Completed Climate, Earth & Environment Public Health & Healthcare

The effects of extreme heat events on mental health in vulnerable urban communities: towards evidence-based policy and practice

In plain English

AI plain-English summary

London’s hottest days are driving more people to seek emergency mental health care, but current heatwave policies ignore this link entirely. This project tackles a blind spot in climate adaptation. While heatstroke and dehydration are well-documented, the mental health toll of extreme heat—on anxiety, psychosis, suicide risk, and medication side effects—remains largely unmeasured and unaddressed in urban planning and public health guidance. The researchers will analyse 15 years of London data (2008–2023), linking satellite temperature readings, electronic medical records, and smartphone mobility data to pinpoint when and where heat triggers mental health crises. They will also model future scenarios under different climate pathways. If successful, the work could reshape how cities prepare for heatwaves. The team aims to amend Greater London Authority planning policies so that local authorities can mandate heat-resilient housing and green spaces designed to buffer mental health impacts. Planners and developers would receive training on these links, and community groups would gain co-developed tools to advocate for change. The project does not test a new therapy or drug—it targets the infrastructure and policy systems that silently shape mental health during extreme weather.

View original technical description
Problem statement: The effects of extreme heat events on mental health in vulnerable urban communities are under-investigated and unrecognised within current policies and practice. Strategic goals: improve evidence base for impacts of extreme hot weather on mental health in vulnerable urban communities using London as case-study; evaluate the mitigation of these impacts by different categories of urban green spaces based on The London Plan; elevate the voices of affected communities, bringing them into discussion with national, local and industry stakeholders; instigate the translation of the findings into policy and practice. Methods: (1) Leading-edge spatio-temporal analytics linking high-resolution environmental data with geo-tagged datasets including electronic medical records and smartphone-based data spanning over 15 years (2008-2023). Integration of satellite data, OpenStreetMap, Google StreetView to estimate exposure to urban green spaces. Microsimulation modelling to generate projections of temperature-related mental health changes under various climate scenarios. (2) Active participation of people with lived experience of mental illness, grassroots organisations, policy and practice experts and industry in all stages of the project. Success indicators: (i) amendments to Greater London Authority/Borough planning policies to empower local authorities; (ii) planners/developers using training and recommendations; (iii) community groups accessing and using co-developed resources to engage with change.

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Researchers

Andrea Mechelli (EPMC Awardee)Antonio Gasparrini (EPMC Awardee)Ioannis Bakolis (EPMC Awardee)JOHANNA GIBBONS (EPMC Awardee)Matthew White (EPMC Awardee)Michael Smythe (EPMC Awardee)Robert Stewart (EPMC Awardee)Stefania Tognin (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Urban adaptation measures to reduce health impacts and inequalities relating to overheating in a changing climate
The Development of a Local Urban Climate Model and its Application to the Intelligent Development of Cities (LUCID)
Seasonal health and climate change resilience for ageing urban populations
Evaluating community-led interventions to maximise the health and well-being of climate change adaptations for extreme events
Co-developed Environmental Solutions to Mitigate the Impact of Temperature Extremes on the Health of Vulnerable Populations

Original classification

Climate Impacts Award

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.