Preparing linked population data for research: cohort study of prisoner perinatal health outcomes

What makes administrative data "research-ready"? A systematic review and thematic analysis of published literature

Introduction

Administrative data are a valuable research resource, but are under-utilised in the UK due to governance, technical and other barriers (e.g., the time and effort taken to gain secure data access). In recent years, there has been considerable government investment in making administrative data "research-ready", but there is no definition of what this term means. A common understanding of what constitutes research-ready administrative data is needed to establish clear principles and frameworks for their development and the realisation of their full research potential.

Objective

To define the characteristics of research-ready administrative data based on a systematic review and synthesis of existing literature.

Methods

On 29th June 2021, we systematically searched seven electronic databases for (1) peer-reviewed literature (2) related to research-ready administrative data (3) written in the English language. Following supplementary searches and snowball screening, we conducted a thematic analysis of the identified relevant literature.

Results

Overall, we screened 2,375 records and identified 38 relevant studies published between 2012 and 2021. Most related to administrative data from the UK and US and particularly to health data. The term research-ready was used inconsistently in the literature and there was some conflation with the concept of data being ready for statistical analysis. From the thematic analysis, we identified five defining characteristics of research-ready administrative data: (a) accessible, (b) broad, (c) curated, (d) documented and (e) enhanced for research purposes.

Conclusions

Our proposed characteristics of research-ready administrative data could act as a starting point to help data owners and researchers develop common principles and standards. In the more immediate term, the proposed characteristics are a useful framework for cataloguing existing research-ready administrative databases and relevant resources that can support their development.

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

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.

Creating the first national linked dataset on perinatal and maternal outcomes in Australia: Methods and challenges

BACKGROUND

Data linkage offers a powerful mechanism for examining healthcare outcomes across populations and can generate substantial robust datasets using routinely collected electronic data. However, it presents methodological challenges, especially in Australia where eight separate states and territories maintain health datasets. This study used linked data to investigate perinatal and maternal outcomes in relation to place of birth. It examined data from all eight jurisdictions regarding births planned in hospitals, birth centres and at home. Data linkage enabled the first Australia-wide dataset on birth outcomes. However, jurisdictional differences in data collection created challenges in obtaining comparable cohorts of women with similar low-risk pregnancies in all birth settings. The objective of this paper is to describe the techniques for managing previously linked data, and specifically for ensuring the resulting dataset contained only low-risk pregnancies.

METHODS

This paper indicates the procedures for preparing and merging linked perinatal, inpatient and mortality data from different sources, providing technical guidance to address challenges arising in linked data study designs.

RESULTS

We combined data from eight jurisdictions linking four collections of administrative healthcare and civil registration data. The merging process ensured that variables were consistent, compatible and relevant to study aims. To generate comparable cohorts for all three birth settings, we developed increasingly complex strategies to ensure that the dataset eliminated women with pregnancies at risk of complications during labour and birth. It was then possible to compare birth outcomes for comparable samples, enabling specific examination of the impact of birth setting on maternal and infant safety across Australia.

CONCLUSIONS

Data linkage is a valuable resource to enhance knowledge about birth outcomes from different settings, notwithstanding methodological challenges. Researchers can develop and share practical techniques to address these challenges. Study findings suggest that jurisdictions develop more consistent data collections to facilitate future data linkage.