目的探究轮班对地铁职工健康相关生命质量的影响,为提高该职业人群的健康素养提供科学依据。方法2019年对武汉市地铁集团职工进行研究,使用调查问卷收集人口学特征和职业史等信息。用生活质量评价量表(the Mos 36-item Short Form Healt...目的探究轮班对地铁职工健康相关生命质量的影响,为提高该职业人群的健康素养提供科学依据。方法2019年对武汉市地铁集团职工进行研究,使用调查问卷收集人口学特征和职业史等信息。用生活质量评价量表(the Mos 36-item Short Form Health Survey,SF-36)测定其健康相关生命质量;按照性别分层,采用单因素和多因素Logistic回归模型分析轮班对该职业人群量表各维度得分的影响。结果9960名职工中有68.6%从事轮班工作。不同性别、工种、被动吸烟状况、锻炼习惯、睡眠状况、个人疾病史亚组人群的SF-36量表得分有显著差异(P<0.05)。与不轮班者相比,不同轮班制职工的一般健康状况、健康变化和社会功能等维度得分为差的风险均较高。并且随着轮班年限的增加,轮班者在身体机能、生理职能、一般健康状况、健康变化、精力、社会功能和精神健康维度得分为差的风险呈现为先升高再趋于平缓甚至下降的趋势。在男职工中,轮班制影响其SF-36量表各维度得分,但在女职工中未见此影响。结论轮班工作可能是影响地铁职工健康相关生命质量的重要危险因素之一,尤以男职工为甚。展开更多
Objective To explore the associations between the monthly number of dengue fever（DF） cases and possible risk factors in Guangzhou, a subtropical city of China. Methods The monthly number of DF cases, Breteau Index ...Objective To explore the associations between the monthly number of dengue fever（DF） cases and possible risk factors in Guangzhou, a subtropical city of China. Methods The monthly number of DF cases, Breteau Index （BI）, and meteorological measures during 2006-2014 recorded in Guangzhou, China, were assessed. A negative binomial regression model was used to evaluate the relationships between BI, meteorological factors, and the monthly number of DF cases. Results A total of 39,697 DF cases were detected in Guangzhou during the study period. DF incidence presented an obvious seasonal pattern, with most cases occurring from June to November. The current month＇s BI, average temperature （Tare）, previous month＇s minimum temperature （Train）, and Tare were positively associated with DF incidence. A threshold of 18.25℃ was found in the relationship between the current month＇s Tmin and DF incidence. Conclusion Mosquito density, Tove, and Tmin play a critical role in DF transmission in Guangzhou. These findings could be useful in the development of a DF early warning system and assist in effective control and prevention strategies in the DF epidemic.展开更多
<strong>Objective</strong><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong>Since the...<strong>Objective</strong><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong>Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. </span><b><span style="font-family:Verdana;">The Conclusions</span></b><span style="font-family:Verdana;">: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality</span></span></span></span><span style="font-family:Verdana;">.</span>展开更多
Malaria is a major cause of morbidity and mortality in Apac district, Northern Uganda. Hence, the study aimed to model malaria incidences with respect to climate variables for the period 2007 to 2016 in Apac district....Malaria is a major cause of morbidity and mortality in Apac district, Northern Uganda. Hence, the study aimed to model malaria incidences with respect to climate variables for the period 2007 to 2016 in Apac district. Data on monthly malaria incidence in Apac district for the period January 2007 to December 2016 was obtained from the Ministry of health, Uganda whereas climate data was obtained from Uganda National Meteorological Authority. Generalized linear models, Poisson and negative binomial regression models were employed to analyze the data. These models were used to fit monthly malaria incidences as a function of monthly rainfall and average temperature. Negative binomial model provided a better fit as compared to the Poisson regression model as indicated by the residual plots and residual deviances. The Pearson correlation test indicated a strong positive association between rainfall and malaria incidences. High malaria incidences were observed in the months of August, September and November. This study showed a significant association between monthly malaria incidence and climate variables that is rainfall and temperature. This study provided useful information for predicting malaria incidence and developing the future warning system. This is an important tool for policy makers to put in place effective control measures for malaria early enough.展开更多
Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest e...Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a more efficient urban spatial structure.展开更多
To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method.It is analyzed the relationship among the width of nonmotorize...To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method.It is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor vehicles.Due to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are established.The results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal distribution.Among them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle conflicts.The width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle conflicts.Peak periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.展开更多
Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in ...Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in the entire country over time remains unknown.This study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015,using four waves of census data and intercensal population sample survey data.Results from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas,moving away from areas with extreme temperature differences.The location of their grandchildren is among the most important attractions.Our findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults,and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.展开更多
文摘目的探究轮班对地铁职工健康相关生命质量的影响,为提高该职业人群的健康素养提供科学依据。方法2019年对武汉市地铁集团职工进行研究,使用调查问卷收集人口学特征和职业史等信息。用生活质量评价量表(the Mos 36-item Short Form Health Survey,SF-36)测定其健康相关生命质量;按照性别分层,采用单因素和多因素Logistic回归模型分析轮班对该职业人群量表各维度得分的影响。结果9960名职工中有68.6%从事轮班工作。不同性别、工种、被动吸烟状况、锻炼习惯、睡眠状况、个人疾病史亚组人群的SF-36量表得分有显著差异(P<0.05)。与不轮班者相比,不同轮班制职工的一般健康状况、健康变化和社会功能等维度得分为差的风险均较高。并且随着轮班年限的增加,轮班者在身体机能、生理职能、一般健康状况、健康变化、精力、社会功能和精神健康维度得分为差的风险呈现为先升高再趋于平缓甚至下降的趋势。在男职工中,轮班制影响其SF-36量表各维度得分,但在女职工中未见此影响。结论轮班工作可能是影响地铁职工健康相关生命质量的重要危险因素之一,尤以男职工为甚。
基金supported by grants from the National Institutes of Health,USA(R01 AI083202,D43 TW009527)National Nature Science Foundation of China(81273139)+1 种基金the Project for Key Medicine Discipline Construction of Guangzhou Municipality(2013-2015-07)Technology Planning Project of Guangdong Province,China(2013B021800041)
文摘Objective To explore the associations between the monthly number of dengue fever（DF） cases and possible risk factors in Guangzhou, a subtropical city of China. Methods The monthly number of DF cases, Breteau Index （BI）, and meteorological measures during 2006-2014 recorded in Guangzhou, China, were assessed. A negative binomial regression model was used to evaluate the relationships between BI, meteorological factors, and the monthly number of DF cases. Results A total of 39,697 DF cases were detected in Guangzhou during the study period. DF incidence presented an obvious seasonal pattern, with most cases occurring from June to November. The current month＇s BI, average temperature （Tare）, previous month＇s minimum temperature （Train）, and Tare were positively associated with DF incidence. A threshold of 18.25℃ was found in the relationship between the current month＇s Tmin and DF incidence. Conclusion Mosquito density, Tove, and Tmin play a critical role in DF transmission in Guangzhou. These findings could be useful in the development of a DF early warning system and assist in effective control and prevention strategies in the DF epidemic.
