Health Foundation (All Analysts Series - Housing Conditions and Young People’s Health), London, 2023

1. Using AI to understand how preventative interventions can
improve the health of children in the UK and reduce winter
pressures on the NHS
All Analysts Series - Housing Conditions and Young People’s Health
Martin Chapman
Tuesday 23 May, 2023
King’s College London

2. Overview
Executive summary
Background
Modelling
Data
Results
1

3. Executive summary

4. Executive summary i
Living in cold, damp and mouldy homes leads to poor health, such as chest conditions in
children and young people (CYP).
2

5. Executive summary ii
Using Artificial Intelligence (AI) and six large UK datasets—including data from national
studies and data from King’s College London (e.g. Lambeth DataNet (LDN) and
eLIXIR)—we have digitally mimicked the household environments of CYP from a variety of
social, ethnic and health backgrounds.
3

6. Executive summary iii
This environment has allowed us to simulate
preventative policy interventions, and thus
predict how effective they are at improving
the health of CYP and reducing winter pres-
sures on the NHS.
We have simulated three key interventions:
issuing government advice, making household
payments and increasing vaccine eligibility.
4

7. Executive summary iv
Although the long-term picture is more complex, our findings show that overall health
utilisation, and disparities between different socio-economic groups, can potentially be
reduced by issuing (continued) government advice during the winter months.
A complete overview is available at digitalmimic.ai.
5

8. Background

9. Artificial Intelligence (AI)
The term ‘Artificial Intelligence’ (AI) is, especially in the current climate, commonly
considered to be equivalent to something like ChatGPT:
Figure 1: A query to OpenAI’s ChatGPT: ‘Explain, in 100 words or less, what ChatGPT is’
6

10. Classifying and predicting from data vs. predicting the behaviour of complex
systems
In reality, this is just one form of AI—based on machine learning—that aims to classify and
predict from data.
This form of AI is very popular, and is very successful at achieving this aim.
Other forms of AI exist, and have much harder (and,
to me, much more interesting) goals, such as predict-
ing the behaviour of complex systems like human
populations1
.
1See this talk by Peter J. Bentley for a full overview of this distinction.
7

11. Agent-Based Modelling (ABM)
One form of AI that aims to predict
the behaviour of complex systems is
Agent-Based Modelling (ABM).
This form of AI involves the design of
conceptual models that capture the
behaviours of individuals (‘AI’), their
state and their environment.
These models are subsequently re-
alised as temporal simulation en-
vironments in code–which are often
powered by real data—the param-
eters of which can be altered, and
emergent behaviour observed.
8

12. ABM and winter pressures
ABM is the form of AI used in this work to explore winter pressures (hence the lay term
‘digital mimic’).
As such, perhaps more accurate title for this work would be: Using AI Using ABM to
understand how preventative interventions can improve the health of children in the UK and
reduce winter pressures on the NHS.
With this in mind, the remainder of this talk is structured around key elements of the ABM
process: modelling, data (including PPIE) and results.
9

13. Modelling

14. Abstraction
How do we model winter pressures populations using ABM? Simple answer: We can’t.
We aren’t (yet) at a stage where the nuances of human behaviours, their state and their
environment can be captured (e.g. in code). But we can get close!
Hurricane forecasting
We can still obtain sound results by mapping complex phe-
nomena to a suitable abstraction of that phenomena and ex-
perimenting at that level.
Insight gained at an abstract level can then provide broad in-
sight into the original phenomena.
This is a common approach in the sciences.
10

15. Resource problems i
There are clear parallels between the winter pressures environment and resource problems.
A search game (‘game’ as in ‘game theory’) is a type of resource problem: how does an
agent efficiently acquire the resources they need?
In a search game, a player (a seeker) is placed in a fixed space (a graph).
A set of resources exist at fixed, unknown positions in the graph.
Each movement (node to node) in the graph has a cost associated with
it, and the player needs to acquire all resources with as low a cost as
possible. Total cost is translated to payoff.
Different strategies exist for how to move through the graph.
11

16. Resource problems ii
v0 v1 v2
v3
v4
v5
v6
v7
10 10
10
10
10
10
10
10
Figure 2: An example search space for a search game, consisting of 8 nodes, 3 of which contain
resources. Starting at v6, a strategy that moves anti-clockwise sequentially would be the best strategy,
acquiring resources in the order shown. Total cost: 30.
12

