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Rationale: Evidence for the association between fine particulate matter (PM2.5) and mortality among patients with tuberculosis (TB) is limited. Whether greenness protects air pollution-related mortality among patients with multidrug-resistant tuberculosis (MDR-TB) is completely unknown.Methods: 2305 patients reported in Zhejiang and Ningxia were followed up from MDR-TB diagnosis until death, loss to follow-up or end of the study (31 December 2019), with an average follow-up of 1724 days per patient. 16-day averages of contemporaneous Normalised Difference Vegetation Index (NDVI) in the 500 m buffer of patient's residence, annual average PM2.5 and estimated oxidant capacity Ox were assigned to patients regarding their geocoded home addresses. Cox proportional hazards regression models were used to estimate HRs per 10 μg/m3 exposure to PM2.5 and all-cause mortality among the cohort and individuals across the three tertiles, adjusting for potential covariates.Results: HRs of 1.702 (95% CI 1.680 to 1.725) and 1.169 (1.162 to 1.175) were observed for PM2.5 associated with mortality for the full cohort and individuals with the greatest tertile of NDVI. Exposures to PM2.5 were stronger in association with mortality for younger patients (HR 2.434 (2.432 to 2.435)), female (2.209 (1.874 to 2.845)), patients in rural (1.780 (1.731 to 1.829)) and from Ningxia (1.221 (1.078 to 1.385)). Cumulative exposures increased the HRs of PM2.5-related mortality, while greater greenness flattened the risk with HRs reduced in 0.188-0.194 on average.Conclusions: Individuals with MDR-TB could benefit from greenness by having attenuated associations between PM2.5 and mortality. Improving greener space and air quality may contribute to lower the risk of mortality from TB/MDR-TB and other diseases. [ABSTRACT FROM AUTHOR]

Background Neural tube defects (NTDs) are congenital birth defects of the central nervous system that affect 0.5–2 per 1000 pregnancies worldwide. Therefore effective interventions for birth defects, especially NTDs, are very important. Methods Yuanping City is a coal mining city in Shanxi Province, China, with a high incidence of NTDs. This study evaluates the effects of NTD interventions in this city after adjusting for covariates that characterize the native environment. The number of NTD cases and births for the 18 towns in Yuanping City from 2007 to 2014 were included in the study. A shared-component zero-inflated Poisson regression was applied to analyse the temporal–spatial variance among the incidence rates of NTDs in Yuanping City before and after the interventions. Results The results showed that existing interventions to mitigate birth defects, such as folic acid supplementation, reduced the incidence of NTDs by 53.5% in coal mining areas in Yuanping City. However, the NTD risk in areas near coal mines, especially unrestored coal mines, was still high, even after the intervention. Conclusions The government should focus on health hazards related to mining and agricultural production and should provide education and resources to reduce environmental exposure. Reducing environmental risks should be regarded as an early intervention strategy to mitigate birth defects. [ABSTRACT FROM AUTHOR]

Risk Assessment and Response Strategies for Extreme Climate Events in Key Nodes of the Belt and Road [ABSTRACT FROM AUTHOR]

Geographically weighted regression (GWR) is an important local method to explore spatial non-stationarity in data relationships. It has been repeatedly used to examine spatially varying relationships between epidemic diseases and predictors. Malaria, a serious parasitic disease around the world, shows spatial clustering in areas at risk. In this article, we used GWR to explore the local determinants of malaria incidences over a 7-year period in northern China, a typical mid-latitude, high-risk malaria area. Normalized difference vegetation index (NDVI), land surface temperature (LST), temperature difference, elevation, water density index (WDI) and gross domestic product (GDP) were selected as predictors. Results showed that both positively and negatively local effects on malaria incidences appeared for all predictors except for WDI and GDP. The GWR model calibrations successfully depicted spatial variations in the effect sizes and levels of parameters, and also showed substantially improvements in terms of goodness of fits in contrast to the corresponding non-spatial ordinary least squares (OLS) model fits. For example, the diagnostic information of the OLS fit for the 7-year average case is R2 = 0.243 and AICc = 837.99, while significant improvement has been made by the GWR calibration with R2 = 0.800 and AICc = 618.54. [ABSTRACT FROM AUTHOR]

Projecting the future distribution of permafrost under different climate change scenarios is essential, especially for the Qinghai–Tibet Plateau (QTP). The altitude-response model is used to estimate future permafrost changes on the QTP for the four RCPs (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). The simulation results show the following: (1) from now until 2070, the permafrost will experience different degrees of significant degradation under the four RCP scenarios. This will affect 25.68%, 40.54%, 45.95%, and 62.84% of the current permafrost area, respectively. (2) The permafrost changes occur at different rates during the periods 2030–2050 and 2050–2070 for the four different RCPs. (1) In RCP2.6, the permafrost area decreases a little during the period 2030–2050 but shows a small increase from 2050 to 2070. (2) In RCP4.5, the rate of permafrost loss during the period 2030–2050 (about 12.73%) is higher than between 2050 and 2070 (about 8.33%). (3) In RCP6.0, the permafrost loss rate for the period 2030–2050 (about 16.52%) is similar to that for 2050–2070 (about 16.67%). (4) In RCP8.5, there is a significant discrepancy in the rate of permafrost decrease for the periods 2030–2050 and 2050–2070: the rate is only about 3.70% for the first period but about 29.49% during the second. [ABSTRACT FROM PUBLISHER]

