The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation st...The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.展开更多
Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biase...Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.展开更多
This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustain-able land management(SLM)practices in Mali using two rounds of the nationally representative survey Enq...This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustain-able land management(SLM)practices in Mali using two rounds of the nationally representative survey Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages.The SLMs considered include the applica-tion of organic fertilizers,the application of inorganic fertilizers,the use of improved seeds,and the practice of intercropping.On average the application of organic fertilizers(39.2%),and inorganic fertilizers(28.7%)are the most frequent SLM practices among Malian farmers,and between 2014 and 2017,we observe a decline in the practice of intercropping.The regression results show that farmers’adoption of different SLMs is significantly associated with biophysical factors(average temperature,climate type,plot size,plot shape,and location),de-mographic factors(age,gender,education,household size),and socioeconomic factors(number of cultivated plots,livelihood diversification,type of crop grown,market access,credit access,economic shocks,and social capital).Our findings suggest that policymakers and agricultural development agencies in Mali need to adopt a multidimensional policy framework to unlock the untapped potential of SLM practices in promoting sustainable agriculture and food security.展开更多
Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. ...Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. This paper applies a Poisson regression model, Negative Binomial regression model and then proposes an Artificial Neural Network model to analyze the 2008-2012 crash data for the Interstate I-90 in the State of Minnesota in the US. By comparing the prediction performance between these three models, this study demonstrates that the Neural Network is an effective alternative method for predicting highway crash frequency.展开更多
The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;"...The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> because in example analyses</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> it either generates better LCV scores or more parsimonious models and requires substantially less time.展开更多
The European Union （EU） Wild Birds Directive recognises that the most serious threats to wild birds＇ conservation in Europe are habitat loss and degradation, and hence, habitats of threatened and migratory species ...The European Union （EU） Wild Birds Directive recognises that the most serious threats to wild birds＇ conservation in Europe are habitat loss and degradation, and hence, habitats of threatened and migratory species must be protected with the establishment of the network of the special protection areas （SPAs） for migratory and endangered bird species in the EU member states. The major European population of the lesser kestrel Falco naumanni, a migratory falcon listed in Annex I of the Birds Directive, occurs in low-input farming systems in the Mediterranean basin, including Greece. The aim of this study was to identify foraging habitats of lesser kestrels and relate them to the delimited SPAs in the agro-ecosystems of Greece, where the stronghold of the species population for Greece occurs. Foraging habitat preferences were assessed using Poisson regression models （PRMs）. SPAs were examined on whether they can effectively protect foraging habitats for breeding lesser kestrels in the study area. Foraging lesser kestrel abundance was positively associated with grasslands and non-irrigated land （dry cereals）, and negatively associated with irrigated land （wet cotton）, scrubland and woodland. Electricity facilities were used as foraging perches by lesser kestrels. The current SPAs cover a small percentage of the species＇ foraging sites and cannot be considered coherent enough to support and protect the foraging habitats of lesser kestrels and other priority species in the agro-ecosystems of the study area. Proposals for effective conservation of low-input farming systems, supporting priority species, are also presented.展开更多
