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
Dec 26, 2015
ENABLING DATA LINKAGETO 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
• Aims and methods of the project• Key findings• The HIC experience• The LMIC experience• Recommendations
Outline of presentation
Introduction
• how data linkage could boost public health research
• the barriers to useful data linkage
Aim: to investigate
Objectives and methods
• “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
• 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
• non-systematic literature review– including conference presentations
• formal and informal interviews• case study examples• internal team perspective
Methods
Objectives and methods
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
Key findings
2. Policy decisions need to be more evidence-based
• research data use is safe
Key findings
Key findings
2. Policy decisions need to be more evidence-based
• ‘intruder’ model → ‘idiot’ model
Key findings
Key findings
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
Key findings
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
Key findings
5. Incentives to manage and share data are weak
• funding bodies have some responsibility• the research community needs to consider its role
Key findings
Key findings
6. Different things matter in difference places
• A hierarchy of problems?– data– organisation– institutions
Key findings
Key findings
• 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
The HIC experience
• What works: stakeholder management– early planning– education
The HIC experience
The HIC experience
• 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
The LMIC experience
• 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
• 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
• 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
• 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
• 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
• Two types– Changing the conceptual framework– Practical guidelines and measures
Recommendations
Recommendations
• Much evidence of what works, but– in the wrong place– not used in decision-making
• Many wheels being re-inventedÞ need for clear, strong, evidence-based
guidance to address fear and ignorance
Recommendations: changing the conceptual framework
Recommendations
• 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
• Establish Research Data Infrastructure to support health data usage and linkagese.g. DataFirst’s Secure Data Service
• Build quantitative skills
Recommendations: practical guidance for LMICs
Recommendations
• Data management is a problem:– shortage of ‘data science’ skills– need to encourage data sharing– data collection and research timetables don’t fit
Þ some funding tailored towards good data collection and curation
Recommendations: planning for and funding data collection
Recommendations