T. Rakhshani, H. Salehiniya, Monire Azizi, A. Ansari-Moghaddam, Hassan okati Aliaba, Seydeh Zeinab Almasi
Introduction: Recent medical and health advances have reduced mortality, consequently a relative increase in life expectancy and aging of population. One of the indices that properly indicate the status of elderly is the quality of life. Identifying the factors affecting the quality of life of the elderly in Zahedan, Iran. Methods: This cross-sectional study was performed on 600 elderly people referring to the Zahedan health centers. Sampling method was two-stage clustering, the data were collected in the check list by using quality of life (QOL) questionnaire SF12 through interview and then were analyzed using independent t-test, one-way ANOVA, Pearson correlation coefficient and multiple linear regression. Results: Of the 600 elderly men and women over 60 years, 472 subjects participated in the study, of whom 291 (61%) were male and 182 (39%) were female. The mean age of the study subjects was 66.2(4.04), and the mean overall quality of life scores in males and females were 28.4(3.7) and 29.07(3.7), respectively. The mean and standard deviation of physical component summary (PCS) and mental component summary (MCS) scores in males and females were 12.3(2.2) and 16.6(2.5), respectively. Age had inverse correlation with QOL and MS and had a direct and significant relationship with PCS. In multiple linear regression, significant relation was observed between chronic illness, hypertension, skeletal disease, diabetes, gastrointestinal disease, marital status, hookah using and smoking with PCS and also between marital status, hypertension and mental illness with MCS. Conclusion: What is obtained from this study and the other relevant studies indicate that QOL is a multifactorial phenomenon that is influenced by demographic, clinical and behavioral factors, but the role of chronic diseases is more obvious. Therefore, it seems necessary to adopt health policies to correct the lifestyle of society.
{"title":"Quality of life in the community-dwelling elderly and associated factors: A Cross-Sectional study","authors":"T. Rakhshani, H. Salehiniya, Monire Azizi, A. Ansari-Moghaddam, Hassan okati Aliaba, Seydeh Zeinab Almasi","doi":"10.18502/jbe.v8i1.10408","DOIUrl":"https://doi.org/10.18502/jbe.v8i1.10408","url":null,"abstract":"Introduction: Recent medical and health advances have reduced mortality, consequently a relative increase in life expectancy and aging of population. One of the indices that properly indicate the status of elderly is the quality of life. Identifying the factors affecting the quality of life of the elderly in Zahedan, Iran. \u0000Methods: This cross-sectional study was performed on 600 elderly people referring to the Zahedan health centers. Sampling method was two-stage clustering, the data were collected in the check list by using quality of life (QOL) questionnaire SF12 through interview and then were analyzed using independent t-test, one-way ANOVA, Pearson correlation coefficient and multiple linear regression. \u0000Results: Of the 600 elderly men and women over 60 years, 472 subjects participated in the study, of whom 291 (61%) were male and 182 (39%) were female. The mean age of the study subjects was 66.2(4.04), and the mean overall quality of life scores in males and females were 28.4(3.7) and 29.07(3.7), respectively. The mean and standard deviation of physical component summary (PCS) and mental component summary (MCS) scores in males and females were 12.3(2.2) and 16.6(2.5), respectively. Age had inverse correlation with QOL and MS and had a direct and significant relationship with PCS. In multiple linear regression, significant relation was observed between chronic illness, hypertension, skeletal disease, diabetes, gastrointestinal disease, marital status, hookah using and smoking with PCS and also between marital status, hypertension and mental illness with MCS. \u0000Conclusion: What is obtained from this study and the other relevant studies indicate that QOL is a multifactorial phenomenon that is influenced by demographic, clinical and behavioral factors, but the role of chronic diseases is more obvious. Therefore, it seems necessary to adopt health policies to correct the lifestyle of society.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43908578","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}
Mohamad Arif Awang Nawi, Wan Mohd Nazlee Wan Zainon, Nor Farid Mohd Noor, M. Khamis, Ali Sultan Al-Rifai, Muhamamd Amirul Mat Lazin, Ibrahim M. Sulaiman