Background:Previous studies have established a link between fluctuations in climate and increased mortality due to coronary artery disease(CAD).However,there remains a need to explore and clarify the evidence for asso...Background:Previous studies have established a link between fluctuations in climate and increased mortality due to coronary artery disease(CAD).However,there remains a need to explore and clarify the evidence for associations between meteorological changes and hospitalization incidences related to CAD and its subtypes,especially in cold regions.This study aimed to systematically investigate the relationship between exposure to meteorological changes,air pollutants,and hospitalization for CAD in cold regions.Methods:We conducted a cross-sectional study using hospitalization records of 86,483 CAD patients between January 1,2009,and December 31,2019.Poisson regression analysis,based on generalized additive models,was applied to estimating the influence of hospitalization for CAD.Results:Significant associations were found between low ambient temperature[-10℃,RR=1.65;95%CI:(1.28-2.13)]and the incidence of hospitalization for CAD within a lag of 0-14 days.Furthermore,O_(3)[95.50μg/m^(3),RR=12;95%CI:(1.03-1.21)]and NO_(2)[48.70μg/m^(3),RR=1.0895%CI:(1.01-1.15)]levels were identified as primary air pollutants affecting the incidence of CAD,ST-segment-elevation myocardial infarction(STEMI),and non-STEMI(NSTEMI)within the same lag period.Furthermore,O_(3)[95.50μg/m^(3),RR=1.12;95%CI:(1.03-1.21)]and NO_(2)[48.70μg/m^(3),RR=1.0895%CI:(1.01-1.15)]levels were identified as primary air pollutants affecting the incidence of CAD,ST-segment-elevation myocardial infarction(STEMI),and non-STEMI(NSTEMI)within the same lag period.The effect curve of CAD hospitalization incidence significantly increased at lag days 2 and 4 when NO_(2)and O_(3)concentrations were higher,with a pronounced effect at 7 days,dissipating by lag 14 days.No significant associations were observed between exposure to PM,SO_(2),air pressure,humidity,or wind speed and hospitalization incidences due to CAD and its subtypes.Conclusion:Our findings suggest a positive correlation between short-term exposure to low ambient temperatures or air pollutants(O_(3)and NO_(2))and hospitalizations for CAD,STEMI,and NSTEMI.These results could aid the development of effective preparedness strategies for frequent extreme weather events and support clinical and public health practices aimed at reducing the disease burden associated with current and future abnormal weather events.展开更多
As an effort to understand the effect of diabetes on the increasing rate of COVID-19 infection, we embarked upon a detailed statistical analysis of various datasets that include COVID-19 infection and mortality rate, ...As an effort to understand the effect of diabetes on the increasing rate of COVID-19 infection, we embarked upon a detailed statistical analysis of various datasets that include COVID-19 infection and mortality rate, diabetes and diseases that may contribute to the severity and risk factor of diabetes in individuals and this impact on COVID-19 and the mortality rate. These diseases include respiratory diseases, cardiovascular diseases, and obesity. Equally significant is the statistical analysis on ethnicity, age, and sex on COVID-19 infection as well as mortality rate. Their possible contributions to increasing the severity and risk factor of diabetes as a risk to mortality to individuals who have COVID-19. Objectives: The ultimate objectives of this investigation are as follow: 1) Is there a risk factor of diabetes on COVID-19 infection and increasing mortality rate? 2) To what extent do other disease conditions that include, obesity, heart failure, and respiratory diseases influence the severity and risk factor of diabetes on increasing COVID-19 infection and mortality rate? 3) To what extent does age, race, and gender increase the mortality of COVID-19 and increase the severity and risk factor of diabetes on COVID-19 mortality rate? 4) How and why COVID-19 virus increases the risk of diabetes in children? 5) Diabetes and COVID-19: Who is most at Risk? Lastly, understanding the misconception of COVID-19 and diabetes.展开更多
Background:In Nigeria,the prevailing realities of ageing in poverty and ill health are becoming obvious.This situation,coupled with the fact that Nigeria has no functional national policy on the care and welfare of ol...Background:In Nigeria,the prevailing realities of ageing in poverty and ill health are becoming obvious.This situation,coupled with the fact that Nigeria has no functional national policy on the care and welfare of older persons is worrisome.There are many factors which contribute to later life frailty which could be direct or indirect.These factors include socioeconomic and demographic factors,biological factors like genetics,lifestyle factors,medical factors such as diseases,sleep disturbances as well as psychological factors.These factors are often interwoven.This study aimed to assess the role of selected socio-economic determinants on later life frailty in Southwestern Nigeria.Data was sourced from the Nigerian general household survey-panel 2018–2019,with a total population size of 4,863 persons aged 45 years and above(mean 52.1±6.4 years).Method:Fried's approach was used to develop a frailty index(non-frail(3%);pre-frail(5.3%);frail(38.7%))and Poisson regression model was utilised.Results:We found a high prevalence of frailty in later life,using some socio-economic