759 8.95 0.0028 **
bmi 0.05302 0.00537 97.52 < 2e-16 ***
chronicity 0.09335 0.42674 0.05 0.8268
diabete 0.18776 0.13320 1.99 0.1587
edu_g 0.03699 0.15569 0.06 0.8122
heartat_stroke 0.04497 0.21905 0.04 0.8373
med_other 0.14509 0.06467 5.03 0.0249 *
statusx -0.01497 0.01918 0.61 0.4351
smoker -0.13740 0.16550 0.69 0.4064
visit 0.12450 0.01086 131.50 < 2e-16 ***
chronicity:edu_g -0.03269 0.25636 0.02 0.8985
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.773 0.138
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.823 0.0475
Number of clusters: 1130 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.054 8.951 0.003
bmi 1.054 97.520 0.000
chronicity 1.098 0.048 0.827
diabete 1.207 1.987 0.159
edu_g 1.038 0.056 0.812
heartat_stroke 1.046 0.042 0.837
med_other 1.156 5.033 0.025
statusx 0.985 0.609 0.435
smoker 0.872 0.689 0.406
visit 1.133 131.503 0.000
chronicity:edu_g 0.968 0.016 0.899
SBP Average African-American
Call:
geeglm(formula = sbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 75.5456 13.2914 32.31 1.3e-08 ***
AGE0 0.8208 0.2293 12.82 0.00034 ***
bmi 0.5779 0.0796 52.67 3.9e-13 ***
cum_avg_unfair -0.2653 0.3423 0.60 0.43844
diabete -3.1063 1.4381 4.67 0.03078 *
edu_g -7.6413 4.4613 2.93 0.08675 .
heartat_stroke -0.5961 1.7580 0.11 0.73454
med_bp -5.6779 0.9912 32.82 1.0e-08 ***
med_other -0.2263 0.7961 0.08 0.77622
statusx 0.4205 0.2626 2.56 0.10930
smoker 3.2081 1.5012 4.57 0.03259 *
visit 0.7023 0.1290 29.65 5.2e-08 ***
cum_avg_unfair:edu_g 0.2944 0.2396 1.51 0.21923
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 281 15.1
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.829 0.0119
Number of clusters: 507 Maximum cluster size: 12
Call:
geeglm(formula = sbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 68.6415 11.2483 37.24 1.0e-09 ***
AGE0 0.8099 0.2251 12.94 0.00032 ***
bmi 0.5973 0.0803 55.33 1.0e-13 ***
chronicity 3.4167 5.1730 0.44 0.50894
diabete -3.0309 1.4416 4.42 0.03552 *
edu_g 0.1724 2.3014 0.01 0.94028
heartat_stroke -0.5660 1.7559 0.10 0.74721
med_bp -5.6593 0.9936 32.44 1.2e-08 ***
med_other -0.2258 0.7957 0.08 0.77655
statusx 0.4112 0.2623 2.46 0.11696
smoker 3.1430 1.4898 4.45 0.03488 *
visit 0.6924 0.1288 28.89 7.7e-08 ***
chronicity:edu_g -4.1950 3.3997 1.52 0.21724
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 279 15.1
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.827 0.013
Number of clusters: 507 Maximum cluster size: 12
DBP Average African-American
Call:
geeglm(formula = dbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 66.6998 7.7753 73.59 < 2e-16 ***
AGE0 0.0825 0.1371 0.36 0.547
bmi 0.2810 0.0484 33.66 6.5e-09 ***
cum_avg_unfair -0.0979 0.2171 0.20 0.652
diabete -2.2712 1.0419 4.75 0.029 *
edu_g -3.6087 2.6849 1.81 0.179
heartat_stroke -0.5106 1.2527 0.17 0.684
med_bp -2.9916 0.5713 27.42 1.6e-07 ***
med_other -0.0110 0.5512 0.00 0.984
statusx 0.0920 0.1623 0.32 0.571
smoker 0.6389 0.8975 0.51 0.477
visit 0.3286 0.0772 18.11 2.1e-05 ***
cum_avg_unfair:edu_g 0.2007 0.1452 1.91 0.167
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 106 4.47
