Dataset Information

Association between NO2 cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China.

ABSTRACT: While accumulating evidence shows that air pollution exposure is an important risk factor to influenza prevalence, their association has been inadequately investigated in mountainous regions with dense populations and high humidity. We aim to estimate the association and exposure-outcome effects between exposure to nitrogen dioxide (NO2) and influenza prevalence in a mountainous region with a dense population and high humidity. We investigated 14,993 patients with confirmed influenza cases from January 2013 to December 2017 in Chongqing, a mountainous city in southwest China. We developed distributed lag non-linear models with quasi-Poisson link to take into account the lag and non-linear effects of NO2 exposure on influenza prevalence. We estimated that the cumulative effect of a 10 ?g/m3 increase in NO2 with seven-day lag (i.e., summing all the contributions up to seven days) corresponded to relative risk of 1.24 (95% CI: 1.17-1.31) in daily influenza prevalence. Comparing to annual mean of the World Health Organization air quality guidelines of 40 ?g/m3 for NO2, we estimated that 14.01% (95% CI: 10.69-17.08%) of the influenza cases were attributable to excessive NO2 exposure. Our results suggest that NO2 exposure could worsen the risk of influenza infection in this mountainous city, filling the gap of relevant researches in densely populated and mountainous cities. Our findings provide evidence for developing influenza surveillance and early warning systems.

SUBMITTER: Zeng W

PROVIDER: S-EPMC7354378 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Association between NO<sub>2</sub> cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China.

Zeng Wen W Zhao Han H Liu Rui R Yan Wei W Qiu Yang Y Yang Fumo F Shu Chang C Zhan Yu Y

Environmental research 20200712

While accumulating evidence shows that air pollution exposure is an important risk factor to influenza prevalence, their association has been inadequately investigated in mountainous regions with dense populations and high humidity. We aim to estimate the association and exposure-outcome effects between exposure to nitrogen dioxide (NO<sub>2</sub>) and influenza prevalence in a mountainous region with a dense population and high humidity. We investigated 14,993 patients with confirmed influenza ...[more]

PMID: 32980014

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Project description:Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Dataset Information

Association between NO2 cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China.

Publications

Association between NO<sub>2</sub> cumulative exposure and influenza prevalence in mountainous regions: A case study from southwest China.

Similar Datasets

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