Citation

Paramasivan K, Raj B, Sudarasanam N, Subburaj R. Heliyon 2023; 9(7): e17865.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.heliyon.2023.e17865

PMID

37456023

PMCID

PMC10339019

Abstract

OBJECTIVES: The Tamil Nadu government mandated several stay-at-home orders, with restrictions of varying intensities, to contain the first two waves of the COVID-19 pandemic. This research investigates how such orders impacted child sexual abuse (CSA) by using counterfactual prediction to compare CSA statistics with those of other crimes. After adjusting for mobility, we investigate the relationship between situational factors and recorded levels of cases registered under the Protection of Children from Sexual Offences Act (POCSO). The situational factors include the victims' living environment, their access to relief agencies, and the competence and responsiveness of the police.

METHODS: We adopt an auto-regressive neural network method to make a counterfactual forecast of CSA cases that represents a scenario without stay-at-home orders, relying on the eight-year daily count data of POCSO cases in Tamil Nadu. Using the insights from Google's COVID-19 Community Mobility Reports, we measure changes in mobility across various community spaces during the various phases of stay-at-home orders in both waves in 2020 and 2021.

RESULTS: The steep falls in POCSO cases during strict stay-at-home periods, compared with the counterfactual estimates, were -72% (Cliff's delta -0.99) and -36% (Cliff's delta -0.65) during the first and second waves, respectively. However, in the post-lockdown phases, there were sharp increases of 68% (Cliff's delta 0.65) and 36% (Cliff's delta 0.56) in CSA cases during the first and second waves, with concomitantly quicker reporting of case registration.

CONCLUSIONS: Considering that the median delay in filing CSA complaints was above 30 days in the mild and post-intervention periods, the upsurge of cases in the more relaxed phases indicates increased occurrences of CSA during strict lockdowns. Overall, higher victimization numbers were observed during the prolonged lockdown-induced school closures. Our findings highlight the time gap between the incidents and their registration during the strict lockdown phases.

Language: en

Keywords

Child abuse; Pandemic; Auto-regressive recurrent neural network; Counterfactual prediction; Sexual abuse of children