BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale c...BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.展开更多
We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartmen...We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United States.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.展开更多
To describe the case fatality rate of SARS in Beijing. Methods Data of SARS cases notified from Beijing Center for Disease Control and Prevention （BCDC） and supplemented by other channels were collected. The data we...To describe the case fatality rate of SARS in Beijing. Methods Data of SARS cases notified from Beijing Center for Disease Control and Prevention （BCDC） and supplemented by other channels were collected. The data were analyzed by rate calculation. Results The case fatality rate of SARS in Beijing was 7.66%, and had an ascending trend while the age of cases was getting older, and a descending trend while the epidemic developmem. The case fatality rate in Beijing was lower than that in other main epidemic countries or regions. Conclusions The risk of death increases with the increment of age of SARS patients. Beijing is successful in controlling and treating SARS.展开更多
<strong>Importance:</strong> Corona virus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pandemic claiming millions of lives since the first outbr...<strong>Importance:</strong> Corona virus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pandemic claiming millions of lives since the first outbreak was reported in Wuhan, China during December 2019. It is thus important to make cross-country comparison of the relevant rates and understand the socio-demographic risk factors. <strong>Methods: </strong>This is a record based retrospective cohort study. <strong>Table 1</strong> was extracted from <a href="https://www.worldometers.info/coronavirus/" target="_blank">https://www.worldometers.info/coronavirus/</a> and from the Corona virus resource center (<strong>Table 2</strong>, <strong>Figures 1-3</strong>), Johns Hopkins University. Data for <strong>Table 1</strong> includes all countries which reported >1000 cases and <strong>Table 2</strong> includes 20 countries reporting the largest number of deaths. The estimation of CFR, RR and PR of the infection, and disease pattern across geographical clusters in the world is presented. <strong>Results:</strong> From <strong>Table 1</strong>, we could infer that as on 4<sup>th</sup> May 2020, COVID-19 has rapidly spread world-wide with total infections of 3,566,423 and mortality of 248,291. The maximum morbidity is in USA with 1,188,122 cases and 68,598 deaths (CFR 5.77%, RR 15% and PR 16.51%), while Spain is at the second position with 247,122 cases and 25,264 deaths (CFR 13.71%, RR 38.75%, PR 9.78%). <strong>Table 2</strong> depicts the scenario as on 8<sup>th</sup> October 2020, where-in the highest number of confirmed cases occurred in US followed by India and Brazil (cases per million population: 23,080, 5007 & 23,872 respectively). For deaths per million population: US recorded 647, while India and Brazil recorded 77 and 708 respectively. <strong>Conclusion:</strong> Studying the distribution of relevant rates across different geographical clusters plays a major role for measuring the disease burden, which in-turn enables implementation of appropriate public healthcare measures.展开更多
Reference values of erythrocyte sedimentation rate(ESR)are the key to interpret ESR blood test in clinic.The common local reference ESR values are more accuracy in blood test that are established with natural geograph...Reference values of erythrocyte sedimentation rate(ESR)are the key to interpret ESR blood test in clinic.The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression(MLR)model and the artificial neural network(ANN).These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited.This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning(CBR)since knowledge domain of ESR and geographical factors is weak.Overall 224 local normal ESR values of China that calculated from 13623 samples were obtained,and the corresponding natural geographical factors and location that include altitude,sunshine hours,relative humidity,temperature,precipitation,annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China.CBR was used to predict the unseen local reference ESR values with cases.The average absolute deviation(AAD),mean square error(MSE),prediction accuracy(PA),and Pearson correlation coefficient(r)between the observed and estimated data of proposed model is 33.07%,9.02,66.93% and 0.78,which are better than those of ANN and MLR model.The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models.The predicted values are very close to the observed values.Model results show significant agreement of cases data.Consequently,the model is used to predict the unseen local reference ESR with natural geographical factors and location.In spatial,the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan,Chongqing,Guangxi and Guizhou provinces,and the reference ESR values are greater than 23 mm/60 min.The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei,Henan,Shaanxi,Shanxi,Jilin and Heilongjiang provinces,and the reference ESR values are greater than 18 mm/60min.The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang,and the reference ESR values are lower than 5 mm/60min.展开更多
