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  • Predictive value of indicators for identifying child maltreatment and intimate partner violence in coded electronic health records: a systematic review and meta-analysis

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Original research
Predictive value of indicators for identifying child maltreatment and intimate partner violence in coded electronic health records: a systematic review and meta-analysis
  1. Shabeer Syed1,2,
  2. Rachel Ashwick2,
  3. Marco Schlosser3,
  4. Arturo Gonzalez-Izquierdo4,
  5. Leah Li1,
  6. Ruth Gilbert1,4
  1. 1 UCL Great Ormond Street Institute of Child Health, Population, Policy and Practice, University College London, London, UK
  2. 2 Oxford Institute of Clinical Psychology Training and Research, University of Oxford, Oxford, UK
  3. 3 Division of Psychiatry, University College London, London, UK
  4. 4 Institute of Health Informatics and Health Data Research UK, University College London, London, UK
  1. Correspondence to Shabeer Syed, UCL Great Ormond Street Institute of Child Health, Population, Policy and Practice, University College London, London WC1N 1E, UK; s.syed.16{at}ucl.ac.uk

Abstract

Objective Electronic health records (EHRs) are routinely used to identify family violence, yet reliable evidence of their validity remains limited. We conducted a systematic review and meta-analysis to evaluate the positive predictive values (PPVs) of coded indicators in EHRs for identifying intimate partner violence (IPV) and child maltreatment (CM), including prenatal neglect.

Methods We searched 18 electronic databases between January 1980 and May 2020 for studies comparing any coded indicator of IPV or CM including prenatal neglect defined as neonatal abstinence syndrome (NAS) or fetal alcohol syndrome (FAS), against an independent reference standard. We pooled PPVs for each indicator using random effects meta-analyses.

Results We included 88 studies (3 875 183 individuals) involving 15 indicators for identifying CM in the prenatal period and childhood (0–18 years) and five indicators for IPV among women of reproductive age (12–50 years). Based on the International Classification of Disease system, the pooled PPV was over 80% for NAS (16 studies) but lower for FAS (<40%; seven studies). For young children, primary diagnoses of CM, specific injury presentations (eg, rib fractures and retinal haemorrhages) and assaults showed a high PPV for CM (pooled PPVs: 55.9%–87.8%). Indicators of IPV in women had a high PPV, with primary diagnoses correctly identifying IPV in >85% of cases.

Conclusions Coded indicators in EHRs have a high likelihood of correctly classifying types of CM and IPV across the life course, providing a useful tool for assessment, support and monitoring of high-risk groups in health services and research.

  • child abuse
  • health services research
  • epidemiology
  • drug withdrawal
  • data collection
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/archdischild-2020-319027

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    Footnotes

    • Contributors Concept: SS and RG. Design: SS. Drafting of the manuscript: SS, RA, RG and LL. Literature search and screening: SS, RA and MS. Acquisition, analysis or interpretation of data: all authors contributed equally. Statistical analysis: SS and LL. Critical revision of the manuscript for important intellectual content: all authors contributed equally. Study supervision: RG and LL.

    • Funding The corresponding author had full access to all of the data and had final responsibility to submit for publication.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.

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