Title: Making a Complex Data Repository Work: Nuts and Bolts
1Making a Complex Data Repository WorkNuts and
Bolts
1
2Technological Innovations
- Technological developments have set the
- scene for information-rich environments
- Increased government data collection
- Powerful computers and programming languages
- Statistical methods for addressing large amounts
of data - Potential of these environments comes at a cost
in complexity
3Research Centres-Innovations
- Registry
- Record linkage
- Government cooperation
- File complexity
- Knowledge management
4Manitoba Population Health Research Data
Repository
-standards tests -high school marks -graduation -r
etention
-income assistance -in care
-birthweight, gestation -injuries -chronic
diseases (asthma, diabetes)
Healthy Child Program Data
-diagnosis
-marital status
-residence
-family size
Census Data at area level
National surveys
Key health databases start in 1970
5Complexity
- Complexity increases with each file
- Each ministry has its own standards
- Both policy and coding change periodically
- Data may be missing for some fields and some
years
6Knowledge Management
- Knowledge management activities create readily
retrievable information -
- Enable these innovations to transform academic
and policy-oriented research - Support of researchers at Manitoba Centre for
Health Policy website
7Three Sections
- Research Protocol Critical steps in using
Manitoba Repository - Glossary Terms commonly used (coordinated with
Centre deliverables) - Concept Dictionary Detailed operational
definitions of variables
8Knowledge Resources and Mission
Project Initiation, Project Management
Knowledge Repository, Documentation, Internal
Training
Knowledge Sharing, Marketing
9MCHP Pillars
- Try to manage knowledge so that more than one
pillar is supported by any given activity - Need for cost-effectiveness in documentation
- Help initiate and manage research
10Repository Pillar
- Need to have organizational memory in
considerable detail. May want to look up material
after several years hiatus. - Need for reflection on what has and has not
worked. - Synthesis of multiple sources of information over
time.
11Knowledge Translation
- Help build community of practice despite other
differences - For teaching and sharing information with other
centres - SAS videos
12Knowledge Communities
- Great diversity of disciplines and collaborators
- Different data
- Different modeling techniques
- Knowledge sharing is essential
13Manitoba Centre for Health Policy
- Magnet for new data sets
- Research partnerships maximize advantages
- Partnerships add to complexity
14Example Complexity in Collaboration(6
Disciplines Represented)
- Local Investigators
- Noralou Roos, Les Roos, Randy Fransoo
- Local (but based in Ontario)
- Marni Brownell
- U of T / OISE
- Ben Levin
- U of T / School of Public Policy
- Marc Stabile
- Columbia University
- Janet Currie
- University of California, Berkeley and SF
- Doug Jutte
15Complexity/Change Imposed on Organization
- New data every year
- Coding changes of varying magnitude
- Educationpolicy changes re data collection
- Educationmajor curriculum change in mathematics
- Intellectual advances (statistical techniques,
research designs) - New deliverablesusing data differently
16Complexity re Longitudinal Information
- Health
- Education
- Social Assistance
- Family information
- Siblings
- Mother
- Family structure
17Concepts
- Provide the basics
- Clarify choices of a particular strategy
- What can and can not be done
- Cross-reference for different ministries and
topics
18Activities Complexity and Change
- Research Protocol
- Users (particularly new users)
- New Statistical Methods
- Change
- Coding (ICD-9-CM to ICD-10-CA)
19Ongoing Issues
- Longitudinal studies are great but tricky. Such
research often involves several shifts in
reporting conventions. - Shift in perspective for survey researchers used
to cleaned up, well-maintained data sets.
20Knowns and Unknowns
- Problems approachable by sensitivity testing
- Example Biases inhospital collection of ER data
21Knowns and Unknowns
- Communication of Known (by Some)
- Changing Formats over Time
- Example Variables miscalculated in complex
cohort study using hospital and physician claims
22Knowns and Unknowns
- Blending Information from Different Ministries
- Example Fiscal year (Health) and Calendar year
(Education) when education data were weak
23Ongoing Issues
- Costs -- How much is enough?
- Cost-sharing of a public good Project funding
creates problem of free-riders
24Ongoing Issues
- Business model must find ongoing balance among
- Bringing in new types of data/research
- Updates of data sets already held
- Documentation of all types
- Funding and new projects
25M
Manitoba Centre for Health Policy
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H
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www.umanitoba.ca/centres/mchp/