RESUMO
Background The benefits of improved air quality on asthma remain understudied. Therefore, our aim was to investigate associations of changes in ambient air pollution with incident asthma from school-age until young adulthood in an area with mostly low air pollution levels. Methods Participants in the BAMSE birth cohort from Stockholm without asthma before the 8-year follow-up were included (N=2371). We estimated the association of change in individual-level air pollutant exposure (particulate matter with diameter ⩽2.5 µm (PM2.5) and, ⩽10 µm (PM10), black carbon (BC) and nitrogen oxides (NOx)) from the first year of life to the 8-year follow-up with asthma incidence from the 8-year until the 24-year follow-up. Multi-pollutant trajectories were identified using Group-Based Multivariate Trajectory model. We also used parametric g-computation to quantify the asthma incidence under different hypothetical interventions regarding air pollution levels. Results Air pollution levels at residency decreased during the period, with median reductions of 5.6% for PM2.5, 3.1% for PM10, 5.9% for BC, and 26.8% for NOx. A total of 395 incident asthma cases were identified from the 8-year until the 24-year follow-up. The odds ratio for asthma was 0.89 (95%CI: 0.80, 0.99) for each interquartile range reduction in PM2.5 (equal to 8.1% reduction). Associations appeared less clear for PM10, BC and NOx. Five multi-pollutant trajectories were identified, where the largest reduction trajectory displayed the lowest odds of asthma (OR=0.55, 95%CI: 0.31, 0.98) compared with the least reduction trajectory. If the PM2.5 exposure had not declined up to the 8-year follow-up, the hypothetical asthma incidence was estimated to have been 10.9% higher (95%CI: 0.8%, 20.8%). Conclusions Decrease in PM2.5 levels during childhood was associated with lower risk of incident asthma from school-age to young adulthood in an area with relatively low air pollution levels, suggesting broad respiratory health benefits from improved air quality. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
RESUMO
BACKGROUND: Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to personal exposure is often not known. OBJECTIVE: We aimed to explore this relation for black carbon (BC) in central Stockholm. METHODS: Families (n = 46) with an infant, one parent working and one parent on parental leave, carried battery-operated BC instruments for 7 days. Routine BC monitoring data were obtained from rural background (RB) and urban background (UB) sites. Outdoor levels of BC at home and work were estimated in 24 h periods by dispersion modelling based on hourly real-time meteorological data, and statistical meteorological data representing annual mean conditions. Global radiation, air pressure, precipitation, temperature, and wind speed data were obtained from the UB station. All families lived in the city centre, within 4 km of the UB station. RESULTS: The average level of 24 h personal BC was 425 (s.d. 181) ng/m3 for parents on leave, and 394 (s.d. 143) ng/m3 for working parents. The corresponding fixed-site monitoring observations were 148 (s.d. 139) at RB and 317 (s.d. 149) ng/m3 at UB. Modelled BC levels at home and at work were 493 (s.d. 228) and 331 (s.d. 173) ng/m3, respectively. UB, RB and air pressure explained only 21% of personal 24 h BC variability for parents on leave and 25% for working parents. Modelled home BC and observed air pressure explained 23% of personal BC, and adding modelled BC at work increased the explanation to 34% for the working parents. IMPACT: Short-term studies of health effects from ambient air pollution usually rely on fixed site monitoring data or spatio-temporal models for exposure characterization, but the relation to actual personal exposure is often not known. In this study we showed that both routine monitoring and modelled data explained less than 35% of variability in personal black carbon exposure. Hence, short-term health effects studies based on fixed site monitoring or spatio-temporal modelling are likely to be underpowered and subject to bias.