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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