Research using population-based administration data integrated with longitudinal data in child protection settings: A systematic review

Abstract

Introduction Over the past decade there has been a marked growth in the use of linked population administrative data for child protection research. This is the first systematic review of studies to report on research design and statistical methods used where population-based administrative data is integrated with longitudinal data in child protection settings. Methods The systematic review was conducted according to Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement. The electronic databases Medline (Ovid), PsycINFO, Embase, ERIC, and CINAHL were systematically searched in November 2019 to identify all the relevant studies. The protocol for this review was registered and published with Open Science Framework (Registration DOI: 10.17605/OSF.IO/96PX8) Results The review identified 30 studies reporting on child maltreatment, mental health, drug and alcohol abuse and education. The quality of almost all studies was strong, however the studies rated poorly on the reporting of data linkage methods. The statistical analysis methods described failed to take into account mediating factors which may have an indirect effect on the outcomes of interest and there was lack of utilisation of multi-level analysis. Conclusion We recommend reporting of data linkage processes through following recommended and standardised data linkage processes, which can be achieved through greater co-ordination among data providers and researchers.

Leveraging Administrative Data to Better Understand and Address Child Maltreatment: A Scoping Review of Data Linkage Studies

“…Overall, many studies did not report sufficient detail relating to data linkage processes (including consent procedures, quality of linkage, risk of bias, and technical considerations) to align with current guidance (e.g. Benchimol et al, 2015 ; Gilbert et al, 2018 ), which is consistent with the findings from Chikwava and colleagues’ recent review ( 2021 ). Understanding data quality is particularly important in assessing the value of data linkage as a method of studying child maltreatment, and the inadequate reporting of linkage processes make it difficult to assess the quality and potential biases of linkages and therefore the robustness of study conclusions.…”

“…During this process, researchers set a threshold in order to balance missed and false matches, though choosing an ‘optimal’ threshold is often not straightforward ( Harron et al, 2017 ). Whilst there is established guidance for how to report on studies using (linked) administrative data, including the RECORD Statement ( Benchimol et al, 2015 ) and GUILD guidance ( Gilbert et al, 2018 ), a recent review of studies using administrative data linked with longitudinal data from child protection settings found that only three of the thirty included studies reported data linkage processes in enough detail to adequately conform with the recommendations of these guidelines ( Chikwava et al, 2021 ).…”

Risk Factors Associated With Primary Care–Reported Domestic Violence for Women Involved in Family Law Care Proceedings: Data Linkage Observational Study

“…The availability of routinely collected population-level administrative data for research provides the opportunity to study cohorts of populations that are hard to reach or considered high risk [38]. This study analyzed administrative records held within the Secure Anonymised Information Linkage (SAIL) Databank [39][40][41] for a cohort of mothers involved in public family law court proceedings and a matched group of mothers in the general population of Wales.…”

Patterns of homelessness and housing instability and the relationship with mental health disorders among young people transitioning from out-of-home care: Retrospective cohort study using linked administrative data

“…The homelessness definition which extends beyond the literal definition of rough sleeping or rooflessness to the different forms, which include housing where there is no security or tenure and where the risk of ending up on the streets is high [14]. The multiple linked datasets provided background and personal characteristics associated with homelessness [50,51] and the large sample size allowed for more accurate prediction of estimates.…”

“…Moreover, it is also important to improve the evidence base for interventions that address mental health and homelessness issues that would result in improved outcomes for both Indigenous and non-Indigenous young people transitioning from care. Further research should ensure they use multiple data sources to augment or validate administrative data [50]. For instance, by conducting face-toface interviews where young people could use a life event calendar to document places they lived, including the duration or time spent in those housing conditions and their quality of life [42].…”