TY - JOUR
T1 - Social Determinants of Severe Injury Among Pediatric Patients During the COVID-19 Pandemic
T2 - An Exploratory Study
AU - Sokol, Rebeccah L.
AU - Sethuraman, Usha
AU - Oag, Katherine
AU - Vitale, Lisa
AU - Donoghue, Lydia
AU - Kannikeswaran, Nirupama
N1 - Publisher Copyright:
© 2022 National Association of Pediatric Nurse Practitioners
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
KW - Association rule mining
KW - pediatric injury
KW - social determinants of health
UR - http://www.scopus.com/inward/record.url?scp=85132939081&partnerID=8YFLogxK
U2 - 10.1016/j.pedhc.2022.05.021
DO - 10.1016/j.pedhc.2022.05.021
M3 - Article
C2 - 35738995
AN - SCOPUS:85132939081
SN - 0891-5245
VL - 36
SP - 549
EP - 559
JO - Journal of Pediatric Health Care
JF - Journal of Pediatric Health Care
IS - 6
ER -