We compared subgroup differences in COVID-19 case and mortality and investigated factors associated with case and mortality rate(MR)measured at the county level in Mississippi.Findings were based on data published by ...We compared subgroup differences in COVID-19 case and mortality and investigated factors associated with case and mortality rate(MR)measured at the county level in Mississippi.Findings were based on data published by the Mississippi State Department of Health between March 11 and July 16,2020.The COVID-19 case rate and case fatality rate(CFR)differed by gender and race,while MR only differed by race.Residents aged 80 years or older and those who live in a non-metro area had a higher case rate,CFR,and MR.After controlling for selected factors,researchers found that the percent of residents who are obese,low income,or with certain chronic conditions were associated with the county COVID-19 case rate,CFR,and/or MR,though some were negatively related.The findings may help the state to identify counties with higher COVID-19 case rate,CFR,and MR based on county demographics and the degree of its chronic conditions.展开更多
目的分析疾病诊断相关分组(diagnosis related groups,DRG)高倍率病例费用结构,研究影响住院费用的相关因素,合理利用DRG评价指标,为公立医院精细化管理和高质量发展提供参考。方法以2021年1月—2022年6月广西某三甲医院为样本,描述分...目的分析疾病诊断相关分组(diagnosis related groups,DRG)高倍率病例费用结构,研究影响住院费用的相关因素,合理利用DRG评价指标,为公立医院精细化管理和高质量发展提供参考。方法以2021年1月—2022年6月广西某三甲医院为样本,描述分析高倍率病例特征,运用SPSS 26.0统计学分析各因素对费用的影响。结果数据来源为从医保平台选取2021年1月—2022年6月DRG终审数据,高倍率病例6217例,占比5.76%,内科组与外科组在高倍率病例占比比较,差异无统计学意义(P>0.05)。在单因素分析中,权重、科室类别对高倍率病例住院费用有显著影响(P<0.05),而医保类别与高倍率费用无关(P>0.05)。结论权重递增结合费用倍数递减的方式制定高倍率病例认定标准是相对科学合理的,但同时应加大对违规入组病例审核力度,重视目前实际付费阶段存在的问题,转变传统医保按项目付费观念,多种付费方式相结合,合理利用DRG评价指标,以更好适应DRG支付方式改革。展开更多
文摘BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.
基金The work has been supported by a grant received from the Ministry of Education,Government of India under the Scheme for the Promotion of Academic and Research Collaboration(SPARC)(ID:SPARC/2019/1396).
文摘We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 diseases.Our proposed SEIHCRD model is an extension of the SEIR model.Three-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and results.We have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United States.After estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic evolution.Themodel calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category scenario.Themodel calculates two types of Case fatality rate one is CFR daily,and the other is total CFR.The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection.The SEIHCRD model outperforms the classic ARXmodel and the ARIMA model.RMSE,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target diagrams.The result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.
文摘To describe the case fatality rate of SARS in Beijing. Methods Data of SARS cases notified from Beijing Center for Disease Control and Prevention （BCDC） and supplemented by other channels were collected. The data were analyzed by rate calculation. Results The case fatality rate of SARS in Beijing was 7.66%, and had an ascending trend while the age of cases was getting older, and a descending trend while the epidemic developmem. The case fatality rate in Beijing was lower than that in other main epidemic countries or regions. Conclusions The risk of death increases with the increment of age of SARS patients. Beijing is successful in controlling and treating SARS.
文摘<strong>Importance:</strong> Corona virus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pandemic claiming millions of lives since the first outbreak was reported in Wuhan, China during December 2019. It is thus important to make cross-country comparison of the relevant rates and understand the socio-demographic risk factors. <strong>Methods: </strong>This is a record based retrospective cohort study. <strong>Table 1</strong> was extracted from <a href="https://www.worldometers.info/coronavirus/" target="_blank">https://www.worldometers.info/coronavirus/</a> and from the Corona virus resource center (<strong>Table 2</strong>, <strong>Figures 1-3</strong>), Johns Hopkins University. Data for <strong>Table 1</strong> includes all countries which reported >1000 cases and <strong>Table 2</strong> includes 20 countries reporting the largest number of deaths. The estimation of CFR, RR and PR of the infection, and disease pattern across geographical clusters in the world is presented. <strong>Results:</strong> From <strong>Table 1</strong>, we could infer that as on 4<sup>th</sup> May 2020, COVID-19 has rapidly spread world-wide with total infections of 3,566,423 and mortality of 248,291. The maximum morbidity is in USA with 1,188,122 cases and 68,598 deaths (CFR 5.77%, RR 15% and PR 16.51%), while Spain is at the second position with 247,122 cases and 25,264 deaths (CFR 13.71%, RR 38.75%, PR 9.78%). <strong>Table 2</strong> depicts the scenario as on 8<sup>th</sup> October 2020, where-in the highest number of confirmed cases occurred in US followed by India and Brazil (cases per million population: 23,080, 5007 & 23,872 respectively). For deaths per million population: US recorded 647, while India and Brazil recorded 77 and 708 respectively. <strong>Conclusion:</strong> Studying the distribution of relevant rates across different geographical clusters plays a major role for measuring the disease burden, which in-turn enables implementation of appropriate public healthcare measures.
