Data LinkageDr Sradha Kotwal
DNT Adelaide, 2017
Staff Specialist,Department of Nephrology
Prince of Wales Hospital,Randwick, Sydney
Post-Doctoral Research Fellow,The George Institute for Global Health,
Sydney
Outline
Data Linkage around
Australia
New Zealand
What has been done
Questions
Data linkage brings together
information relating to the same
individual from different sources
Kotwal et al, Nephrology 2016
Admitted Patient Data
Person A: Demographic data
Person A: Health data
Death registry
Person B: Demographic data
Person B: Health data
Cancer Registry
Person A: Health data
Person A: Demographic data
Researcher
Person A: Health data
Person A: Health data
Person A: Health data
Data Linkage Unit
Person A Demographic data Person B Demographic data
Person B: Demographic data
Person B: Health data
Person A: Demographic data
Person A: Health data
Person B: Health data
Person B: Demographic data
Person B: Health data
Person B: Health data
Person B: Health data
Encrypted Identifier
Encrypted Identifier
Encrypted Identifier
Encrypted Identifier
Kotwal et al, Nephrology 2016
Datasets
State based administrative datasets
Commonwealth datasets
Clinical registries
State based administrative datasets
• Mandatory
• Admitted patient data
• Birth and Death registry
• Emergency department data
• Mental Health
• Perinatal data
Commonwealth datasets
Medicare Benefits Schedule (MBS)
Pharmaceutical Benefits Schedule (PBS)
National Death Index
Clinical registries
State based
Commonwealth
Disease specific
Consent
De-identified data usually does not require consent
MBS/PBS data depends on design
Specific consent form
Registries tend to use opt-out approach
Population Health Research Network
New Zealand
National Health Index number – unique for each person
Statistics NZ
Data linkage unit nationally
Created a cross-agency data-sharing solution
Integrated Data Infrastructure
Limitations
Purpose of data is not research
Quality of coding
Hypothesis generating research
Validated algorithms
Robust and validated risk adjustment methods
Strengths
Includes all patients/ minimizes bias
Cost-effective
Sensitive information
Longitudinal outcomes
Service utilization research
ICD10 code N18
Kidney transplantation
is associated with a marked
increase in cancer risk at a wide variety of
sites.
Chronic kidney disease could be added to the list of criteria defining people at highest risk of future coronary events
CKD
CKD
DM & CKD
DM
DM
DM & CKD
Transferred patients have a better survival
than non-transferred
patients
More Hospitalisations in rural patients
More Inter-hospital transfers in rural patients
Summary
Important research methodology to
complement current research
Allows us to answer questions that
cannot be answered by other studies
Understanding of how health services are
used
What we can do with data linkage
Re-admissions
Health service use
Comorbidity recording to calculate risk scores
Post-marketing surveillance of new medications
Impact of new medications on outcomes
National KPI’s
Questions
Question 1
Do you agree with administrative data pertaining to your
patients being used for research purposes?
1. Yes
2. No
Question 2
Are you happy for such data to be reported at a
centre/hospital level?
1. Yes, with centre/hospital consent
2. Yes
3. No
Question 3
Would you be willing to routinely consent patients for data
linkage as part of all ANZ clinical trials (investigator driven)?
1. Yes
2. No
Question 4
Should all treated ESKD patients be routinely consented for
data linkage to federal datasets (MBS & PBS – Australia)?
1. Yes
2. No
Thank you!
Further informationPopulation Health Research Network www.phrn.org.auState specific Data linkage units through PHRNAIHW
