1.
Mapping estimated heat-related
mortality in London due to
population age, urban heat island,
and dwelling characteristics
Jonathon Taylor1, Paul Wilkinson2, Mike Davies1, Ben Armstrong2, Zaid
Chalabi2, Anna Mavrogianni1, Phil Symonds1, Roberto Picetti2, Eleni
Oikonomou3
1 Institute for Environmental Design and Engineering, The Bartlett School of Environment,
Energy and Resources, UCL
2 London School of Hygiene and Tropical Medicine
3 Energy Institute, The Bartlett School of Environment, Energy and Resources, UCL
London, City Hall, 28th October 2015Heat Risk in London group meeting

2.
2
Ongoing research
Ongoing projects Duration Deliverables
NERC Air pollution and WEather-
related health impacts: methodological
study based on Spatio-temporally
disaggregated multi-pollutant models
for present-day and future
(AWESOME) - WP3
2011-2015 • Markers of indoor overheating risk for the UK
housing stock at the unit postcode level
(completed)
• Linkage of housing markers with health data to
assess the modifying effect of indoor
environment exposure on heat-related health
risk
NIHR Health Protection Research Unit
(HPRU) - Theme 2 - Healthy
Sustainable Cities
2014-2019 • Expansion of the AWESOME heat vulnerability
metamodel to factor in urban transformations
(housing stock growth, urban greening) and
occupancy behaviour scenarios
Arup Global Research Challenge -
Seasonal health and climate change
resilience for ageing urban
populations: The development of
vulnerability indices for selected cities
and prioritisation of targeted
responses
2014-2016 • A network of collaborators
• Urban heat vulnerability indices for ageing urban
populations in 3 cities (London, New York and
Shanghai)

3.
• Climate change is predicted to increase the frequency of hot
spells and heatwaves in the future.
• Urban development and densification may increase Urban Heat
Island (UHI) risks.
• The elderly, and those with pre-existing health problems are
most vulnerable to health risks during hot weather.
• The population will be getting older, and therefore more
vulnerable to heat.
• A drive to make homes more energy-efficient may increase
indoor overheating risks.
• Housing shortage may lead to increased frequency of loft
conversions, converted flat, and small flats.
3
The problem

4.
Combined ‘triple jeopardy’
Image source:
LUCID project
3. Urban heat island
(LUCID LondUM)
2. Population age
(Census 2011)
1. Building characteristics
(EnergyPlus building physics
model)
The objective of this work is to estimate the overall mortality risk in
London, accounting for the ‘Triple Jeopardy’ of:

5.
The London Urban Heat
Island (UHI) is an increase
in temperatures in urban
areas relative to
surrounding rural areas.
This map shows the UHI
effect on average maximum
outdoor temperature across
London wards from the 26th
of May to 19th July, 2006
modelled as part of the
LUCID project3.
5
Urban heat island

6.
But, the UHI can change
due to weather patterns.
This is the modelled UHI
during a 4-day hot period
modelled in LUCID.
6
Urban heat island

7.
The elderly, particularly those
over 75, have an elevated risk
of mortality during hot
weather.
This map indicates the wards
in London with high
proportions of elderly
residents according to the
2011 Census1.
7
Population age

8.
8
Empirical and modelling studies
demonstrate variations in
overheating risk of dwellings
based on their built form and fabric
types. We used building
archetypes developed by
Oikonomou et al4 with building
fabric features derived using the
English Housing Survey (EHS)5,
for nine different age bands based
on the most common
constructions for London in the
EHS. Modelled in EnergyPlus6.
Indoor temperature estimates

9.
Indoor temperature
estimates can be mapped
to individual addresses in
the GeoInformation
Group’s Build Class
database7.
This shows the ward-
mean indoor temperature
anomaly (the deviation of
indoor temperatures from
London-wide mean).
9
Indoor temperature estimates

10.
The baseline mortality rate of
each ward can be estimated
using 2011 Census data and
age-standardised mortality
rates for all causes during the
summer2.
This map shows the
estimated mortality during the
LUCID modelling period (May
26th - July 19th) per million
population.
10
Baseline mortality