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.805 0.0133
Number of clusters: 507 Maximum cluster size: 12
Call:
geeglm(formula = dbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 61.5805 6.8660 80.44 < 2e-16 ***
AGE0 0.0937 0.1373 0.47 0.49
bmi 0.2891 0.0496 33.95 5.7e-09 ***
chronicity 4.3344 3.1642 1.88 0.17
diabete -2.2634 1.0444 4.70 0.03 *
edu_g 1.3276 1.3574 0.96 0.33
heartat_stroke -0.5096 1.2552 0.16 0.68
med_bp -3.0122 0.5727 27.67 1.4e-07 ***
med_other -0.0256 0.5512 0.00 0.96
statusx 0.0905 0.1624 0.31 0.58
smoker 0.5567 0.8956 0.39 0.53
visit 0.3233 0.0772 17.55 2.8e-05 ***
chronicity:edu_g -1.9696 2.0716 0.90 0.34
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 106 4.66
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.807 0.0141
Number of clusters: 507 Maximum cluster size: 12
HTN African-American
Call:
geeglm(formula = htn_formula1, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -3.48196 0.91713 14.41 0.00015 ***
AGE0 0.04010 0.01441 7.74 0.00540 **
bmi 0.02079 0.00532 15.28 9.2e-05 ***
cum_avg_unfair -0.03111 0.02988 1.08 0.29794
diabete 0.05144 0.08295 0.38 0.53518
edu_g -0.62113 0.37471 2.75 0.09739 .
heartat_stroke 0.05179 0.09884 0.27 0.60026
med_other 0.04428 0.05093 0.76 0.38461
statusx 0.02293 0.01960 1.37 0.24217
smoker -0.00888 0.06314 0.02 0.88813
visit 0.09004 0.00809 123.79 < 2e-16 ***
cum_avg_unfair:edu_g 0.02875 0.01912 2.26 0.13271
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.614 0.0405
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.818 0.0179
Number of clusters: 507 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.041 7.742 0.005
bmi 1.021 15.285 0.000
cum_avg_unfair 0.969 1.083 0.298
diabete 1.053 0.385 0.535
edu_g 0.537 2.748 0.097
heartat_stroke 1.053 0.275 0.600
med_other 1.045 0.756 0.385
statusx 1.023 1.368 0.242
smoker 0.991 0.020 0.888
visit 1.094 123.794 0.000
cum_avg_unfair:edu_g 1.029 2.261 0.133
Call:
geeglm(formula = htn_formula2, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -3.97917 0.74255 28.72 8.4e-08 ***
AGE0 0.04009 0.01438 7.78 0.0053 **
bmi 0.02107 0.00538 15.31 9.1e-05 ***
chronicity -0.17150 0.32556 0.28 0.5983
diabete 0.05419 0.08348 0.42 0.5163
edu_g -0.12234 0.14942 0.67 0.4129
heartat_stroke 0.05287 0.10062 0.28 0.5992
med_other 0.04461 0.05145 0.75 0.3860
statusx 0.02269 0.01964 1.33 0.2479
smoker -0.01435 0.06273 0.05 0.8191
visit 0.08994 0.00809 123.51 < 2e-16 ***
chronicity:edu_g 0.07743 0.22067 0.12 0.7257
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.613 0.0405
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.818 0.018
Number of clusters: 507 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.041 7.776 0.005
bmi 1.021 15.308 0.000
chronicity 0.842 0.278 0.598
diabete 1.056 0.421 0.516
edu_g 0.885 0.670 0.413
heartat_stroke 1.054 0.276 0.599
med_other 1.046 0.752 0.386
statusx 1.023 1.335 0.248
smoker 0.986 0.052 0.819
visit 1.094 123.506 0.000
chronicity:edu_g 1.081 0.123 0.726
SBP Average Chinese
Call:
geeglm(formula = sbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 24.868 17.061 2.12 0.14494
AGE0 1.252 0.297 17.73 2.5e-05 ***
