Application of geospatial analysis in health research: a systematic review of methodological aspects of studies on violence against children and young people

Shinyemba, Tobias and Shiode, Shino and Devries, K. (2024) Application of geospatial analysis in health research: a systematic review of methodological aspects of studies on violence against children and young people. Child Abuse & Neglect 151 (106730), ISSN 0145-2134.

Abstract

Background: Geographical variation exists in violence experienced by children and young people; however, there is limited research applying geospatial techniques to study this variation, and the methodological quality of this body of work is unclear. Objective: This study aimed to review the application of geospatial analysis in research on violence against children (VAC) and evaluate how essential methodological aspects are reported. Methods: Twelve databases were searched for studies on VAC using geospatial techniques. Two independent reviewers screened the papers for eligibility. Findings were narratively synthesised. Results: Sixty studies were included. Six studies estimated the prevalence of VAC and 54 investigated the associations between VAC and covariates. Most studies were conducted in the US (68 %), and the broad definition of ‘child maltreatment’ (53 %) was the most common form of violence explored. Most studies (83 %) used administrative data, whereas 23 % used an ecological study design to estimate the associations between risk factors and official reports of VAC. Frequentist modelling approaches were used in 54 % of the studies, and 47 % investigated VAC at census tract level. Model fit metrics were reported in 69 % of studies. Conclusions: Current knowledge of the geographical distribution of VAC is severely limited because of the reliance on administrative data, which vastly underestimates the prevalence of VAC compared with self-reports and poor reporting of quality characteristics. There is a huge opportunity for applying geospatial methods in VAC research in diverse geographic contexts. Future research must adopt rigorous and standardised approaches to model fitting and validation and make better use of self-reported data.

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