Introduction

Prior studies have demonstrated that the number of child maltreatment (CM) reports decreased during the onset of the coronavirus disease 2019 (COVID-19) pandemic (Brown et al., 2022; Shusterman et al., 2022). The opportunity for traditional report sources (e.g., school and daycare personnel) to identify and report CM greatly decreased following official stay-at-home orders and other social distancing measures (Brown et al., 2022; Katz, et al., 2021a; Shusterman et al., 2022). Moreover, stay-at-home orders may have had negative repercussions for the health of rural and urban communities such as increased controlled substance use and serious psychological distress (Maeng et al., 2022; McGinty et al., 2020). Stay-at-home orders may have increased the risk of CM as well (Katz, et al., 2021a; Shusterman et al., 2022). The decrease in CM report rates is concerning in rural areas where children are already at an increased risk for maltreatment due to less surveillance by mandated report sources (Maguire-Jack & Kim, 2021; Yuma et al., 2022). Findings indicate that parental tolerance for children’s disobedience decreased during COVID-19 and that the initial lockdown period in 2020 increased CM risk (Sari et al., 2022). While social distancing measures were in place, families spent more time together while coping with the stress from loneliness and job loss (Kovacs et al., 2021; Lawson et al., 2020). Therefore, the decrease in CM report rates associated with COVID-19 across the world should not be equated with a decrease in CM in general (Sari et al., 2022).

Report rates of CM decreased across the United States (Shusterman et al., 2022), but studies have not analyzed if CM report rate disparities between rural and urban areas persisted at the onset of the COVID-19 pandemic. Recent pre-pandemic CM report rate trends (i.e., between 2007 and 2018) suggest that increases were more pronounced in rural and small urban areas compared with large urban areas (Kim & Maguire-Jack, 2021). An explanation for these pre-pandemic disparities could be that children in rural communities may be at greater risk of maltreatment, which is reflected by increased CM report rates, due to reduced access to mental and physical health care (Orsi et al., 2018). The COVID-19 pandemic impacted child and adult health and well-being significantly in rural areas (Mueller et al., 2021), so past CM report rate disparities between rural and urban areas could have increased at the onset of the pandemic. There is evidence that intimate partner violence, which is often related to CM and CM reporting, increased in rural areas due to the COVID-19 pandemic (Monteith et al., 2021). Victims of violence in rural communities experience reduced access to essential services such as child and family medical care, and emergency shelter assistance (Grossman et al., 2005). Without access to services, children in rural communities could have been at a greater risk of maltreatment at the onset of the COVID-19 pandemic.

There is evidence that COVID-19 stay-at-home orders could have influenced the health of children and families across the United States. Moreover, child protective services (CPS) reporting systems were impacted in a way where at-risk children may not have received services, but no studies have taken into consideration geography when analyzing these effects. A study with a limited sample from North Carolina found that families in both rural and suburban areas experienced an increase in violence during the initial period of the pandemic (Machlin et al., 2022), but to our knowledge, no other studies have analyzed the differential impact of the COVID-19 pandemic on CM reporting in rural and urban counties. CM reporting is essential for the continued delivery of services to children during the COVID-19 pandemic (Katz et al., 2021b). Considering the CM report rate disparities that existed between rural and urban areas before the pandemic, and the public health challenges associated with the COVID-19 pandemic, more research is needed on the effects of the COVID-19 pandemic in different geographic areas.

Our study analyzes how CPS reporting systems operated in different geographies in the United States during the initial COVID-19-related stay-at-home orders. We used quantitative methods and national data to analyze the impact of the COVID-19 pandemic on CPS reporting systems in rural and urban counties across the United States. For this study, we pose two primary research questions. First: Were the effects of the COVID-19 pandemic on rates of children with a maltreatment report different in rural counties compared to urban counties in the United States? Second: Controlling for two factors that are commonly associated with CM (i.e., poverty and food access), to what extent did the presence of COVID-19 cases at the county level influence the rates of children with a maltreatment report in rural and urban counties in the United States?

Methods

Sample

We used administrative child welfare data submitted by states to the National Child Abuse and Neglect Data System (NCANDS) from federal fiscal years (FFYs) 2019 and 2020. NCANDS is a voluntary federal data collection program sponsored by the Children’s Bureau in the U.S. Department of Health and Human Services, Administration on Children, Youth and Families (U.S. Department of Health & Human Services, 2022). For all analyses, we used the NCANDS Child File, a collection of case-level information from all children involved in maltreatment reports that received an investigation (i.e., screened-in reports) by state and county CPS agencies (U.S. Department of Health & Human Services, 2022). Each record contains one child and one report (i.e., a report can have multiple children and a child can be included in multiple reports). While NCANDS includes 52 “states” (i.e., 50 states plus the District of Columbia and the Commonwealth of Puerto Rico), our sample only included 50 “states” (i.e., excluding Missouri and the Commonwealth of Puerto Rico due to data issues related to county COVID-19 reporting).

