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Interactive Health Indicators Tool (i-hit): Building on the Proof of Concept
Knowledge & Intelligence Team (North West)
Introduction• The i-hit is a tool that was developed in 2010/11 in response to a specific
request from a local authority by the North West Public Health Observatory and Liverpool John Moores University (LJMU)
• The tool has been used within the local authority to:1. Identify Health and Wellbeing Board/JSNA priorities
2. Identify the scale of change necessary to achieve sustainable improvement
3. Model how changes in one area might be ASSOCIATED with other areas of interest
• The tool is underpinned by conditional independence and Bayesian Network mathematics
• The tool is founded in a health inequalities context
• The tool is different from many epidemiology tools…
Association not Causation
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Making the case for associative modelling
• LJMU Maths and Computing Science Department – ground breaking developments to produce an advanced algorithm for mapping associations (see references)
• Philosophy of Science – ontology and epistemology distinctions – important to balance research to expand our knowledge of both “What” and “How”
The retail example: Champagne and Shampoo…
1.There are actions that can be taken simply by knowing “What” (e.g. discount deals, product placement)
2.Spurious links; proactive challenge and insight
3.Demistifying clustered behaviour; targeting and sequencing interventions
4.Directing further research on the “How”
D. Bacciu, T.A. Etchells, P.J.G Lisboa and J. Whittaker, 2013. Efficient identification of independence networks using mutual information. Computational Statistics, vol. 28(2), 621-646
P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction and Search, 2nd edition. MIT Press, New York, 2000
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Applying this to indicators and measures
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Making progress
Getting worse
The Tool
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The Tool
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The Tool
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The Tool
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Developing the Tool – Phase 1
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Data refresh (June 2014);
Inclusion of new indicators
Update to previous indicator and measure definitions;
Inclusion of new advances in the mathematical theory and applications that underpin the model;
Increased interactivity and functionality in the tool so that the indicator maps can be presented in alternative ways
Expansion to enable more local areas to view their data in the tool
Developing the Tool – Phase 2
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Change multiple or fix indicators - on the fly solution; not lookups
Include socio-economic and demographic measures – tailored solution
Linking to the NICE evidence and ROI tools where causation is determined
Develop a front-end to process new data and variables
Work with users to develop understanding around the limits of interpretation - indicator ejection and outliers
Evaluate
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