status such as educational level(b=0.024;p=0.004),wealth status(b=0.029;p=0.001),smoking habit(b=0.073;p=0.003)Rohrer index(b=0.005;p=0.002)and current health status(b=0.020;p=0.001).Our findings provided further evidence that socio-economic status impacts later-life frailty outcomes.This study uses cross sectional data which limits the study of the factors influencing the socioeconomic determinants of frailty.Conclusion:These results underline the need to adopt social protection systems in Nigeria to moderate the impact of health and economic shocks over the lifespan and to maintain the reserve capacity individuals bring in later life.State actors are to mainstream ageing issues into national development planning and the implementation of equal access for all older persons to affordable and quality healthcare and long-term care.展开更多
This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males,unemployed females,percentage of poor house...This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males,unemployed females,percentage of poor households i.e.,poverty prevalence,night lights index,literacy rate,household food security,and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios(LLR),global Moran’s I,local indicator of spatial association-LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression(GWPR)and semi-parametric GWPR(s-GWPR)were used to determine the spatial association between HIV incidence and socio-economic factors.HIV incidence(number of cases per 1000)ranged from 0.6(Buhera district)to 13.30(Mangwe district).Spatial clustering of HIV incidence was observed(Global Moran’s I=-0.150;Z score 3.038;p-value 0.002).Significant clusters of HIV were observed at district level.HIV incidence and its association with socio-economic factors varied across the districts except percentage of females unemployed.Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.展开更多
This paper concerns with optimal designs for a wide class of nonlinear models with informa-tion driven by the linear predictor.The aim of this study is to generate an R-optimal design which minimizes the product of th...This paper concerns with optimal designs for a wide class of nonlinear models with informa-tion driven by the linear predictor.The aim of this study is to generate an R-optimal design which minimizes the product of the main diagonal entries of the inverse of the Fisher informa tion matrix at certain values of the parameters.An equivalence theorem for the locally R optimal designs is provided in terms of the intensity function.Analytic solutions for the locally saturated R-optimal designs are derived for the models having linear predictors with and without intercept,respectively.The particle swarm optimization method has been employed to generate locally non-saturated R-optimal designs.Numerical examples are presented for ilustration of the locally R-optimal designs for Poisson regression models and proportional hazards regression models.展开更多
Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management.Recently,the development of deep learning technology and big data of street view i...Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management.Recently,the development of deep learning technology and big data of street view images,makes it possible to quantitatively explore the relationship between streetscape and crime.This study computed eight streetscape indexes of the street built environment using Google Street View images firstly.Then,the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model.An experiment was conducted in downtown and uptown Manhattan,New York.Global regression results show that the influences of Motorization Index on crimes are significant and positive,while the effects of the Light View Index and Green View Index on crimes depend heavily on the socioeconomic factors.From a local perspective,the Pedestrian Space Index,Green View Index,Light View Index and Motorization Index have a significant spatial influence on crimes,while the same visual streetscape factors have different effects on different streets due to the combination differences of socioeconomic,cultural and streetscape elements.The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association.The results provide both theoretical and practical implications for crime theories and crime prevention efforts.展开更多
文摘The present paper proposes a new robust estimator for Poisson regression models. We used the weighted maximum likelihood estimators which are regarded as Mallows-type estimators. We perform a Monte Carlo simulation study to assess the performance of a suggested estimator compared to the maximum likelihood estimator and some robust methods. The result shows that, in general, all robust methods in this paper perform better than the classical maximum likelihood estimators when the model contains outliers. The proposed estimators showed the best performance compared to other robust estimators.
