Most air pollution studies assume people breathe the same dirty air at home all day, but this project will track individuals as they move through their neighbourhoods to measure what they actually inhale. Current health impact assessments rely on crude exposure estimates that miss how pollution varies street by street and hour by hour. This leads to inaccurate risk calculations and policies that may protect some groups while leaving others exposed. The researcher will combine GPS tracking, activity diaries, and accelerometry from three large UK cohort studies—EPIC-Norfolk, UK Biobank, and Fenland—to build exposure-response functions that account for mobility, inhaled dose, and individual susceptibility. If successful, the work will produce open-access tools that allow local authorities to simulate how different emission-reduction policies affect specific subgroups—older adults, people with existing heart conditions, or those living in high-traffic areas. This could shift air quality policy from one-size-fits-all approaches toward targeted interventions that reduce inequalities in cardiovascular disease and stroke, the two leading causes of death globally. The tools will be integrated into existing international health impact assessment frameworks used by organisations such as the WHO.
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Background Ambient air pollution (AAP) is the biggest threat to human health, surpassing smoking as the top contributor to global burden of disease. The WHO estimates 40% of 4.2 million premature deaths relating to AAP annually are due to ischaemic heart disease (IHD) and stroke, the top two ranked causes of mortality globally. These exposure and outcomes are also drivers of inequalities. Health impact assessments (HIAs), a public health tool to judge potential health effects of policies, are reliant on exposure-response functions (ERFs, i.e., risk of disease based on pollutant concentration changes) from epidemiological studies. Limitations in study methods however, lead to exposure misclassification, heterogeneous outcome measurements and insufficient evidence on how inhaled dose influences susceptibility. New methods that minimise these errors are needed to prevent inaccurate conclusions and subsequent inequitable public health policies. Aims and objectives To develop and apply ERFs, which account for individual-level exposure variability and susceptibility, for cardiometabolic outcomes in air quality HIAs to inform equitable policies. This will be achieved through: Developing exposure assessment techniques capturing mobility. Calculating exposure-outcome associations that align with aetiology. Developing dose based ERFs, to better understand subgroup susceptibility. Identifying differences in health impacts of emission and air pollution reducing policies in different subgroups.. Methods Four work packages align with each study objective, using European Prospective Study on Nutrition and Cancer (EPIC)-Norfolk, UK Biobank (UKB) and Fenland studies' data. Each has high spatial heterogeneity in AAP and has linkage to healthcare records for cardiovascular and cerebrovascular diseases and type 2 diabetes outcomes. Pollutants are Nitrogen Dioxide, Ozone, Particulate Matter 10 and 2.5 from EXPANSE's 2000-2019 pollutant maps at 25x25m resolution. SO1. Using EPIC-Norfolk GPS tracking, lifestyle and activity data, travel-activity patterns from routing algorithms will be derived and linked to pollutant estimates. I will compare exposure estimates from this dynamic method to the current approach of exposure at residence. SO2. I will run cox regression models to identify associations between SO1 exposures and aetiologically defined cardiometabolic outcome, adjusting for total and direct effect. SO1 and SO2 will be replicated in UKB to test reproducibility. SO3. Fenland's GPS data is linked to pollution estimates, capturing granular geographical and hourly temporal variability. Individuals' annual inhaled pollutant dose can be calculated from integrating this with accelerometry and anthropometry data. ERFs will be calculated based using dose, mobility exposure or residential exposure and stratified by subgroups to assess changes in susceptibility. SO4. I will input subgroup specific ERFs from SO1-SO3 into an open-access HIA tool to simulate effects of emission reducing policies. Timelines for delivery SO1, SO2 and SO3 will be completed within the first 12, 18 and 24 months, respectively. SO4 will start in year one and be implemented on completion of SO1-SO3. Anticipated impact and dissemination Dissemination will be through academic journals, open access media platforms, patient forums, local authority networks and international organisations. I will co-create databases and toolkits for local authorities and collaborate with international organisations to integrate subgroup specific ERFs into existing global HIA tools.
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