a public health approach to preventing and treating child
TRANSCRIPT
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A Public Health Approach to Preventing and Treating Child Abuse and Neglect
September 11, 2019
NGA Human Services Policy Institute
Duke Storen
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Copyright Predict Align Prevent 2019 2
U.S. Screened-in Referral Rates per 1,000 Children
Source: U.S. Department of Health & Human Services, Administration for Children and Families, Administration on Children, Youth and Families,
Children’s Bureau. (2019). Child Maltreatment 2017. Available from https://www.acf.hhs.gov/cb/research-data-technology/ statistics-research/child-
maltreatment
The rate of child
maltreatment in the United
States is increasing.
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3Step 1 – Define and Monitor the Problem
Step 2 – Identify Risk and Protective Factors
Step 3 – Develop and Test Prevention Strategies
Step 4 – Assure Widespread Adoption
Public Health Approach - CDC
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4Predictive Analytics: “The practice of extracting information from data sets to determine patterns and predict outcomes and trends” (Predictive Analytics, Applications in Child Welfare, Casey Family Programs)
Predictive Risk Modeling: a specific type of predictive analytics, focused on using data patterns to identify predictors of risk and assign risk categories based on these patterns to individuals and/or families (Predictive Analytics, Applications in Child Welfare, Casey Family Programs)
Defining Predictive Analytics
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5
Amazon Product Recommendations
Examples of Predictive Analytics
Facebook Ads
Healthcare Screenings
Fraud Detection
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6Predictive Analytics in Child Welfare
Identify Potential Risk and
Protective Factors
Assemble Spatial
Data Sets for Each Factor
Geocoding (locating)
Spatial Data
Assign Polygon
Hierarchy (geographic
areas used to organize
resources)
Kernel Density
Mapping (test
concentrations and
correlations)
Combine Correlated
Factors into a Map
Gather Intelligence Factors and Patterns to
Target
Design Strategies to
Leverage Community Resources
Data AnalysisGathering
DataCommunity Alignment
Geospatial Risk Modeling/Analysis
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7• Social connections
• Parental supports
• Health
• Stable housing
• Employment
• More parental education
• Well resourced schools
• Places of worship
• Parks
• Community Centers
• Licensed child care
• Early learning centers
• Health clinics
Protective Variables
Gathering Data
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8• Physical abuse
• Sexual abuse
• Emotional abuse
• Physical and emotional neglect
• Exposure to:
• Domestic Violence
• Substance misuse within household
• Household mental illness
• Parental separation or divorce
• Incarcerated household member
Risk Factors
Gathering Data
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9
If violent behavior is purely a psychological characteristic of the individual, where someone lives should have no bearing
on their likelihood of experiencing ACEs and child maltreatment,
We would expect it to occur randomly across geographical regions.
This is not what we see.
Geospatial Risk Modeling applied to Child Abuse
Data Analysis
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10• Geospatial risk models are built on the notion that
• Place matters and should be taken into account when estimating risk
• Physical elements of the environment contribute to or reduce the likelihood of risk
• Information on these elements can be used to improve prediction – their frequency and distance from locations where child abuse occurs
Geospatial Risk Modeling applied to Child Abuse
Data Analysis
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11• We are interested in identifying the socio-economic and
physical elements that make a neighborhood risky for children
• If features that enable or contribute to the likelihood of ACEs and child maltreatment can be identified, it is likely they can also be mitigated
Geospatial Risk Modeling applied to Child Abuse
Data Analysis
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1,910 individual 1,000 ft2 cells
• 62.5 square miles• Population: 227,032• Children: 41,000• Under age five: 13,850
• 48% African American• 44.7% White• 6.5% Latino
US Census 2010
Study Area -- City of Richmond:
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13Geocode Founded Child Maltreatment Cases
Data Analysis
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14Look For Clustering and Associated Risk or Protective Factors
Data Analysis
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15Founded CPS Investigations, Crime and Domestic Violence in Richmond, VA
Data Analysis
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16Map Risk and Protective Factor Layers
Data Analysis
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17Predict High Risk Areas
Data Analysis
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Feature Importance
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How does the distribution of poverty relate to maltreatment events?
These tables illustrate the relationship between poverty rate and predicted maltreatment count. The scatter plot
shows that the correlation between poverty and predictive risk is marginal. This visual relationship is confirmed by
a correlation coefficient of 0.29. The weak relationship persists even when the zero count grid cells are removed.
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20Aligning Project Components
Alignment and Monitoring
Asset Mapping
Service Mapping
Resource Alignment
Gap Analysis
Capacity Needs Analysis
Strategies for Community Engagement and Resource
Allotment
Community Alignment
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21• What is the type of abuse to be targeted by the intervention?
• For each risk factor identified in the model, what is its relationship to the type of abuse?• What mechanisms link the risk factor to the type of abuse?
• What actions can DSS, local police, VDH, and other community partners take to mitigate the spatial influence of each risk factor?
• Given available resources and our understanding of how the risk factors are related to the type of abuse, what risk factors should be prioritized to receive attention?
Community Engagement
Community Alignment
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22Community Engagement
• Could be based on order of relative risk
• Could be based on financial cost of intervention
• Could be based on feasibility of coordinating and deploying necessary resources
• Could be based on community willingness to implement the intervention
Community Alignment
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