基金Under the auspices of National Natural Science Foundation of China(No.40971060)
文摘Reference values of erythrocyte sedimentation rate(ESR)are the key to interpret ESR blood test in clinic.The common local reference ESR values are more accuracy in blood test that are established with natural geographical factors by using the multiple linear regression(MLR)model and the artificial neural network(ANN).These knowledge-based methods have limitations since the knowledge domains of ESR and natural geographical factors are limited.This paper presents a new cases-depended model to establish reference ESR values with natural geographical factors and location using case-based reasoning(CBR)since knowledge domain of ESR and geographical factors is weak.Overall 224 local normal ESR values of China that calculated from 13623 samples were obtained,and the corresponding natural geographical factors and location that include altitude,sunshine hours,relative humidity,temperature,precipitation,annual temperature range and annual average wind speed were obtained from the National Geomatics Center of China.CBR was used to predict the unseen local reference ESR values with cases.The average absolute deviation(AAD),mean square error(MSE),prediction accuracy(PA),and Pearson correlation coefficient(r)between the observed and estimated data of proposed model is 33.07%,9.02,66.93% and 0.78,which are better than those of ANN and MLR model.The results show that the proposed model provides higher prediction accuracy than those of the artificial neural network and multiple linear regression models.The predicted values are very close to the observed values.Model results show significant agreement of cases data.Consequently,the model is used to predict the unseen local reference ESR with natural geographical factors and location.In spatial,the highest ESR reference areas are distributed in the southern-western district of China that includes Sichuan,Chongqing,Guangxi and Guizhou provinces,and the reference ESR values are greater than 23 mm/60 min.The higher ESR reference values are distributed in the middle part and northern-eastern of China which include Hubei,Henan,Shaanxi,Shanxi,Jilin and Heilongjiang provinces,and the reference ESR values are greater than 18 mm/60min.The lowest ESR reference values are distributed in the northern-western of China that includes Tibet and Xinjiang,and the reference ESR values are lower than 5 mm/60min.
文摘We compared subgroup differences in COVID-19 case and mortality and investigated factors associated with case and mortality rate(MR)measured at the county level in Mississippi.Findings were based on data published by the Mississippi State Department of Health between March 11 and July 16,2020.The COVID-19 case rate and case fatality rate(CFR)differed by gender and race,while MR only differed by race.Residents aged 80 years or older and those who live in a non-metro area had a higher case rate,CFR,and MR.After controlling for selected factors,researchers found that the percent of residents who are obese,low income,or with certain chronic conditions were associated with the county COVID-19 case rate,CFR,and/or MR,though some were negatively related.The findings may help the state to identify counties with higher COVID-19 case rate,CFR,and MR based on county demographics and the degree of its chronic conditions.
文摘目的分析疾病诊断相关分组(diagnosis related groups,DRG)高倍率病例费用结构,研究影响住院费用的相关因素,合理利用DRG评价指标,为公立医院精细化管理和高质量发展提供参考。方法以2021年1月—2022年6月广西某三甲医院为样本,描述分析高倍率病例特征,运用SPSS 26.0统计学分析各因素对费用的影响。结果数据来源为从医保平台选取2021年1月—2022年6月DRG终审数据,高倍率病例6217例,占比5.76%,内科组与外科组在高倍率病例占比比较,差异无统计学意义(P>0.05)。在单因素分析中,权重、科室类别对高倍率病例住院费用有显著影响(P<0.05),而医保类别与高倍率费用无关(P>0.05)。结论权重递增结合费用倍数递减的方式制定高倍率病例认定标准是相对科学合理的,但同时应加大对违规入组病例审核力度,重视目前实际付费阶段存在的问题,转变传统医保按项目付费观念,多种付费方式相结合,合理利用DRG评价指标,以更好适应DRG支付方式改革。
