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In March of 2020, the COVID-19 Data Project was created to satisfy the driving need for accessible data tracking the spread of the coronavirus across the United States. Following changes in levels of reporting over the following year and a half, in December of 2021, the Community Data Project was created. The Project started as small action teams looking beyond COVID-19 data and has since expanded to become multiple tracks and teams focused on collecting data on important public health topics outside of the pandemic.
As of July 2021, the BroadStreet Child Abuse and Neglect Dataset was initiated as a pilot program working towards creating a national, county-level dataset on child abuse by type. This was done in response to discovering that the national dataset on Child Abuse only details data at the state level. In addition, child abuse data generally does not have an overall database where casa data and types of abuse are easily accessible to the public. Similar to other major population health issues, ongoing surveillance of child maltreatment can inform the development of policies that respond to the needs of children and families. Studies relying on alternative data sources from public health have reported different trends in some forms of child maltreatment. The variation in reported prevalence from different sources highlights the importance of using multiple sources of information to better track child maltreatment at the population level1.
The Community Data Project’s dataset on child abuse and neglect is unique in that it is the only currently known dataset to exist that is collecting child abuse data at the county-level across the United States. This data gives the public knowledge regarding the rate of child abuse cases occuring across the United States. The Child Abuse Data Track is continuously evolving because of how recent this up-and-coming sector is within the greater data curriculum of the Broadstreet Institute.
According to the Child Abuse Prevention and Treatment Act (CAPTA), child abuse and neglect at a federal level is defined as “any recent act or failure to act on the part of a parent or caregiver that results in death, serious physical or emotional harm, sexual abuse, or exploitation, or an act or failure to act that presents an imminent risk of serious harm.”
| Term | Definition3 | Notes3 |
|---|---|---|
| Physical Abuse | The intentional use of physical force that can result in physical injury. | Examples include hitting, kicking, shaking, burning, or other shows of force against a child. |
| Sexual Abuse (may also be referred as Sexual Maltreatment) |
The involvement of pressuring or forcing a child to engage in sexual acts. | Child Sexual Abuse refers to the involvement of a child (person less than 18 years old) in sexual activity that violates the laws or social taboos of society and that they a. Do not fully comprehend b. Do not consent to, or is unable to, give informed consent to c. Not developmentally prepared for and cannot give consent to |
| Emotional Abuse (may also be referred as Emotional Maltreatment or Mental Abuse) | Behaviors that harm a child’s self worth or emotional well-being. | Examples include name-calling, shaming, rejection, withholding love, and threatening. |
| Neglect | A failure to meet a child’s basic physical and emotional needs. | These needs include housing, food, clothing, education, and access to medical care. |
| Variable | Definition |
|---|---|
| County | The county for which the data is reported. |
| FIPS_Code | Federal Information Processing Standards code that uniquely identifies countries and county equivalents in the United States. |
| [year]_Physical | Counts of reported physical abuse for that year. |
| [year]_Emotional | Counts of reported emotional abuse for that year. |
| [year]_Neglect | Counts of reported neglect abuse for that year. |
| [year]_Sexual | Counts of reported sexual abuse for that year. |
Data entry is conducted by the Data Entry and Quality Assurance (DE & QA) Team. DE & QA team members enter data once assigned by the DE & QA Team Lead. This entry is done per state, county, assigned year, and type of abuse. This information is entered into Google Sheets, available on a shared Google Doc in the Institute’s Google Drive. This data entry occurs in a set period of time to be completed and made ready for review on a bi-weekly basis. To limit errors on the Google Sheet, each state has its own tab with counties and columns for types of abuse reported. After entering data for a given state, the BroadStreet interns check to make sure the counties match up per the state website or KidsCount website, as some counties do not report on certain types of abuse. Once the intern has completed their assigned state, year, and type of abuse, they then mark it as completed on an additional Google Sheet. After entry is completed, the team reviews the state sheet for Quality Assurance to ensure data was filled in completely and matches what the states reported. Quality Assurance interns identify any data entry mistakes and make the appropriate corrections. If the value entered is incorrect, the Quality Assurance team members make corrections.
