Research / Big and linked data

Integrated data to address inequitable health outcomes

Investigating data linkage bias in the IDI and implementation gaps in H. pylori testing and treatment

illustrated representation of the IDI simplified
Project Status: Active Funding: $500,000 Timeframe: January 2021 – December 2023

Tā mātou e tūhura ana

What we are investigating

Take | Issue

Politicians, policy-makers and planners rely on robust data and research evidence to improve health services for our population. Aotearoa New Zealand has a rich resource of big data sets, available – in anonymised form – through the Statistics New Zealand Integrated Data Infrastructure (IDI).

A major gap in the IDI is primary healthcare data, which is particularly valuable for understanding the full clinical pathway for non-communicable diseases and examining potential service failure.

Another problem is that the power of big data relies on a huge number of data linkages, and errors can occur in matching records belonging to the same person. Data linkage errors are often correlated with ethnicity, which reduces the value of big and linked data for studies of health inequities.

Whāinga | Aim

This project has dual aims.  At a foundational level, it aims to improve the data available for future health research by investigating ways to address data linkage errors in the IDI, and by examining primary care laboratory test data linked to health data.

A further aim is to reduce inequities in stomach cancer death rates in New Zealand, which vary up to six-fold by ethnicity.  By analysing the current testing and treatment of Helicobacter pylori (H. pylori) infection, the major contributor to inequitable stomach cancer outcomes, this project will provide evidence to support the New Zealand Cancer Action Plan 2019-2029 aim to address H. pylori infection in priority populations.

Huarahi I Whāia | Approach

The first part of the study will examine the extent of data linkage error and linkage bias in the IDI on measures of ethnic inequalities in cardiovascular disease, cancer and diabetes.  It will investigate whether correcting for linkage errors would change estimates of inequitable outcomes for these non-communicable diseases.

The second part of the study will examine the feasibility and value of linking community laboratory test data to the wider health data system, using the testing and treatment of H. pylori infection as a model.

Project Team

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