Introduction: Handling the COVID-19 outbreak is one of the most novelties modern work is facing by many countries today. Massive outbreak needs countries efficacy and talent in creating new approaches. These approaches need to prevent the spread of the outbreak and increase the citizens' belief as the outbreak will damage the countries' functional capacity. Technical efficiency is used maximally to gain total control of the conditions. This study aims to measure the relative efficiency level of Southeast Asian countries in dealing with COVID-19 pandemic over one year. Methods: The relative efficiency level of the most successful countries in Southeast Asia in managing COVID-19 infection was determined using Frontier 4.1 through Stochastic Frontier Analysis (SFA) and Excel software. The technical efficiency of the SFA model is defined as the ratio of observed output to maximum feasible production. If the country's technical efficiency (TE) is greater than 80%, it is the most effective in Southeast Asia at managing COVID-19 infection, but if it is less than 80% or close to 0, it is inefficient. Results: This research aims at the COVID-19 epidemic in a Southeast Asian country, where the country with the highest technical efficiency score is the most efficient and indicates the country's ability to deal with the COVID-19 outbreak without any complications. Laos was ranked first (TE = 0.99901), with a technical efficiency score that was higher than that of most other Southeast Asian countries. Singapore comes in second position with a technical efficiency score of 0.99882. Brunei is in third place for COVID-19, with a technical efficiency score of 0.99870. Cambodia is in last place, with a score of 0.84675 for technical efficiency. Conclusion: Laos is the highest technical efficiency score among the southeast Asian countries. Various things that can lead to inefficiency include lack of awareness about standard operating procedures (SOP) among the causes of COVID-19 case infection in the workplace, and the community continues to increase. This condition may also be due to the lack of medication or vaccines to cure COVID-19. All communities around the world are expected to adopt standard operating procedures (SOP) such as wearing face masks, hand sanitizers, and social distance to curb the increasingly violent spread of COVID-19.
{"title":"Measurement of Relative Efficiency Levels of Southeast Asian Countries in Dealing with the Covid-19 Pandemic Over One Year","authors":"Mohamad Arif Awang Nawi, Wan Mohd Nazlee Wan Zainon, Nor Farid Mohd Noor, M. Khamis, Ali Sultan Al-Rifai, Muhamamd Amirul Mat Lazin, Ibrahim M. Sulaiman","doi":"10.18502/jbe.v7i4.10398","DOIUrl":"https://doi.org/10.18502/jbe.v7i4.10398","url":null,"abstract":"Introduction: Handling the COVID-19 outbreak is one of the most novelties modern work is facing by many countries today. Massive outbreak needs countries efficacy and talent in creating new approaches. These approaches need to prevent the spread of the outbreak and increase the citizens' belief as the outbreak will damage the countries' functional capacity. Technical efficiency is used maximally to gain total control of the conditions. This study aims to measure the relative efficiency level of Southeast Asian countries in dealing with COVID-19 pandemic over one year. \u0000Methods: The relative efficiency level of the most successful countries in Southeast Asia in managing COVID-19 infection was determined using Frontier 4.1 through Stochastic Frontier Analysis (SFA) and Excel software. The technical efficiency of the SFA model is defined as the ratio of observed output to maximum feasible production. If the country's technical efficiency (TE) is greater than 80%, it is the most effective in Southeast Asia at managing COVID-19 infection, but if it is less than 80% or close to 0, it is inefficient. \u0000Results: This research aims at the COVID-19 epidemic in a Southeast Asian country, where the country with the highest technical efficiency score is the most efficient and indicates the country's ability to deal with the COVID-19 outbreak without any complications. Laos was ranked first (TE = 0.99901), with a technical efficiency score that was higher than that of most other Southeast Asian countries. Singapore comes in second position with a technical efficiency score of 0.99882. Brunei is in third place for COVID-19, with a technical efficiency score of 0.99870. Cambodia is in last place, with a score of 0.84675 for technical efficiency. \u0000Conclusion: Laos is the highest technical efficiency score among the southeast Asian countries. Various things that can lead to inefficiency include lack of awareness about standard operating procedures (SOP) among the causes of COVID-19 case infection in the workplace, and the community continues to increase. This condition may also be due to the lack of medication or vaccines to cure COVID-19. All communities around the world are expected to adopt standard operating procedures (SOP) such as wearing face masks, hand sanitizers, and social distance to curb the increasingly violent spread of COVID-19.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42466583","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}