文摘Stop frequency models, as one of the elements of activity based models, represent an important part of travel behavior. Unobserved heterogeneity across the travelers should be taken into consideration to prevent biasedness and inconsistency in the estimated parameters in the stop frequency models. Additionally, previous studies on the stop frequency have mostly been done in larger metropolitan areas and less attention has been paid to the areas with less population. This study addresses these gaps by using 2012 travel data from a medium sized U.S. urban area using the work tour for the case study. Stop in the work tour were classified into three groups of outbound leg, work based subtour, and inbound leg of the commutes. Latent Class Poisson Regression Models were used to analyze the data. The results indicate the presence of heterogeneity across the commuters. Using latent class models significantly improves the predictive power of the models compared to regular one class Poisson regression models. In contrast to one class Poisson models, gender becomes insignificant in predicting the number of tours when unobserved heterogeneity is accounted for. The commuters are associated with increased stops on their work based subtour when the employment density of service-related occupations increases in their work zone, but employment density of retail employment does not significantly contribute to the stop making likelihood of the commuters. Additionally, an increase in the number of work tours was associated with fewer stops on the inbound leg of the commute. The results of this study suggest the consideration of unobserved heterogeneity in the stop frequency models and help transportation agencies and policy makers make better inferences from such models.
文摘This study uses logistic and Poisson regression models to examine the factors influencing the adoption of sustain-able land management(SLM)practices in Mali using two rounds of the nationally representative survey Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages.The SLMs considered include the applica-tion of organic fertilizers,the application of inorganic fertilizers,the use of improved seeds,and the practice of intercropping.On average the application of organic fertilizers(39.2%),and inorganic fertilizers(28.7%)are the most frequent SLM practices among Malian farmers,and between 2014 and 2017,we observe a decline in the practice of intercropping.The regression results show that farmers’adoption of different SLMs is significantly associated with biophysical factors(average temperature,climate type,plot size,plot shape,and location),de-mographic factors(age,gender,education,household size),and socioeconomic factors(number of cultivated plots,livelihood diversification,type of crop grown,market access,credit access,economic shocks,and social capital).Our findings suggest that policymakers and agricultural development agencies in Mali need to adopt a multidimensional policy framework to unlock the untapped potential of SLM practices in promoting sustainable agriculture and food security.
文摘Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. This paper applies a Poisson regression model, Negative Binomial regression model and then proposes an Artificial Neural Network model to analyze the 2008-2012 crash data for the Interstate I-90 in the State of Minnesota in the US. By comparing the prediction performance between these three models, this study demonstrates that the Neural Network is an effective alternative method for predicting highway crash frequency.
文摘The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> because in example analyses</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> it either generates better LCV scores or more parsimonious models and requires substantially less time.
文摘The European Union （EU） Wild Birds Directive recognises that the most serious threats to wild birds＇ conservation in Europe are habitat loss and degradation, and hence, habitats of threatened and migratory species must be protected with the establishment of the network of the special protection areas （SPAs） for migratory and endangered bird species in the EU member states. The major European population of the lesser kestrel Falco naumanni, a migratory falcon listed in Annex I of the Birds Directive, occurs in low-input farming systems in the Mediterranean basin, including Greece. The aim of this study was to identify foraging habitats of lesser kestrels and relate them to the delimited SPAs in the agro-ecosystems of Greece, where the stronghold of the species population for Greece occurs. Foraging habitat preferences were assessed using Poisson regression models （PRMs）. SPAs were examined on whether they can effectively protect foraging habitats for breeding lesser kestrels in the study area. Foraging lesser kestrel abundance was positively associated with grasslands and non-irrigated land （dry cereals）, and negatively associated with irrigated land （wet cotton）, scrubland and woodland. Electricity facilities were used as foraging perches by lesser kestrels. The current SPAs cover a small percentage of the species＇ foraging sites and cannot be considered coherent enough to support and protect the foraging habitats of lesser kestrels and other priority species in the agro-ecosystems of the study area. Proposals for effective conservation of low-input farming systems, supporting priority species, are also presented.
基金This research was partially supported by the National Natural Science Foundation of China(No.72074065)the Harbin Medical University Innovative Scientific Research Funding Project(No.0202-31041220023).
