This document discusses building a Child Data Centre of Alberta to analyze linked population data and inform social and health policy. It would utilize secondary data, including administrative, service delivery, and research data. The Child Data Centre would include a Child Population Data Repository, Child Research Data Repository, and Community Service Agency Data Repository. It would generate new knowledge through secondary analysis of longitudinal linked data from multiple ministries to study outcomes over time and evaluate policies. Challenges include ensuring privacy, security, and ethical use of data from various sources. The goal is to improve outcomes for children and families through timely evidence-informed policy.

1. Xinjie Cui BMed PhD MBA
Chief Analytics Officer
Building a Child Data Center
with Linked Population Data
to Inform Policy

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The Need and The Opportunity
Complex social, health, and education policies REQURE
• More
• Relevant
• Timely
Evidence for decision
Advancement of information technology ENABLES
• More and better data collection and store (digital storage)
• More, better, new methodologies for data discovery

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Secondary Data and Secondary Analysis
Secondary analysis – utilization of existing data
Secondary data
• Administrative data – collected to conduct business
• Service delivery data – collected to provide services
• Research data – collected to answer research questions
Benefits
• Potentially faster
• Relatively low cost

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Usefulness of Secondary Data
• Monitor/track health and wellbeing of the
population (identify trends and issues)
• Discover patterns and relationships of
risk/resilient factors and outcomes
• Support development of strategies and policies
• Evaluate effectiveness of policies, programs,
and services
• Inform performance measurement and result
based budgeting

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Child Data Centre of Alberta
A data and research facility
–Wide variety of data holdings with
potential for linkage
–Value added services
–Broad access by qualified policy
analysts and researchers

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Vision
Improved outcomes for children and
family by informing social and health
policy, practice, and service planning
Through
New knowledge generation
Timely and relevant evidence for policy and practice
Capacity building

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ACCFCR Board
ACCFCR
Child
Population
Data
Repository
Child
Research Data
Repository
Child Data Centre of Alberta
Community
Service
Agency Data
Repository
Children First Act

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Government Administrative Data
Pros:
• Populations based (all Albertans)
• Longitudinal
• Linked data may address system-wide and policy relevant
questions well and provides opportunity to study broad
determinants of health
Cons:
• Lack details on some measures such as behavioral risk
factors, physical measurements etc.
• Inconsistent measures; recording and coding errors
• Subject to misinterpretation if not familiar with data
collection and manipulation process

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Service Delivery Data
From community agencies and service organizations
Pros
• Detailed in depth data
• Intervention and services focused
• May contain risk factors and qualitative measures
Cons
• Inconsistency across agencies/clinics
– standards, capacity, systems, services…
• Large number of organization of different kinds

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Research Data
• Public funded research data exist in isolation
• Often times, when research project is done,
data gets destroyed or shelved – seldom gets
reused or further analyzed
Modern Research
• Collaborative, multi-disciplinary, and answer
complex questions
• Data are large in volume and digitized
• Great value for repurpose and reuse

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Research Data Sharing
• Trend around the world:
– Funding Agencies: NIH USA, MRC UK,
Tri-council Canada
– Open access content: PLOS
• Lack of centralized place for storage,
management and further utilization
• Especially lack of support around
secondary use

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Child Data Centre of Alberta
• Securely stored, cleaned, catalogued,
enhanced, managed, and potentially
linkable. (where and when it is possible and
appropriate).
• Access and use of data facilitated, managed
and supported – value add
• Information privacy protected

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ACCFCR Board
ACCFCR
Child
Population
Data
Repository
Child
Research Data
Repository
Child Data Centre of Alberta
Community
Service
Agency Data
Repository
Children First Act

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Child and Youth Data Lab
As the Administrative Data component of CDCA
• Links and analyzes government
administrative data – cross-sector
• For policy relevant research
• With a focus on children, youth and families
in Alberta

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Partnering Ministries
• Human Services (Lead ministry)
• Health (HUB for data matching)
• Education
• Innovation and Advanced Education
• Justice and Solicitor General
• Aboriginal Relations

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CYDL Research Process
• Partnership – working closely together
• Process
– Identification of research priorities
– Project development/Study design
– Identification of data elements
– Feasibility assessment (data and privacy)
– Research Ethics approval
– Data sharing agreements and Privacy Impact Assessment
– Data linkage and transfer
– Data validation, analysis, and interpretation
– Report generation and dissemination/knowledge mobilization
• Involvement of ministries throughout the
process

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Characteristics of CYDL
• Authentic Partnership
– Ministries involvement throughout the full
research cycle
• Anonymous Linkage
– Data hashed at source
• Policy Relevant
– Research questions are policy driven
• Project Based Model
– No population repository yet

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Experience of Albertan Youth over Time
• Longitudinal design
• Six years of data 2005/06 to 2010/11
• Broad scope:
• 5 ministries
• 25 databases covering more program and service
areas
• Over 250 variables
• Key indicators:
• Demographic
• Socio-economic status
• Mental health status
• Educational achievement
• Health service use

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
All programs
Health
Child intervention
Child support services
FSCD
Child health benefits
Child care subsidy
Education
Corrections
Offence charges
Advanced Education
Income support learners
Income support
AISH
Adult health benefits
PDD
Advancing Futures bursary
Age (years)
CYDL Longitudinal Project Sample Indicators
Young adulthood
Childhood
Youth
Early childhood
Court outcomes
Type of charge, charge history
Program and institution details
Household type, enrollment details
Client type, household type
Disability type, household indicators
Type of benefits
Type and amount of support
Enrollment and support details
Perinatal indicators, diagnoses, mental health status
Type of abuse/neglect, type of services provided, history
Type of order, reason for file closure
Type of disability, duration of services, reason for file closure
Type of benefit received, age at first use
Type of program, reason for care, duration
Achievement, school indicators, special programs, high school completion
Gender, age, socio-economic status, region of residence/service, Aboriginal status (if available)

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Types of Analysis
Time trends
Policy impact over time
Service use patter over time
Specific topics such as
• Youth transition
• Resiliency
• Early childhood
development
• Poverty
• Persistent offenders
• FASD
• Aboriginal youth
• Special education
• Self-harm

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Research Data Repository
• Stakeholders consultation
• Best practice - technical, management,
ethical, metadata, and data governance
• Principal Investigators of two cohort studies
on board and other datasets
• Design of processes and structure

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Research Data Repository
• Set up governance structure
• Build technical infrastructure – enclave
• Transfer initial datasets
• Develop metadata
• Pilot early adopter projects
• Support access and use

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Research Data Repository
• Develop communication/marketing plan to
promote CDCA
• Identify potential research datasets for
repository
• Provide training opportunities for students
and trainees
• Identify opportunities to develop Service
Delivery Data component
• Explore feasibility of integration with CYDL
component

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Issues for Consideration
Research Ethics
• Consent, Re-consent, Agreements
Information Privacy and Confidentiality
• Right to privacy and confidentiality as public good
Data Security
• Technology (increased risk but also new tools to
protect)
• Data linkage (increased risk for re-identifiability)
Original Researchers’ Involvement
• Researchers’ input, flexibility

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Success Factors
• Independent
• Outside of government
• Outside of universities
• Authentic Partnership
• Engagement of research community
• Political and Executive support
• Enabling data sharing legislation
• Sponsors and Champions at multiple levels
• Strong Governance and Accountability structure
and process
• Sustainable funding and Designated resources
• Strong project management and coordination
• Dedicated staff and technical/research expertise
• Persistence and patience

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Questions?