Pub Date : 2020-06-09DOI: 10.15406/BBIJ.2020.09.00306
R. Shanker, Kamlesh Kumar Shukla, Ravi Shanker
In this paper some important properties including coefficients of variation, skewness, kurtosis and index of dispersion of size–biased new quasi Poisson–Lindley distribution (SBNQPLD) have been discussed and their behaviors have been explained graphically for varying values of parameters. Some applications of SBNQPLD have also been discussed.
{"title":"A note on size– biased new quasi Poisson– Lindley distribution","authors":"R. Shanker, Kamlesh Kumar Shukla, Ravi Shanker","doi":"10.15406/BBIJ.2020.09.00306","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00306","url":null,"abstract":"In this paper some important properties including coefficients of variation, skewness, kurtosis and index of dispersion of size–biased new quasi Poisson–Lindley distribution (SBNQPLD) have been discussed and their behaviors have been explained graphically for varying values of parameters. Some applications of SBNQPLD have also been discussed.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85836707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-20DOI: 10.15406/bbij.2020.09.00304
G. Mekebo, Adinew Handiso, O. Reddy
ven though the use of ART has brought a significant reduction in the mortality and morbidity of patients living with HIV/AIDS, a number of patients still die after the start of ART. This study was aimed at identifying factors associated with mortality among adult HIV infected patients who are on ART in HQEMM Hospital. The data for the study was obtained from HQEMM Hospital ART clinic. The HIV infected patients 15 years of age and who were under ART from March 2009 up to May 2015 were included in the study. Logistic regression model was employed to analyze the data to identify factors associated with Mortality of HIV infected patients. A total of 400 adult HIV infected patients who were taking ART were included in the study. Out of these patients, 18.75% of them died. The results obtained from logistic regression analysis showed that age, level of education, alcohol, baseline weight, TB status, and baseline CD4 count were significant factors of mortality of HIV infected patients taking ART in HQEMM Hospital. Patients with no education were more likely to die than those who attended at least primary school. Patients who drink alcohol were also more likely to die than those who do not. Health workers need to support those patients with no or little education by continuous awareness creation of taking care of themselves and knowing what factors facilitate death. Patients who drink alcohol need to be given advice to reduce excessive drinking.
{"title":"Analysis of risk factors for mortality among adult HIV infected patients on antiretroviral therapy: A case of hossana queen elleni mohammad memorial hospital, hossana, Ethiopia","authors":"G. Mekebo, Adinew Handiso, O. Reddy","doi":"10.15406/bbij.2020.09.00304","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00304","url":null,"abstract":"ven though the use of ART has brought a significant reduction in the mortality and morbidity of patients living with HIV/AIDS, a number of patients still die after the start of ART. This study was aimed at identifying factors associated with mortality among adult HIV infected patients who are on ART in HQEMM Hospital. The data for the study was obtained from HQEMM Hospital ART clinic. The HIV infected patients 15 years of age and who were under ART from March 2009 up to May 2015 were included in the study. Logistic regression model was employed to analyze the data to identify factors associated with Mortality of HIV infected patients. A total of 400 adult HIV infected patients who were taking ART were included in the study. Out of these patients, 18.75% of them died. The results obtained from logistic regression analysis showed that age, level of education, alcohol, baseline weight, TB status, and baseline CD4 count were significant factors of mortality of HIV infected patients taking ART in HQEMM Hospital. Patients with no education were more likely to die than those who attended at least primary school. Patients who drink alcohol were also more likely to die than those who do not. Health workers need to support those patients with no or little education by continuous awareness creation of taking care of themselves and knowing what factors facilitate death. Patients who drink alcohol need to be given advice to reduce excessive drinking.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80434826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-02DOI: 10.15406/bbij.2020.09.00305