17. Resource problems and winter pressures
Viewing our seekers are abstractions of families, our search space can be configured to
represent the challenging resource-based environment found in the winter months:
v0 v1 v2
v3
v4
v5
v6
v7
10 10
10
10
10
10
10
10
Figure 3: Non-winter search game configuration
(as seen). 3 nodes explored to find resources. Total
cost: 30.
v0 v1 v2
v3
v4
v5
v6
v7
10 10
10
10
10
10
10
10
Figure 4: Winter search game
configuration. 7 nodes explored to find
resources. Total cost: 60.
13

18. Payoff i
Payoff in our model is based upon
whether a traverser has had a ‘suc-
cessful’ game; a game in which all re-
sources have been collected with suit-
able cost (less than the total cost of
all edges in the graph).
v0 v1 v2
v3
v4
v5
v6
v7
10 10
10
10
10
10
10
10
Figure 5: An ‘unsuccessful’ game, in which the seeker’s
backtracking (⟨v5, v6, v7, v6, v5⟩, with an additional cost of 40)
makes their total cost (100) more than that of the total edge
cost in the graph (80).
14

19. Payoff ii
As such, we can view
‘a seeker in a search space where hidden objects are position at maximum distance who is
unable to obtain all resources has a low payoff’
as an abstraction of
‘a family in a winter environment who is unable to obtain the resources they need (e.g a heated
home) will have poor health outcomes and higher health utilisation’.
15

20. Resource problems and winter pressures i
Continuing this idea, we can abstract other aspects of the winter pressures environment to
our search game environment to create the remainder of our model.
All (configurable) aspects of our model can be viewed here.
Use case Model
Environment (as described)
Non-winter Random dispersal of resources
Winter Dispersal of resources at maximum distance
Population
Families containing CYP with respiratory
conditions
Random search; consequences on payoff for
lack of resource acquisition (as described)
Families containing CYP with mental health
condition
Random search + additional barriers; conse-
quences for lack of resource acquisition
16

21. Resource problems and winter pressures ii
Families containing CYP without condition Random search; limited impact on payoff for
lack of resource acquisition∗
Low, middle and high SEC families Low, medium and high ‘gas’ levels, to offset
traversal costs
White British and Ethnic Minority families Different ‘gas’ levels, to offset traversal costs
Interventions
Government advice Seekers adopt targeted search mechanisms
Support payments Increased gas levels across seekers
Increased vaccine eligibility Increased proportion of seekers for whom
there are limited consequences on payoff for
lack of resource acquisition
*Consequences are less severe if you don’t have any preexisting conditions
17

22. Resource problems and winter pressures iii
Behaviour
Uptake of targeted education % distribution between random and targeted
search (meta-strategy distribution)
Uptake of support payments % chance to use gas
Uptake of vaccines % chance to leverage resource immunity
18

23. Data

24. Data
Despite being carefully created, one might argue that the abstract model described so far could
capture a wide variety of phenomena.
To offset this, we use real data to configure the
variables in our model.
19

25. Datasets i
Six key datasets used in the project:
Name Content Size Owner More
London
eLIXIR (Born in SE Lon-
don)
Maternity and Neonatal data ∼32K KCL Link
Lambeth DataNet (LDN) All primary care data from
42 ethnically and socio-
economically diverse GP
practices in SE London, with
an established data linkage
with eLIXIR
∼420K KCL Link
20

26. Datasets ii
Resilience, Ethnicity &
AdolesCent Mental Health
(REACH)
Mental health data for adoles-
cents aged 11-18 years old
∼4K KCL Link
National
Millennium Cohort Study
(MCS)
Demographic data (e.g. occu-
pation)
∼19K UCL Link
GOV.uk (various) Demographic (e.g. income) National
level
UK
Gov.
Link
ONS National Statistics
Socio-economic classifica-
tion (NS-SEC) breakdowns
Demographic (e.g. occupa-
tion)
National
level
ONS Link
21

27. Model to Data i
Each element of our model was configured using this data.
Derived distributions are listed in our model configuration and our simulation configuration.
Use case Model Data Analysis
Population
Low, middle and high
SEC families
Low, medium and high
‘gas’ levels, to offset
traversal costs
SEC income lev-
els (Gov.uk); SEC
distributions∗
(LDN,
REACH and eLIXIR -
London; MCS + ONS -
National)
Link
*Of 100 families represented in each simulation, what is the distribution of different SEC groups
22

