Table 4.
Interaction between SLE status and race/ethnicity on the risk of outcomes
| NH White compared with… | |||
|---|---|---|---|
| Asian1 | NH Black2 | Hispanic3 | |
| Pneumonia | |||
| Relative excess risk due to interaction (95% CI) | −1.02 (−1.71, −0.33)* | −0.87 (−2.00, 0.25) | −0.95 (−1.81, −0.09)* |
| Multiplicative interaction parameter (95% CI) | 0.94 (0.67, 1.31) | 1.02 (0.49, 2.10) | 1.16 (0.76, 1.79) |
| Infections | |||
| Relative excess risk due to interaction (95% CI) | −1.00 (−1.72, −0.27)* | −1.07 (−2.21, 0.08) | −1.35 (−2.12, −0.58)* |
| Multiplicative interaction parameter (95% CI) | 0.99 (0.71, 1.38) | 1.07 (0.53, 2.15) | 0.92 (0.60, 1.39) |
| Cardiovascular diseases | |||
| Relative excess risk due to interaction (95% CI) | −0.85 (−1.93, 0.23) | −1.41 (−2.97, 0.15) | −1.02 (−2.56, 0.53) |
| Multiplicative interaction parameter (95% CI) | 1.42 (0.97, 2.08) | 1.23 (0.52, 2.90) | 1.35 (0.82, 2.23) |
| Neoplasms | |||
| Relative excess risk due to interaction (95% CI) | −0.07 (−0.65, 0.51) | −0.22 (−1.14, 0.70) | −0.23 (−0.88, 0.41) |
| Multiplicative interaction parameter (95% CI) | 0.91 (0.64, 1.30) | 0.90 (0.44, 1.86) | 0.89 (0.57, 1.40) |
| Malignant neoplasms | |||
| Relative excess risk due to interaction (95% CI) | 0.78 (−0.33, 1.88) | 1.24 (−1.44, 3.92) | 0.83 (−0.55, 2.21) |
| Multiplicative interaction parameter (95% CI) | 1.18 (0.56, 2.49) | 1.66 (0.40, 6.99) | 1.34 (0.57, 3.17) |
NH Black=Non-Hispanic Black; NH White=Non-Hispanic White
RERI appear to be statistically significant
To calculate interaction on the additive and multiplicative scales, we extracted three datasets comprising Asians and whites only (dataset 1), blacks and whites only (dataset 2), and Hispanics and whites only (dataset 3).
Dataset 1;
Dataset 2;
Dataset 3. In each model, we included potential confounders, as well as an interaction term for race/ethnicity and SLE status. For example, the regression equation modeling malignant neoplasms as the outcome comparing Asians to whites (using dataset 1) would be: Y(Malignant neoplasm) = β0 + β1(SLE) + β2(Asian) + β3(Gender) + β4(Age) + β5(Smoking) + β6(BMI) + β7(SLE ∗ Asian). In another example, the regression equation modeling CVD as the outcome comparing blacks to whites (using dataset 2) would be Y(CVD) = β0 + β1(SLE) + β2(Black) + β3(Gender) + β4(Age) + β5(Smoking) + β6(BMI) + β7(Diabetes) + β8(SLE ∗ Black).