Multisite data linkage projects in mental health research

Using data linkage for mental health research in Australia

Data linkage is a powerful tool for understanding the multifaceted needs and priorities of mental health care from the perspective of users and providers. Its potential remains underutilised in Australian settings - the Productivity Commission Inquiry into Mental Health in 2020 highlighted a significant gap: routinely collected administrative data are seldom leveraged in mental health research and service evaluation. In this manuscript, we provide insights into how data linkage has been used in mental health research, the type of questions that can be addressed, the steps involved in conducting data linkage research and the benefits and limitations of the use of this methodology. We propose crucial recommendations for advancing this field including: enhancing education for stakeholders (including the public, data custodians, ethics committees and policy makers); fostering stronger collaborative relationships with individuals with lived experiences throughout the research journey; improving infrastructure and resources for data linkage activities and linking data across sectors to address complex meaningful research questions. Data linkage is not just a method but a critical strategy to transform mental health research and service evaluation, ensuring more informed, effective and holistic mental health care.

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Identification of child mental health problems by combining electronic health record information from different primary healthcare professionals: a population-based cohort study

OBJECTIVES

To investigate the potential value of combining information from electronic health records from Dutch general practitioners (GPs) and preventive youth healthcare professionals (PYHPs) in predicting child mental health problems (MHPs).

DESIGN

Population-based retrospective cohort study.

SETTING

General practice, children who were registered with 76 general practice centres from the Leiden University Medical Centre (LUMC) primary care academic network Extramural LUMC Academic Network in the Leiden area, the Netherlands. For the included children we obtained data regarding a child's healthy development from preventive youth healthcare.

PARTICIPANTS

48 256 children aged 0-19 years old who were registered with participating GPs between 2007 and 2017 and who also had data available from PYHPs from the period 2010-2015. Children with MHPs before 2007 were excluded (n=3415).

PRIMARY OUTCOME

First MHPs based on GP data.

RESULTS

In 51% of the children who had MHPs according to GPs, PYPHs also had concerns for MHPs. In 31% of the children who had no MHPs according to GPs, PYHPs had recorded concerns for MHPs. Combining their information did not result in better performing prediction models than the models based on GP data alone (c-statistics ranging from 0.62 to 0.64). Important determinants of identification of MHPs by PYHPs 1 year later were concerns from PHYPs about MHPs, borderline or increased problem scores on mental health screening tools, life events, family history of MHPs and an extra visit to preventive youth healthcare.

CONCLUSIONS

Although the use of combined information from PYHPs and GPs did not improve prediction of MHPs compared with the use of GP data alone, this study showed the feasibility of analysing a combined dataset from different healthcare providers what has the potential to inform future studies aimed at improving child MHP identification.