Data Linkage. Dr Sradha Kotwal DNT Adelaide, Staff Specialist, Department of Nephrology Prince of Wales Hospital, Randwick, Sydney

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1 Data Linkage Dr 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

2 Outline Data Linkage around Australia New Zealand What has been done Questions

3 Data linkage brings together information relating to the same individual from different sources

4 Kotwal et al, Nephrology 2016

5 Encrypted Identifier Data Linkage Unit Encrypted Identifier Kotwal et al, Nephrology 2016 Person A Demographic data Person B Demographic data Admitted Patient Data Death registry Cancer Registry Person A: Demographic data Person A: Health data Person B: Demographic data Person B: Health data Person A: Demographic data Person A: Health data Person B: Demographic data Person B: Health data Person A: Demographic data Person A: Health data Person B: Demographic data Person B: Health data Researcher Person A: Health data Encrypted Identifier Person A: Health data Person A: Health data Person B: Health data Encrypted Identifier Person B: Health data Person B: Health data

6 Datasets State based administrative datasets Commonwealth datasets Clinical registries

7 State based administrative datasets Mandatory Admitted patient data Birth and Death registry Emergency department data Mental Health Perinatal data

8 Commonwealth datasets Medicare Benefits Schedule (MBS) Pharmaceutical Benefits Schedule (PBS) National Death Index

9 Clinical registries State based Commonwealth Disease specific

10 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

11 Population Health Research Network

12 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

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15 Limitations Purpose of data is not research Quality of coding Hypothesis generating research Validated algorithms Robust and validated risk adjustment methods

16 Strengths Includes all patients/ minimizes bias Cost-effective Sensitive information ICD10 code N18 Longitudinal outcomes Service utilization research

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18 Kidney transplantation is associated with a marked increase in cancer risk at a wide variety of sites.

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20 Chronic kidney disease could be added to the list of criteria defining people at highest risk of future coronary events DM & CKD CKD DM DM & CKD CKD DM

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22 Transferred patients have a better survival than nontransferred patients

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24 More Hospitalisations in rural patients

25 More Inter-hospital transfers in rural patients

26 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

27 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

28 Questions

29 Question 1 Do you agree with administrative data pertaining to your patients being used for research purposes? 1. Yes 2. No

30 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

31 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

32 Question 4 Should all treated ESKD patients be routinely consented for data linkage to federal datasets (MBS & PBS Australia)? 1. Yes 2. No

33 Further information Population Health Research Network State specific Data linkage units through PHRN AIHW Thank you!