S. Soltani, Armin Zakeri, I. Pakzad, A. Tabibzadeh, A. Zakeri, Milad Zandi, Sara Akhavan Rezayat, M. Jafarzadeh, Parinaz Khalkhali Asl, Arghavan Zebardast, Y. Erfani, Ramin Shahbahrami, R. Pakzad, P. Malekifar
Introduction: Since the start of a pandemic from Wuhan, China in 2019, there is tremendous attention on the COVID-19 manifestation. One of the most important COVID-19 clinical presentations is gastrointestinal symptoms. The current systematic review study aims to focus on the implication of the gastrointestinal tract in patients infected with SARS-CoV-2. Methods: We searched literature in MEDLINE, Scopus, Web of Science, and Embase to find related article by using the following keywords "COVID-19", "SARS-CoV-2 infection", "Gastrointestinal Tract", "digestive system". The heterogeneity of included studies was quantified with the I2 statistic. A random-effects model was used to estimate the pooled prevalence and a meta-regression method was utilized to investigate the factors affecting heterogeneity between studies. Results: Of 3028 retrieved documents, 111 studies with 21126 COVID-19 cases were included. The prevalence of any Gastrointestinal symptoms was 17.22% (14.48 to 20.13). The prevalence of diarrhea was 13.75% (12.07 to 15.44), anorexia 27.41% (21.53 to 33.29) and Nausea/vomiting 8.11% (6.87 to 9.35). Furthermore, the prevalence of other symptoms in current study was fever 76.70% (73.42 to 79.83), cough 58.07% (54.59 to 61.52) and dyspnea/shortness of breath 24.63% (20.06 to 29.48). According to meta-regression results, age (p: 0.027) and fever (p<0.001) had significant effect on prevalence of any Gastrointestinal symptoms. Conclusion: The anorexia, diarrhea, nausea, and vomiting are the most common Gastrointestinal presentations.
{"title":"Gastrointestinal Manifestations of the COVID-19: A Systematic Review and MetaAnalysis with 111 studies","authors":"S. Soltani, Armin Zakeri, I. Pakzad, A. Tabibzadeh, A. Zakeri, Milad Zandi, Sara Akhavan Rezayat, M. Jafarzadeh, Parinaz Khalkhali Asl, Arghavan Zebardast, Y. Erfani, Ramin Shahbahrami, R. Pakzad, P. Malekifar","doi":"10.18502/jbe.v7i4.10389","DOIUrl":"https://doi.org/10.18502/jbe.v7i4.10389","url":null,"abstract":"Introduction: Since the start of a pandemic from Wuhan, China in 2019, there is tremendous attention on the COVID-19 manifestation. One of the most important COVID-19 clinical presentations is gastrointestinal symptoms. The current systematic review study aims to focus on the implication of the gastrointestinal tract in patients infected with SARS-CoV-2. \u0000Methods: We searched literature in MEDLINE, Scopus, Web of Science, and Embase to find related article by using the following keywords \"COVID-19\", \"SARS-CoV-2 infection\", \"Gastrointestinal Tract\", \"digestive system\". The heterogeneity of included studies was quantified with the I2 statistic. A random-effects model was used to estimate the pooled prevalence and a meta-regression method was utilized to investigate the factors affecting heterogeneity between studies. \u0000Results: Of 3028 retrieved documents, 111 studies with 21126 COVID-19 cases were included. The prevalence of any Gastrointestinal symptoms was 17.22% (14.48 to 20.13). The prevalence of diarrhea was 13.75% (12.07 to 15.44), anorexia 27.41% (21.53 to 33.29) and Nausea/vomiting 8.11% (6.87 to 9.35). Furthermore, the prevalence of other symptoms in current study was fever 76.70% (73.42 to 79.83), cough 58.07% (54.59 to 61.52) and dyspnea/shortness of breath 24.63% (20.06 to 29.48). According to meta-regression results, age (p: 0.027) and fever (p<0.001) had significant effect on prevalence of any Gastrointestinal symptoms. \u0000Conclusion: The anorexia, diarrhea, nausea, and vomiting are the most common Gastrointestinal presentations.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68130003","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}
H. Esmaily, Rasoul Alimi, Z. Meshkat, A. Hoseinpour, Omid Kiani, Fatemeh Khorashadizadeh
