Sciensano

Poor air quality causes many health issues, such as respiratory and cardiovascular problems and is the number one environmental cause of premature death across the EU. Although great efforts have been made in the past 10 years to reduce the air pollution levels in Belgium, a considerable part of the population is still exposed to concentrations exceeding the World Health Organization Air Quality Guidelines. Therefore, quantifying the effectiveness of policies that further reduce those levels is becoming particularly important in supporting policymaking. In Belgium and worldwide, the adverse health impact of long-term air pollution exposure on morbidity and mortality is already well documented. However, methodological challenges remain to provide actionable evidence on the potential health benefits of realistic interventions. Advanced statistical modeling and causal inference will be used to evaluate the impact of hypothetical intervention scenarios targeting long-term exposure to PM 2.5, PM10, NO2, and black carbon on reducing non-communicable diseases, multi-morbidity and mortality in Belgium using data from the Belgian Health Interview surveys from 2008 to 2018. Data will be enriched with cause-of-death data and objective environmental indicators related to air pollution, noise and green space based on the residential address of the survey participants. Compared with classic analytical methods, the statistical approach used in this study has the advantage to provide causal effect estimates that are more intuitive to policy makers. Research findings will be displayed in the form of percentage of disease cases or deaths that could be avoided in Belgium under specific air pollution reduction interventions. The main results will allow decision makers to have a deeper insight on the potential health benefits of air pollution reduction policies in Belgium and facilitate the prioritization of air quality strategies.