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Nationally linked data to improve health services and policy

Tom G Briffa, Louisa Jorm, Rodney T Jackson, Christopher Reid and Derek P Chew
Med J Aust 2019; 211 (9): . || doi: 10.5694/mja2.50368
Published online: 4 November 2019

A well designed National Integrated Health Services Information Analysis Asset will improve services and policy

New Zealand continues to set best practice standards internationally for cardiovascular disease (CVD) risk prediction and management. A 2018 study, using data from the PREDICT general practice cohort linked with demographic, prior medical history and drug‐dispensing data highlighted that the risk of CVD is best estimated from longitudinal follow‐up of a contemporary nationally representative cohort initially free of disease.1 PREDICT is a risk tool that incorporates new predictors of socio‐economic deprivation and ethnicity and better reflects the population whose risk is being assessed. In comparison, the application of earlier international risk equations to Australasian populations, such as the Pooled Cohort Risk Equations (PCEs)2 in the United States and QRISK in the United Kingdom,3 are likely to substantially underestimate or overestimate risk, leading to either undertreatment or overtreatment. Where underestimates of risk occur, the individual is falsely reassured and no indication to commence preventive treatment is apparent. The reverse is true for overestimates of risk, where an indication to start preventive treatment is unnecessary. This is particularly true among socially disadvantaged and ethnically diverse populations, where both PCEs and QRISK underperform. New Zealand's ability to integrate its administrative health datasets with other data sources — in this case, primary care — has enabled the conduct of this policy changing research. The New Zealand's Ministry of Health has adopted and supported the roll‐out of the updated CVD risk management guidelines recommending that general practitioners use the new PREDICT‐derived CVD risk equation. It is thus important that Australia has a national repository that enables the combination of routine health datasets with other data sources, existing and emerging, to permit evaluation of health care and inform policy decisions.


  • 1 University of Western Australia, Perth, WA
  • 2 Centre for Big Data Research in Health, UNSW Sydney, Sydney, NSW
  • 3 University of Auckland, Auckland, New Zealand
  • 4 Curtin University, Perth, WA
  • 5 Flinders University, Adelaide, SA


Correspondence: tom.briffa@uwa.edu.au

Competing interests:

No relevant disclosures.

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