Table 2.
Multinomial logistic regression models adjusted for the dependent variable injury type for each sex
| Dependent variable | Predictor | B (Std error) | p | odds ratio | 95% CI odds ratio |
|---|---|---|---|---|---|
| Type of injury1 | Boys3 | ||||
| Sprain | Intercept | − 0.793 (0.276) | .004 | ||
| SP level (0) | 0.100 (0.672) | .882 | 1.105 | (0.296, 4.125) | |
| SP level (1) | 2.180 (0.838) | .009 | 8.842 | (1.713, 45.651) | |
| Fracture | Intercept | − 0.480 (0.250) | .055 | ||
| SP level (0) | − 1.600 (1.090) | .142 | 0.202 | (0.024, 1.709) | |
| SP level (1) | 1.984 (0.821) | .016 | 7.269 | (1.455, 36.306) | |
| Type of injury2 | Girls4 | ||||
| Strain | Intercept | 2.272 (0.810) | .005 | ||
| Maturity offset | − 0.538 (0.224) | .016 | 0.584 | (0.376, 0.906) | |
| SP level(0) | − 1.249 (0.756) | .098 | 0.287 | (0.065, 1.262) | |
| SP level(1) | − 2.012 (0.824) | .015 | 0.134 | (0.027, 0.673) | |
| SP level(2) | − 3.029 (1.239) | .015 | 0.048 | (0.004, 0.549) | |
| Fracture | Intercept | 2.050 (0.895) | .022 | ||
| Maturity offset | − 0.842 (0.253) | < .001 | 0.431 | (0.262, 0.707) | |
| SP level(o) | − 1.869 (0.974) | .055 | 0.154 | (0.023, 1.041) | |
| SP level(1) | − 1.541 (0.932) | .098 | 0.214 | (0.034, 1.330) | |
| SP level(2) | − 0.572 (0.945) | .545 | 0.564 | (0.089, 3.596) |
1The reference category is strain
2The reference category is sprain
3Model X2(4) = 15.165, p = .004; Cox & Snell R2 = .120; Nagelkerke R2 = .135; McFadden R2 = .059
4Model X2(8) = 28.770, p < .001; Cox & Snell R2 = .290; Nagelkerke R2 = .328; McFadden R2 = .158