Predict-Align-Prevent (PAP) is a 501(c)(3) non-profit organization dedicated to discovering the components of an effective primary prevention bundle for child maltreatment. PAP was founded in 2017 by Dr. Dyann Daley, a board-certified pediatric anesthesiologist with 13 years of clinical experience. She found her passion for child maltreatment prevention while caring for severely abused and neglected children in the operating room. Through her research, PAP was built with a commitment to open science, objective metrics, and child-centered outcomes. Our mission is to prevent child maltreatment through place-based geospatial risk analysis, strategic alignment of community initiatives, and implementation of accountable prevention programs.
In order to accomplish this mission, we developed a novel continuous quality improvement cycle which includes geospatial machine learning to predict the locations of future child maltreatment events, community-based strategic planning to optimize allocation of existing prevention resources, and longitudinal measurement of population health and safety metrics to determine the effectiveness of aligned prevention resources and supports.
By taking a place-based approach to prevention, PAP aims to help communities and governments uncover, evaluate, and replicate effective prevention initiatives. Ultimately, PAP is seeking the combination(s) of programs, services, and infrastructure that reliably prevents child maltreatment and related fatalities across jurisdictions. Our approach is built around three phases: Predict, Align, and Prevent.
PREDICT: GEOSPATIAL RISK AND PROTECTIVE FACTOR ANALYSIS
Predict-Align-Prevent identifies where child maltreatment will occur in the future
in 1000 ft. sq. areas using place-based predictive analytics. A geographic risk and protective factor analysis determines which risk factors are most harmful and which protective factors are most helpful in each unique community.
Derived from Adverse Childhood Experiences studies, social determinants of health, and resilience research, risk and protective factor data are culled from public and private data sources including public health, law enforcement, and child welfare systems, among others. Predict-Align-Prevent supports 'owners' of protected data to perform geospatial risk analysis in-house, thereby minimizing the risk of a security or privacy breach.
The resulting maps show where and what prevention efforts are likely to have the
greatest prevention impact.
ALIGN: COMMUNITY-BASED COLLABORATIVE STRATEGIC PLANNING
Utilizing the predictive maps overlaid with health and community asset locations, PAP works with community leaders, stakeholders, and coalitions to align existing prevention efforts and introduce new evidence-based prevention programs. A few program goals include:
• 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
PREVENT: IDENTIFY THE COMPONENTS OF AN EFFECTIVE PRIMARY PREVENTION BUNDLE
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.
Learn more about PAP by visiting www.Predict-Align-Prevent.org or contact us at Info@predict-align-prevent.org.
Comments (0)