文摘Background:Previous studies have established a link between fluctuations in climate and increased mortality due to coronary artery disease(CAD).However,there remains a need to explore and clarify the evidence for associations between meteorological changes and hospitalization incidences related to CAD and its subtypes,especially in cold regions.This study aimed to systematically investigate the relationship between exposure to meteorological changes,air pollutants,and hospitalization for CAD in cold regions.Methods:We conducted a cross-sectional study using hospitalization records of 86,483 CAD patients between January 1,2009,and December 31,2019.Poisson regression analysis,based on generalized additive models,was applied to estimating the influence of hospitalization for CAD.Results:Significant associations were found between low ambient temperature[-10℃,RR=1.65;95%CI:(1.28-2.13)]and the incidence of hospitalization for CAD within a lag of 0-14 days.Furthermore,O_(3)[95.50μg/m^(3),RR=12;95%CI:(1.03-1.21)]and NO_(2)[48.70μg/m^(3),RR=1.0895%CI:(1.01-1.15)]levels were identified as primary air pollutants affecting the incidence of CAD,ST-segment-elevation myocardial infarction(STEMI),and non-STEMI(NSTEMI)within the same lag period.Furthermore,O_(3)[95.50μg/m^(3),RR=1.12;95%CI:(1.03-1.21)]and NO_(2)[48.70μg/m^(3),RR=1.0895%CI:(1.01-1.15)]levels were identified as primary air pollutants affecting the incidence of CAD,ST-segment-elevation myocardial infarction(STEMI),and non-STEMI(NSTEMI)within the same lag period.The effect curve of CAD hospitalization incidence significantly increased at lag days 2 and 4 when NO_(2)and O_(3)concentrations were higher,with a pronounced effect at 7 days,dissipating by lag 14 days.No significant associations were observed between exposure to PM,SO_(2),air pressure,humidity,or wind speed and hospitalization incidences due to CAD and its subtypes.Conclusion:Our findings suggest a positive correlation between short-term exposure to low ambient temperatures or air pollutants(O_(3)and NO_(2))and hospitalizations for CAD,STEMI,and NSTEMI.These results could aid the development of effective preparedness strategies for frequent extreme weather events and support clinical and public health practices aimed at reducing the disease burden associated with current and future abnormal weather events.
文摘As an effort to understand the effect of diabetes on the increasing rate of COVID-19 infection, we embarked upon a detailed statistical analysis of various datasets that include COVID-19 infection and mortality rate, diabetes and diseases that may contribute to the severity and risk factor of diabetes in individuals and this impact on COVID-19 and the mortality rate. These diseases include respiratory diseases, cardiovascular diseases, and obesity. Equally significant is the statistical analysis on ethnicity, age, and sex on COVID-19 infection as well as mortality rate. Their possible contributions to increasing the severity and risk factor of diabetes as a risk to mortality to individuals who have COVID-19. Objectives: The ultimate objectives of this investigation are as follow: 1) Is there a risk factor of diabetes on COVID-19 infection and increasing mortality rate? 2) To what extent do other disease conditions that include, obesity, heart failure, and respiratory diseases influence the severity and risk factor of diabetes on increasing COVID-19 infection and mortality rate? 3) To what extent does age, race, and gender increase the mortality of COVID-19 and increase the severity and risk factor of diabetes on COVID-19 mortality rate? 4) How and why COVID-19 virus increases the risk of diabetes in children? 5) Diabetes and COVID-19: Who is most at Risk? Lastly, understanding the misconception of COVID-19 and diabetes.
