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Little Rock, Arkansas Technical Report for the Arkansas Division of Children and Family Services

REPORT SECTIONS:    Intro    Predict     Align     Psychographics     Community     Recommendations     Ahead    

Stopping Child Maltreatment before it happens

The children at the greatest risk for maltreatment and related fatality are aged 0-3 years and share the distinctive characteristic of potential lack of visibility to the most common types of mandatory reporters such as teachers, medical professionals, and law enforcement before they sustain harm.

Indeed, approximately half of infants and children who die from child maltreatment are not known to child protection agencies before their deaths occur.

Children who survive abuse, neglect, and adversity in early childhood often suffer a lifetime of physical, mental, educational, and social health problems.

Long-term outcomes include shorter life expectancy, chronic disease and disability, obesity, smoking, alcohol and drug abuse, risk of intimate partner and sexual violence, depression and anxiety, suicidality, sexually transmitted infections, unintended and teenage pregnancies, low birth weight and fetal death, psychological disorders, and risk of aggressive and/or criminal behavior.

Efforts to stop child maltreatment must focus on a preemptive approach.

According to the 2016 final report of the Presidential Commission to Eliminate Child Abuse and Neglect Fatalities, strategies for prevention of child maltreatment, related fatality, and the host of sequelae is at a crossroads.

Communities across the nation are challenged to reliably prevent child abuse and neglect, related fatality, recurrence of abuse and neglect, or keep a child safe while in foster or kinship care.

They do not know if expensive social resources are being allocated to the people who need them most, or if the services provided are effective in improving objective measures of child health and safety.

Stopping child maltreatment before it happens in Arkansas’ capitol


innovative and transparent approach to child welfare

Governor Asa Hutchinson, First Lady Susan Hutchinson, and the Department of Children and Family Services Director Mischa Martin have taken bold steps to address challenging child welfare issues in Arkansas.

In 2016, DHS and DCFS released a report titled Moving Beyond Crisis, which outlined an honest look at system weakness and set an agenda of strategic, aggressive reforms to the state child welfare and foster care system. The Governor has shown continued commitment to improving and funding these critical child welfare programs.

Director Martin and her team’s reform efforts are working, and the Arkansas child welfare system is continuously improving for the state’s most vulnerable population: from the 2019 report Family First Fits Us, between August 2016 and August 2019, key metrics have seen positive changes:

August 2016 August 2019
Children in foster care 5,196 4,285
Children placed with relatives 23.40% 30.30%
Children placed in family-like setting 77.60% 86.90%
Ratio of foster home beds to children 0.69 0.79
Average caseload 28 19
Overdue investigations 721 104

The report concluded with this thought:

"As the federal Family First Prevention Services Act becomes active, we see that Family First Fits Us. Our efforts and our values are directly in line with this landmark piece of federal legislation. It provides a roadmap and the support for us to continue to build upon the foundation DCFS has laid over the last three years. The work will not be easy, but the effort to move from crisis to stability to progress is worth it because it is the right thing to do for children and families in Arkansas."

Significant progress has been made in Arkansas, and DCFS continues to expand their tools and strategies to carry on this most important work.

ACEs in

in Arkansas

Adverse childhood experiences (ACEs) are a focus for Arkansas state, with the highest prevalence of children in the nation who have experienced at least one ACE, and one in seven children experiencing three or more ACEs.

ACEs are traumatic events that occur before the age of 18 years, and include:

  • Physical, emotional, and sexual abuse
  • Physical and emotional neglect
  • Having a caregiver with mental illness or substance use
  • Losing a parent to death or divorce
  • Having an incarcerated relative
  • Exposure to domestic violence

Arkansans’ high exposure to adverse childhood experiences is apparent in its national ranking for multiple problems with well established links to ACEs.

All of these problems share common risk and protective factors, and prevention will require a multi-agency, collaborative approach that multiplies the impact of scarce prevention resources.

Arkansas National Ranking
(1=best  51=worst)
Child maltreatment fatality 51
Teen birth 51
Infant mortality 46
3rd grade reading proficiency 46
Premature birth 45
Domestic violence homicide 45
Homicide 44
Child Poverty 44
Maternal morbidity and mortality 43
Suicide 42
Poverty 41
No. of children available for adoption 31

Using what you have

How existing prevention resources can be targeted to make a measurable impact

Knowing that there is limited and often unreliable funding for child maltreatment prevention and related social services, Predict-Align-Prevent (PAP) works to leverage existing funding, local expertise, and community-wide participation in prevention activities with accountability to objective measures of population health and safety.



a continuous quality improvment prevention cycle

PAP implements a novel continuous quality improvement process with jurisdictions committed to the prevention of child abuse and neglect.

PAP is at work on a national scale seeking the combination(s) of programs, services, and infrastructure that reliably prevents child maltreatment and related fatalities across jurisdictions.

AR DCFS and PAP partner

to improve child maltreatment prevention efforts for the city of Little Rock

A city of nearly two hundred thousand,
Little Rock was chosen as the first city in Arkansas to participate in the PAP Program


PAP uses place-based geospatial machine learning to identify where children are at the greatest risk of maltreatment. Data, including child welfare, health, crime, code violations, and infrastructure, are analyzed to create a relationship model of maltreatment across space. We validate the model against prior known maltreatment incidents, creating a powerful tool: a predictive maltreatment risk map at scales starting from a few city blocks.