Data Preparation

Child-level NCANDS data and population estimates (U.S. Census Bureau, Population Division, 2021) were aggregated at the county level for the FFYs 2019 and 2020 to produce CM report rates. All data in these analyses related to CM reports are based on maltreatment report date. This is an important consideration because NCANDS data collection for any given FFY is based on the disposition date rather than the report date. Data for FFY 2020 at the time this analysis was conducted were only available for reports that had a disposition date in the same fiscal year. In the changepoint analysis (see below) used to address our first research question, we used data from both the FFY 2019 and FFY 2020 Child Files.

The changepoint analysis analyzed inter- and intra-region CM report incidence rate ratios (IRRs) from a portion of calendar year 2019 (i.e., weeks 2–26, which include the dates January 7, 2019, through June 30, 2019) and a portion of calendar year 2020 (i.e., weeks 2–26, which include the dates January 6, 2020, through June 28, 2020). We defined the time period of our analysis by taking into consideration the fact that CM reports typically decline during the summer months when children are not in school (Shusterman et al., 2022). Moreover, reports that come to CPS toward the end of the FFY but have not yet reached a disposition prior to September 30 are included in the Child File for the subsequent FFY. We excluded reports that came in during FFY 2019 but did not reach a disposition within the same fiscal year (i.e., some reports with report dates in FFY 2019 only reached a disposition in FFY 2020) to maintain consistency between the NCANDS FFY 2019 and 2020 datasets. The changepoint analyses included 2,967 counties (1,843 rural and 1,124 urban) in 2019 and 2,963 counties (1,835 rural and 1,128 urban) in 2020 out of the 3,029 (i.e., 3,143 minus Missouri) total counties in the United States.

For the multilevel analysis used to address our second research question and the actual effects of the presence of COVID-19, we only used CM report-related data to construct the dependent variable from the FFY 2020 Child File. The data in the multilevel analysis included 2,959 counties (i.e., 1,835 rural and 1,124 urban counties) out of the 3,029 (i.e., 3,143 minus Missouri) total counties in the United States. The full model with the control variables included 2,941 (i.e., 1,823 rural and 1,118 urban) counties due to listwise deletion resulting from the inclusion of control variables that had missing data for 18 counties.

In addition to the Census Bureau’s population estimates and the NCANDS data, the changepoint analysis and the multilevel analysis also included the Rural-Urban Continuum Code (RUCC) dataset (U.S. Department of Agriculture, 2013) to geographically classify the counties that had NCANDS CM report data. The multilevel models included COVID-19 data from the New York Times’ GitHub repository (The New York Times, 2021). Moreover, we merged New York County (Manhattan), Kings County (Brooklyn), Bronx County (The Bronx), Richmond County (Staten Island), and Queens County (Queens) into one “county” (i.e., New York City) since the COVID-19 data merges these counties. Finally, the multilevel analysis also included variables from the Food Environment Atlas (U.S. Department of Agriculture, 2020). Unlike the CM report and COVID-19 data, however, the Food Environment Atlas data are time invariant.

Dependent Variable

Rate of Children with a Maltreatment Report Per 10,000 Children

The average rate of unique children with a maltreatment report in each county each week is the dependent variable in both the changepoint analysis and multilevel analysis. Since this study focuses on differences between counties, we used rate per 10,000 children in the county population instead of a count variable. In the multilevel analysis, we used a log transformation of the CM report rate variable to improve bivariate linearity with the predictors, and to reduce skewness.

Independent Variables

COVID-19 Case Occurrence

Due to the severely skewed distribution of the COVID-19 cases per 10,000 people variable and given that the study time span was at the beginning of the pandemic, we chose to use a dichotomous variable detecting the presence of COVID-19 cases in the county each week. When one or more cases of COVID-19 was recorded in the county, the variable equals 1, and when no COVID-19 cases were recorded, the variable equals 0. Given the difference in availability of health status indicators (i.e., COVID-19 infection and hospitalization rates) between rural and urban areas (Centers for Disease Control and Prevention, 2022), our COVID-19 case variable is a proxy for community spread of the virus.

Rural-Urban Designation

We used the Rural-Urban Continuum Codes (RUCC) to create our rural-urban designation variable. The codes (1 being the most urban through 9 being the most rural) classify metropolitan counties in the United States by the population size and nonmetropolitan counties by level of urbanization and proximity to a metropolitan county. These codes were dichotomized as “urban” (1 through 3) or “rural” (4 through 9) (U.S. Department of Agriculture, 2013).

