Ximena Camacho, The Melbourne School of Population and Global Health, University of Melbourne

2. The potential of linked population health data-sets to support population, health services and clinical research Ximena Camacho, The Melbourne School of Population and Global Health, University of Melbourne David Henry, Bond University and Institute for Clinical Evaluative Sciences, Toronto, Canada

3. Health data networks – importance of foundational population layer Ancestry.com/ 23andMe Food purchase/ gym membership Social Media/ Mobile Apps Linkage key Research Cohorts Biobanks Randomised trials Electronic Health Records Specialized Registries Routine Surveys Central Registry Census/ Vital Statistics/ Notifiable Diseases/ Administrative data (hospitalizations, insurance claims, prescriptions) / Routine Lab data

4. And can be distributed – multiple jurisdictions Coordinating centre Ancestry.com/ 23andMe Food purchase/ gym membership Social Media/ Mobile Apps Cohorts Biobanks Randomised trials Electronic Health Records Specialized Registries Routine Surveys Census/ Vital Statistics/ Notifiable Diseases/ Administrative data (hospitalizations, insurance claims, prescriptions) / Routine Lab data

5. Examples • Comprehensive use of linked population data: Example of T2 Diabetes • Exploring an emerging public health risk: prescribed opioids • Monitoring quality and safety of care at institutional level • Examining the data, their governance and use • Relevance in Australia

6. Type 2 Diabetes Mellitus • Versatility of routinely collected (administrative) data • Defining condition in routinely collected data (RCD) • Surveillance: charting the epidemic • Predicting population risk (survey linkage) • Exploring risk factors: obesity, ethnicity, built environments (survey linkage) • Evaluating the safety of treatments – incretin analogues

7. Diabetes algorithm • One Hospital discharge abstract DA or two Physician Service Claims for diabetes mellitus within 2 years • For validation, diagnostic data abstracted from primary care charts (n=3,317) of 57 randomly selected physicians were linked to the administrative data cohort, and sensitivity and specificity were calculated • Sensitivity for diabetes was 86% for the 2-claim algorithm, specificity was 97% • Predictive value of positive result was 80%

8. Lorraine Lipscombe Jan Hux Development of algorithm based on administrative data validated by chart review and applied to the Ontario population Lipscombe LL, Hux JE. Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995–2005: a population-based study. The Lancet. 2007 Mar 9;369(9563):750-6.

9. The influence of ethnicity on BMI cut-points in the development of Type 2 Diabetes Mellitus Maria Chiu Multi-ethnic cohort study of 59,824 nondiabetic adults aged >30 years living in Ontario, Canada. Subjects were identified from Statistics Canada’s population health surveys, linked to federal immigrant file and followed for up to 12.8 years for diabetes incidence using record linkage to health administrative data. BMI was estimated from survey data Chiu M, Austin PC, Manuel DG, Shah BR, Tu JV. Deriving ethnic-specific BMI cutoff points for assessing diabetes risk. Diabetes Care. 2011 Aug 1;34(8):1741-8.

10. Predicting population risk of T2DM Laura Rosella Linkage of Ontario sample of the Canadian Community Health Survey to administrative Data Validation study using The National Population Health Survey data in Manitoba Rosella LC, Manuel DG, Burchill C, Stukel TA. A population-based risk algorithm for the development of diabetes: development and validation of the Diabetes Population Risk Tool (DPoRT). Journal of Epidemiology & Community Health. 2011 Jul 1;65(7):613-20.

11. Impact of built environment on rates of diabetes Gillian Booth et. al. Application of diabetes algorithm to the Toronto population and linkage to GIS data in the Registered Persons Data-Base Toronto Star Nov 1 2007: ‘Diabetes lurks in suburbs’

12. Immigrants identified by linking federal file to provincial registered persons data-base and tagged with the walkability index for their new neighbourhood. Freedom from diabetes at inception by negative algorithm. Follow up through administrative data to detect development of diabetes. Comparisons adjusted for age and income. Booth GL, Creatore MI, Moineddin R, Gozdyra P, Weyman JT, Matheson FI, Glazier RH. Unwalkable neighborhoods, poverty, and the risk of diabetes among recent immigrants to Canada compared with long-term residents. Diabetes Care. 2013 Feb 1;36(2):302-8.

13. Using a distributed network to assess drug safety

14. Distributed Network Coordinating centre Ancestry.com/ 23andMe Food purchase/ gym membership Social Media/ Mobile Apps Cohorts Biobanks Randomised trials Electronic Health Records Specialized Registries Routine Surveys Census/ Vital Statistics/ Notifiable Diseases/ Administrative data (hospitalizations, insurance claims, prescriptions) / Routine Lab data

15. CNODES - a distributed network of population health data centres

16. Safety of incretin mimetic drugs in diabetes • Injectable agents that stimulate insulin release and inhibit glucagon release • Randomised trials had raised flags around cardiac and pancreatic safety • CNODES quickly performed 3 analyses that demonstrated safety using a cohort of 972 384 patients initiating antidiabetic drugs

18. New onset diabetes with statins – comparison of randomised and non-randomised studies

20. Tara Gomes et. al. Ontario Opioid Observatory

22. Arch Intern Med. 2011;171(7):686-691

23. Using linked data to assess the quality and safety of institutions

24. Coronary Artery Bypass Grafting (CABG) Surgery Report Cards in Ontario Modeled after New York State’s Cardiac Surgery Reporting System (first US cardiac report card) Produced by ICES in collaboration with Cardiac Care Network of Ontario (CCN) since 1993 • CCN clinical database linked to ICES administrative database • Risk-adjustment methods extensively tested and published in the medical literature Results shared with hospital CEOs, Chiefs of cardiac surgery and surgeons at each institution Hospital results first publicly released in 1999