Chen Qian, Jayesh P. Rai, Jianmin Pan, A. Bhatnagar, C. McClain, S. Rai
Machine learning has been a trending topic for which almost every research area would like to incorporate some of the technique in their studies. In this paper, we demonstrate several machine learning models using two different data sets. One data set is the thermograms time series data on a cancer study that was conducted at the University of Louisville Hospital, and the other set is from the world-renowned Framingham Heart Study. Thermograms can be used to determine a patient’s health status, yet the difficulty of analyzing such a high-dimensional dataset makes it rarely applied, especially in cancer research. Previously, Rai et al.1 proposed an approach for data reduction along with comparison between parametric method, non-parametric method (KNN), and semiparametric method (DTW-KNN) for group classification. They concluded that the performance of two-group classification is better than the three-group classification. In addition, the classifications between types of cancer are somewhat challenging. The Framingham Heart Study is a famous longitudinal dataset which includes risk factors that could potentially lead to the heart disease. Previously, Weng et al.2 and Alaa et al.3 concluded that machine learning could significantly improve the accuracy of cardiovascular risk prediction. Since the original Framingham data have been thoroughly analyzed, it would be interesting to see how machine learning models could improve prediction. In this manuscript, we further analyze both the thermogram and the Framingham Heart Study datasets with several learning models such as gradient boosting, neural network, and random forest by using SAS Visual Data Mining and Machine Learning on SAS Viya. Each method is briefly discussed along with a model comparison. Based on the Youden’s index and misclassification rate, we select the best learning model. For big data inference, SAS Visual Data Mining and Machine Learning on SAS Viya, a cloud computing and structured statistical solution, may become a choice of computing.
机器学习已经成为一个热门话题，几乎每个研究领域都希望在他们的研究中纳入一些技术。在本文中，我们使用两个不同的数据集演示了几个机器学习模型。一组数据是在路易斯维尔大学医院进行的一项癌症研究的热成像时间序列数据，另一组来自世界著名的弗雷明汉心脏研究。热像图可用于确定患者的健康状况，但分析这种高维数据集的难度使其很少应用，特别是在癌症研究中。之前，Rai et al.1提出了一种数据约简方法，并比较了参数方法、非参数方法(KNN)和半参数方法(dww -KNN)进行分组分类。他们得出结论，两组分类的表现优于三组分类。此外，癌症类型之间的分类有些挑战性。弗雷明汉心脏研究是一个著名的纵向数据集，其中包括可能导致心脏病的风险因素。此前，Weng et al.2和Alaa et al.3得出结论，机器学习可以显著提高心血管风险预测的准确性。由于原始的Framingham数据已经被彻底分析过，所以看看机器学习模型如何改进预测将是一件很有趣的事情。在本文中，我们使用SAS可视化数据挖掘和SAS Viya上的机器学习，利用梯度增强、神经网络和随机森林等几种学习模型进一步分析了热像图和Framingham心脏研究数据集。每种方法都进行了简要讨论，并进行了模型比较。基于约登指数和误分类率，选择最佳学习模型。对于大数据推理，基于SAS Viya的SAS可视化数据挖掘和机器学习，这是一种云计算和结构化统计解决方案，可能成为计算的选择。
{"title":"Target classification using machine learning approaches with applications to clinical studies","authors":"Chen Qian, Jayesh P. Rai, Jianmin Pan, A. Bhatnagar, C. McClain, S. Rai","doi":"10.15406/bbij.2020.09.00305","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00305","url":null,"abstract":"Machine learning has been a trending topic for which almost every research area would like to incorporate some of the technique in their studies. In this paper, we demonstrate several machine learning models using two different data sets. One data set is the thermograms time series data on a cancer study that was conducted at the University of Louisville Hospital, and the other set is from the world-renowned Framingham Heart Study. Thermograms can be used to determine a patient’s health status, yet the difficulty of analyzing such a high-dimensional dataset makes it rarely applied, especially in cancer research. Previously, Rai et al.1 proposed an approach for data reduction along with comparison between parametric method, non-parametric method (KNN), and semiparametric method (DTW-KNN) for group classification. They concluded that the performance of two-group classification is better than the three-group classification. In addition, the classifications between types of cancer are somewhat challenging. The Framingham Heart Study is a famous longitudinal dataset which includes risk factors that could potentially lead to the heart disease. Previously, Weng et al.2 and Alaa et al.3 concluded that machine learning could significantly improve the accuracy of cardiovascular risk prediction. Since the original Framingham data have been thoroughly analyzed, it would be interesting to see how machine learning models could improve prediction. In this manuscript, we further analyze both the thermogram and the Framingham Heart Study datasets with several learning models such as gradient boosting, neural network, and random forest by using SAS Visual Data Mining and Machine Learning on SAS Viya. Each method is briefly discussed along with a model comparison. Based on the Youden’s index and misclassification rate, we select the best learning model. For big data inference, SAS Visual Data Mining and Machine Learning on SAS Viya, a cloud computing and structured statistical solution, may become a choice of computing.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82313754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-30DOI: 10.15406/bbij.2020.09.00303
