Air pollution is a ubiquitous environmental exposure with a number of well-documented adverse health effects. The Global Burden of Disease Study from 2015 estimated that 4 million deaths and 100 million lost years of healthy life worldwide were attributed to fine particulate matter (PM2.5), the major burden caused by cardiovascular disease, followed by chronic respiratory diseases, cerebrovascular disease, and lung cancer. A recent calculation reported a dramatic doubling in number of deaths attributable to air pollution, and a reduction in the mean life expectancy by 2.2 years.
This doubling in number of deaths is largely explained by inclusion of diseases, such as diabetes and hypertension, which were only recently linked to air pollution. Overall, the burden of air pollution is not yet fully elucidated and current research is rapidly pushing forward to identify new links between air pollution and (neuro)degenerative diseases and cancers other than lung as well as underlying mechanism by which air pollution accelerates biological ageing and shortens life.
While inflammation and oxidative stress are the most relevant mechanisms behind adverse effects of air pollution, literature on other ageing biomarkers is limited to blood plasma DNA methylation and telomere length. Recent mapping of residential air pollution levels for entire Danish population has brought massive new possibilities for studying health effects of air pollution in big data context. Research into health effects of air pollution in Denmark has to date mainly focused on death, hospitalization, and medication registers. This leaves an enormous unexploited potential in exploring novel ageing trajectories and biomarkers from nationwide archive of pathological samples as well as other, yet unexplored internationally unique datasets in the public domain such as school records and IQ tests.
Our research will identify (new) relevant air pollutants and quantify the effects these polluters have on a number of ageing related outcomes. Prospective research will include amongst other environmental exposures, including traffic noise, green and blue spaces, built environment, distance to roads, and light at night. This knowledge will be used to inform and support policy efforts towards better air quality as one step in promotion of healthy ageing.
Particulate matter (PM) air pollution increases morbidity and mortality and shortens life expectancy. Research over the last 20 years has led to a more accurate quantification of the health impact of air pollution with increased understanding of source apportionment, susceptible populations, and of proposed biological pathways. Yet, considerable gaps in knowledge of PM-related health effects remain, particularly with identifying social inequities in PM exposure.
Socially vulnerable populations (e.g. low socioeconomic status (SES) communities) may experience a “double burden” from PM exposure such that low SES communities not only experience higher PM exposure, but also higher PM-related health effects. Previous studies in North America and Europe often relied on cross-sectional data or addressed few social determinants beyond race/ethnicity, income, and education. Reinforcing this limitation is that many countries do not have spatiotemporally sequenced social determinant data of its residents at the individual level.
The current project leverages the CHALLENGE infrastructure by integrating state-of-the-art model estimates for PM and other air pollutants, and a wide array of social determinant data from Statistics Denmark’s data repository (country of origin, education, family income, social benefits, employment, occupation, pension, housing, incarceration/detention, civic engagement, public safety, built environment, health-related behavior), with the individual life history trajectories for all Danish residents over the time period between 2000 and 2017. We will develop multilevel repeated measure models of air pollution as a function of social determinants at the individual and area level. We will also examine whether the social determinants modify the associations between air pollution and aging-related phenotypes.
In summary, we will quantify and describe differences in air pollution exposure and air pollution-related health effects across communities and subpopulations according to their social determinant profile, all of which will contribute to health impact analyses in Denmark and thereby informing policy makers in the regulatory setting. We will also extend the proof-of-concept models to other major environmental exposures (e.g. traffic and wind turbine noise, green space, light at night) and health outcomes.