TY  - JOUR
PY  - 2022//
TI  - Leveraging administrative data to better understand and address child maltreatment: a scoping review of data linkage studies
JO  - Child maltreatment
A1  - Soneson, Emma
A1  - Das, Shruti
A1  - Burn, Anne-Marie
A1  - van Melle, Marije
A1  - Anderson, Joanna K.
A1  - Fazel, Mina
A1  - Fonagy, Peter
A1  - Ford, Tamsin
A1  - Gilbert, Ruth
A1  - Harron, Katie
A1  - Howarth, Emma
A1  - Humphrey, Ayla
A1  - Jones, Peter B.
A1  - Moore, Anna
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - BACKGROUND: This scoping review aimed to overview studies that used administrative data linkage in the context of child maltreatment to improve our understanding of the value that data linkage may confer for policy, practice, and research. <br><br>METHODS: We searched MEDLINE, Embase, PsycINFO, CINAHL, and ERIC electronic databases in June 2019 and May 2020 for studies that linked two or more datasets (at least one of which was administrative in nature) to study child maltreatment. We report findings with numerical and narrative summary. <br><br>RESULTS: We included 121 studies, mainly from the United States or Australia and published in the past decade. Data came primarily from social services and health sectors, and linkage processes and data quality were often not described in sufficient detail to align with current reporting guidelines. Most studies were descriptive in nature and research questions addressed fell under eight themes: descriptive epidemiology, risk factors, outcomes, intergenerational transmission, predictive modelling, intervention/service evaluation, multi-sector involvement, and methodological considerations/advancements. <br><br>CONCLUSIONS: Included studies demonstrated the wide variety of ways in which data linkage can contribute to the public health response to child maltreatment. However, how research using linked data can be translated into effective service development and monitoring, or targeting of interventions, is underexplored in terms of privacy protection, ethics and governance, data quality, and evidence of effectiveness.<p />  <p>Language: en</p>
LA  - en
SN  - 1077-5595
UR  - http://dx.doi.org/10.1177/10775595221079308
ID  - ref1
ER  -