Controls

Population, Low Access to Food Store (%), 2015

Low Access to Food Store (LAFS) is the percent of people in a county living more than 1 mile from a supermarket or large grocery store if in a metropolitan area, or more than 10 miles from a supermarket or large grocery store if in a nonmetropolitan area. Studies have found that LAFS is often a significant predictor of CM (Helton et al., 2018). We used the log of the original variable to improve univariate normality.

Child Poverty Rate, 2015

Child poverty rate is the percent of the county population under age 18 living in families with income below the poverty threshold. We used this variable as a control to measure the effects of child economic hardship in each county. As with LAFS, child poverty rate is a common factor in analyses of CM (Helton et al., 2018). We top-coded this variable at 55% (i.e., data points above this threshold were recorded to 55%) in the regression models to improve univariate normality.

Analytic Strategy

Changepoint

To conduct the changepoint analysis (Brown et al., 2022; Killick & Eckley, 2014), we applied the “changepoint” package in R (Killick et al., 2014; R Core Team 2021). With the methodology of “at most one change” reflecting our assumption that only one significant change in rural and urban CM report IRRs occurred during the time frame of the initial national response to the COVID-19 pandemic. This statistical procedure examines significant differences in mean IRR over a determined time frame. The null hypothesis of this type of analysis is that no significant change in the mean of the given continuous variable occurs during the determined time frame and the alternative hypothesis is that a significant difference in mean occurs at one point in the time frame. This analysis involves the calculation of a general likelihood ratio test statistic for detecting mean differences (Hinkley, 1970). We analyzed whether statistically significant differences in inter- and intra-region rural-urban CM report IRRs occurred during a specified time frame (i.e., weeks 2–26) in both 2019 and 2020. If a changepoint was detected, we compared the pre- and post-changepoint means.

Multilevel Analysis

We estimated the effect of COVID-19 on rates of children with maltreatment reports in rural and urban counties in the United States using random effects regression models. Our analysis included two models: Model 1 focused on just the effects of COVID-19 on report rates of CM in rural and urban counties and Model 2 focused on the inclusion of the control variables to risk adjust the association between COVID-19 and CM reporting in rural and urban counties. We chose random effects models because they can include both time-variant and time-invariant variables (i.e., while the occurrence of COVID-19 in each county is a time-varying variable, the variable measuring rural and urban designation is not). Random effects models are considered efficient, but they assume that county-level residuals are uncorrelated with predictors and that differences across counties have the same association to CM report rates as changes over time within counties (Calvo et al., 2013). Considering the random effects estimator assumption of equicorrelated errors, which in many cases is too stringent for observational data, we chose to obtain cluster-robust standard errors (Cameron & Trivedi, 2022). We clustered our models by state taking into consideration the differences that exist among state CPS systems and structures (Drake et al., 2022). In both models, we mean-centered and standardized all continuous independent variables for ease of interpretation of the constant and calculation of predicted values. We also screened all the data for univariate normality and bivariate linearity. We used IBM SPSS Statistics for Windows, Version 24.0 for data cleaning and preparation (IBM Corp., 2016) and for the diagnostic tests, data screening, and statistical procedures of the regression models, we used Stata/MP 16.1 (StataCorp. 2019).

Results

Changepoint Analysis

In the changepoint analysis, we included weeks 2–26 in calendar years 2019 and 2020. Figure 1 presents the results of the inter-region CM report IRR analysis. As can be seen in Figure 1, the average CM report rate among rural counties appears to be higher than in urban counties in both years. In 2020, however, this disparity significantly increased at week 11 (i.e., between March 9 and March 15) where the average rural over urban CM report IRR increased from 1.36 to 1.5. At the changepoint location, children in rural areas had an average CM report rate 1.5 times larger than children in urban areas in 2020. Figure 1 also indicates that the 2020 CM report IRR remained high throughout the analysis’ time period. No changepoint in the inter-region CM report IRR was detected at the week 11 time point in 2019.