bmi 0.680 0.210 10.49 0.00120 **
cum_avg_unfair 0.767 0.531 2.08 0.14905
diabete -0.677 2.540 0.07 0.78979
edu_g 10.212 6.343 2.59 0.10739
heartat_stroke -2.146 2.370 0.82 0.36522
med_bp -5.502 1.568 12.31 0.00045 ***
med_other 0.669 1.015 0.43 0.51005
statusx 0.155 0.306 0.26 0.61333
smoker 0.836 2.423 0.12 0.73019
visit 0.132 0.120 1.19 0.27454
cum_avg_unfair:edu_g -0.585 0.340 2.97 0.08486 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 181 13.1
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.872 0.0128
Number of clusters: 211 Maximum cluster size: 12
Call:
geeglm(formula = sbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 33.812 15.064 5.04 0.02479 *
AGE0 1.297 0.302 18.40 1.8e-05 ***
bmi 0.674 0.209 10.45 0.00122 **
chronicity 5.429 6.480 0.70 0.40218
diabete -0.712 2.529 0.08 0.77832
edu_g 1.351 2.727 0.25 0.62045
heartat_stroke -2.186 2.374 0.85 0.35705
med_bp -5.514 1.559 12.50 0.00041 ***
med_other 0.673 1.017 0.44 0.50796
statusx 0.153 0.307 0.25 0.61922
smoker 0.806 2.438 0.11 0.74085
visit 0.139 0.121 1.32 0.25028
chronicity:edu_g -3.336 4.079 0.67 0.41350
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 181 13
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.871 0.0126
Number of clusters: 211 Maximum cluster size: 12
DBP Average Chinese
Call:
geeglm(formula = dbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 39.5374 10.4624 14.28 0.00016 ***
AGE0 0.4026 0.1909 4.45 0.03494 *
bmi 0.4654 0.1354 11.81 0.00059 ***
cum_avg_unfair 0.2259 0.3467 0.42 0.51475
diabete -0.6472 1.1764 0.30 0.58223
edu_g 5.3249 4.0629 1.72 0.19000
heartat_stroke -3.2199 2.0137 2.56 0.10981
med_bp -3.6273 1.1431 10.07 0.00151 **
med_other 1.2466 0.8767 2.02 0.15504
statusx -0.1091 0.2202 0.25 0.62041
smoker -0.2855 2.7167 0.01 0.91629
visit -0.3910 0.0869 20.23 6.9e-06 ***
cum_avg_unfair:edu_g -0.2465 0.2152 1.31 0.25190
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 81 5.73
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.849 0.0142
Number of clusters: 211 Maximum cluster size: 12
Call:
geeglm(formula = dbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 43.0503 9.3269 21.30 3.9e-06 ***
AGE0 0.4064 0.1938 4.40 0.0359 *
bmi 0.4621 0.1347 11.77 0.0006 ***
chronicity 1.3122 4.6982 0.08 0.7800
diabete -0.6879 1.1703 0.35 0.5567
edu_g 1.4427 1.9542 0.55 0.4603
heartat_stroke -3.2235 2.0184 2.55 0.1103
med_bp -3.6487 1.1444 10.17 0.0014 **
med_other 1.2452 0.8758 2.02 0.1551
statusx -0.1118 0.2203 0.26 0.6118
smoker -0.2737 2.7170 0.01 0.9198
visit -0.3859 0.0873 19.53 9.9e-06 ***
chronicity:edu_g -1.4399 2.8058 0.26 0.6078
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 81.4 5.8
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.85 0.0144
Number of clusters: 211 Maximum cluster size: 12
HTN Chinese
Call:
geeglm(formula = htn_formula1, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -3.48196 0.91713 14.41 0.00015 ***
AGE0 0.04010 0.01441 7.74 0.00540 **
bmi 0.02079 0.00532 15.28 9.2e-05 ***
cum_avg_unfair -0.03111 0.02988 1.08 0.29794
diabete 0.05144 0.08295 0.38 0.53518
edu_g -0.62113 0.37471 2.75 0.09739 .