Introduction: Iran is facing to a rapid change in the epidemiological pattern of HIV. The trend of annual number of new HIV infections and factors associated with the development of HIV over the last two decades in Iran, were examined. Methods: The data were retrospectively collected from patients who attended the Behavior Diseases Consultation Center of Mashhad University of Medical Sciences in Iran. Individuals were contributed if they were HIV-positive. Baseline factors including demographic, behavioral and clinical characteristics for all participants were examined. Changes in population characteristics were assessed using chi-square test or the Fisher’s exact test. Results: From 1989 to 2014, 584 individuals were eligible for present analysis according to the inclusion criteria. Most patients were men (87%) with most being 36–52 years old, unemployed (58%), married (39.3%), less educated (47.7%) and had a history of imprisonment (83%). The most common modes of HIV transmission were injection drug use (IDU) (51.4%) and IDU along with the sexual relationship (26.5%). These transmission modes varied significantly according to gender (p-value<0.001). Similarly, IDU and sexual relationship were most important transmission modes in males and females, respectively. The number of new HIV infections through sexual-related modes has risen sharply in recent years. Conclusion: In Iran, the pattern of HIV transmission is shifting from unsafe injection drug to unsafe sexual contact for forthcoming years. Also, increasing trend of HIV incidence in women is remarkable. Accordingly, HIV-prevention strategies need to be changed.
{"title":"An Alarming Shift of HIV Infection from Injection Drug to Sexual Transmission in Mashhad, Iran, 1989-2014","authors":"H. Esmaily, Rasoul Alimi, Z. Meshkat, A. Hoseinpour, Omid Kiani, Fatemeh Khorashadizadeh","doi":"10.18502/jbe.v7i4.10399","DOIUrl":"https://doi.org/10.18502/jbe.v7i4.10399","url":null,"abstract":"Introduction: Iran is facing to a rapid change in the epidemiological pattern of HIV. The trend of annual number of new HIV infections and factors associated with the development of HIV over the last two decades in Iran, were examined. \u0000Methods: The data were retrospectively collected from patients who attended the Behavior Diseases Consultation Center of Mashhad University of Medical Sciences in Iran. Individuals were contributed if they were HIV-positive. Baseline factors including demographic, behavioral and clinical characteristics for all participants were examined. Changes in population characteristics were assessed using chi-square test or the Fisher’s exact test. \u0000Results: From 1989 to 2014, 584 individuals were eligible for present analysis according to the inclusion criteria. Most patients were men (87%) with most being 36–52 years old, unemployed (58%), married (39.3%), less educated (47.7%) and had a history of imprisonment (83%). The most common modes of HIV transmission were injection drug use (IDU) (51.4%) and IDU along with the sexual relationship (26.5%). These transmission modes varied significantly according to gender (p-value<0.001). Similarly, IDU and sexual relationship were most important transmission modes in males and females, respectively. The number of new HIV infections through sexual-related modes has risen sharply in recent years. \u0000Conclusion: In Iran, the pattern of HIV transmission is shifting from unsafe injection drug to unsafe sexual contact for forthcoming years. Also, increasing trend of HIV incidence in women is remarkable. Accordingly, HIV-prevention strategies need to be changed.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44050455","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}
M. Afrash, L. Erfannia, Morteza Amrae, N. Mehrabi, Saeed Jelvay, Raoof Nopour, M. Shanbehzadeh
Introduction: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary to deliver the best possible care for patients and, accordingly, diminish the pressure on the healthcare industries. Hence our paper aims to present an intelligent algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the COVID-19 and finally opted for the best-performing algorithm. Methods: In this developmental study, the clinical data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were used. The Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm identified the most relevant variables. Then, chosen features feed into the several data mining methods, including K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, HistGradient Boosting Classifier, and Support Vector Machine. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms, and finally, the best model was implemented. Results: Out of the 34 included features, 11 variables were selected as the essential features. The results of using ML algorithms indicated that the best performance belongs to the AdaBoost classifier with mean accuracy = 92.9%, mean specificity = 89.3%, mean sensitivity = 94.2%, mean F-measure = 91.6 %, mean KAPA = 94.3% and mean ROC = 92.1 %. Conclusion: The empirical results reveal that the Adaboost model yielded higher performance than other classification models and developed our Clinical Decision Support Systems (CDSS) interface to discriminate positive COVID-19 from negative cases.
