Evidence map›Paper›PMID 40259178›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2025

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

Rhema Vaithianathan, Diana Benavides-Prado, Rebecca Rebbe, Emily Putnam-Hornstein

Abstract read
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Using a Predictive Risk Model to Prioritize Families for Prevention Services: The Hello Baby Program in Allegheny County, PA.Prevention science : the official journal of the Society for Prevention Research · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rhema VaithianathanCentre for Social Data Analytics, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand. rhema.vaithianathan@aut.ac.nz.ORCID 0000-0002-8899-5254
Diana Benavides-PradoSchool of Computer Science, The University of Auckland, Auckland, New Zealand.
Rebecca RebbeSchool of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Emily Putnam-HornsteinSchool of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population-based efforts to prevent child abuse and neglect are challenging because annual incidence rates are relatively low. Even among families that meet eligibility and risk criteria for intensive home-visiting programs, the baseline rate of maltreatment tends to be low because we use simple criteria. This creates both service (i.e., cost) and evaluation (i.e., power) challenges because a large number of families need to receive the preventive intervention to produce detectable changes in subsequent maltreatment. The increase in the availability of administrative data has made it possible to use predictive risk models (PRMs) to risk-stratify whole birth cohorts and identify children at the highest risk of maltreatment and other early childhood adversities. The current paper describes the development and validation of a PRM implemented in Allegheny County, Pennsylvania, to stratify families and newborn infants into three levels of prioritized services based on the predicted risk of child removal due to maltreatment by age 3. Using a research dataset of anonymized records for children born in Allegheny County between 2012 and 2015, predictive features were coded using data available in the county's administrative data systems. This spine was linked to child removal outcomes between 2012 and 2018, so we had a 3-year follow-up for each child. A PRM was trained to predict removals in the first 3 years of life using the least absolute shrinkage and selection operator. Predictive accuracy was measured for the highest 5% of risk scores in a holdout dataset. The model was validated using nontraining outcomes such as maternal mortality, infant mortality, and maltreatment-related fatalities and near-fatalities. The model achieved an area under the receiver operating characteristic curve of .93 (95% CI [0.92, 0.95]), recall of 19.93%, and precision of 54.10%. Children identified for the top tier of services had a relative risk ratio of maltreatment-related fatality or near-fatality of 5.54 (95% CI [3.41, 9.00]). Using alternative eligibility approaches (e.g., poverty, teen maternal age) proved far inferior to using PRM in targeting services for children at high baseline risk of maltreatment.

Indexed as

Child AbuseChild, PreschoolFemaleHumansInfantInfant, NewbornMalePennsylvaniaRisk AssessmentChild maltreatmentHome visitingPredictive risk modelPrevention

Identifiers

PMID40259178
PMCPMC12064473

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.