G. Srinivasa Rao, Edward R. Paul
Control charts are considered as important tools when producer wants to produce goods or services of high–quality. These charts help producers to manufacture products based on specified limits by monitoring the quality beforehand.1 There are a number of control charts developed to monitor production process in different situations. One of the major characteristics of many control charts is that the production process should follow normal distribution. Ouyang et al.2 and Pearn and Wu3 they mentioned efficiency of process capability (PC) based on the production process which follows normally distributed processes. According to Aslam and Jun1 there are also other control charts which are developed based on non–normal distributions which are being used when the production process follows other distributions rather than normal. Rao4 developed a control chart for time truncated life tests using exponentiated half logistic distribution and Rao et al.5 constructed attribute control charts for the Dagum distribution under truncated life tests.
{"title":"Time truncated control chart using log logistic distribution","authors":"G. Srinivasa Rao, Edward R. Paul","doi":"10.15406/bbij.2020.09.00303","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00303","url":null,"abstract":"Control charts are considered as important tools when producer wants to produce goods or services of high–quality. These charts help producers to manufacture products based on specified limits by monitoring the quality beforehand.1 There are a number of control charts developed to monitor production process in different situations. One of the major characteristics of many control charts is that the production process should follow normal distribution. Ouyang et al.2 and Pearn and Wu3 they mentioned efficiency of process capability (PC) based on the production process which follows normally distributed processes. According to Aslam and Jun1 there are also other control charts which are developed based on non–normal distributions which are being used when the production process follows other distributions rather than normal. Rao4 developed a control chart for time truncated life tests using exponentiated half logistic distribution and Rao et al.5 constructed attribute control charts for the Dagum distribution under truncated life tests.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89911257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-30DOI: 10.15406/bbij.2020.09.00302
R. Dharmasena, Lakshika S. Nawarathna, Ruwan D. Nawarathna, V. Vithanaarachchi
In recent years, dental care has received increasing attention from people across the globe. With growing living conditions, people are more aware of preventable conditions that might be avoided. Malocclusion is one among the most studied problems in orthodontics. The statistical predictive model building plays a vital role in dentistry particularly, for clinical decision making. Developing a model for predicting the factors affecting for discontinuation of treatment is a vital step in assessing the therapeutic effect of treatment, resource management and cost reduction in the healthcare industry. Logistic regression and Probit regression models are considered as a successful widely used approach to analyze a classification problem with factor predictor variables. In this study, Naïve Bayes classifier and random forest classification models are introduced to predict discontinuation of orthodontic treatments of dental patients. Based on this study the duration of active treatment was the most significant factor affecting the discontinuation of the treatment. When comparing the four approaches, random forest classifier showed the highest accuracy and specificity, while Naïve Bayes model indicated the highest sensitivity on the prediction of discontinuation of the treatment. Besides, the classification-based approach with modern predictive algorithms shows a robust result for orthodontic data.
