Table 4.
Ratio of geometric means derived from regression models for specific PBDE congeners measured in maternal blood collected upon delivery
| |
PBDE 47 |
PBDE 99 |
PBDE 100 |
PBDE 153 |
|---|---|---|---|---|
|
# < LOD / Total observationsa |
3 / 311 |
50 / 313 |
28 / 316 |
5 / 304 |
|
Model distribution |
General Gamma |
Log logistic |
Log logistic |
General Gamma |
| Ratio of geometric means (95% CI) | Ratio of geometric means (95% CI) | Ratio of geometric means (95% CI) | Ratio of geometric means (95% CI) | |
|
Race/ethnicity |
|
|
|
|
| Non Hispanic White |
1 |
|
|
|
| African-American |
1.47 (0.95, 2.29)b |
|
|
|
| Hispanic |
1.21 (0.92, 1.57) |
|
|
|
|
Maternal education |
|
|
|
|
| Less than high school |
1 |
1 |
|
1 |
| High school or equivalent |
1.76 (1.30, 2.37)* |
1.39 (0.98, 1.97)b |
|
1.21 (0.92, 1.60) |
| College |
1.59 (1.20, 2.10)* |
1.27 (0.93, 1.73) |
|
1.42 (1.10, 1.83)** |
| Graduate |
1.45 (0.99, 2.12)b |
1.00 (0.69, 1.45) |
|
0.95 (0.67, 1.34) |
|
Household income |
|
|
|
|
| < $25,000 |
|
|
|
1 |
| $25,000 – 50,000 |
|
|
|
1.22 (0.93, 1.62) |
| >$50,000 |
|
|
|
1.47 (1.15, 1.87)** |
|
Pre-pregnancy BMI |
|
|
|
|
| Normal |
|
|
|
1 |
| Underweight |
|
|
|
0.81 (0.57, 1.16) |
| Overweight |
|
|
|
0.99 (0.81, 1.21) |
| Obese |
|
|
|
0.65 (0.50, 0.85)** |
|
Solid Dairy (reference:) |
|
|
|
|
| < 5 servings per week |
|
|
|
1 |
| 5-10 servings per week |
|
|
|
1.19 (0.97, 1.47) |
| >10 servings per week |
|
|
|
1.25 (1.01, 1.56)* |
|
Processed Meat |
|
|
|
|
| ≤1 serving per week |
|
|
1 |
1 |
| Not reported |
|
|
1.06 (0.79, 1.40) |
1.14 (0.91, 1.44) |
| >1 servings per week |
|
|
1.39 (1.10,1.76)** |
1.53 (1.26, 1.86)** |
|
Household electronics |
|
|
|
|
| < 10 electronics |
1 |
1 |
1 |
1 |
| ≥ 10 electronics |
1.70 (1.35, 2.13)** |
1.41 (1.12, 1.77)** |
1.37 (1.08, 1.74)** |
1.32 (1.07, 1.63)** |
| Generalized R2c | 12.2% | 4.6% | 5.7% | 24.5% |
aSample size for each congener changes due to missing covariates.
b0.05 < p < 0.09, *p < 0.05, ** p < 0.01.
cGeneralized R2 indicates the proportion of variability in the data that is accounted for by the statistical model with multiple predictors.