De-identified data on child abuse and neglect reports were extracted from California’s Child Welfare Services Case Management System (CWS/CMS) for all 58 counties for the period from January 1st, 2016 through July 31st 2020 (1,828,135 reports involving 2,422,058 children). Maltreatment data included the date of report, a binary indicator (yes/no) if the reporter alleged concerns of DV in the maltreatment report, and reporter type (non-mandated reporter, social service professional, medical provider, law enforcement, or educator). We included all reports to CPS regardless of the screening decision (i.e., whether it was investigated or not). Our estimates are all at the report or incident level; we did not make any adjustments for the number of children on a report or children who may have been reported more than once. This decision is consistent with our interest in the nature of the information communicated in reports to CPS rather than the potential impact on individual children.
We calculated the proportion of DV allegations as a function of total CPS reports by week and year. For the weeks from January through the end of July, we calculated the percentage difference in the count of all CPS reports (regardless of whether DV was alleged) versus those with DV alleged. We made comparisions between the year 2020 relative to the mean counts between 2016 and 2019. To capture known seasonality in CPS reporting, we created a variable identifying those weeks that corresponded with schools being in session, or not (e.g., summer break, winter/Thanksgiving break), or periods of COVID-19 closures.
To examine whether allegations of DV varied over time and exhibited seasonal trends, we plotted: (1) maltreatment report counts and the proportion of reports with DV allegations by week (limiting months from January through July to maintain consistency for all years of data); (2) the percent difference by week for all CPS reports (regardless of whether DV was alleged) versus those with DV alleged between 2020 and the mean of counts for years 2016–2019; (3) the proportion of reports with DV allegations by year with seasonal patterns of school closures; and (4) the count of CPS reports with DV alleged (versus not) and the proportion with DV alleged by reporter type throughout the study period.
We implemented an interrupted-time series (ITS) analysis to evaluate whether or not there was a change in DV allegations in CPS reports associated with the COVID-19 pandemic. ITS has been identified as the strongest quasi-experimental design that uses data before and after an event to measure the effects of said event, while controlling for trends in the data (Penfold & Zhang, 2013). An ITS was selected as in-person school closures in California (starting on March 16, 2020 and continued through the summer through California Executive Order N-26-20) constituted a well-defined pre- and post-COVID-19 period. Although there are more than 1,000 school districts in California, most students were enrolled in a district that closed between Friday March 13, 2020 and Wednesday March 18, 2020 (Johnson, 2020), almost all schools were closed as of Monday March 16 (Xie et al., 2020). Given the rapid changes that occurred in California, our outcome (counts of CPS reports with a DV allegation) was expected to shift relatively quickly in response to COVID-19 related stressors, thus allowing ascertainment within our study period (Bernal et al., 2017).
We first ran a Poisson model given the use of count data and identified overdispersion through the ratio of residual deviance to degrees of freedom (12.7) being higher than 1 and the ‘dispersiontest’ function in the R package “AER” (Kleiber & Zeileis, 2008). As a result, we used a quasi-Poisson regression model to account for the overdispersion and assumed a level change following March 16, 2020. The following model was used (Bernal et al., 2017):
$${Y}_{t}= {\beta }_{0}+ {\beta }_{1}T+ {\beta }_{2}{X}_{t}+ {\beta }_{3}T{X}_{t}$$
where Yt is the weekly counts of CPS reports with DV allegations, β0 is the baseline level at T = 0, β1 is the underlying pre-COVID-19 change associated with time, β2 is the level change associated with COVID-19, and β3 is the slope change following COVID-19. Due to the observed weekly and school-related seasonal variation in reporting over time, we included both a linear term for time and a Fourier term of four sine/cosine pairs to account for seasonality (Bhaskaran et al., 2013). We observed very consistent patterns aligned with seasonal school breaks in the data which Bhaskaran et al. (2013) identified as a good fit with Fourier terms and has the benefit of modeling patterns smoothly. We ran two sets of models – unadjusted and adjusted for standardized counts of overall CPS reports as an offset term to convert the outcome into a rate (or proportion).
We tested for autocorrelation (when consecutive observations are similar to each other (Bernal et al., 2017)) and partial auto-correlations. We stratified the ITS analyses by reporter type to investigate whether changes in DV allegations varied by who made the CPS report. ITS results are presented as relative risks (RR) with 95% confidence intervals. All analyses were completed using R (R Core Team, 2019). This analysis of de-identified data for this study fell under an existing data sharing agreement with the California Department of Social Services and both state and university human subject approvals.