{"title":"Predicting cessation of orthodontic treatments using a classification-based approach","authors":"R. Dharmasena, Lakshika S. Nawarathna, Ruwan D. Nawarathna, V. Vithanaarachchi","doi":"10.15406/bbij.2020.09.00302","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00302","url":null,"abstract":"In recent years, dental care has received increasing attention from people across the globe. With growing living conditions, people are more aware of preventable conditions that might be avoided. Malocclusion is one among the most studied problems in orthodontics. The statistical predictive model building plays a vital role in dentistry particularly, for clinical decision making. Developing a model for predicting the factors affecting for discontinuation of treatment is a vital step in assessing the therapeutic effect of treatment, resource management and cost reduction in the healthcare industry. Logistic regression and Probit regression models are considered as a successful widely used approach to analyze a classification problem with factor predictor variables. In this study, Naïve Bayes classifier and random forest classification models are introduced to predict discontinuation of orthodontic treatments of dental patients. Based on this study the duration of active treatment was the most significant factor affecting the discontinuation of the treatment. When comparing the four approaches, random forest classifier showed the highest accuracy and specificity, while Naïve Bayes model indicated the highest sensitivity on the prediction of discontinuation of the treatment. Besides, the classification-based approach with modern predictive algorithms shows a robust result for orthodontic data.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86123493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-29DOI: 10.15406/bbij.2020.09.00301
K. V. Jayamol, K. K. Jose
In this paper we study a stochastic ordering namely alternate probability generating function (a.p.g.f .... ) ordering and its properties. The life distribution H(t) of a device subject to shocks governed by a Poisson process is considered as a function of the probabilities Pk of surviving the first k shocks. Various properties of the discrete failure distribution Pk are shown to be reflected in corresponding properties of the continuous life distribution H(t). A certain cumulative damage model and various applications of these models in reliability modeling are also considered.
{"title":"Shock models leading to G* class of lifetime distributions","authors":"K. V. Jayamol, K. K. Jose","doi":"10.15406/bbij.2020.09.00301","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00301","url":null,"abstract":"In this paper we study a stochastic ordering namely alternate probability generating function (a.p.g.f .... ) ordering and its properties. The life distribution H(t) of a device subject to shocks governed by a Poisson process is considered as a function of the probabilities Pk of surviving the first k shocks. Various properties of the discrete failure distribution Pk are shown to be reflected in corresponding properties of the continuous life distribution H(t). A certain cumulative damage model and various applications of these models in reliability modeling are also considered.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83185621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-06DOI: 10.15406/bbij.2020.09.00300
M. Eltehiwy, Abu-Bakr A. AbdulMotaal
The primary objective of this paper is to introduce a new measure for detecting skewness for grouped data, which is simpler than the current measures in its application. The new proposed coefficient of skewness based on the cumulative frequency data and hence uses more information from the tails of the distribution and thus will be more appropriate to detect asymmetry in the data. Another advantage of the new statistic is that it is bounded by -1 and +1; hence, the coefficients of skewness can be interpreted easily. Simulation study is employed to assess the performance of the proposed coefficient of skewness with three of the classical measure of skewness appeared in the literature using the mean square error (MSE) and mean absolute error (MAE). The simulation study strongly supports the use of the proposed measure for comparing the degrees of skewness of different frequency distributions.
