Evidence map›Paper›PMID 33375836›Full record

ArticleChild maltreatment2022

Reports and Removals of Child Maltreatment-Related Hospitalizations: A Population-Based Study.

Rebecca Rebbe, Joseph A Mienko, Melissa L Martinson

Abstract read
In one paragraph

Article in Child maltreatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Rebecca RebbeSuzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0002-6759-1701
Joseph A MienkoCenter for Social Sector Analytics & Technology, School of Social Work, University of Washington, Seattle, WA, USA.ORCID 0000-0003-3207-1455
Melissa L MartinsonSchool of Social Work, University of Washington, Seattle, WA, USA.

Funding

Scientific/Technical CoreP2CHD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI SARA R. CURRAN · 2017 to 2026
$6.3M
NICHD NIH HHS P2C HD042828
6 · The paper itself

Abstract

Despite U.S. child protective services (CPS) agencies relying on mandated reporters to refer concerns of child maltreatment to them, there is little data regarding which children mandated reporters decide to report and not to report. This study addresses this gap by utilizing a population-based linked administrative dataset to identify which children who are hospitalized for maltreatment-related reasons are reported to CPS and which are removed by CPS. The dataset was comprised of all children born in Washington State between 1999 and 2013 (N = 1,271,416), all hospitalizations for children under the age of three, and all CPS records. We identified maltreatment-related hospitalizations using standardized diagnostic codes. We examined the records for children with maltreatment-related hospitalizations to identify hospitalization-related CPS reports and if the child was removed from their parents. We tested for differences in these system responses using multinomial regression. About two-thirds of children identified as experiencing a child maltreatment-related hospitalization were not reported to CPS. We found differences in responses by maltreatment subtype and the type of diagnostic code. Children whose hospitalizations were related to abuse and associated with a specific maltreatment code had increased odds of being both reported to CPS and subsequently removed by CPS.

Indexed as

Child AbuseChild Protective ServicesChildChild WelfareHospitalizationHumansParentsabusechild maltreatmentlinked administrative datamandated reportingneglect

Identifiers

PMID33375836
PMCPMC11326083

What OpenQuestion holds

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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.