{"title":"Machine Learning-Based Clinical Decision Support System for Automatic Diagnosis of COVID-19 based on Clinical Data","authors":"M. Afrash, L. Erfannia, Morteza Amrae, N. Mehrabi, Saeed Jelvay, Raoof Nopour, M. Shanbehzadeh","doi":"10.18502/jbe.v8i1.10407","DOIUrl":"https://doi.org/10.18502/jbe.v8i1.10407","url":null,"abstract":"Introduction: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary to deliver the best possible care for patients and, accordingly, diminish the pressure on the healthcare industries. Hence our paper aims to present an intelligent algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the COVID-19 and finally opted for the best-performing algorithm. \u0000Methods: In this developmental study, the clinical data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were used. The Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm identified the most relevant variables. Then, chosen features feed into the several data mining methods, including K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, HistGradient Boosting Classifier, and Support Vector Machine. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms, and finally, the best model was implemented. \u0000Results: Out of the 34 included features, 11 variables were selected as the essential features. The results of using ML algorithms indicated that the best performance belongs to the AdaBoost classifier with mean accuracy = 92.9%, mean specificity = 89.3%, mean sensitivity = 94.2%, mean F-measure = 91.6 %, mean KAPA = 94.3% and mean ROC = 92.1 %. \u0000Conclusion: The empirical results reveal that the Adaboost model yielded higher performance than other classification models and developed our Clinical Decision Support Systems (CDSS) interface to discriminate positive COVID-19 from negative cases.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45292907","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}
Introduction: The growth curve are time dependece regression models which commonly are useful in describing the rapid growth of total cases or deaths in a pandemic situation. Methods: The Gompertz and logistic functions are useful to describe the growth curve of a population or any time dependence variable such as metabolic rate, growth of tumors and total number of cases or deaths in a pervasive disease. The logistics family of growth curve including logistic, SSlogistic, generalized logistic and power logistic and Gompertz models were considered to describe the growth curve of total_cases_per_million (t_c_p_m) of COVID-19 in Iran during the 19-Feb-2020 to 28-May-2021. The models were fitted to data using nls function in R and the fitting accuracy was evaluated using the numerical and graphical approaches. Results: The logistic family and Gompertz growth curve were applied to fit the total_cases_per_million of COVID-19 in Iran as the response versus the time in days as predictor variable. The training and testing RMSE criterions were considered as the numerical criterions to assess the model accuracy. The growth curve of fitted models was compared with the growth curve of observed data. Results indicated that the logistic and Gompertz models provided a better description of target variable than the alternatives. Conclusion: As results shown, the logistic and Gompertz models provided a better description of response variable than the alternatives. Therefore, the logistic and Gompertz models are able to describe and forecast the COVID-19 variables (including total cases, death, recovered and so on) very well.