County-level data in the United States was collected from KidsCount and various federal websites (such as Department of Children & Families) that reported any child abuse per state. KidsCount is a website that provides state and local datasets on topics regarding children's well-being.2 Sources were expanded as necessary to include government or institutional sources with county-level or other sub-state level data (i.e. zip code, health district, or regional data). In BroadStreet’s initiative, types of abuse are provided comprehensively within yearly reports where cases are to be reported into the dataset. To ensure we are providing a comprehensive report, interns are responsible for seeking out the source for each state’s county-level data. Data sources per state can vary and if KidsCount is not available for a specific state, federal websites are used, such as Department of Social Services or Department of Children & Families reports.
Data is collected weekly for each county in the United States that is reporting any extent of child abuse.
Data is collected as a cumulative count rather than a daily count. Types of abuse are provided comprehensively within yearly reports where cases are to be reported into an entire data set. Numerical cases reported under the “substantiated” are data of interest; this indicates the case(s) has evidence of abuse.
When the county is not listed nor its numerical data, often from lack of collection (e.g. geological effects, data release phases), a “-” is used to denote this. When 0 cases are reported in a category, a 0 is used as the placeholder.
Table 1. Total cases
| Variable Name | Variable Description |
|---|---|
| Substantiated | Number of substantiated child abuse/neglect investigations for children under 18. This indicator represents the number of substantiated cases, not an unduplicated count of children who experienced child abuse/neglect.4 |
| Units Reported | Total number of cases4 |
Table 2. Types of abuse
| Variable Name | Variable Description |
|---|---|
| Substantiated | A finding that a preponderance of evidence exists to conclude abuse/neglect has occurred or is occurring as a result of the observation of visible signs, physical and/or credible verbal evidence provided to the Children’s Service Worker by the child, perpetrator or witnesses in accordance with the definitions of abuse/neglect. This includes cases which are adjudicated by the courts.5 |
| Units Reported | Total number of cases5 |
Where applicable, state total numbers were converted into rates per 1,000 cases using R software. The DPLYR R-package was used to clean and manipulate the data.
Data Challenges Certain organized counties on published reports are organized based on region. The way in which interns organize these counties, through Google Sheets, are in alphabetical order. This discrepancy may present some difficulty when entering data. On occasions, data can become mixed up, thus fortifying the possibility of publishing incorrect data.
Accessibility of Data: Formatting Child Abuse data may be reported in various formats, which can complicate any efforts to automate data entry. Formats used to report child abuse data include .pdf, .csv, excel, and Microsoft Bl, to name a few.
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Medina SP, Sell K, Kavanagh J, Curtis C, Wood JN. Tracking Child Abuse and Neglect: The Role of Multiple Data Sources in Improving Child Safety. PolicyLab, 2012. Accessed April 24, 2022. https://policylab.chop.edu/sites/default/files/pdf/publications/PolicyLab_EtoA_Tracking_Child_Abuse_and_Neglect_2012.pdf
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KidsCount. The Annie E. Casey Foundation. Kids Count Data Center website. Accessed April 24, 2022. https://datacenter.kidscount.org/
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Centers for Disease Control and Prevention. Fast Facts: Preventing Child Abuse & Neglect. Centers for Disease Control and Prevention website. https://www.cdc.gov/violenceprevention/childabuseandneglect/fastfact.html
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KidsCount. Substantiated child abuse/neglect in Missouri. Kids Count Data Center website. Accessed April 18, 2022. https://datacenter.kidscount.org/data/tables/9567-substantiated-child-abuse-neglect?loc=27&loct=2#detailed/2/any/false/1729,37,870,573,869,36,868,867,133,38/any/21983,21984
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Missouri Department of Social Services. Missouri Child Abuse and Neglect Annual Report. Accessed April 18, 2022. https://dss.mo.gov/re/canar.htm
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Ernesto Lopez; Brenna Jarvis, MPH; Meenal Khandaker; Sarah Murata; Riya Kumar.
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