28. Model to Data ii
White British and Eth-
nic Minority families
Different ‘gas’ levels, to
offset traversal costs
Ethnicity income levels
(Gov.uk); Ethnicity distri-
butions (LDN, REACH and
eLIXIR - London)
Link
Families containing
CYP with respiratory
condition
Random search; conse-
quences on payoff lack
of resource acquisition
Asthma presentation
(LDN)
Link
Families containing
CYP with mental
health condition
Random search + ad-
ditional barriers; conse-
quences for lack of re-
source acquisition
Depression presentation
(REACH)
Link
23

29. Model to Data iii
Interventions
Support payments Increased gas levels
across seekers
+5% of income -
Increased vaccine eligi-
bility
Increased proportion
of seekers for whom
there are limited con-
sequences on payoff
for lack of resource
acquisition
+2 years (as an additional
proportion of the popula-
tion)
-
24

30. Model to Data iv
Behaviour
Uptake of targeted edu-
cation
% distribution between
random and targeted
search (meta-strategy
distribution)
? Link
Uptake of support pay-
ments
% chance to use gas ? Link
Uptake of vaccines % chance to leverage re-
source immunity
? Link
25

31. PPIE i
An important aspect of our
model is the uptake of in-
terventions (behaviours), as
seen.
To further increase the speci-
ficity of our model, we can
configure these behaviours us-
ing data on real human be-
haviour.
In this project, we obtained
this data via PPIE (Patient
and Public Involvement and En-
gagement).
26

32. PPIE ii
Specifically, questionnaires were issued, the answers to which were translated to behaviour
distributions:
...
Part 3. Household Support Fund
A fund (usually a one-off payment) provided by local councils to ‘help
households most in need to pay for essentials such as food and utilities
this winter’.
...
2. If you felt that you needed the support, would you be willing to access the
Household Support Fund? (Yes/No)
...
All questions are listed here. Derived behaviour distributions are listed in our KCL analysis.
27

33. Outcomes and Utilisation
v0 v1 v2
v3
v4
v5
v6
v7
10 10
10
10
10
10
10
10
Recall each game has a payoff, which abstractly rep-
resents health utilisation (low payoff = worse health
outcomes and higher utilisation).
Our payoff values themselves only make sense in the
context of our abstract model. Therefore, to an-
chor these values to real potential health utilisa-
tion data we can simulate an existing environment
for which we already have this data (e.g. non-winter),
match the payoff for this simulation to the utilisation
data we have, and then use this as a baseline for the
other simulations.
Utilisation figures are listed in our LDN analysis and
in our model configuration.
28

34. Implementation
We realise our model in Java, providing us with
a simulation platform that we can configure
with our data, and through which we can run
experiments and obtain results.
The implementation can be viewed here.
29

35. Results

36. Results
83,776 individuals analysed
30+ engagement activities
37 subgroups of interest
33,000 simulations run
30

37. Note
1. Our model is designed to be an objective entity, and as such its outputs may not
necessarily align with our own personal thoughts or preferences on the best way forward.
2. Any mention of ‘behaviours’ refers to behaviours in the context of winter pressures (e.g.
use of heating) as opposed to behaviours in a mental health context.
31

38. Non-winter vs. winter (SEC + Asthma)
1.62
1.625
1.63
1.635
1.64
1.645
1.65
1.655
1.66
0
0
3
L
O
W
S
E
C
-
R
E
S
P
0
0
4
M
E
D
S
E
C
-
R
E
S
P
0
0
5
H
I
G
H
S
E
C
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment
Non-winter
1.66
1.62
1.66
Non-winter2
1.6
1.7
1.8
1.9
2
2.1
2.2
2.3
2.4
2.5
2.6
0
0
3
L
O
W
S
E
C
-
R
E
S
P
0
0
4
M
E
D
S
E
C
-
R
E
S
P
0
0
5
H
I
G
H
S
E
C
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment
Winter
2.55
2.21
1.7
Winter
A jump in health utilisation (primary care) between the non-winter and winter months.
2We create measurable groups, e.g. ‘003LOWSEC-RESP’: from those individuals with asthma, the proportion
that also have a low SEC (LDN)
32

