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  • Clinical Research Article
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COVID-19’s causal impact on child abuse and socioeconomic status: a Bayesian time series study

Abstract

Background

The COVID-19 pandemic intensified psychosocial stressors, potentially contributing to increased rates of child abuse. This study aimed to compare trends in child abuse/traumatic brain injury (TBI) admissions and socioeconomic status before and after the pandemic.

Methods

A 7-year retrospective study was conducted at a Level-1 Pediatric Trauma Center. TBI cases were identified using ICD-10 codes based on the modified CDC framework. Neighborhood disadvantage and injury severity were measured using the Social Deprivation Index (SDI) and Injury Severity Score (ISS), respectively, with higher scores indicating greater disadvantage and severity. A Bayesian structural time series (BSTS) model was employed to assess the causal impact of COVID-19 on monthly child abuse/TBI admissions, SDI, and ISS.

Results

The study included 560 child abuse cases, with 62.3% involving TBI. Before COVID-19, monthly admissions averaged 5.89 for child abuse and 3.70 for child abuse with TBI, with corresponding SDI scores of 60.07 and 57.60. During the COVID era, monthly averages rose to 8.77 and 5.58 (p = 0.001, p < 0.001), and SDI scores increased to 66.32 and 61.60 (p = 0.053, p = 0.370). BSTS analysis inferred a causal impact of COVID-19 on monthly child abuse admissions (p = 0.001), monthly child abuse admissions sustaining TBI (p = 0.001), an upward trend in average monthly SDI scores (p = 0.033), and a decrease in average monthly ISS (p = 0.001).

Conclusions

The study indicates a significant increase in child abuse/TBI admissions and heightened neighborhood disadvantage during the COVID-19 pandemic.

Impact

  • This study uses Bayesian structural time series analysis to assess the COVID-19 pandemic’s causal impact on child abuse and traumatic brain injury (TBI) admissions.

  • The pandemic is linked to increased child abuse admissions and TBI cases, correlating with worsening socioeconomic conditions indicated by higher Social Deprivation Index scores.

  • Admissions did not rise significantly during the early pandemic (first 3 months, p = 0.160), but mid-to-late phases showed a significant increase (p = 0.001).

  • Injury severity, as measured by Injury Severity Score, declined, suggesting less severe injuries during the pandemic.

  • These findings emphasize the need for proactive interventions and continuous surveillance to protect vulnerable populations.

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Fig. 1: Bayesian structural time series analysis.
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Fig. 2: Bayesian structural time series analysis.
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Fig. 3: Bayesian structural time series analysis.
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Fig. 4: Bayesian structural time series analysis.
The alternative text for this image may have been generated using AI.

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Data availability

The datasets and R codes generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data: F.K. and A.C. Drafting the article: F.K. and J.L. Revising the article critically for important intellectual content: all authors. Final approval of the version to be published: all authors.

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Correspondence to Foad Kazemi.

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Kazemi, F., Liu, J., Nasr, I.W. et al. COVID-19’s causal impact on child abuse and socioeconomic status: a Bayesian time series study. Pediatr Res 98, 599–610 (2025). https://doi.org/10.1038/s41390-025-03996-0

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