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Using a Predictive Risk Model to Prioritize Families for Prevention Services: The Hello Baby Program in Allegheny

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This summary is machine-generated.

Predictive risk models (PRMs) can identify infants at high risk for child maltreatment, improving targeted prevention services. This approach is more effective than traditional methods for stratifying families and allocating resources to those most in need.

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Area of Science:

  • Child welfare
  • Public health
  • Data science

Background:

  • Population-based child abuse prevention is challenging due to low incidence rates.
  • Identifying high-risk families for interventions is difficult with traditional criteria.
  • Predictive risk models (PRMs) offer a data-driven approach to risk stratification.

Purpose of the Study:

  • To develop and validate a PRM for identifying infants at high risk of maltreatment-related removal by age 3.
  • To stratify families into prioritized service levels based on predicted risk.
  • To compare PRM effectiveness against traditional eligibility criteria.

Main Methods:

  • Utilized administrative data from Allegheny County for children born 2012-2015.
  • Developed a PRM using the least absolute shrinkage and selection operator (LASSO).
  • Validated the model using child removal data and other adverse outcomes (e.g., mortality).

Main Results:

  • The PRM achieved an Area Under the Curve (AUC) of 0.93.
  • The top 5% risk stratum showed a 5.54 relative risk ratio for maltreatment-related fatality/near-fatality.
  • PRM significantly outperformed traditional methods like poverty or teen maternal age for risk targeting.

Conclusions:

  • PRMs are effective in identifying high-risk infants for targeted child welfare interventions.
  • This data-driven approach enhances the efficiency and impact of prevention services.
  • PRMs offer a superior method for resource allocation in child protective services.