BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still...BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.展开更多
This study investigates the impact of the New Rural Cooperative Medical Scheme(NRCMS)on rural households to escape poverty.We employ the instrumental variable method,the IVProbit model,to analyze the national data fro...This study investigates the impact of the New Rural Cooperative Medical Scheme(NRCMS)on rural households to escape poverty.We employ the instrumental variable method,the IVProbit model,to analyze the national data from the rural-resident field survey by the China Family Panel Studies(CFPS)in 2016.Based on the large-scale data,we found that,first,the hospitalization of family members is the key factor in increasing the risk of the family falling into poverty.The NRCMS has significantly reduced the likely risk of falling into poverty.Second,the impact of the NRCMS on poverty alleviation varies among groups with different levels of income.There is no impact on the upper-middle and high-income groups;in contrast,the NRCMS has substantially improved the capacity of low-income rural families to prevent poverty due to illness,especially for the lower-middle-income group.Third,there exist significant regional differences in the impact of NRCMS on the health poverty alleviation of rural households in China.The NRCMS has successfully reduced the risk of rural households in the western region falling into poverty,simultaneously,no significant impact on those in the eastern and central regions.In order to diminish and eliminate poverty eventually and boost rural residents'capacity for income acquisition,we propose the following:raise the actual compensation ratio of the NRCMS,control the rising expense of NRCMS by promoting the payment method reform,construct the comprehensive healthcare system in the western region,strengthen the medical security for the poor in remote area,and enhance the living environment for rural residents.展开更多
Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to re...Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction.The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference(DID)model to assess the declines of population mobility at the city level,and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020.After 4 weeks,population mobility declined by-54.81%(interquartile range,-65.50%to-43.56%).The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks(ie,1%decline of population mobility was associated with 0.72%[95%CI:0.50%-0.93%]reduction of cumulative cases for 1 week,1.42%2 weeks,1.69%3 weeks,1.72%4 weeks,1.64%5 weeks,and 1.52%6 weeks).The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter.The effects on cumulative cases differed by cities of different population sizes,with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved.Implementation of mobility restrictions in major cities with large population sizes may be even more important.展开更多
基金Supported by the Project of NINGBO Leading Medical Health Discipline,No.2022-B11Ningbo Natural Science Foundation,No.202003N4206Public Welfare Foundation of Ningbo,No.2021S108.
文摘BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.
基金supports by the National Social Science Fund of China(18FGL014)the Key Project of Humanities and Social Science Base of Anhui Province of China(SK2019A0491)+4 种基金the Humanities and Social Science Foundation of the Ministry of Education of China(18YJA790065)the Social Science Foundation of Anhui Province of China(AHSKY2017D01)the Outstanding Scholar Project of Anhui Province of China(gxbj ZD12)the Key Project of the Social Science Foundation of Anhui Province of China(AHSKY2020D44)the 2019 Major Project of the Social Science Foundation of Anhui Province of China(AHSKZD2019D04)。
文摘This study investigates the impact of the New Rural Cooperative Medical Scheme(NRCMS)on rural households to escape poverty.We employ the instrumental variable method,the IVProbit model,to analyze the national data from the rural-resident field survey by the China Family Panel Studies(CFPS)in 2016.Based on the large-scale data,we found that,first,the hospitalization of family members is the key factor in increasing the risk of the family falling into poverty.The NRCMS has significantly reduced the likely risk of falling into poverty.Second,the impact of the NRCMS on poverty alleviation varies among groups with different levels of income.There is no impact on the upper-middle and high-income groups;in contrast,the NRCMS has substantially improved the capacity of low-income rural families to prevent poverty due to illness,especially for the lower-middle-income group.Third,there exist significant regional differences in the impact of NRCMS on the health poverty alleviation of rural households in China.The NRCMS has successfully reduced the risk of rural households in the western region falling into poverty,simultaneously,no significant impact on those in the eastern and central regions.In order to diminish and eliminate poverty eventually and boost rural residents'capacity for income acquisition,we propose the following:raise the actual compensation ratio of the NRCMS,control the rising expense of NRCMS by promoting the payment method reform,construct the comprehensive healthcare system in the western region,strengthen the medical security for the poor in remote area,and enhance the living environment for rural residents.
基金supported by the grants from the National Natural Science Foundation of China(Nos.71704122 and 71974138)National Science and Technology Major Project(No.2018ZX10302206)1·3·5 project for disciplines of excellence,West China Hospital,Sichuan University(No.ZYYC08003)。
文摘Background:Since the outbreak of coronavirus disease 2019(COVID-19),human mobility restriction measures have raised controversies,partly because of the inconsistent findings.An empirical study is promptly needed to reliably assess the causal effects of the mobility restriction.The purpose of this study was to quantify the causal effects of human mobility restriction on the spread of COVID-19.Methods:Our study applied the difference-in-difference(DID)model to assess the declines of population mobility at the city level,and used the log-log regression model to examine the effects of population mobility declines on the disease spread measured by cumulative or new cases of COVID-19 over time after adjusting for confounders.Results:The DID model showed that a continual expansion of the relative declines over time in 2020.After 4 weeks,population mobility declined by-54.81%(interquartile range,-65.50%to-43.56%).The accrued population mobility declines were associated with the significant reduction of cumulative COVID-19 cases throughout 6 weeks(ie,1%decline of population mobility was associated with 0.72%[95%CI:0.50%-0.93%]reduction of cumulative cases for 1 week,1.42%2 weeks,1.69%3 weeks,1.72%4 weeks,1.64%5 weeks,and 1.52%6 weeks).The impact on the weekly new cases seemed greater in the first 4 weeks but faded thereafter.The effects on cumulative cases differed by cities of different population sizes,with greater effects seen in larger cities.Conclusions:Persistent population mobility restrictions are well deserved.Implementation of mobility restrictions in major cities with large population sizes may be even more important.