A profile of the Centre for Health Record Linkage

Agreement of acute serious events recorded across datasets using linked Australian general practice, hospital, emergency department and death data: implications for research and surveillance

Introduction

Understanding the level of recording of acute serious events in general practice electronic health records (EHRs) is critical for making decisions about the suitability of general practice datasets to address research questions and requirements for linking general practice EHRs with other datasets.

Objectives

To examine data source agreement of five serious acute events (myocardial infarction, stroke, venous thromboembolism (VTE), pancreatitis and suicide) recorded in general practice EHRs compared with hospital, emergency department (ED) and mortality data.

Methods

Data from 61 general practices routinely contributing data to the MedicineInsight database was linked with New South Wales administrative hospital, ED and mortality data. The study population comprised patients with at least three clinical encounters at participating general practices between 2019 and 2020 and at least one record in hospital, ED or mortality data between 2010 and 2020. Agreement was assessed between MedicineInsight diagnostic algorithms for the five events of interest and coded diagnoses in the administrative data. Dates of concordant events were compared.

Results

The study included 274,420 general practice patients with at least one record in the administrative data between 2010 and 2020. Across the five acute events, specificity and NPV were excellent (>98%) but sensitivity (13%-51%) and PPV (30%-75%) were low. Sensitivity and PPV were highest for VTE (50.9%) and acute pancreatitis (75.2%), respectively. The majority (roughly 70-80%) of true positive cases were recorded in the EHR within 30 days of administrative records.

Conclusion

Large proportions of events identified from administrative data were not detected by diagnostic algorithms applied to general practice EHRs within the specific time period. EHR data extraction and study design only partly explain the low sensitivities/PPVs. Our findings support the use of Australian general practice EHRs linked to hospital, ED and mortality data for robust research on the selected serious acute conditions.

A bespoke data linkage of an IVF clinical quality registry to population health datasets; methods and performance

Introduction

Assisted reproductive technologies (ART), such as in-vitro fertilisation (IVF), have revolutionised the treatment of infertility, with an estimated 8 million babies born worldwide. However, the long-term health outcomes for women and their offspring remain an area of concern. Linking IVF treatment data to long-term health data is the most efficient method for assessing such outcomes.

Objectives

To describe the creation and performance of a bespoke population-based data linkage of an ART clinical quality registry to state-based and national administrative datasets.

Methods

The linked dataset was created by deterministically and probabilistically linking the Australia and New Zealand Assisted Reproduction Database (ANZARD) to New South Wales (NSW) and Australian Capital Territory (ACT) administrative datasets (performed by NSW Centre for Health Record Linkage (CHeReL)) and to national claims datasets (performed by Australian Institute of Health and Welfare (AIHW)). The CHeReL’s Master Linkage Key (MLK) was used as a bridge between ANZARD’s partially identifiable patient data (statistical linkage key) and NSW and ACT administrative datasets. CHeReL then provided personal identifiers to the AIHW to obtain national content data. The results of the linkage were reported, and concordance between births recorded in ANZARD and perinatal data collections (PDCs) was evaluated.

Results

Of the 62,833 women who had ART treatment in NSW or ACT, 60,419 could be linked to the CHeReL MLK (linkage rate: 96.2%). A reconciliation of ANZARD-recorded births among NSW residents found that 94.2% (95% CI: 93.9–94.4%) of births were also recorded in state/territory-based PDCs. A high concordance was found in plurality status and birth outcome ≥99% agreement rate, Cohen’s kappa ranged: 0.78–0.98) between ANZARD and PDCs.

Conclusion

The data linkage resource demonstrates that high linkage rates can be achieved with partially identifiable data and that a population spine, such as the CHeReL’s MLK, can be successfully used as a bridge between clinical registries and administrative datasets.