{"title":"A New coefficient of Skewness for grouped data","authors":"M. Eltehiwy, Abu-Bakr A. AbdulMotaal","doi":"10.15406/bbij.2020.09.00300","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00300","url":null,"abstract":"The primary objective of this paper is to introduce a new measure for detecting skewness for grouped data, which is simpler than the current measures in its application. The new proposed coefficient of skewness based on the cumulative frequency data and hence uses more information from the tails of the distribution and thus will be more appropriate to detect asymmetry in the data. Another advantage of the new statistic is that it is bounded by -1 and +1; hence, the coefficients of skewness can be interpreted easily. Simulation study is employed to assess the performance of the proposed coefficient of skewness with three of the classical measure of skewness appeared in the literature using the mean square error (MSE) and mean absolute error (MAE). The simulation study strongly supports the use of the proposed measure for comparing the degrees of skewness of different frequency distributions.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88477885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-09DOI: 10.15406/bbij.2020.09.00299
Heon-Jae Jeong, S. Han, H. Liao, Wui-Chiang Lee
The Safety Attitudes Questionnaire (SAQ) is a popular instrument to measure safety culture; however, its six domains have not been equally analyzed and used. Perception of management (PM), one of the underutilized domains, consists of two sets of the same items: one set for unit-level managers and the other for hospital-level managers. The SAQ was administered in a large tertiary hospital in Seoul, with 1,381 questionnaires being returned, including approximately 74% from women and 54% from nurses, which reflects Korea’s healthcare professional composition well. Respondents were asked to score management’s behavior in improving quality and safety. To calculate the score difference (unit managers’ score less hospital managers’ score), the generalized estimating equation was used to take the clinical unit’s clustering effects into account. In all subgroups and all PM items, the unit managers’ score was higher than that of hospital managers; most differences were statistically significant. On a scale of 0 to 100, the greatest difference was observed in the pharmacist group (14.5). In most cases, the score difference was around four to six. Various hypothetical explanations were offered. In Korea, many hospital managers are evaluated by hospitals’ financial performance and, quite often, monetary compensation for adverse events costs less than investing in improving safety, although there is no concrete evidence for this yet. In addition, hospital management’s term lasts around two to three years, which is too short of a time for a hospital’s reputation to drop in Korea’s healthcare environment. Consequently, hospital managers naturally put less emphasis on preventing medical errors. Another explanation arises from healthcare professionals’ fear of being reprimanded after giving a low score to unit managers. Although this survey was administered anonymously, respondents could have felt uncomfortable being critical of their unit managers, who will supervise respondents for a long time. These reasons are all conjecture. Further study is needed.
{"title":"Healthcare professionals’ perception of hospital and unit-Level managers’ contribution to improving safety","authors":"Heon-Jae Jeong, S. Han, H. Liao, Wui-Chiang Lee","doi":"10.15406/bbij.2020.09.00299","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00299","url":null,"abstract":"The Safety Attitudes Questionnaire (SAQ) is a popular instrument to measure safety culture; however, its six domains have not been equally analyzed and used. Perception of management (PM), one of the underutilized domains, consists of two sets of the same items: one set for unit-level managers and the other for hospital-level managers. The SAQ was administered in a large tertiary hospital in Seoul, with 1,381 questionnaires being returned, including approximately 74% from women and 54% from nurses, which reflects Korea’s healthcare professional composition well. Respondents were asked to score management’s behavior in improving quality and safety. To calculate the score difference (unit managers’ score less hospital managers’ score), the generalized estimating equation was used to take the clinical unit’s clustering effects into account. In all subgroups and all PM items, the unit managers’ score was higher than that of hospital managers; most differences were statistically significant. On a scale of 0 to 100, the greatest difference was observed in the pharmacist group (14.5). In most cases, the score difference was around four to six. Various hypothetical explanations were offered. In Korea, many hospital managers are evaluated by hospitals’ financial performance and, quite often, monetary compensation for adverse events costs less than investing in improving safety, although there is no concrete evidence for this yet. In addition, hospital management’s term lasts around two to three years, which is too short of a time for a hospital’s reputation to drop in Korea’s healthcare environment. Consequently, hospital managers naturally put less emphasis on preventing medical errors. Another explanation arises from healthcare professionals’ fear of being reprimanded after giving a low score to unit managers. Although this survey was administered anonymously, respondents could have felt uncomfortable being critical of their unit managers, who will supervise respondents for a long time. These reasons are all conjecture. Further study is needed.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77655738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-28DOI: 10.15406/bbij.2020.09.00297
Reta Habtamu Bacha
Malnutrition among children under age five is the major public health delinquent issue in the developing world, particularly in Ethiopia. This study aimed to figure out determinants of Ethiopian children malnutrition by applying Bayesian approach with Markov chain Monte Carlo (MCMC) techniques on the 2011 EDHS data. The preliminary analysis indicated that the overall prevalence of underweight among children in Ethiopia is found 36.4%. Bayesian generalized additive regression model applied to flexibly estimate effects of socio-economic, demographic, health and environmental covariates. The estimation result showed that covariates succeeding birth interval, gender of child, child by choice not by chance, vaccination and cough are significantly affect the children nutritional status in Ethiopia. The effect of child age, mother’s age at child birth, succeeding birth intervals, number of household member and birth order were also explored non-parametrically as determinants of children nutritional status. Based up on this biometric analysis, concerned governmental and non-governmental bodies should give emphasis on the significant covariates to improve the children nutritional status of the country.