Rationale: Evidence for the association between fine particulate matter (PM 2.5 ) and mortality among patients with tuberculosis (TB) is limited. Whether greenness protects air pollution-related mortality among patients with multidrug-resistant tuberculosis (MDR-TB) is completely unknown., Methods: 2305 patients reported in Zhejiang and Ningxia were followed up from MDR-TB diagnosis until death, loss to follow-up or end of the study (31 December 2019), with an average follow-up of 1724 days per patient. 16-day averages of contemporaneous Normalised Difference Vegetation Index (NDVI) in the 500 m buffer of patient's residence, annual average PM 2.5 and estimated oxidant capacity O x were assigned to patients regarding their geocoded home addresses. Cox proportional hazards regression models were used to estimate HRs per 10 μg/m 3 exposure to PM 2.5 and all-cause mortality among the cohort and individuals across the three tertiles, adjusting for potential covariates., Results: HRs of 1.702 (95% CI 1.680 to 1.725) and 1.169 (1.162 to 1.175) were observed for PM 2.5 associated with mortality for the full cohort and individuals with the greatest tertile of NDVI. Exposures to PM 2.5 were stronger in association with mortality for younger patients (HR 2.434 (2.432 to 2.435)), female (2.209 (1.874 to 2.845)), patients in rural (1.780 (1.731 to 1.829)) and from Ningxia (1.221 (1.078 to 1.385)). Cumulative exposures increased the HRs of PM 2.5 -related mortality, while greater greenness flattened the risk with HRs reduced in 0.188-0.194 on average., Conclusions: Individuals with MDR-TB could benefit from greenness by having attenuated associations between PM 2.5 and mortality. Improving greener space and air quality may contribute to lower the risk of mortality from TB/MDR-TB and other diseases., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Maternal gestational PM 2.5 exposure was associated with small for gestational age (SGA). Identifying potential mediating factors may help design preventive strategies to reduce this risk., Objective: This study aimed to explore the roles of maternal blood pressure and hemoglobin may play in the PM 2.5 exposure and SGA relationship among 117,162 births in 16 counties across China during 2014-2018., Methods: Daily PM 2.5 concentration was collected from China National Environmental Monitoring Center. According to maternal residency during pregnancy, the PM 2.5 exposure for each trimester and the whole pregnancy was assessed using an inverse-distance weighting approach. Repeated measurements of maternal blood pressure and hemoglobin during pregnancy were collected for each woman. We estimated the total effect of gestational PM 2.5 exposure on SGA, and further tested the mediation effects of maternal blood pressure and hemoglobin concentration during pregnancy., Results: Of 117,162 included mother-infant pairs, 11,361 (9.7 %) were SGA. The odds ratios of SGA associated with PM 2.5 exposure (per 10 μg/m 3 increase) in the second trimester and the whole pregnancy were 1.023 (95 % CI: 1.009, 1.037) and 1.024 (1.001, 1.048), respectively. We identified the independent mediating effect of blood pressure and hemoglobin in the second and third trimesters, with the proportion of mediation ranging from 1.64 % to 5.78 % and 2.40 % to 8.70 %, respectively. When considering the mediators jointly, we found a stronger mediating effect with a proportion of mediation ranging from 3.93 % to 13.69 %., Discussion: Increases in maternal blood pressure and hemoglobin in the second and third trimesters can independently and jointly mediate the effects of gestational PM 2.5 exposure on SGA. Monitoring and managing maternal blood pressure and hemoglobin during prenatal care may constitute a promising avenue to reducing SGA risk associated with gestational PM 2.5 exposure., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Competing Interests: The authors declared no competing interests.

Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM 10 ) on CHDs are inconsistent. We used two machine learning models (i.e., random forest (RF) and gradient boosting (GB)) to investigate the non-linear effects of PM 10 exposure during the critical time window, weeks 3-8 in pregnancy, on risk of CHDs. From 2009 through 2012, we carried out a population-based birth cohort study on 39,053 live-born infants in Beijing. RF and GB models were used to calculate odds ratios for CHDs associated with increase in PM 10 exposure, adjusting for maternal and perinatal characteristics. Maternal exposure to PM 10 was identified as the primary risk factor for CHDs in all machine learning models. We observed a clear non-linear effect of maternal exposure to PM 10 on CHDs risk. Compared to 40μgm -3 , the following odds ratios resulted: 1) 92μgm -3 [RF: 1.16 (95% CI: 1.06, 1.28); GB: 1.26 (95% CI: 1.17, 1.35)]; 2) 111μgm -3 [RF: 1.04 (95% CI: 0.96, 1.14); GB: 1.04 (95% CI: 0.99, 1.08)]; 3) 124μgm -3 [RF: 1.01 (95% CI: 0.94, 1.10); GB: 0.98 (95% CI: 0.93, 1.02)]; 4) 190μgm -3 [RF: 1.29 (95% CI: 1.14, 1.44); GB: 1.71 (95% CI: 1.04, 2.17)]. Overall, both machine models showed an association between maternal exposure to ambient PM 10 and CHDs in Beijing, highlighting the need for non-linear methods to investigate dose-response relationships., (Copyright © 2018 Elsevier B.V. All rights reserved.)