11.
Studies indicate an overall
increase in the Relative
Risk (RR) of mortality during
hot weather8-14.
In London, this occurs
above a mean daily
maximum temperature
threshold of 24.8°C, and
represents a 3.8% increase
in RR per °C8.
Amended to give age-
specific slopes using data
from Gasparrini et al9.
11
Mean maximum temperature (oC)
RelativeRisk
Numberofdays
Temperature-mortality curve

12.
The population attributable burden
of heat death over the 55-day
LUCID study period per million
population. Inclusive of average
maximum temperature when
temperature mortality threshold is
exceeded, population age, size,
and mortality rates, UHI, and
dwelling characteristics.
Heat death is strongly driven by
population age. The total number
of excess deaths due to heat
during this period is estimated to
be 274 people.
12
Mortality estimates

13.
Population additional net attributable burden of heat death per million
population due to age and UHI and indoor temperatures. The estimated
UHI-attributable and MMDT-attributable deaths during LUCID is estimated
to be 6.1 and 23.5, respectively. UHI in total would cause an estimated
8.14 excess heat deaths a day.
13
Mortality estimates

14.
There are a number of limitations in this study due to the assumptions
required and the data available. These include:
• There is little data available on the age of people within specific
dwelling types. We have had to assume an equal probability of age
groups living across all dwelling types.
• The study is based on LUCID UHI and indoor temperature models
run using weather files from London in 2006. Depending on weather
patterns, the UHI may change. Future climates have not been
modelled, but may be in further studies.
• The building physics models do not account for a range of occupant
behaviours, which can be an important contributor to indoor
temperatures. Some of the most vulnerable individuals may not be
able to adequately ventilate their dwellings, meaning the indoor
temperatures will rise even high than the model estimates, adding
significant risk.
14
Study limitations

15.
Without knowing the type of people who
live in individual dwelling types, we must
assume an equal probability across all
age groups.
Individual-building level maps may be
more informative.
15
Building-level vulnerability

16.
• Greatest mortality levels seen in outer London where the population
tends to be older.
• Indoor temperatures have a larger range than UHI temperatures.
• We modelled the ‘mean’ house and ‘mean’ person-age; some will be
much more vulnerable.
• Individual-building maps may be more useful for identifying at-risk
dwellings, and avoiding housing the most vulnerable in these
houses.
• Further work should look at future climate, housing stock, and UHI
changes.
16
Conclusions

17.
1UK Data Service (2013) UK Census Data – Age and Sex by Ward, London, UK.
2ONS (2013) Death Registrations Summary Statistics, England and Wales, 2012. Office of National Statistics, London, UK.
3LUCID (2010). The Development of a Local Urban Climate Model and its Application to the Intelligent Design of Cities.
4Oikonomou et al (2012) Modelling the relative importance of the urban heat island and the thermal quality of dwellings for
overheating in London. Building and Environment, 57(2012) 223-238.
5DCLG (2008) English Housing Survey 2008, London, UK, Department for Communities and Local Government.
6US DOE EERE. EnergyPlus energy simulation software, version 3.1.0.027. Available online at:
http://apps1.eere.energy.gov/buildings/energyplus/
7GG (2013) National Building Class Database, Cambridge, UK, The Geoinformation Group.
8Armstrong et al (2010). Association of mortality with high temperatures in a temperature climate: England and Wales. J Epidemiol
Community Health, doi:10.1136/jech.2009.093161
9Gasparrini et al. (2012) The effect of high temperatures on cause-specific mortality in England and Wales. Occup Environ Med,
69:56-61.
10Vandentorren, et al. (2006) August 2003 Heat Wave in France: Risk Factors for Death of Elderly People Living at Home.
European Journal of Public Health, 16:583-591.
11Hajat et al (2007) Heat-related and cold-related deaths in England and Wales: who is at risk? Occup Environ Med, 64:93-100.
12Medina-Ramon et al. (2006) Extreme temperatures and mortality: assessing effect modification by personal characteristics and
specific cause of death in a multi-city case-only analysis. Environ Health Perspect, 114:1331-6.
13O’Neill et al. (2005) Disparities by race in heat-related mortality in four US cities: the role of air conditioning prevalence. J Urban
Health, 82:191-7.
14Schwartz J. (2005) Who is sensitive to extremes of temperature?: a case-only analysis. Epidemiology, 16:67-72.
17
References