{"title":"Identifying prognosticators covariates of child nutritional status in ethiopia: A bayesian generalized additive modelling approach","authors":"Reta Habtamu Bacha","doi":"10.15406/bbij.2020.09.00297","DOIUrl":"https://doi.org/10.15406/bbij.2020.09.00297","url":null,"abstract":"Malnutrition among children under age five is the major public health delinquent issue in the developing world, particularly in Ethiopia. This study aimed to figure out determinants of Ethiopian children malnutrition by applying Bayesian approach with Markov chain Monte Carlo (MCMC) techniques on the 2011 EDHS data. The preliminary analysis indicated that the overall prevalence of underweight among children in Ethiopia is found 36.4%. Bayesian generalized additive regression model applied to flexibly estimate effects of socio-economic, demographic, health and environmental covariates. The estimation result showed that covariates succeeding birth interval, gender of child, child by choice not by chance, vaccination and cough are significantly affect the children nutritional status in Ethiopia. The effect of child age, mother’s age at child birth, succeeding birth intervals, number of household member and birth order were also explored non-parametrically as determinants of children nutritional status. Based up on this biometric analysis, concerned governmental and non-governmental bodies should give emphasis on the significant covariates to improve the children nutritional status of the country.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88446919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-28DOI: 10.15406/bbij.2020.09.00295
M. Hossain, Shahnaj Sultana Sathi, Md. Sabbir Hossain, Mst Farzana Akter, M. O. Ullah
Fish is considered as one of the most essential food items that provide proteins to build our body throughout the world. In Bangladesh, Fish and Fisheries sectors play an immensely important role in terms of nutrition, income, employment generation and foreign exchange earnings. Most of the lands in Sunamganj covered by haors and cannals and therefore many people in this area are involved in capturing fish than fish farming. In this study we aimed to assess the livelihood status of fishermen at Sunamganj. For this we randomly collected data based on a questionnaire from 425 fishermen during April 2018. We found most of the fishermen belong to middle age group and had middle family size. Around 56.3% fishermen took loan from different banks while only 3.1% received loan from NGO. The financial condition of fishers was observed very poor as the land owned by them was decreasing day by day. Though there was significant increase in monthly income compare to last 10 years, however it’s not sufficient for better livelihood. Their socio-economic condition doesn’t match with national economic progress. The results of logistic regression model shows that earning members had significant influence (OR=1.77, CI: 0.965,3.272, P<0.10) on taking loan, indicates that households with only one earning member are 1.77 times as likely to take loan than household with more than one earning members. That is taking loan is likely to increase around 77% for households with only one earning member. About 89.9% fishermen were afraid about their future earnings due to early/flash flood. Taken together, we may conclude that overall situation of the livelihood status is not so good because of more illiteracy, more loan and natural disasters like flood. So government and non-government organizations should play role to improve their economic status by providing well education to their children as well as give more incentives so that they don’t need to take loan. In addition, need to construct of embankment or dam for protecting them from flood as well.
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