文摘Background:In Nigeria,the prevailing realities of ageing in poverty and ill health are becoming obvious.This situation,coupled with the fact that Nigeria has no functional national policy on the care and welfare of older persons is worrisome.There are many factors which contribute to later life frailty which could be direct or indirect.These factors include socioeconomic and demographic factors,biological factors like genetics,lifestyle factors,medical factors such as diseases,sleep disturbances as well as psychological factors.These factors are often interwoven.This study aimed to assess the role of selected socio-economic determinants on later life frailty in Southwestern Nigeria.Data was sourced from the Nigerian general household survey-panel 2018–2019,with a total population size of 4,863 persons aged 45 years and above(mean 52.1±6.4 years).Method:Fried's approach was used to develop a frailty index(non-frail(3%);pre-frail(5.3%);frail(38.7%))and Poisson regression model was utilised.Results:We found a high prevalence of frailty in later life,using some socio-economic status such as educational level(b=0.024;p=0.004),wealth status(b=0.029;p=0.001),smoking habit(b=0.073;p=0.003)Rohrer index(b=0.005;p=0.002)and current health status(b=0.020;p=0.001).Our findings provided further evidence that socio-economic status impacts later-life frailty outcomes.This study uses cross sectional data which limits the study of the factors influencing the socioeconomic determinants of frailty.Conclusion:These results underline the need to adopt social protection systems in Nigeria to moderate the impact of health and economic shocks over the lifespan and to maintain the reserve capacity individuals bring in later life.State actors are to mainstream ageing issues into national development planning and the implementation of equal access for all older persons to affordable and quality healthcare and long-term care.
文摘This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males,unemployed females,percentage of poor households i.e.,poverty prevalence,night lights index,literacy rate,household food security,and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios(LLR),global Moran’s I,local indicator of spatial association-LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression(GWPR)and semi-parametric GWPR(s-GWPR)were used to determine the spatial association between HIV incidence and socio-economic factors.HIV incidence(number of cases per 1000)ranged from 0.6(Buhera district)to 13.30(Mangwe district).Spatial clustering of HIV incidence was observed(Global Moran’s I=-0.150;Z score 3.038;p-value 0.002).Significant clusters of HIV were observed at district level.HIV incidence and its association with socio-economic factors varied across the districts except percentage of females unemployed.Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.
基金Lei He’s work is supported by the National Natural Science Foundation of China[Grant Number 12101013]the Natural Science Foundation of Anhui Province[Grant Number 2008085QA15]Rong-Xian Yue’s work is supported by the National Natural Science Foundation of China[Grant Numbers 11971318,11871143].
文摘This paper concerns with optimal designs for a wide class of nonlinear models with informa-tion driven by the linear predictor.The aim of this study is to generate an R-optimal design which minimizes the product of the main diagonal entries of the inverse of the Fisher informa tion matrix at certain values of the parameters.An equivalence theorem for the locally R optimal designs is provided in terms of the intensity function.Analytic solutions for the locally saturated R-optimal designs are derived for the models having linear predictors with and without intercept,respectively.The particle swarm optimization method has been employed to generate locally non-saturated R-optimal designs.Numerical examples are presented for ilustration of the locally R-optimal designs for Poisson regression models and proportional hazards regression models.
基金supported by the National Natural Science Foundation of China(Grant No.61872050,No.62172066)the Chongqing Basic and Frontier Research Program(cste2018jcyjAX0551),the FundamentaRl esearchFundsforthe,Central Universityes(2018CDJSK03XK01)the Chongqing Technology Innovation and Application Development Key Project(ctsc2019jscx-gksbx0066)。
文摘Understanding the influencing mechanism of the urban streetscape on crime is fairly important to crime prevention and urban management.Recently,the development of deep learning technology and big data of street view images,makes it possible to quantitatively explore the relationship between streetscape and crime.This study computed eight streetscape indexes of the street built environment using Google Street View images firstly.Then,the association between the eight indexes and recorded crime events was revealed with a poisson regression model and a geographically weighted poisson regression model.An experiment was conducted in downtown and uptown Manhattan,New York.Global regression results show that the influences of Motorization Index on crimes are significant and positive,while the effects of the Light View Index and Green View Index on crimes depend heavily on the socioeconomic factors.From a local perspective,the Pedestrian Space Index,Green View Index,Light View Index and Motorization Index have a significant spatial influence on crimes,while the same visual streetscape factors have different effects on different streets due to the combination differences of socioeconomic,cultural and streetscape elements.The key streetscape elements of a given street that affect a specific criminal activity can be identified according to the strength of the association.The results provide both theoretical and practical implications for crime theories and crime prevention efforts.
