1. ENABLING DATA LINKAGE TO MAXIMISE THE VALUE OF PUBLIC HEALTH RESEARCH DATA Presentation of findings to the Public Health Research Data Forum University of the West of England, Bristol DataFirst, University of Cape Town CIPRB, Dhaka 1

2. Aims and methods of the project Key findings The HIC experience The LMIC experience Recommendations Outline of presentation Introduction

3. how data linkage could boost public health research the barriers to useful data linkage Aim: to investigate Objectives and methods

4. “to produce a synthesis fully grounded in both theory and empirical evidence to generate recommendations and practical guidelines for short- and long-term public health data strategies” practical and useful rather than exhaustive Objective Objectives and methods

5. Faculties of business and health, UWE DataFirst, University of Cape Town Centre for Injury Prevention Research, Bangladesh –Mix of expertise in data access, socioeconomic data, and public health and clinical data Project team Objectives and methods

6. non-systematic literature review –including conference presentations formal and informal interviews case study examples internal team perspective Methods Objectives and methods

7. Key findings

8. 1.Change the tone of the debate 1.Data should not be used for research or linked unless it can be done safely and securely 2.Data should be available for research and linking unless it cannot be done safely and securely Key findings

9. 1.Change the tone of the debate default closed → default open Key findings

10. 2. Policy decisions need to be more evidence-based research data use is safe Key findings

11. 2. Policy decisions need to be more evidence-based ‘intruder’ model → ‘idiot’ model Key findings

12. 3. Narrow informed consent is not enough for good epidemiological research broad consent supported by public/researchers where broad consent not feasible, we know how to manage the social contract Key findings

13. 4. Maintaining good relationships is the key relationships with everyone: data depositors, ethics committees, general public, researchers early planning with stakeholders vital –especially for strategic projects Key findings

14. 5. Incentives to manage and share data are weak funding bodies have some responsibility the research community needs to consider its role Key findings

15. 6. Different things matter in difference places A hierarchy of problems? –data –organisation –institutions Key findings

16. Data issues exist Dominated by institutional issues –relationships with data depositors/ethics committees –public acceptability –unrealistic risk-assessment, worst-case scenario planning The HIC experience

17. What works: stakeholder management –early planning –education The HIC experience

18. Dominated by operational and quality issues The LMIC experience

19. Operational issues: access to health data –Publicly funded health data held by state research institutes, universities only available to research collaborators –No data sharing requirement from national funding bodies –Data sharing requirements of international funders not enforced No critical mass of researchers engaged in quantitative research – rather “pools of expertise” The LMIC experience

20. The base situation –We have useful, linkable data –ADHSS, other household survey, hospital information systems, civil registration, laboratory data, drug dispensation, encounters, episodic data, social grants and schools The LMIC experience in SA

21. What data linkage has there been? –ADHSS to civil registration systems, clinical data (PHCU, HIV/AIDS, hypertension clinics) –Data harmonisation project –HIV cohort data to national population registers The LMIC experience in SA

22. Operational Barriers –High level data skills and database management skills rare –Outsourcing of complex information system management –Pay scale issues and incentives, public vs private The LMIC experience in SA The LMIC experience

23. Statistical Barriers –ID numbers not always available –ID number penetration correlated with individual characteristics –Probabilistic matching issues: date of birth, names, twins The LMIC experience in SA

24. Ethical Concerns –Protection of personal information perceived as more important if data used for research purposes (vs clinical) –WCDoH trying to operationalise due diligence by setting up preapproved database procedures, anonymize data effectively The LMIC experience in SA

25. Two types –Changing the conceptual framework –Practical guidelines and measures Recommendations

26. Much evidence of what works, but –in the wrong place –not used in decision-making Many wheels being re-invented  need for clear, strong, evidence-based guidance to address fear and ignorance Recommendations: changing the conceptual framework Recommendations

27. Everything has been solved somewhere Make sure this information is known –Technical information managing access; collecting good ID data –Institutional tips getting ethics/data depositors on your side Recommendations: practical guidance Recommendations

28. Establish Research Data Infrastructure to support health data usage and linkages e.g. DataFirst’s Secure Data Service Build quantitative skills Recommendations: practical guidance for LMICs Recommendations

29. Data management is a problem: –shortage of ‘data science’ skills –need to encourage data sharing –data collection and research timetables don’t fit  some funding tailored towards good data collection and curation Recommendations: planning for and funding data collection Recommendations

30. http://www.wellcome.ac.uk/About-us/Policy/Spotlight-issues/Data- sharing/Public-health-and-epidemiology/WTP056860.htm Thank you Questions? Next steps