Library journals viewThe Virtual Health Information Network (VHIN) will build on the ‘big data’ approach to healthcare. We will be utilising and growing the data available in the Statistics New Zealand Integrated Data Infrastructure (SNZ IDI).

The SNZ IDI provides infrastructure in which linked health data sets can be maintained, grown and accessed efficiently. The SNZ IDI already holds multiple social sector datasets and is starting to hold health data. This has potential for research integrating health data with other types of data.

The VHIN Project will help answer research questions about the productivity costs of cardiovascular disease, the long-term impact of the Canterbury earthquakes on cardiovascular disease, and questions that support the He Pikinga Waiora and Biomarkers for Cancer Detection projects.

 

Virtual Health Information Network: Capitalising on New Zealand’s health data

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The Capitalising on Health Data project is aligned with the Virtual Health Information Network (vhin.co.nz) and is based out of the Health Inequalities Research Programme, Department of Public Health, University of Otago, Wellington. This project is exploring four areas of research.

 

The impact of the Canterbury earthquakes on cardiovascular disease (CVD)

This study assesses the impact of earthquake damage on CVD in the five years after a major earthquake sequence. The study uses linked administrative datasets to identify individuals 45+ years old living in Christchurch at the time of the first Canterbury earthquake on 4th September 2010. Individuals are assigned the level of damage from their residential mesh-block using the ratio of the insurance claim relative to the property value in Earthquake Commission data. Age-standardised rates of CVD mortality, and CVD hospitalisation and hospitalisation with myocardial infarction, were compared by level of damage in the first year and four subsequent years post-earthquake. Rate ratios are adjusted for age, sex, ethnicity, small area deprivation index and Inland Revenue data on personal income using Poisson regression. This research question was designed in collaboration with the Predicting cardiovascular disease and diabetes project.

 

Productivity costs of cardiovascular disease

This study quantifies the level of income loss and unemployment in individuals who get CVD. Propensity score modelling is used to identify a comparable group who do not develop CVD. Income, diagnosis and socio-demographic data will be assembled from linked administrative and health data sources. Productivity outcomes are examined by age, sex and ethnicity. This research question was designed in collaboration with the Predicting cardiovascular disease and diabetes project, and the BODE3 programme.

 

Protective factors against the progression of prediabetes to diabetes

This study examines the modifiable and structural risk factors that influence the progression from prediabetes to diabetes among Māori and non-Māori in New Zealand, using primary care and administrative datasets. This research question was designed and developed in collaboration with He Pikinga Waiora project.

 

The prevalence of cancer in New Zealand

This research estimates the prevalence of cancer among the resident population of New Zealand using the Integrated Data Infrastructure. Cancer prevalence is defined as a diagnosis of cancer recorded in the Cancer Registry in the past one year, five years or eighteen years.  Prevalence is being disaggregated by sex, age, ethnicity, deprivation, District Health Board and possibly by urban / rural differences and specific cancer types. This research question was designed in collaboration with the Biomarkers for cancer detection project.

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