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, UCL2 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

Ongoing researchOngoing 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)

• 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

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:

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

But, the UHI can change

due to weather patterns.

This is the modelled UHI

during a 4-day hot period

modelled in LUCID.

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Urban heat island

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.

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Population age

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

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).

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Indoor temperature estimates

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.

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Baseline mortality

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.

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Mean maximum temperature (oC)

Rela

tive R

isk

Num

ber o

f days

Temperature-mortality curve

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.

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Mortality estimates

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.

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Mortality estimates

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.

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Study limitations

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.

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Building-level vulnerability

• 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.

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Conclusions

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.2009.0931619Gasparrini 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.

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References