1. Mapping estimated heat-related mortality in London due to population age, urban heat island, and dwelling characteristics Jonathon Taylor 1, Paul Wilkinson 2, Mike Davies 1, Ben Armstrong 2, Zaid Chalabi 2, Anna Mavrogianni 1, Phil Symonds 1, Roberto Picetti 2, Eleni Oikonomou 3 1 Institute of 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 August 31 st - September 3 rd, 2015 ISEESão Paulo

2. Financial disclosure All authors of this presentation have read the definition of Financial Conflict of Interest, and certify that there are no financial conflicts of interests to declare.

3. Vulnerability to heat is predicted to increase because of: -- climate change -- the increasing size and density of cities (UHI effect) -- population ageing Measures to make homes more energy-efficient may alter indoor ‘overheating’ risk Housing shortage may lead to increased frequency of loft conversions and other dwellings susceptible to high indoor temperatures The Problem

4. To estimate and map the heat-related mortality risk in London accounting for: Population age Urban Heat Island (UHI) effects Building characteristics Objective

5. Population Age 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 census 1.

6. Baseline Mortality Baseline mortality per million population

7. Urban Heat Island 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, 26 th of May to 19 th July, 2006 (modelled as part of the LUCID project 3 )

8. Urban Heat Island (2) But, the UHI can change due to weather patterns. This is the modelled UHI during a 4-day hot period during LUCID.

9. Indoor Temperature Estimates 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 al 4 with fabric types and permeabilities derived using English Housing Survey (EHS) 5, for nine different age bands based on the most common constructions for London in the EHS. Modelled in EnergyPlus 6.

10. Indoor Temperature Estimates (2) Indoor temperature estimates can be mapped to individual addresses in the Geoinformation Groups Build Class database 7. This shows the ward-mean indoor temperature anomaly (i.e. the deviation of indoor temperatures from the London-wide mean).

11. Combined ‘Triple Jeopardy’ Population Age Urban Heat Island Dwelling Characteristics

12. Relationship between Temperature and Mortality Studies indicate an overall increase in the Relative Risk (RR) of mortality during hot weather 8-14. In London, this occurs above a mean daily maximum temperature threshold of 24.8°C, and represents a 3.8% increase in RR per °C 8. Amended to give age-specific slopes using data from Gasparrini et al 9. Mean maximum temperature (C ) Relative Risk Number of Days

13. Mortality Estimates RR heat is the relative risk of mortality at temperature T max, as described by the relationship in the previous slide. Subscript i refers to age group and j to ward. T max is taken as the outdoor temperature when mortality threshold are exceeded plus the UHI anomaly for a dwelling and the indoor temperature anomaly for a dwelling. Where n j is the number of dwellings in the ward.

14. Mortality Estimates Estimated 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 (25.8°C), 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.

15. Limitations of the study include… Limited data on the age of people within specific dwelling types UHI and indoor temperatures are based on weather files for London in 2006, which may not be representative of other years The building physics models do not account for a range of occupant behaviours (e.g. ventilation behaviours), which might appreciably alter temperatures and associated risks No inclusion of nursing homes, where vulnerability due to heat will be high.

16. Individual-Building Vulnerability 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.

17. Conclusions Age is the most important determinant of population risk to heat 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

18. References 1 UK Data Service (2013) UK Census Data – Age and Sex by Ward, London, UK. 2 ONS (2013) Death Registrations Summary Statistics, England and Wales, 2012. Office of National Statistics, London, UK. 3 LUCID (2010). The Development of a Local Urban Climate Model and its Application to the Intelligent Design of Cities. 4 Oikonomou 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. 5 DCLG (2008) English Housing Survey 2008, London, UK, Department for Communities and Local Government. 6 US DOE EERE. EnergyPlus energy simulation software, version 3.1.0.027. Available online at: http://apps1.eere.energy.gov/buildings/energyplus/ http://apps1.eere.energy.gov/buildings/energyplus/ 7 GG (2013) National Building Class Database, Cambridge, UK, The Geoinformation Group. 8 Armstrong 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 9 Gasparrini et al. (2012) The effect of high temperatures on cause-specific mortality in England and Wales. Occup Environ Med, 69:56- 61. 10 Vandentorren, 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. 11 Hajat et al (2007) Heat-related and cold-related deaths in England and Wales: who is at risk? Occup Environ Med, 64:93-100. 12 Medina-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. 13 O’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. 14 Schwartz J. (2005) Who is sensitive to extremes of temperature?: a case-only analysis. Epidemiology, 16:67-72.