heartat_stroke 0.05179 0.09884 0.27 0.60026
med_other 0.04428 0.05093 0.76 0.38461
statusx 0.02293 0.01960 1.37 0.24217
smoker -0.00888 0.06314 0.02 0.88813
visit 0.09004 0.00809 123.79 < 2e-16 ***
cum_avg_unfair:edu_g 0.02875 0.01912 2.26 0.13271
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.614 0.0405
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.818 0.0179
Number of clusters: 507 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.041 7.742 0.005
bmi 1.021 15.285 0.000
cum_avg_unfair 0.969 1.083 0.298
diabete 1.053 0.385 0.535
edu_g 0.537 2.748 0.097
heartat_stroke 1.053 0.275 0.600
med_other 1.045 0.756 0.385
statusx 1.023 1.368 0.242
smoker 0.991 0.020 0.888
visit 1.094 123.794 0.000
cum_avg_unfair:edu_g 1.029 2.261 0.133
Call:
geeglm(formula = htn_formula2, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -3.97917 0.74255 28.72 8.4e-08 ***
AGE0 0.04009 0.01438 7.78 0.0053 **
bmi 0.02107 0.00538 15.31 9.1e-05 ***
chronicity -0.17150 0.32556 0.28 0.5983
diabete 0.05419 0.08348 0.42 0.5163
edu_g -0.12234 0.14942 0.67 0.4129
heartat_stroke 0.05287 0.10062 0.28 0.5992
med_other 0.04461 0.05145 0.75 0.3860
statusx 0.02269 0.01964 1.33 0.2479
smoker -0.01435 0.06273 0.05 0.8191
visit 0.08994 0.00809 123.51 < 2e-16 ***
chronicity:edu_g 0.07743 0.22067 0.12 0.7257
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.613 0.0405
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.818 0.018
Number of clusters: 507 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.041 7.776 0.005
bmi 1.021 15.308 0.000
chronicity 0.842 0.278 0.598
diabete 1.056 0.421 0.516
edu_g 0.885 0.670 0.413
heartat_stroke 1.054 0.276 0.599
med_other 1.046 0.752 0.386
statusx 1.023 1.335 0.248
smoker 0.986 0.052 0.819
visit 1.094 123.506 0.000
chronicity:edu_g 1.081 0.123 0.726
SBP Average Hispanic
Call:
geeglm(formula = sbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 78.7185 17.2267 20.88 4.9e-06 ***
AGE0 0.5407 0.2459 4.83 0.0279 *
bmi 0.4393 0.1024 18.41 1.8e-05 ***
cum_avg_unfair 0.2215 0.7458 0.09 0.7665
diabete 0.5820 2.7266 0.05 0.8310
edu_g 0.7764 8.8675 0.01 0.9302
heartat_stroke -7.7358 4.1191 3.53 0.0604 .
med_bp 4.9588 1.8627 7.09 0.0078 **
med_other -1.2708 2.3172 0.30 0.5834
statusx 0.2200 0.5045 0.19 0.6628
smoker 3.2119 1.5572 4.25 0.0392 *
visit 0.5233 0.2057 6.47 0.0109 *
cum_avg_unfair:edu_g -0.0618 0.6681 0.01 0.9263
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 120 9.98
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.374 0.0518
Number of clusters: 124 Maximum cluster size: 8
Call:
geeglm(formula = sbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 81.781 13.197 38.40 5.8e-10 ***
AGE0 0.532 0.244 4.76 0.0291 *
bmi 0.439 0.104 17.75 2.5e-05 ***
chronicity 0.852 7.087 0.01 0.9044
diabete 0.551 2.725 0.04 0.8397
edu_g -0.226 2.932 0.01 0.9386
heartat_stroke -7.858 4.079 3.71 0.0540 .
