Mental Health Consequences of Air Pollution

Lead Supervisor
Dr Ioannis Bakolis
Senior Lecturer in Biostatistics
Department of Biostatistics and Health Informatics, Health Services and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London
ioannis.bakolis@kcl.ac.uk

Co-supervisor
Dr Ian Mudway
Senior Lecturer Analytical, Environmental & Forensic Sciences, King’s College London
Environmental Research Group MRC-PHE Centre for Environment and Health
School of Public Health, Imperial College London

Project Details

The World Health Organization (WHO) recently estimated that ambient air pollution causes 482,000 premature deaths within the WHO European Region 1 with an estimated economic cost of 1.575 trillion US$ including morbidity costs 2. However, the significant potential health and societal costs of poor mental health in relation to air quality is not represented in the WHO report 1. This reflects the limited number of studies directly linking air pollution exposure to adverse mental health outcomes published to date 3-12 and gaps and uncertainties in our knowledge of the underlying pathophysiologic mechanisms that drive the reported associations 13.

Benefiting from collaboration with University of Leicester and University College London and local partners providing access to UK data on air pollution (KCL urban model) and mental health (Clinical Record Interactive Search (CRIS) database, South East London Community Survey (SELCoH) and 1946 birth cohort) and wellbeing (Urban Mind), this project aims to systematically explore longitudinal associations between air pollution and mental function using a range of advanced Geographical Information Systems (GIS) and state of the art statistical techniques. Throughout the project, the PhD candidate will gain a deep understanding of how air pollution stressors could affect mental function (under main supervision of IM) and learn and apply a range of modelling techniques, including Bayesian hierarchical and structural equation models under a causal inference framework (under main supervision of IB).

Yr1: A systematic review and meta-analysis of the associations between air pollution and a range of psychiatric and neurological outcomes
Yr2: Augment the linkages of existing air pollution databases with Urban Mind, CRIS, SELCoH and 1946 birth cohort;
Yr3: Conduct statistical analysis;
Yr4: Synthesise findings and draft a list of recommendations for tackling air pollution levels and identifying populations at risk.

References:
1. WHO. Burden of disease from air pollution 2014.
2. WHO. Economic costs of the health impact of air pollution in Europe 2015.
3. Clifford, Environ Res 2016; 147: 383-98.
4. Power, Environ Health Perspect 2011; 119(5): 682-7.
5. Peters, Age Ageing 2015; 44(5): 755-60.
6. Porta, Epidemiology 2016; 27(2): 228-36.
7. Becerra, Environ Health Perspect 2013; 121(3): 380-6.
8. Jung. PloS one 2013; 8(9): e75510.
9. Raz, Environ Health Perspect 2015; 123(3): 264-70.
10. Oudin Environ Health Perspect 2016; 124(3): 306-12.
11. Weuve J, Arch Int Med 2012; 172(3): 219-27.
12. Chen, Lancet 2017; 389(10070): 718-26.
13. Block, Neurotox 2012; 33(5): 972-84.

Datasets

Clinical Interactive Research Database (CRIS) MRC National Survey of Health and Development (1946 birth cohort) South East London Community Survey (SELcoH) Urban Mind app 

Keywords

air pollution, mental health, birth cohorts, routinely collected data