Fig. 1
figure 1

IRRs of unique children with a maltreatment report between rural-urban designation for each year. Rural rates of unique children reported for maltreatment over the same rates in urban counties in weeks 2–26. The straight line at 1.00 on the y-axis represents parity in the rate ratios. The dashed line represents weekly 2019 data and the solid line represents weekly 2020 data. The vertical dotted line represents the changepoint location at week 11

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Figure 2 shows the results from the intra-region IRR analysis. As can be seen in Figure 2, the IRRs between average CM report rates in 2019 and 2020 among both rural and urban counties appear to be stable at the beginning of the time period but then increase after week 11. Based on the results of the changepoint analysis, we find that for rural counties, the CM report IRR between 2019 and 2020 significantly increased at the week 11 time point from 0.98 to 1.33 and from 0.99 to 1.45 in urban areas. At the changepoint location, children in rural areas in 2019 had an average CM report rate 1.33 times larger than children in rural areas in 2020. For children in urban areas, the average CM report rate was 1.45 times larger in 2019 than in 2020 at the changepoint location.

Fig. 2
figure 2

IRRs of unique children with a maltreatment report between years for each rural-urban designation. Rates of unique children reported for maltreatment in 2019 over the same rates in 2020 for both urban and rural counties in weeks 2–26. The straight line at 1.00 on the y-axis represents parity in the rate ratios. The dashed line represents weekly rural data and the solid line represents weekly urban data. The vertical dotted line represents the changepoint location at week 11

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Regression Models

Descriptive statistics before transformations for the variables in the regression models, including CM report rates, county geography type, COVID-19 case occurrence, and control variables are presented in Table 1. In weeks 2 through 26, the observed mean CM report rate per 10,000 children was 11.19 in all counties, 12.93 in rural counties, and 8.9 in urban counties. COVID-19 cases were recorded in 46.67% of all counties, 39.57% of rural counties, and 55.97% of urban counties. The mean child poverty rate was 23.32 in all counties, 25.64 in rural counties, and 20.28 in urban counties. The mean population with LAFS was 20.07 in all counties, 19.46 in rural counties, and 20.85 in urban counties. Approximately 56.72% of all observations came from rural counties.

Table 1 Regression Descriptive Statistics: Weeks 2–26 of Calendar Year 2020
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Table 2 displays the results of random effects regression models for CM report rates. In Model 1, we see that if a county had a COVID-19 case occurrence and a rural designation, then CM report rates were significantly lower (B = –0.28, p < .001) than in rural counties without a COVID-19 occurrence. If a county had an urban designation but did not have a COVID-19 occurrence, then CM report rates were also significantly lower (B = –0.36, p < .001) compared to rural counties without a COVID-19 occurrence. In Model 1, the interaction between COVID-19 occurrence and rural-urban designation was significant (B = –0.07, p < .001). In Model 2, we see that the significance and direction of the main effects and the interaction term did not change with the inclusion of control variables. While the effects of rural-urban designation remained significant with the inclusion of control variables, they also decreased in strength. In Model 2, if a county had an urban designation but did not have a COVID-19 occurrence, then CM report rates were also significantly lower (B = –0.26, p < .001) compared to rural counties without a COVID-19 occurrence. Less access to food in counties was associated with a higher CM report rate (B = 0.07, p < .05). Higher child poverty rates were also associated with higher CM report rates (B = 0.18, p < .001). This confirms our expectation that COVID-19 and rural-urban designation are associated with CM reporting even when child poverty and LAFS are taken into consideration.

Table 2 Regression Results for the Effects of COVID-19 and Rural-Urban Designation on Maltreatment Rates
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Regarding the interaction term in the models, Table 3 displays the additive effects of COVID-19 occurrence and rural-urban designation. Additive effects are indicative of how predictor variables synergistically influence the outcome variable (i.e., CM report rates). We found that the largest difference in effects on CM reporting was between urban counties with a COVID-19 occurrence and rural counties without a COVID-19 occurrence in both Model 1 and Model 2. The significant difference between the rural and urban counties on the CM report IRRs in the changepoint analysis, and the significant interaction term between COVID-19 case occurrence and rural-urban designation in the multilevel analysis were key results. They enable us to conclude that CM reporting in urban counties was impacted significantly more than in rural counties during the initial stage of the COVID-19 pandemic.

Table 3 Additive Effects of COVID-19 occurrence and Rural-Urban Designation on Maltreatment Rates
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Discussion

The results of this analysis show that CM reporting significantly decreased across the Unites States in week 11 in 2020 compared with previous years. We analyzed whether past differences in CM report rates between urban and rural jurisdictions were maintained during the initial COVID-19 lockdown. Indeed, CM report rates in rural areas remained higher than in urban areas, but it is also clear that in both urban and rural counties, CM report rates significantly declined during the initial COVID-19 lockdown period. Our analyses provide empirical evidence in support of the hypothesis that CM reporting in urban areas was impacted more than in rural areas during the early months of the COVID-19 pandemic. This evidence is further supported by our analysis that included variables (i.e., access to food and poverty) that are associated with CM report rates and rural-urban designation. Our findings support our hypothesis that CM reporting was impacted in rural areas differently than in urban areas in two ways. First, the changepoint analysis of the inter- and intra-region IRRs across years showed differences between rural and urban counties. Second, a significant finding of the random effects models is that the interaction between COVID-19 case occurrence and rural-urban designation showed that urban counties with a COVID-19 case occurrence were associated with decreased CM report rates compared with rural counties with no COVID-19 case occurrence.