{"title":"Modeling the Number of COVID-19 Total Cases in Iran Using Gompertz and Logistic Growth Curves","authors":"Hossein Zamani","doi":"10.18502/jbe.v7i4.10396","DOIUrl":"https://doi.org/10.18502/jbe.v7i4.10396","url":null,"abstract":"Introduction: The growth curve are time dependece regression models which commonly are useful in describing the rapid growth of total cases or deaths in a pandemic situation. \u0000Methods: The Gompertz and logistic functions are useful to describe the growth curve of a population or any time dependence variable such as metabolic rate, growth of tumors and total number of cases or deaths in a pervasive disease. The logistics family of growth curve including logistic, SSlogistic, generalized logistic and power logistic and Gompertz models were considered to describe the growth curve of total_cases_per_million (t_c_p_m) of COVID-19 in Iran during the 19-Feb-2020 to 28-May-2021. The models were fitted to data using nls function in R and the fitting accuracy was evaluated using the numerical and graphical approaches. \u0000Results: The logistic family and Gompertz growth curve were applied to fit the total_cases_per_million of COVID-19 in Iran as the response versus the time in days as predictor variable. The training and testing RMSE criterions were considered as the numerical criterions to assess the model accuracy. The growth curve of fitted models was compared with the growth curve of observed data. Results indicated that the logistic and Gompertz models provided a better description of target variable than the alternatives. \u0000Conclusion: As results shown, the logistic and Gompertz models provided a better description of response variable than the alternatives. Therefore, the logistic and Gompertz models are able to describe and forecast the COVID-19 variables (including total cases, death, recovered and so on) very well.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47353872","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}
Introduction: Unplanned pregnancy is a public health problem that affects maternal and child health, including maternal death, abortion, and low birth weight. Consequently, the government established family planning for action to prevent and reduce the health problems for most disadvantaged women. Objective: This study was conducted to examine the overall prevalence of unplanned pregnancy and its associated risk factors in Ethiopia. Methods: A Population based cross-sectional study was conducted from Ethiopian 2016 demographic health survey data. A total of 3894 pregnant women were included in the study and the samples were selected trough multistage stratified cluster sampling. Uni-variate and multiple logistic regression analysis were used to identify factors associated with an unplanned pregnancy. Variables with p-value < 0.05 were identified as significant factors. Results: Among 3894 understudy pregnant women, 31.02% (95% CI = 28.21-38.58 %) of pregnancies was unplanned. The analysis result revealed that women whose age ≤ 30 years [adjusted odds ratio(AOR) = 5.42, 95 % CI=2.38 - 12.34], women in rural [AOR =1.11, 95% CI=1.03 - 2.39], illiterate women [AOR =2.3,95% CI=2.02 - 4.09], women drinks alcohol [AOR =1.45,95% CI=1.31- 1.67], smoker women [AOR = 1.52, 95% CI 1.49 - 2.65 ],women chewing chat[AOR =1.66, 95% CI=1.66 1.18 - 2.33], unemployed women[AOR =4.97, 95%CI=1.31-12.38], poor economic level [AOR =8.42,95%CI=5.87- 14.39] and noneuser contraceptive methods [AOR =1.7, 95% CI=1.14 – 3.87] were found to be associated with unplanned pregnancy. Conclusion: The prevalence of unplanned pregnancy in the study area was 31.02%. The findings suggest that certain groups of women are at increased risk of unplanned pregnancy and would benefit from targeted family planning interventions.
