Introduction: This study sought to identify social determinants of health (SDH) patterns associated with severe pediatric injuries. Method: We used cross-sectional data from children (≤18 years) admitted to a pediatric trauma center between March and November 2021 (n = 360). We used association rule mining (ARM) to explore SDH patterns associated with severe injury. We then used ARM-identified SDH patterns in multivariable logistic regressions of severe injury, controlling for patient and caregiver demographics. Finally, we compared results to naive hierarchical logistic regressions that considered SDH types as primary exposures rather than SDH patterns. Results: We identified three SDH patterns associated with severe injury: (1) having child care needs in combination with neighborhood violence, (2) caregiver lacking health insurance, and (3) caregiver lacking social support. In the ARM-informed logistic regression models, the presence of a child care need in combination with neighborhood violence was associated with an increased odds of severe injury (aOR, 2.77; 95% CI, 1.01–7.62), as was caregiver lacking health insurance (aOR, 2.29; 95% CI, 1.02–5.16). In the naive hierarchical logistic regressions, no SDH type in isolation was associated with severe injury. Discussion: Our exploratory analyses suggest that considering the co-occurrence of negative SDH that families experience rather than isolated SDH may provide greater insights into prevention strategies for severe pediatric injury.
- Association rule mining
- pediatric injury
- social determinants of health