目的 描述陕西省COVID-19确诊病例的空间流行病学特征,分析其相关因素,为陕西省新型冠状病毒肺炎的防控工作的开展提供参考依据.方法 收集陕西省COVID-19确诊病例信息及相关社会经济学数据,分析确诊病例的时间及空间分布特征,采用广义线性模型探索人群COVID-19发病与社会经济因素之间的关联.结果 2020年1月23日,陕西省首次报告4例,2月4日新增确诊病例最高达23例,2月19日后再无新增.输入型病例比本地病例更早出现并达到新增高峰,且更早进入归零期.空间分析结果显示,确诊病例数最多的地市为西安市(120例),占总数的48.98％,确诊病例较多的区县为莲湖区、雁塔区、新城区和未央区.与各区县确诊病例数相关的社会经济学因素为教育支出(IRR=0.287,95％ CI:O.134～0.612)、人均生产总值(IRR=1.143,95％ CI:1.049～1.245)及各区县与武汉市的距离(IRR=0.995,95％ CI:0.992～0.998).结论 应针对重点地区和人群在疫情出现早期积极采取相应的措施,及早控制疫情的发展蔓延.

Exposure to PM2.5 pollution is a significant health concern and increases risks for cancers in China. However, the studies regarding the effect of PM2.5 and esophageal cancer incidence (ECI) among urban-rural areas are limited. In this study, we examined the sex- and area-specific association between exposure to PM2.5 and ECI, as well as explored the corresponding lag effects on ECI using a geographical weighted Poisson regression. We found significantly positive effect on ECI for males and females in different models, with the greatest increase of 1.44% (95% CI: 1.30%, 1.59%) and 2.42% (95% CI: 2.17%, 2.66%) in per 10 ug/m(3) increase of PM2.5 for males and females at single year lag7 and lag4 after all covariates controlled, respectively. We also found that the long-term effect of PM2.5 on ECI was relatively stable at all moving average year lags. Moreover, rural areas had higher ECI risks for males (0.17%) and females (0.64%) with longer lag period than urban areas. In addition, higher risks for both sexes appeared in north, northwestern, and east China. The findings indicated that long-term exposure to PM2.5 was significantly associated with increased risks for ECI, which reinforce a comprehensive understanding for ECI related to PM2.5.

Objective To analyse the spatial clustering of COVID-19 case fatality risks in the districts of Bangladesh and to explore the association of sociodemographic indicators with these risks. Study design Ecological study. Study setting Secondary data were collected for a total of 64 districts of Bangladesh. Methods The data for district-wise COVID-19 cases were collected from the Ministry of Health and Family Welfare, Bangladesh from March 2020 to June 2020. Socioeconomic and demographic data were collected from National Census Data, 2011. Retrospective spatial analysis was conducted based on district-wise COVID-19 cases in Bangladesh. Global Moran’s I was adopted to find out the significance of the clusters. Furthermore, generalised linear model was conducted to find out the association of COVID-19 cases with sociodemographic variables. Results Total 87 054 COVID-19 cases were included in this study. The epidemic hotspots were distributed in the 11 most populous cities. The most likely clusters are primarily situated in the central, south-eastern and north-western regions of the country. High-risk clusters were found in Dhaka (Relative Risk (RR): 5.22), Narayanganj (RR: 2.70), Chittagong (RR: 1.69), Munshiganj (RR: 2.31) Cox’s Bazar (RR: 1.63), Faridpur (RR: 1.65), Gazipur (RR: 1.33), Bogra (RR: 1.35), Khulna (RR: 1.22), Barishal (RR: 1.07) and Noakhali (RR: 1.06). Weekly progression of COVID-19 cases showed spatially clustered by Moran’s I statistics (p value ranging from 0.013 to 0.436). After fitting a Poisson linear model, we found a positive association of COVID-19 with floating population rate (RR=1.542, 95% CI 1.520 to 1.564), and urban population rate (RR=1.027, 95% CI 1.026 to 1.028). Conclusion This study found the high-risk cluster areas in Bangladesh and analysed the basic epidemiological issues; further study is needed to find out the common risk behaviour of the patients and other relative issues that involve the spreading of this infectious disease.

Rapid urbanization and industrialization in China have incurred serious air pollution and consequent health concerns. In this study, we examined the modifying effects of urbanization and socioeconomic factors on the association between PM2.5 and incidence of esophageal cancer (EC) in 2000?2015 using spatiotemporal techniques and a quasi-Poisson generalized linear model. The results showed a downward trend of EC and high-risk areas aggregated in North China and Huai River Basin. In addition, a stronger association between PM2.5 and incidence was observed in low urbanization group, and the association was stronger for females than males. When exposure time-windows were adjusted as 0, 5, 10, 15 years, the incidence risk increased by 2.48% (95% CI: 2.23%, 2.73%), 2.20% (95% CI: 1.91%, 2.49%), 2.18% (95% CI%: 1.92%, 2.43%), 1.87% (95% CI%:1.64, 2.10%) for males, respectively and 4.03% (95% CI: 3.63%, 4.43%), 2.20% (95% CI: 1.91%, 2.49%), 3.97% (95% CI: 3.54%, 4.41%), 3.06% (95% CI: 2.71%, 3.41%) for females, respectively. The findings indicated people in low urbanization group faced with a stronger EC risk caused by PM2.5, which contributes to a more comprehensive understanding of combating EC challenges related to PM2.5 pollution.

<span id="ChDivSummary" name="ChDivSummary" class="abstract-text">目的分析中国内地境外输入新型冠状病毒肺炎确诊病例的流行病学特征,为进一步评估境外输入风险和防控工作提供依据。方法根据国家及各省市卫生健康委员会公布的确诊病例活动轨迹建立境外输入确诊病例数据库;对确诊病例的三间分布进行流行病学特征分析。结果境外输入病例在各地级市的时间及空间分布均表现出一定的区域性,截至5月20日中国内地有9个地级市境外输入病例超过50例。输入国主要为俄罗斯、英国、美国、法国及西班牙。输入病例中留学生及外籍人员分别占40%、10%。结论境外输入疫情时空表现存在明显差异,且与各地市自身特点及管控政策辐射有关;相关政策对境外输入疫情的调控效果显而易见。</span>