[Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study]. / æ¹–åŒ—æ©æ–½åœŸå®¶æ—è‹—æ—è‡ªæ²»å·žæ–°ç”Ÿå„¿çª’æ¯å±é™©å› ç´ åˆ†æžåŠåˆ—çº¿å›¾é¢„æµ‹æ¨¡åž‹çš„æž„å»ºï¼šä¸€é¡¹å¤šä¸­å¿ƒç ”ç©¶.

OBJECTIVES: To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia. METHODS: A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively. RESULTS: Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia. CONCLUSIONS: The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.

[Evaluation of Air Pollution Characteristics and Air Quality Improvement Effect in Beijing and Chengdu].

In recent years, China's air quality has been improving, and the concentration of atmospheric particulate matter has decreased significantly. In this study, the pollution characteristics and trends of two typical representative cities (Beijing and Chengdu) were analyzed. The geographical locations, pollution emissions, and meteorological diffusion conditions of the two cities were compared, to evaluate the relative contribution of meteorological conditions and pollution reduction regulations in decreasing fine particulate matter (PM2.5) concentrations. The results showed that the number of heavily polluted days and pollution episodes in Beijing and Chengdu decreased significantly from 2013 to 2018, and the concentration of SO2 and PM2.5 decreased substantially. Compared to 2013, SO2 concentration in Beijing and Chengdu has decreased by 77.8% and 70.9%, whereas PM2.5 concentration has decreased by 42.7% and 48.5%, respectively. The largest reduction appeared in winter, when PM2.5 decreased at an annual rate of 13.5 µg ·m-3 for Beijing and 14.1 µg ·m-3 for Chengdu. During the study period, the wind speed in Chengdu was less than that in Beijing, temperature was approximately 3℃ higher, and static wind in winter was more frequent. A significantly lower mixed-layer height, atmospheric capacity index, and ventilation coefficient in Chengdu resulted in more unfavorable atmospheric diffusion conditions. The static and stable weather index and the environmental meteorological index (EMI) also showed that the atmospheric diffusion conditions were better in Beijing than in Chengdu. The EMI of the two cities showed a decreasing trend during the study period, and the decline in EMI in Chengdu was the most significant in 2018, indicating an evident improvement in meteorological conditions. In 2018, emission reductions are estimated to have contributed 33.5% and 24% to the decrease in PM2.5 in Beijing and Chengdu, respectively, and meteorological conditions contributed 7.2% and 11.1% to the reduction in these two cities. In winter, emission reductions respectively contributed 31.7% and 32.5% to reduction in Beijing and Chengdu, while meteorological conditions made a larger contribution.

[Relationship Between Atmospheric Visibility and PM2.5 Concentrations and Distributions].

Monitoring data were used to analyze the relationships among relative humidity (RH), visibility, and PM2.5 concentrations. A strong, linear relationship expression between the extinction coefficient and PM2.5 concentrations at different relative humidities is proposed. The mean correlation coefficient at RH 40%-90% was higher than 0.75 for most of Central and Eastern China, and reached 0.9 in Beijing. Comparatively, the extinction efficiency of PM2.5 was much larger in Beijing, the Yangzi River Delta Region, and Sichuan than in other regions. However, the influence of RH on visibility varied from region to region. In Beijing, RH dominated the decrease in visibility when RH>90%, while in Guangzhou, this was the case when RH>80%. From 1980 to 1996, the annual variation in PM2.5 concentrations was not significant in Beijing and the PM2.5 concentrations were significantly higher than in the 2000s because of the dominant mode of heating. From 1997 to 2009, PM2.5 concentrations in Beijing showed a slow downward trend, and from 2010 to 2012, showed an upward trend. Since 1980, PM2.5 concentrations in the entire country have been rising. PM2.5 concentrations have always been higher in North China than in other parts of the country.