Using spatio-temporal lagged association pattern to unravel the acute effect of air pollution on mortality

Effects of nitrogen dioxide exposure on the risk of eye and adnexa diseases among children in Shenzhen, China: an assessment using the generalized additive modeling approach

This study aimed to assess the relationship between nitrogen dioxide (NO₂) exposure and the risk of eye and adnexa diseases (EADs) among children in Shenzhen, China. Information about the daily number of outpatients with EADs and the concentration of air pollutants and meteorological data were collected. A generalized additive model was used to assess the effect of NO₂ exposure on the risk of EADs during outpatient visits. An increase in NO₂ level by 10 μg/m³ was related to an increase in the daily number of outpatients by 5.43% (95% confidence interval [CI]: 2.25%, 8.70%) at lag0, by 4.35% (95% CI: 1.15%, 7.66%) at lag1, and by 3.21% (95% CI: 0.05%, 6.47%) at lag3. In addition, the relationship between NO₂ exposure and the risk of dacryoadenitis was the strongest, with an estimated value of 15.0% (95% CI: 7.99%, 22.5%) at lag0. Moreover, the association between NO₂ exposure and the risk of EADs among children in Shenzhen was confirmed in this study. Therefore, the government should introduce stringent environmental policies to control air pollution and protect human health, particularly that of the vulnerable population.

Influence of atmospheric parameters on human mortality data at different geographical levels

Human mortality data are often modeled using a demographic approach as a function of time. This approach does not present an adequate fit model for the number of deaths with great variability. For this reason, additional information (social, economic and environmental) is required for complementing and improving demographic modelling. This article evaluated the association between human mortality data (segregated by age and sex) with meteorological and air pollutant covariates at three geographical levels: country, macro-climate regions and county. The modelling was based on a generalized linear modelling framework and takes into account the common characteristic of overdispersion in human mortality data through the application of negative binomial distribution. The proposed approach improved the dynamic behavior of the Farrington-like model (basic demographic model) and took into account the extreme meteorological and natural air pollution events. Notably, the proposed modelling worked well in cases where the amount of data was scarce.

Personal exposure to ambient PM2.5, PM10, O3, NO2, and SO2 for different populations in 31 Chinese provinces

Most epidemiological studies usually employ ambient air pollutant concentrations as a proxy of personal exposure to air pollutants originating outdoors, which could lead to a biased estimation of health effects. Herein, we modeled infiltration and exposure factors as the modifications of personal exposure to ambient PM_2.5, PM₁₀, O₃, NO₂, and SO₂ for all seasons, genders, and ages in 31 Chinese provinces. The annual average exposure factors of PM₁₀, PM_2.5, O₃, NO₂, and SO₂ were 0.42 ± 0.13 (arithmetic mean ± standard deviation), 0.68 ± 0.14, 0.34 ± 0.12, 0.50 ± 0.14, and 0.40 ± 0.13, respectively. We observed significant age, gender, seasonal, and geographical differences in infiltration and exposure factors for all studied ambient air pollutants. These factors were higher in southern China than in the north, and they were the highest in summer and the lowest in winter. The exposure factor of minors (age < 18 years) was significantly lower than that of adults (age ≥ 18 years, P < 0.01). Adult males had higher exposure factors than females (P < 0.01). Epidemiological studies utilizing outdoor concentrations of air pollutants could overestimate personal exposure to these pollutants. The present study could help in reducing the bias in the estimation of the health effects of air pollutants.

Attributable Risk to Assess the Health Impact of Air Pollution: Advances, Controversies, State of the Art and Future Needs

Despite the increased attention given to the health impact assessment of air pollution and to the strategies to control it in both scientific literature and concrete interventions, the results of the implementations, especially those involving traffic, have not always been satisfactory and there is still disagreement about the most appropriate interventions and the methods to assess their effectiveness. This state-of-the-art article reviews the recent interpretation of the concepts that concern the impact assessment, and compares old and new measurements of attributable risk and attributable fraction. It also summarizes the ongoing discussion about the designs and methods for assessing the air pollution impact with particular attention to improvements due to spatio-temporal analysis and other new approaches, such as studying short term effects in cohorts, and the still discussed methods of predicting the values of attributable risk (AR). Finally, the study presents the more recent analytic perspectives and the methods for directly assessing the effects of not yet implemented interventions on air quality and health, in accordance with the suggestion in the strategic plan 2020-2025 from the Health Effect Institute.