med_bp 4.937 1.870 6.97 0.0083 **
med_other -1.209 2.359 0.26 0.6084
statusx 0.239 0.502 0.23 0.6347
smoker 3.155 1.538 4.21 0.0402 *
visit 0.523 0.204 6.56 0.0104 *
chronicity:edu_g 0.365 5.972 0.00 0.9513
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 120 10
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.378 0.0519
Number of clusters: 124 Maximum cluster size: 8
DBP Average Hispanic
Call:
geeglm(formula = dbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 59.6098 10.8995 29.91 4.5e-08 ***
AGE0 0.2605 0.1512 2.97 0.0849 .
bmi 0.2273 0.0671 11.48 0.0007 ***
cum_avg_unfair 0.1624 0.5546 0.09 0.7697
diabete 2.0742 1.8085 1.32 0.2514
edu_g -0.5628 6.2807 0.01 0.9286
heartat_stroke -3.9391 3.8211 1.06 0.3026
med_bp 3.6648 1.2059 9.24 0.0024 **
med_other -0.0352 1.3487 0.00 0.9792
statusx -0.6069 0.3745 2.63 0.1051
smoker 2.0108 0.9366 4.61 0.0318 *
visit -0.4296 0.1455 8.72 0.0032 **
cum_avg_unfair:edu_g 0.0159 0.5101 0.00 0.9752
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 59.9 4.5
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.231 0.0535
Number of clusters: 124 Maximum cluster size: 8
Call:
geeglm(formula = dbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 59.7021 8.0468 55.05 1.2e-13 ***
AGE0 0.2607 0.1494 3.05 0.0809 .
bmi 0.2207 0.0686 10.36 0.0013 **
chronicity 7.0445 4.2760 2.71 0.0995 .
diabete 2.0408 1.7963 1.29 0.2559
edu_g 1.3402 1.5849 0.72 0.3978
heartat_stroke -4.1305 3.7713 1.20 0.2734
med_bp 3.6146 1.2015 9.05 0.0026 **
med_other 0.1586 1.3446 0.01 0.9061
statusx -0.5877 0.3748 2.46 0.1169
smoker 2.0445 0.9520 4.61 0.0317 *
visit -0.4255 0.1443 8.70 0.0032 **
chronicity:edu_g -5.5672 3.1578 3.11 0.0779 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 60.1 4.52
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.235 0.0555
Number of clusters: 124 Maximum cluster size: 8
HTN Hispanic
Call:
geeglm(formula = htn_formula1, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -6.2386 2.3706 6.93 0.00850 **
AGE0 0.1186 0.0286 17.17 3.4e-05 ***
bmi 0.0409 0.0123 11.04 0.00089 ***
cum_avg_unfair -0.1511 0.0831 3.30 0.06920 .
diabete 0.0503 0.1885 0.07 0.78969
edu_g -3.3542 1.4941 5.04 0.02477 *
heartat_stroke -0.3189 0.4992 0.41 0.52303
med_other 0.2705 0.1783 2.30 0.12911
statusx 0.0486 0.0528 0.85 0.35698
smoker 0.2271 0.1864 1.48 0.22321
visit 0.1112 0.0178 39.12 4.0e-10 ***
cum_avg_unfair:edu_g 0.2082 0.0716 8.47 0.00362 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.69 0.157
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.61 0.106
Number of clusters: 124 Maximum cluster size: 8
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.126 17.167 0.000
bmi 1.042 11.043 0.001
cum_avg_unfair 0.860 3.302 0.069
diabete 1.052 0.071 0.790
edu_g 0.035 5.040 0.025
heartat_stroke 0.727 0.408 0.523
med_other 1.311 2.303 0.129
statusx 1.050 0.848 0.357
smoker 1.255 1.484 0.223
visit 1.118 39.115 0.000
cum_avg_unfair:edu_g 1.232 8.466 0.004
Call:
geeglm(formula = htn_formula2, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -9.0537 1.7733 26.07 3.3e-07 ***
AGE0 0.1208 0.0287 17.74 2.5e-05 ***
bmi 0.0421 0.0122 11.85 0.00058 ***
chronicity 2.2441 1.5953 1.98 0.15952
diabete 0.0277 0.1912 0.02 0.88472
edu_g -0.0994 0.5430 0.03 0.85478
heartat_stroke -0.3437 0.4844 0.50 0.47800
med_other 0.3262 0.1788 3.33 0.06805 .
