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Manchester, Winnipesaukee Region, and Greater North Country Technical Report for the New Hampshire Department of Health and Human Services

REPORT SECTIONS:    Intro    Predict     Psychographics     Align     Recommendations     Ahead    

Stopping Child Maltreatment before it happens

Approximately 1 in 7 children experienced child abuse and neglect in the last year across the United States, and this is likely an underestimate.[1]

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.

Approximately half of infants and children who die from child maltreatment are not known to child protection agencies before their deaths occur.[2]

Stopping child maltreatment before it happens

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 or criminal behavior.

The total lifetime economic burden associated with child abuse and neglect was approximately $428 billion in 2015.1

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

New Hampshire

The New Hampshire Department of Health and Human Services (DHHS) is shifting to a child maltreatment prevention-focused organization that provides voluntary services to high-risk families and family strengthening programming to all families while placing emphasis on systems coordination and use of population health and safety needs data.

Under the DHHS umbrella, Division of Public Health Services (DPHS) and Division for Children, Youth, and Families (DCYF) have taken the lead to advance the use of population health and safety data to create needs assessment maps in order to advance child maltreatment prevention efforts through the Community Collaborations to Strengthen and Preserve Families grant initiative.

The selected communities to date include Manchester, the Winnipesaukee Region, and the Greater North Country area. All three locations have established multi-sector partnerships, a history of collaboration, and a level of readiness to implement the proposed family-strengthening interventions successfully.

An Overview of
the PAP Program

DHHS, DPHS and DCYF partnered with Predict-Align-Prevent (PAP) as part of their collaborative prevention efforts.

The PAP Program focuses on reducing ACEs in the underlying population using spatial risk predictions, prevention asset optimization, and psychographic intelligence, resulting in a continuous quality improvement cycle based on objective population metrics.

The New Hampshire collaboration worked to expand upon the program, adding subject matter expertise and site specific knowledge. The following is an overview of the PAP Program, followed by New Hampshire’s specific findings, experiences, and approach to implementation.

ACEs and Child

PAP’s research has indicated that areas with a high-risk for child maltreatment represent the accumulation of ACEs in the underlying population.

Preventing ACEs will prevent maltreatment and a host of other dire, ACEs-related outcomes.

ACEs Outcomes

ACEs outcomes share common risk and protective factors, and prevention will require a collaborative approach that multiplies the impact of scarce resources.

The PAP Program identifies and specifically targets the where, what, and how for prevention efforts, ensuring resources achieve maximum impact.

Geospatial Machine Learning

Predicting Where Child Maltreatment is Likely to Occur

The three-phase program starts by identifying the risk and protective features present in the environment and applies geospatial machine learning to predict the places where child maltreatment is likely to occur in the future.

Phase 1: Predict

Data, including child welfare, health, crime, code violations, and infrastructure, are analyzed to create a relationship model of maltreatment across space. We then validate the model against prior known maltreatment incidents, creating a predictive maltreatment risk/needs 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 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 results specifically identify where and what to focus prevention efforts on for the greatest impact.

Next, we conduct a psychographic market segmentation analysis utilizing the predictive maps to determine the psychographic characteristics unique to each community’s high-risk/needs areas.

This analysis supports multiple efforts, including optimizing community engagement, developing targeted prevention messaging, resource and infrastructure allocation, and marketing of prevention opportunities.

Phase 2: Align

Utilizing the predictive maps overlaid with existing and potential health and community asset locations, showing their effective reach and impact, work begins with community leaders and members to align existing prevention efforts.

Prioritized research-backed interventions are introduced, ensuring communities are informed so the most impactful practices and programs can be selected.

Community awareness and transparency are an essential part of the Align process; each community’s voice and psychographic characteristics are reflected in the design and decision-making of the proposed work.

The development of new social norms to support desired outcomes, optimized access to critical supports, improved professional response, capacity development for vital services, and utilization of programs that hire community members in high-risk/needs areas are examples of Align process goals.

Phase 3: Prevent

Once the community has rallied around high-risk/needs locations by aligning prevention services, supports, resources, and initiatives, prevention campaigns are implemented.

The results of the interventions are measured, and adjustments are carried out to ensure continuous improvement.

Repeated population-level impact measurement of aligned services and supports will demonstrate reduction of ACEs and related risk factors, clarifying the need for new or different strategies, the expansion of effective programs, and the refinement of resource allocation.

This continuous prevention improvement approach adapts to each community's changing needs and influences prevention efforts to address and impact those who are unseen and unheard.

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