文摘<strong>Objective</strong><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong>Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. </span><b><span style="font-family:Verdana;">The Conclusions</span></b><span style="font-family:Verdana;">: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality</span></span></span></span><span style="font-family:Verdana;">.</span>
文摘Malaria is a major cause of morbidity and mortality in Apac district, Northern Uganda. Hence, the study aimed to model malaria incidences with respect to climate variables for the period 2007 to 2016 in Apac district. Data on monthly malaria incidence in Apac district for the period January 2007 to December 2016 was obtained from the Ministry of health, Uganda whereas climate data was obtained from Uganda National Meteorological Authority. Generalized linear models, Poisson and negative binomial regression models were employed to analyze the data. These models were used to fit monthly malaria incidences as a function of monthly rainfall and average temperature. Negative binomial model provided a better fit as compared to the Poisson regression model as indicated by the residual plots and residual deviances. The Pearson correlation test indicated a strong positive association between rainfall and malaria incidences. High malaria incidences were observed in the months of August, September and November. This study showed a significant association between monthly malaria incidence and climate variables that is rainfall and temperature. This study provided useful information for predicting malaria incidence and developing the future warning system. This is an important tool for policy makers to put in place effective control measures for malaria early enough.
基金Under the auspices of National Natural Science Foundation of China(No.40971075)
文摘Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a more efficient urban spatial structure.
基金This work was supported in part by the Ministry of Education of the People’s Republic of China Project of Humanities and Social Sciences under Grant No.19YJCZH208,author X.X,http://www.moe.gov.cn/in part by the Social Sciences Federation Think Tank Project of Hunan Province under Grant No.ZK2019025,author X.X,http://www.hnsk.gov.cn/+3 种基金in part by the Education Bureau Research Foundation Project of Hunan Province under Grant No.20A531,author X.X,http://jyt.hunan.gov.cn/in part by the Science and Technology Project of Changsha City,under Grant No.kq2004092,author X.X,http://kjj.changsha.gov.cn/in part by Key Subjects of the State Forestry Bureau in China under Grant No.[2016]21,author X.X,http://www.forestry.gov.cn/and in part by“Double First-Class”Cultivation Discipline of Hunan Province in China under Grant No.[2018]469,author X.X,http://jyt.hunan.gov.cn/.
文摘To study riding safety at intersection entrance,video recognition technology is used to build vehicle-bicycle conflict models based on the Bayesian method.It is analyzed the relationship among the width of nonmotorized lanes at the entrance lane of the intersection,the vehicle-bicycle soft isolation form of the entrance lane of intersection,the traffic volume of right-turning motor vehicles and straight-going non-motor vehicles,the speed of right-turning motor vehicles,and straight-going non-motor vehicles,and the conflict between right-turning motor vehicles and straight-going nonmotor vehicles.Due to the traditional statistical methods,to overcome the discreteness of vehicle-bicycle conflict data and the differences of influencing factors,the Bayesian random effect Poisson-log-normal model and random effect negative binomial regression model are established.The results show that the random effect Poisson-log-normal model is better than the negative binomial distribution of random effects;The width of non-motorized lanes,the form of vehicle-bicycle soft isolation,the traffic volume of right-turning motor vehicles,and the coefficients of straight traffic volume obey a normal distribution.Among them,the type of vehicle-bicycle soft isolation facilities and the vehicle-bicycle traffic volumes are significantly positively correlated with the number of vehicle-bicycle conflicts.The width of non-motorized lanes is significantly negatively correlated with the number of vehicle-bicycle conflicts.Peak periods and flat periods,the average speed of right-turning motor vehicles,and the average speed of straight-going non-motor vehicles have no significant influence on the number of vehicle-bicycle conflicts.
基金National Natural Science Foundation of China,No.42001153,No.42001161。
文摘Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in China.The full picture of their migration in the entire country over time remains unknown.This study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015,using four waves of census data and intercensal population sample survey data.Results from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas,moving away from areas with extreme temperature differences.The location of their grandchildren is among the most important attractions.Our findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults,and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.