{"title":"Prevalence of Unplanned Pregnancy and associated risk factors among Pregnant Women in Ethiopia","authors":"Melkamu A Zeru","doi":"10.18502/jbe.v7i4.10397","DOIUrl":"https://doi.org/10.18502/jbe.v7i4.10397","url":null,"abstract":"Introduction: Unplanned pregnancy is a public health problem that affects maternal and child health, including maternal death, abortion, and low birth weight. Consequently, the government established family planning for action to prevent and reduce the health problems for most disadvantaged women. Objective: This study was conducted to examine the overall prevalence of unplanned pregnancy and its associated risk factors in Ethiopia. \u0000Methods: A Population based cross-sectional study was conducted from Ethiopian 2016 demographic health survey data. A total of 3894 pregnant women were included in the study and the samples were selected trough multistage stratified cluster sampling. Uni-variate and multiple logistic regression analysis were used to identify factors associated with an unplanned pregnancy. Variables with p-value < 0.05 were identified as significant factors. \u0000Results: Among 3894 understudy pregnant women, 31.02% (95% CI = 28.21-38.58 %) of pregnancies was unplanned. The analysis result revealed that women whose age ≤ 30 years [adjusted odds ratio(AOR) = 5.42, 95 % CI=2.38 - 12.34], women in rural [AOR =1.11, 95% CI=1.03 - 2.39], illiterate women [AOR =2.3,95% CI=2.02 - 4.09], women drinks alcohol [AOR =1.45,95% CI=1.31- 1.67], smoker women [AOR = 1.52, 95% CI 1.49 - 2.65 ],women chewing chat[AOR =1.66, 95% CI=1.66 1.18 - 2.33], unemployed women[AOR =4.97, 95%CI=1.31-12.38], poor economic level [AOR =8.42,95%CI=5.87- 14.39] and noneuser contraceptive methods [AOR =1.7, 95% CI=1.14 – 3.87] were found to be associated with unplanned pregnancy. \u0000Conclusion: The prevalence of unplanned pregnancy in the study area was 31.02%. The findings suggest that certain groups of women are at increased risk of unplanned pregnancy and would benefit from targeted family planning interventions.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49425171","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}
M. Ghodsi, B. Larijani, Shahin Roshani, M. Mohammad Amoli, F. Razi, A. Keshtkar, P. Khashayar, Fariba Zarrabi, M. R. Mohajeri-Tehrani
Introduction: An important part of preventing major common diseases is identifying genetic factors that contribute to their occurrence. For the first time in our knowledge, we investigated the association between five polymorphisms of vitamin D receptor (VDR) gene (ApaI, BsmI, FokI, EcoRV, and TaqI) and low bone density/osteopenia/osteoporosis in individuals with type 2 diabetes using classification and regression tree (CART) algorithms. Methods: Data from 158 participants with T2D were used to develop the CART analysis. The binary output variable was "bone state" with low or normal values. Age and BMI (continuous variables), vitamin D deficiency (yes/no), and gender (binary variables), as well as the studied polymorphism of the VDR gene (categorical variables) all played a role in the explanatory model. A 5-fold cross-validation process was used for model validation. Results: Participants were divided into three groups: men, women, and both sexes. In all groups, age was the major factor predicting the low state in the final obtained tree model. The second most significant predictor in each model was BMI in both sexes (accuracy:75.30% ± 2.80%, AUC: 0.740 ± 0.064), EcoRV polymorphism in women (accuracy: 80.79% ± 6.58%, AUC:0.785 ± 0.063), and TaqI polymorphism in men (accuracy: 76.36% ± 3.05%, AUC:0.706 ± 0.125). Conclusion: Model validation of the final tree models demonstrated that the use of CART algorithms could be an acceptable technique for risk factors of osteoporosis among individuals with T2D. Our recommendation is to conduct more population-based studies. We hope this study will serve as a basis for future research.
{"title":"An application of CART algorithms for detection of an association between VDR polymorphisms and reduced bone density in individuals with type 2 diabetes: a population-based cross-sectional study","authors":"M. Ghodsi, B. Larijani, Shahin Roshani, M. Mohammad Amoli, F. Razi, A. Keshtkar, P. Khashayar, Fariba Zarrabi, M. R. Mohajeri-Tehrani","doi":"10.18502/jbe.v8i1.10406","DOIUrl":"https://doi.org/10.18502/jbe.v8i1.10406","url":null,"abstract":"Introduction: An important part of preventing major common diseases is identifying genetic factors that contribute to their occurrence. For the first time in our knowledge, we investigated the association between five polymorphisms of vitamin D receptor (VDR) gene (ApaI, BsmI, FokI, EcoRV, and TaqI) and low bone density/osteopenia/osteoporosis in individuals with type 2 diabetes using classification and regression tree (CART) algorithms. \u0000Methods: Data from 158 participants with T2D were used to develop the CART analysis. The binary output variable was \"bone state\" with low or normal values. Age and BMI (continuous variables), vitamin D deficiency (yes/no), and gender (binary variables), as well as the studied polymorphism of the VDR gene (categorical variables) all played a role in the explanatory model. A 5-fold cross-validation