Application of the
Benefits Analysis Tools
for MRC population health studies
Professor Dipak Kalra
Centre for Health Informatics and Multiprofessional Education
(CHIME)
University College London
d.kalra@ucl.ac.uk
Background:
MRC Data Support Service (2009-11)
• Funded by MRC to better understand the different approaches
to data sharing across its population health studies
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identify drivers
identify approaches
identify barriers
provide support to studies where needed
contribute to a review of MRC policy and to future policy
guidance from MRC
6 exemplar studies were investigated in detail, working closely
with study Directors, PIs and data managers
Today’s review of the benefits tools draws on all six, and from
less detailed review of other population health studies
(a kind of virtual study based on aggregated and anonymised
insights)
MRC study data sharing context
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(Not like a central data archive)
Each MRC study:
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defines the overall research mission
might have other funding parties e.g. CRUK, international
undertakes the data collection
has to maintain cohort relationships for ongoing collection
is responsible for maintaining confidentiality
designs the data schema
documents some metadata, but the depth needed for shared use is often
complex
undertakes most of the data cleaning and derivations
analyses and publishes its own research on the data
shares voluntarily, as a complement to its core mission
The wide range of study types and their maturity means
the opportunities and benefits will vary between studies
Stimulating new networks and
collaborations
• Localised expression: Enriched quality and scope of
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grant applications, greater chance of funding and/or a
larger award
Action: Form collaborations with other research groups
for joint applications for funding and/or share resources
from individual grant awards
KRDS Outcome type: Direct benefit, in 2-4 years
Stakeholders: Internal: PIs, academics; External: grant
funders
Quantitative benefit: Larger scale of research funding
through larger grant or pooling of grant incomes
Qualitative benefit: Increased visibility and kudos
Weighting: 3
Re-purposing and re-use of data
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Localised expression: Lower costs of data collection
and curation, faster route to usable data
Action: Agree shared use of pre-existing data held by
another study to avoid new data collection
KRDS Outcome type: Indirect benefit, in 1 year
Stakeholders: Internal: PIs, academics, data managers
Quantitative benefit: Cost saving, shorter time to
publications
Qualitative benefit: ~
Weighting: 4
Conclusions
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The Benefits Framework and the Impact Tool can
accommodate the kinds of benefit from good data
curation practice and from data sharing in MRC
population health studies
Detailing the active steps to realise each benefit, when
the result might be realised and who benefits seem to be
useful ingredients for putting forward a case for funding
or for prioritising resource utilisation with a study
Whilst initial population might be done by one person,
completing the spreadsheet and working out weightings
might be nicely undertaken in a team workshop