39. Interventions (SEC + Asthma)
1.68
1.69
1.7
1.71
1.72
1.73
1.74
1.75
0
0
3
L
O
W
S
E
C
-
R
E
S
P
0
0
4
M
E
D
S
E
C
-
R
E
S
P
0
0
5
H
I
G
H
S
E
C
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment (Intervention: Advice)
Winter
1.71
1.74
1.68
Issuing government advice
1.6
1.7
1.8
1.9
2
2.1
2.2
2.3
2.4
2.5
0
0
3
L
O
W
S
E
C
-
R
E
S
P
0
0
4
M
E
D
S
E
C
-
R
E
S
P
0
0
5
H
I
G
H
S
E
C
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment (Intervention: Support payments)
Winter
2.49
2.16
1.7
Making household payments
1.6
1.7
1.8
1.9
2
2.1
2.2
2.3
0
0
3
L
O
W
S
E
C
-
R
E
S
P
0
0
4
M
E
D
S
E
C
-
R
E
S
P
0
0
5
H
I
G
H
S
E
C
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment (Intervention: Vaccinations)
Winter
2.26
2.08
1.7
Increasing vaccine eligibility
A reduction in health utilisation when issuing government advice during the winter months
33

40. Interventions (Ethnicity + Asthma)
1.687
1.6875
1.688
1.6885
1.689
1.6895
1.69
1.6905
1.691
1.6915
0
1
7
E
M
E
T
H
-
R
E
S
P
0
1
8
W
B
E
T
H
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment (Intervention: Advice)
Winter
1.69 1.69
Issuing government advice
1.7
1.71
1.72
1.73
1.74
1.75
1.76
1.77
1.78
0
1
7
E
M
E
T
H
-
R
E
S
P
0
1
8
W
B
E
T
H
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment (Intervention: Support payments)
Winter
1.77
1.7
Making household payments
1.7
1.71
1.72
1.73
1.74
1.75
1.76
1.77
1.78
1.79
0
1
7
E
M
E
T
H
-
R
E
S
P
0
1
8
W
B
E
T
H
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment (Intervention: Vaccinations)
Winter
1.79
1.7
Increasing vaccine eligibility
A reduction in health utilisation when issuing government advice during the winter months
34

41. Potential policy implications (SEC + Asthma)
1.68
1.69
1.7
1.71
1.72
1.73
1.74
1.75
0
0
3
L
O
W
S
E
C
-
R
E
S
P
0
0
4
M
E
D
S
E
C
-
R
E
S
P
0
0
5
H
I
G
H
S
E
C
-
R
E
S
P
Health
utilisation
(average
visits/year)
Environment (Intervention: Advice)
Winter
1.71
1.74
1.68
1. Overall health utilisation can potentially
be reduced by issuing government
advice.
2. Disparities between different SEC
groups can potentially be reduced by
issuing government advice.
Requires further validation before implementation
into policy.
35

42. Long-term picture
Using the temporal aspect of our
model we can gain some early in-
sight into how interventions will
perform over time.
The long-term picture shows
that the impact of giving govern-
ment advice may not be sus-
tained, and interventions that
tackle more fundamental is-
sues may be required.
Very early insight.
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90 100
Payoff
(Unmapped)
Time (Intervention: Advice)
003LOWSEC-RESP 004MEDSEC-RESP 005HIGHSEC-RESP
36

43. Summary
We can use ABM as a mechanism for exploring the solutions to complex problems.
Insight gained at an abstract level can provide, subject to validation, insight into potential
solutions at a more concrete level.
In the case of winter pressures, we can abstract to higher-level entities–made specific by the
use of real data–and consider solutions at that level.
What’s next?
• More validation (e.g. against existing intervention implementations)
• More data (e.g. additional health utilisation data)
• More PPIE (e.g. additional SEC insight)
• More funding!
37

44. Team
Dr. Martin Chapman (PI)
Abigail G-Medhin (Data Scientist)
Kian Daneshi (Research Software Engineer)
Professor Mark Ashworth (GP)
Professor Laia Becares (Racism and Health)
Dr. Harriet Boulding (Policy)
Professor Vasa Curcin (Health Informatics)
Professor Seeromanie Harding (Inequalities)
Professor Craig Morgan (Mental Health)
Dr. Divya Parmar (Health Systems)
Professor Lucilla Poston (Maternal and Child
Health)
Dr. Ingrid Wolfe (Paediatrician)
Dr. Mariam Molokhia (GP)
38

45. Links
github.com/digitalmimic/model github.com/digitalmimic/winter-analysis
Everything else: digitalmimic.ai
Contact: @martin chap man.
39