Using risk and protective factor data selections derived from ACEs studies, social determinants of health, and resilience research, we then develop a geographic risk and protective factor analysis to determine which risk factors are most harmful and which protective factors are most helpful across each community.

The resulting maps show where and what prevention efforts are likely to have the greatest prevention impact.


Utilizing the predictive maps overlaid with health and community asset locations, Predict-Align-Prevent works with existing community leaders, stakeholders, community members, and coalitions to align existing prevention efforts.

  • Optimize access to critical supports
  • Develop capacity for vital services
  • Develop supportive infrastructure
  • Improve professional response
  • Standardize cross-sector prevention messaging
  • Strengthen and build community resilience
  • Match employment opportunities with populations using psychographics
  • Create new social norms


Once a community has rallied around high-risk locations by aligning prevention services, supports, resources, and initiatives Predict-Align-Prevent and collaborators can learn what combination results in prevention.

Repeated population-level measurement of the impact of aligned services and supports will demonstrate if there is a reduction of child maltreatment and related risk factors over time. The purpose of this measurement is to clarify the need for new or different strategies, to expand effective programs, and continue to improve allocation of resources.

We expect an effective prevention bundle to offer communities and states benefits beyond the reduction of child maltreatment including improved allocation of resources, quality improvement in prevention services, and a reduction in redundant or conflicting efforts.


We set to work

to engineer better outcomes for the children of Little Rock through PAP’s three-phase approach

Our first step:

to predict where child maltreatment is likely to occur in the future, using place-based predictive analytics

Partnering with a data science research team from the University of Arkansas, risk maps for child maltreatment were created.

The city of Little Rock was covered by a grid, with each grid cell measuring 1000 ft sq.

Based on the PAP methodology, risk categories from 1 to 5 were assigned.

The dark blue grid cells, Risk Category 5, represent the highest risk places for child maltreatment.

While only 15% of the total population lives in the highest risk areas, approximately 60% of child maltreatment, 40% of child maltreatment deaths, and 50% of all ACEs-related child deaths occur there.

By geographically defining these small areas, we have an opportunity to align prevention resources where they will do the most good.

Just think - if more than half of all of these problems occur in 15% of a city's population and 10% of its geography, we have a better chance of actually moving the needle as we now know where to focus efforts.

And this was only the beginning

let’s look at all we have discovered and accomplished so far...

Click to Continue to Predict Section


  • [1] (Sacks & Murphey, 2018) Sacks, V., & Murphey, D. (2018). The prevalence of adverse childhood experiences, nationally, by state, and by race or ethnicity. Child Trends. Retrieved from https://www.childtrends.org/publications/prevalence-adverse-childhood-experiences-nationally-state-race-ethnicity
  • [2] The Child and Adolescent Health Measurement Initiative., ARKANSAS| FACT SHEET 2019, https://www.cahmi.org
  • [3] U.S. Department of Health and Human Services, Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. (2018). Child Maltreatment 2018: Table 4-1 Child Fatalities by Submission Type
  • [4] HHS https://www.hhs.gov/ash/oah/adolescent-development/reproductive-health-and-teen-pregnancy/teen-pregnancy-and-childbearing/trends/index.html
  • [5] America's Health Rankings analysis of CDC WONDER Online Database, Underlying Cause of Death, Multiple Cause of Death files; Natality public-use data, United Health Foundation, AmericasHealthRankings.org
  • [6] The Nation's Report Card https://www.nationsreportcard.gov/profiles/stateprofile?chort=1&sub=RED&sj=AL&sfj=NP&st=MN&year=2019R3
  • [7] CDC Wonder Online Database, Natality public-use data, 2017
  • [8] Violence Policy Center. When Men Murder Women: An Analysis of 2016 Homicide Data
  • [9] CDC https://www.cdc.gov/nchs/pressroom/sosmap/homicide_mortality/homicide.htm
  • [10] America's Health Rankings analysis of U.S. Census Bureau, American Community Survey, United Health Foundation, AmericasHealthRankings.org
  • [11] https://www.usnews.com/news/best-states/articles/2019-06-12/these-states-have-the-highest-maternal-mortality-rates https://worldpopulationreview.com/states/maternal-mortality-rate-by-state
  • [12] CDC https://www.cdc.gov/nchs/pressroom/sosmap/suicide-mortality/suicide.htm
  • [13] https://www.americashealthrankings.org/explore/senior/measure/poverty_sr/state/AR
  • [14] U.S. Department of Health and Human Services, Administration for Children and Familie 2017 https://www.acf.hhs.gov/sites/default/files/cb/afcars_state_data_tables_07thru16.xlsx Census.gov https://data.census.gov/cedsci/table?g=0100000US.04000.001&tid=ACSST1Y2016.S0101&vintage=2016&hidePreview=true&moe=false&tp=false Children available for adoption as percentage of state child population.
  • Daley D., Bachmann M., Bachmann B.A., Pedigo C., Bui. M.T., & Coffman J. (2016). Risk terrain modeling predicts child maltreatment. Child Abuse Neglect. 62:29-38. doi:10.1016/j.chiabu.2016.09.014. https://www.sciencedirect.com/science/article/pii/S0145213416301922
  • Predict Align Prevent (2019). Richmond, Virginia Technical Report. https://b9157c41-5fbe-4e28-8784-ea36ffdbce2f.filesusr.com/ugd/fbb580_2f1dda2ff6b84f32856bc95d802d6629.pdf
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