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Data science, public health and prevention
Julian Flowers, Head of Public Health Data Science, PHEHonorary Clinical Professor, Institute of Health Informatics, UCL
Digital and data science are driving population health analytics
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PHI 1.0 (nowish) PHI 2.0 (nextish)
Structured/ small data => Structured + unstructured/ big data
Profiling => Analysis and insight
Collation and description => Prediction and prescription
Excel/ stats packages => R/ Python/ PowerBI
Static reports => Interactive reporting
Manual processing => Automated processing
Waterfall project Mx => Agile
User feedback => User need
Epidemiology and stats =>Epidemiology + models + machine
learning
Bias and confounding/ noise => Bias and confounding/ noise
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What can data science do?Data science:
1. Change data narrative
2. Automation/ scale/ pace
3. Drive ICT (what we need to use data for drives ICT not the other way round)
4. Do clever things - Machine learning and AI - models
• Hypothesise
• Explain
• Segment
• Simplify
• Predict
• Personalise
5. Create new understanding – genomes, phenomes and exposomes
6. Evaluate - real world evidence/ natural experiments
7. Continually improve (reinforcement learning)
8. Ethics/ governance
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Prevention
“an ounce of prevention is worth a pound of cure” Benjamin
Franklin
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Table 9: Areas where respondents would like to see more preventative
health activity within their council
Priority 2015 % Number 2017 %
Mental Health 79 30 73
Obesity in
children
71 27 66
Obesity in adults 42 24 59
Physical Inactivity 58 24 59
Drug misuse 17 21 51
Dementia 52 19 46
Alcohol misuse 40 17 41
Smoking 29 14 34
Sexual health 19 6 15
Other 6 5 12
Source: https://www.local.gov.uk/public-health-perceptions-survey
Levels of prevention
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https://www.med.uottawa.ca/sim/data/Prevention_e.htm
For example take a “prevention view’ of dementia…
• Overall objective might be to reduce the burden on
dementia in the population
http://ihmeuw.org/4dbi
How much dementia is preventable?
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http://ihmeuw.org/4dbj
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Behavioural:
• Smoking
• Alcohol
• Diet
• Physical activity
‘Metabolic’:
• Diabetes
• Hypertension
• Obesity
• Cholesterol
• Primordial prevention - preventing CVD
• reducing risk of dementia through improving lifestyle, tackling obesity, physical acticity, smoking strategy
• Primary prevention - reducing incidence of dementia
• risk stratification and cardiovascular prevention strategies in higher risk groups
• Secondary prevention - slowing progression
• memory clinics for early detection, cardiovascular prevention/ long-term planning
• Tertiary prevention - maintaining independence
• care-planning/ social care
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Measuring prevention• Cannot really measure at individual level
• By definition cannot know if event does not happen if it has been prevented or delayed
• Can assess at population level by looking at trends and comparison
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Level of
prevention
Possible metric Can we measure? Data issues - thoughts
Primordial Risk factors rates
Sociodemographic
Yes - some Adult obesity measurement
Risk factor clustering
Incidence of dementia
Primary Incidence CFAS II From EHR or CFAS.
Big data/ data science
Secondary Progression
Events
Proxies? E.g admission
rates.
% newly diagnosed patients
on dementia drugs
Proportion of dementia
cases diagnosed via
emergency admission
Need better access to primary care data.
Need data linkage
Need objective measure of cognitive decline
Big data
Tertiary Independence Proxies e.g. care home
admission ratio
Deaths IUPR
Assessment rates
Data linkage
Using Fingertips
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Putting it all together: modelling
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Incidence of dementia
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http://www.bmj.com/content/358/bmj.j2856
Prevalence of dementia
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Data science in public health locally
1. In its infancy
2. Some LAs modernising analytics (R, tableau, automation)
3. Universities can support (data science postgrad degrees in health cropping
up all the time)
4. Lots of modelling (academic and commercial)
5. Need to develop skills in the workforce – build capacity and capability –
PHE
6. New: Health Data Research-UK
£54 million funding to transform health through data science
From April this year, the six sites will work collaboratively as foundation partners in Health Data Research UK to make
game-changing improvements in people’s health by harnessing data science at scale across the UK.
Midlands – University of Birmingham, University of Leicester, University of Nottingham, University of Warwick,
University Hospitals Birmingham NHS Foundation Trust
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