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Background: Macrosomia has increased rapidly worldwide in the past few decades, with a huge impact on health. However, the effect of PM 2.5 and extreme high-temperature (EHT) on macrosomia has been ignored., Objective: This study aimed to explore the association between maternal exposure to EHT, PM 2.5 and macrosomia based on the Seventh Demographic and Health Survey (DHS) in 14 countries of Africa., Methods: The study included detailed demographic information on 106 382 births and maternal. Satellite inversion models estimated monthly mean PM 2.5 and mean surface temperature of 2 m (SMT 2m ). Macrosomia was defined as the birth weight ≥ 4000 g. We used a Cox proportional risk regression model to estimate the association between PM 2.5 , EHT and macrosomia. We further explored the susceptibility of exposure to EHT and PM 2.5 at different pregnancy periods to macrosomia, and plotted the expose-response curve between PM 2.5 and macrosomia risk using a restricted cubic spline function. In addition, the Interplot model was used to investigate the interaction between EHT and PM 2.5 on macrosomia. Finally, some potential confounding factors were analysed by stratification., Results: There was the positive association between EHT, PM 2.5 and macrosomia, and the risk of macrosomia with the increase in concentrations of PM 2.5 without clear threshold. Meanwhile, EHT and PM 2.5 had a higher effect on macrosomia in middle/later and early/middle stages of pregnancy, respectively. There was a significant interaction between EHT and PM 2.5 on macrosomia., Conclusions: Maternal exposure to EHT, PM 2.5 during pregnancy was associated with an increased risk of macrosomia in Africa., (© 2023 World Obesity Federation.)

Recently, the burden of cardiovascular disease (CVD) has attracted global attention. Meanwhile, CVD has become the leading cause of death in China. Some epidemiological studies have indicated that ambient air pollution may contribute to increased mortality from CVD diseases. Many studies have found a strong association between air pollutants and the risk of CVD deaths in some big cities, but few have focused on the effects of six pollutants in rural areas. Our study aimed to investigate the effects of six air pollutants (CO, NO 2 , O 3 , PM 2.5 , PM 10 , and SO 2 ) on CVD deaths of rural areas in Anhui Province and to further clarify which populations were susceptible to air pollution. First, the generalized additive models were combined with the distributed lag nonlinear models to evaluate the individual effects of air pollution on CVD deaths in each area. Then, random-effects models were used to aggregate the associations between air pollutants and CVD mortality risk in nine regions. Overall, all six pollutants had a statistically significant effect on the risk of CVD deaths on the lag 07 days. The associations between PM 2.5 , PM 10 , and SO 2 and daily CVD deaths were strongest, with maximum cumulative RR (lag 07) of 1.91 (1.64-2.18), 2.27 (1.50-3.05), and 2.13 (1.44-2.82). In general, we found that six air pollutants were the important risk factors for CVD and specific CVD deaths in Anhui Province. The elderly were susceptible to PM 2.5 , PM 10 , and SO 2 ., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Obesity has caused a huge burden of disease. Few studies have explored individuals' environmental exposure level and the impact of multiple environmental exposures on obesity. The aim of this study was to explore individual air pollution exposure evaluation, and the association between and multiple environmental factors and obesity among adult residents in rural areas of China. In this study, 8400 residents of 14 districts and counties in eastern of China were selected by multistage stratified cluster sampling, and a total of 8377 residents were included in the final analysis. We adopted BMI (Body Mass Index) > 28 kg/m 2 as the definition of obesity. First, an individual air pollution evaluation model was established based on the monitoring data of air pollution stations closest to residential address, different demographic characteristics of residents and daily living habits using generalized linear model and random forest model. Then, we used Bayesian Kernel Machine Regression (BKMR) and Quantile g-Computation (QgC) models to explore multiple environmental exposures on obesity. The results showed that six air pollutants were significantly positively associated with obesity, and green space had a significant protective effect on obesity. The BKMR model showed that the effects of different air pollutants on obesity were significantly enhanced by each other, while green space significantly reduced the positive effect of air pollution on obesity. The QgC model showed a significant positive association with obesity when all environmental factors were exposed as a whole, especially in males, higher household incomes and young people. It suggested that relevant authorities should improve regional air quality and green space to reduce the burden of disease caused by obesity., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Hypertension (HTN) was a major preventable cause of cardiovascular disease (CVD), contributing to a huge disease burden. Ambient temperature, air pollution and green space were important influencing factors of HTN, and few studies have assessed the effects and interactions of ambient temperature, air pollution and green space on HTN in rural areas. In this study, we selected 8400 individuals randomly in rural areas of Anhui Province by a multi-stage stratified cluster sampling. A total of 8383 individuals were included in the final analysis. We collected particulate pollutants and meteorological data from the local air quality monitoring stations and National Center for Meteorological Science from January 1 to December 31, 2020, respectively. The normalized differential vegetation index (NDVI) of Anhui Province in 2020 was produced and processed by remote sensing inversion on the basis of medium resolution satellite images. The average annual mean exposure concentrations of air pollution, meteorological factors, and NDVI were calculated for each individual based on the geocoded residential address. HTN was defined according the Chinese Guidelines for Prevention and Treatment of HTN. The effects and interactions of ambient temperature, air pollution and green space on HTN were evaluated by generalized linear model and interaction model, respectively. In this study, the prevalence of HTN was 24.14%. The adjusted odd ratio of HTN for each 1 μg/m 3 increasing in PM 2.5 and PM 10 , 1 °C of ambient temperature, and 0.1 of NDVI were:1.276 (1.013, 1.043), 1.012 (1.006, 1.018), 0.862 (0.862, 0.981) and 0.669 (0.611, 0.733), respectively. The results showed that air pollutants were positively correlated with HTN, while ambient temperature and green space were negatively correlated with HTN. Meanwhile, the negative associations of green space on HTN could decrease with the increasing concentrations of air pollution, but increase with the rising of ambient temperature., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Air pollution and its negative effects on health of people have been a global concern. Many studies had found a strong association between air pollutants and risk of death, but few had focused on the effects of six pollutants and rural areas. Our study aimed to investigate the effects of six air pollutants (CO, NO 2 , O 3 , PM 2.5 , PM 10 , and SO 2 ) on non-accidental and respiratory deaths in rural areas of Anhui Province by adjusting for confounding factors, and to further clarify which populations were susceptible to death associated with air pollution. In the first phase of the analysis, the generalized additive models were combined with the distributed lag non-linear models to evaluate the individual effects of air pollution on death in each area. In the second stage, random-effects models were used to aggregate the associations between air pollutants and mortality risk in nine areas. Overall, six pollutants had the strongest effects on the risk of death on the lag 07 days. The associations between PM 2.5 and NO 2 and daily non-accidental deaths were strongest, with maximum RR (lag 07): 1.63 (1.37-1.88) and 1.67 (1.37-1.96). The maximum pooled effects of association between six air pollutants and RD were PM 2.5 , with RR (lag 07): 1.89 (1.45-2.34). PM 2.5 and PM 10 had significant differences between the elderly and the non-elderly with respectively, RRR: 1.22 (1.04-1.41) and 1.26 (1.11-1.42). In general, we found that six air pollutants were the important risk factors for deaths (deaths from respiratory disease and non-accidental) in rural areas of Anhui Province. PM 10 and PM 2.5 had a considerable impact on the elderly., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)