There are several possible reasons for our results. One explanation could be that children living in rural counties did not experience drastic changes in living conditions during the initial stay-at-home orders compared with children in urban counties. Some research findings indicate that older adults living in rural areas had more social interactions during the initial COVID-19 stay-at-home orders (Pérès et al., 2021). Children in rural areas could have had more social interactions than their urban counterparts as well which could have increased the opportunity for report sources to observe and report CM. Moreover, people in rural areas were significantly less likely to participate in COVID-19-related preventive health behaviors (Callaghan et al., 2021) so traditional report sources (e.g., school and daycare personnel) exposure to children who may have experienced CM may have been less suppressed in rural than in urban communities. Thus, the effects of the lockdowns and social distancing measures that obstructed efforts to protect children from maltreatment during the early stages of the pandemic (Nay, 2020) could have affected urban communities more than rural communities. Interestingly, global positioning system data indicates that during the initial phase of the COVID-19 pandemic, people in rural communities traveled more than in urban communities (Showalter et al., 2021). This could also be indicative that more physical interactions occurred between report sources and children in rural communities compared with urban communities where adherence to social distancing restrictions was more prevalent.

Whether the actual social distancing restrictions led to increased CM report rate disparities between rural and urban communities cannot be determined by our analyses. Nevertheless, variations in actual community spread and the concomitant application of measures to mitigate spread are viable explanations for increased disparities between rural and urban areas. Data and analyses that take into consideration more nuanced variables would be able to discern why these disparities exist and why they increased during the pandemic. During the time period of our analyses, many changes occurred in CPS systems that could have impacted CM report rates in both rural and urban counties. For example, workers were able to conduct welfare visits remotely (Goldhaber, 2020) and child welfare agencies adapted policies and practices to address the impact of COVID-19 on service delivery (Schwab-Reese et al., 2020; Shusterman et al., 2022). The causal effects of the COVID-19 pandemic on CM reporting and total CM (i.e., observed plus unobserved CM) should therefore be studied in future analyses. Recent reviews on the impact of the COVID-19 pandemic on interpersonal violence indicate that challenges persist that affect the availability and quality of data on CM (Cappa & Jijon, 2021). Also, due to the limited availability of COVID-19 testing during the beginning of the pandemic, case-level COVID-19 data present an incomplete picture of the outbreak (The New York Times, 2021).

Notwithstanding the limitations presented above, the present study is the first to consider how CM reporting was affected by the COVID-19 pandemic in rural counties compared with urban counties. While previous findings on the prevalence of violence against children during the COVID-19 pandemic are not generalizable because of the vast differences in scope and study design (Cappa & Jijon, 2021), the findings of this study help explain how public health crises (e.g., COVID-19) exacerbate disparities in CM reporting between rural and urban regions. Research has generally shown that CM report rates are greater in rural areas compared with urban areas (Kim & Maguire-Jack, 2021). A major finding of this study is that during the initial outbreak of the pandemic, CM reporting in rural counties was significantly affected, but this effect was less severe than in urban counties. It is possible that children in rural areas were seen more by report sources and that CPS offices in rural counties operated at a capacity similar to before the pandemic.

The state of the pandemic continues to evolve and therefore requires on-going longitudinal data analyses to understand its impact on vulnerable populations (e.g., children and older adults) who are at risk of maltreatment. Research with longer time frames is needed to better explain the findings presented here and to uncover any additional trends associated with subsequent COVID-19 infection waves. As an avenue for future research, it would also be beneficial to supplement CM report data with agency survey data to assess how CPS agencies across the United States continue to respond to COVID-19-related policies. It is possible that CPS systems eventually became more strained in rural areas once COVID-19 infections reached every county.

The results presented here offer opportunities for future research that can leverage the natural experiment that was precipitated by the COVID-19 outbreak and the ensuing national lockdown. The COVID-19 pandemic impacted communities differently in the United States, particularly related to urban-rural designation. Insights drawn from the results of our analyses can guide future intervention and policy implementation that take into consideration the geography where CPS systems operate. Our findings can also guide efforts to anticipate future health emergencies and natural disasters. Our goal is for this knowledge to improve prevention and intervention planning and policy to better protect children across the country.