25. Trends in In-hospital Mortality Rates After Isolated CABG Surgery in Ontario, 1991-2006 Public reporting Confidential reporting

26. Enhanced Feedback for Effective Cardiac Treatment (EFFECT) Study Objective: To evaluate whether the public release of hospital report cards would improve the quality of cardiac care provided Methods: Cluster randomized trial of 86 hospitals in Ontario, Canada Hospitals were randomized to receive early or delayed feedback on their performance on AMI and CHF process of care indicators Reference: Tu JV, et al. JAMA 2009; 302 (21) 26

27. Hospital performance and Choosing Wisely Objective: to minimise unnecessary pre-operative testing before low-risk surgeries

28. Using linked data to assess primary care performance

29. Practice Reports Healthc Q. 2015; 18(1):7-10. Reference: Haj-Ali W, et al. Healthc Policy. 2017 Feb; 12(3):66-79.

30. Clinical Quality Registries

31. Registries : Mortality Rates Amongst Participants and Non-Participants Reference: Tu et al 2004. N Engl J Med; 350:1414-1421

32. Institute for Clinical Evaluative Sciences

33. The Institute for Clinical Evaluative Sciences (ICES) • Independent, non-profit corporation and research institution established in 1992 • Principal data steward for Ontario • Prescribed Entity status (PHIPA s.45) • Population-based health services and clinical research • Evaluation of health care delivery and outcomes • 200+ scientists across 7 sites in Ontario • Joint governance of First Nations data under OCAP principles

34. ICES Reach: Ontario ICES Sites Location (date opened) ICES Central Toronto (1992) ICES Queen’s Queen’s University (Oct 2007) ICES uOttawa University of Ottawa (May 2010) ICES UofT University of Toronto (Jul 2012) ICES Western Western University (Dec 2012) ICES McMaster McMaster University (June 2016) ICES North Health Sciences North (Sep 2018) Research Programs at ICES Cancer Cancer CV Cardiovascular and Diagnostic Imaging CDP Chronic Disease and Pharmacotherapy HSPE Health System Planning and Evaluation KDT Kidney, Dialysis, and Transplantation (Aug 2012) MHA Mental Health and Addictions (Dec 2013) PCPH Primary Care and Population Health

35. Key principles • Population spine

36. ICES CORE Data Repository: Coded and Linkable Derived Chronic Conditions Health Service Encounters Provider/ Facilities Real-time Project Specific Research Data People & Geography Special Collections* Physicians Hospitals Complex care Long-term care homes Home care Health Outcomes for Better Information Care (nursing home) Implantable Cardiac Defibrillators People in Ontario eligible for health care since 1985 Unique individual ICES Key Number (IKN) used for linking across all data sets Demographics Deaths Census Disease registries (ex. cancer, stroke, cardiac, perinatal) HIV clinics Immigration First Nation Métis Social Assistance Disabilities Early Development Instrument Physician claims Hospital discharge abstracts Emergency visits Ontario Drug Benefit claims Narcotics Monitoring System Home care Rehab Long-term care Using routine ICES data Diabetes Respiratory problems (ex. Asthma, chronic obstructive pulmonary disease) Cardiac problems (ex. heart attacks, hypertension) Schizophrenia Many others Unique algorithm based on Ontario health card number • * Special • governance Linked data set

37. Key principles • Population spine • Accessible data

38. Role-based level of access

39. Key principles • Population spine • Accessible data • Streamlined governance

40. Data governance General Use data Controlled Use data • Health “core”: • Vital stats • Insurance claims • Discharge abstracts • Pharmaceuticals • Etc. • “Other” or sensitive data: • Disease registries (e.g. HIV) • Immigration • Etc. Custodian approval required ICES approves project ICES = custodian

41. Key principles • Population spine • Accessible data • Streamlined governance • Responsiveness

42. Efficient workflow • Streamlined governance and data access • 2 week turnaround for standard project approvals • 24 hours for rapid response projects • In most cases data custodians devolve responsibility to ICES for use of the de-identified linked data • Resourced to scale • Run by researchers

43. Collaboration

44. Conclusions • Great potential for linked health administrative data to address a wide range of questions ranging from population health to clinical care • Concerns about poor quality data and misclassification have generally been addressed by validation studies • A foundational population layer from a universal healthcare program is a critical component – everything else should ‘plug in’ to that to create a ‘symbiotic’ relationship • The versatility of routinely collected data is increased by multiple linkages – e.g. Census data/ administrative data/ GIS/ immigrant status/ survey • Important to understand that such data are used routinely to investigate health problems and to analyse policy impacts in many jurisdictions

45. Conclusions • Key issues in Australia: • Inter-jurisdictional tensions – Commonwealth and States • Proposed solutions must meet the challenges of scalability – e.g. could AIHW or SURE facility meet the challenge of 500 additional requests/ year with satisfactory response times (weeks)? • Independent research centres are more effective and better meet the broad needs of researchers and analysts. They are better able to attract HQP with the necessary data science and analytical skills • A distributed network of data centres – one in each State/ Territory would be ideal, in addition to centralized facilities (AIHW/ ABS). This will require that the States demand regular updated access to Commonwealth data for their constituents