statusx 0.0460 0.0509 0.82 0.36528
smoker 0.2201 0.1794 1.51 0.21987
visit 0.1095 0.0174 39.47 3.3e-10 ***
chronicity:edu_g -1.5567 1.4875 1.10 0.29534
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.706 0.173
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.583 0.114
Number of clusters: 124 Maximum cluster size: 8
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.128 17.741 0.000
bmi 1.043 11.847 0.001
chronicity 9.432 1.979 0.160
diabete 1.028 0.021 0.885
edu_g 0.905 0.033 0.855
heartat_stroke 0.709 0.503 0.478
med_other 1.386 3.330 0.068
statusx 1.047 0.820 0.365
smoker 1.246 1.505 0.220
visit 1.116 39.474 0.000
chronicity:edu_g 0.211 1.095 0.295
SBP Average Japanese
Call:
geeglm(formula = sbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 36.837 15.284 5.81 0.016 *
AGE0 1.204 0.281 18.29 1.9e-05 ***
bmi 0.449 0.200 5.04 0.025 *
cum_avg_unfair 0.616 0.495 1.55 0.213
diabete -1.316 1.645 0.64 0.424
edu_g 3.625 5.210 0.48 0.487
heartat_stroke 10.205 5.901 2.99 0.084 .
med_bp -8.431 2.144 15.47 8.4e-05 ***
med_other 0.975 1.057 0.85 0.357
statusx 0.282 0.308 0.84 0.360
smoker -0.518 1.838 0.08 0.778
visit 0.948 0.136 48.53 3.2e-12 ***
cum_avg_unfair:edu_g -0.356 0.306 1.35 0.245
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 213 21.5
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.881 0.0097
Number of clusters: 228 Maximum cluster size: 12
Call:
geeglm(formula = sbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 46.188 14.090 10.75 0.001 **
AGE0 1.239 0.283 19.14 1.2e-05 ***
bmi 0.438 0.198 4.90 0.027 *
chronicity -2.361 8.458 0.08 0.780
diabete -1.389 1.641 0.72 0.397
edu_g -3.587 2.185 2.69 0.101
heartat_stroke 10.163 5.919 2.95 0.086 .
med_bp -8.373 2.144 15.25 9.4e-05 ***
med_other 0.965 1.058 0.83 0.362
statusx 0.282 0.308 0.84 0.359
smoker -0.514 1.810 0.08 0.776
visit 0.942 0.136 47.90 4.5e-12 ***
chronicity:edu_g 4.261 5.021 0.72 0.396
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 210 21
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.878 0.0101
Number of clusters: 228 Maximum cluster size: 12
DBP Average Japanese
Call:
geeglm(formula = dbp_formula1, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 39.0708 11.0662 12.47 0.00041 ***
AGE0 0.5392 0.1986 7.37 0.00662 **
bmi 0.2852 0.1380 4.27 0.03875 *
cum_avg_unfair 0.3285 0.3298 0.99 0.31928
diabete -0.1721 1.0512 0.03 0.86994
edu_g 0.7223 3.7316 0.04 0.84652
heartat_stroke 5.7974 2.5095 5.34 0.02088 *
med_bp -5.5707 1.3690 16.56 4.7e-05 ***
med_other -0.2363 0.7690 0.09 0.75860
statusx 0.1818 0.2202 0.68 0.40889
smoker -0.0837 1.3735 0.00 0.95141
visit 0.0346 0.0917 0.14 0.70634
cum_avg_unfair:edu_g -0.1170 0.2243 0.27 0.60192
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 94.4 7.61
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.871 0.0111
Number of clusters: 228 Maximum cluster size: 12
Call:
geeglm(formula = dbp_formula2, data = race_data, id = race_data$ID,
corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) 44.8120 10.1006 19.68 9.1e-06 ***
AGE0 0.5467 0.1983 7.60 0.0058 **
bmi 0.2881 0.1366 4.45 0.0350 *
chronicity -3.3047 4.9844 0.44 0.5073
diabete -0.1674 1.0367 0.03 0.8717
edu_g -2.2745 1.3967 2.65 0.1034
heartat_stroke 5.8461 2.5171 5.39 0.0202 *
med_bp -5.5211 1.3659 16.34 5.3e-05 ***
med_other -0.2217 0.7689 0.08 0.7731
statusx 0.1798 0.2202 0.67 0.4143
smoker -0.0629 1.3437 0.00 0.9627
visit 0.0280 0.0916 0.09 0.7596
chronicity:edu_g 3.6066 3.1601 1.30 0.2538
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 93.1 7.42
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.867 0.0114
Number of clusters: 228 Maximum cluster size: 12
HTN Japanese
Call:
geeglm(formula = htn_formula1, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -8.0145 2.3608 11.53 0.00069 ***
AGE0 0.0835 0.0395 4.46 0.03472 *
bmi 0.0209 0.0280 0.56 0.45560
cum_avg_unfair 0.0722 0.0894 0.65 0.41952
diabete 0.0335 0.1683 0.04 0.84206
edu_g 0.2375 1.0067 0.06 0.81347
heartat_stroke -0.2393 0.0854 7.85 0.00509 **
med_other -0.0220 0.1911 0.01 0.90831
statusx 0.0672 0.0511 1.73 0.18848
smoker -0.3230 0.2981 1.17 0.27864
visit 0.1310 0.0213 37.89 7.5e-10 ***
cum_avg_unfair:edu_g -0.0328 0.0613 0.29 0.59246
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.841 0.287
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.857 0.0756
Number of clusters: 228 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.087 4.459 0.035
bmi 1.021 0.557 0.456
cum_avg_unfair 1.075 0.652 0.420
diabete 1.034 0.040 0.842
edu_g 1.268 0.056 0.813
heartat_stroke 0.787 7.849 0.005
med_other 0.978 0.013 0.908
statusx 1.070 1.729 0.188
smoker 0.724 1.174 0.279
visit 1.140 37.890 0.000
cum_avg_unfair:edu_g 0.968 0.287 0.592
Call:
geeglm(formula = htn_formula2, family = poisson(link = "log"),
data = race_data, id = race_data$ID, corstr = "ar1")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -7.2837 2.0907 12.14 0.00049 ***
AGE0 0.0877 0.0399 4.83 0.02791 *
bmi 0.0226 0.0278 0.66 0.41668
chronicity 0.4743 0.8774 0.29 0.58876
diabete 0.0355 0.1735 0.04 0.83775
edu_g -0.2213 0.3175 0.49 0.48588
heartat_stroke -0.2280 0.0886 6.63 0.01003 *
med_other -0.0197 0.1944 0.01 0.91940
statusx 0.0671 0.0515 1.70 0.19231
smoker -0.3249 0.3077 1.11 0.29110
visit 0.1305 0.0212 37.95 7.3e-10 ***
chronicity:edu_g -0.1617 0.6079 0.07 0.79027
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 0.836 0.275
Correlation: Structure = ar1 Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.857 0.0722
Number of clusters: 228 Maximum cluster size: 12
Risk Ratios:
Estimate Wald Pr(>|W|)
AGE0 1.092 4.833 0.028
bmi 1.023 0.660 0.417
chronicity 1.607 0.292 0.589
diabete 1.036 0.042 0.838
edu_g 0.801 0.486 0.486
heartat_stroke 0.796 6.630 0.010
med_other 0.981 0.010 0.919
statusx 1.069 1.700 0.192
smoker 0.723 1.115 0.291
visit 1.139 37.948 0.000
chronicity:edu_g 0.851 0.071 0.790