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