process was used for model validation. \u0000Results: Participants were divided into three groups: men, women, and both sexes. In all groups, age was the major factor predicting the low state in the final obtained tree model. The second most significant predictor in each model was BMI in both sexes (accuracy:75.30% ± 2.80%, AUC: 0.740 ± 0.064), EcoRV polymorphism in women (accuracy: 80.79% ± 6.58%, AUC:0.785 ± 0.063), and TaqI polymorphism in men (accuracy: 76.36% ± 3.05%, AUC:0.706 ± 0.125). \u0000Conclusion: Model validation of the final tree models demonstrated that the use of CART algorithms could be an acceptable technique for risk factors of osteoporosis among individuals with T2D. Our recommendation is to conduct more population-based studies. We hope this study will serve as a basis for future research.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46539614","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}
Introduction: In clinical practices, multiple biomarkers are frequently used on the same subjects for the diagnosis of an adverse outcome. This study compares two alternative multiple linear regression approaches as the logistic regression model and the discriminant function score in combing several markers. Methods: Ten thousand simulated data sets were generated from binormal and non-binormal pairs of distributions with different sample sizes and correlation structures. Each dataset underwent a logistic regression and the discriminant analysis simultaneously. The ROC analysis was performed with each marker alone and also their combining scores. For two alternative approaches, the average of AUC and its root mean square error (RMSE) were estimated over 10000 replications trials for all configurations and sample sizes used. The practical utility of the two methods is further illustrated with a clinical example of real data as well. Results: The two approaches yielded identical accuracy in particular with binormal data. With non- binormal data, the logistic regression risk score produced an equal or slightly better accuracy than the discriminate function score. Conclusion: Overall, the two approaches yield rather identical results. However, adopting the logistic regression model may incorporate a slightly better accuracy index than discriminant analysis with nonbinormal data.
{"title":"Statistical Considerations in Combining Multiple Biomarkers for Diagnostic Classification: Logistic Regression Risk Score Versus Discriminant Function Score","authors":"K. Hajian-Tilaki, Z. Graili, V. Nassiri","doi":"10.18502/jbe.v8i2.10412","DOIUrl":"https://doi.org/10.18502/jbe.v8i2.10412","url":null,"abstract":"Introduction: In clinical practices, multiple biomarkers are frequently used on the same subjects for the diagnosis of an adverse outcome. This study compares two alternative multiple linear regression approaches as the logistic regression model and the discriminant function score in combing several markers. \u0000Methods: Ten thousand simulated data sets were generated from binormal and non-binormal pairs of distributions with different sample sizes and correlation structures. Each dataset underwent a logistic regression and the discriminant analysis simultaneously. The ROC analysis was performed with each marker alone and also their combining scores. For two alternative approaches, the average of AUC and its root mean square error (RMSE) were estimated over 10000 replications trials for all configurations and sample sizes used. The practical utility of the two methods is further illustrated with a clinical example of real data as well. \u0000Results: The two approaches yielded identical accuracy in particular with binormal data. With non- binormal data, the logistic regression risk score produced an equal or slightly better accuracy than the discriminate function score. \u0000Conclusion: Overall, the two approaches yield rather identical results. However, adopting the logistic regression model may incorporate a slightly better accuracy index than discriminant analysis with nonbinormal data.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47839307","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}
Introduction: Anemia is the most common public problem caused by nutritional deficiency diseases among women of reproductive age. The main objective of this study was determining the regional variation and associated factors of anemia status among women of reproductive age in Ethiopia. Methods: A cross-sectional study was conducted among 14,489 women who enrolled in Ethiopia demographic and health survey data of 2016. Binary and multilevel logistic regression was carried out for variables to determine associated factors with anemia status of women and its regional variations at ascertained of 5% level. This study was used information criteria to compared candidates models. Results: This finding shows that women who use improved source of drinking water (OR=1.98, 95%CI=1.05, 3.72), being in middle wealth index (OR=0.25, 95%CI=0.10, 0.63), being in rich wealth index (OR=0.42, 95%CI=0.19, 0.94), having age at 1st birth in 20-24 years(OR=0.24, 95%CI=0.11, 0.53), having number of living children 1-2(OR=3.68, 95%CI=3.48, 4.98), having number of living children 3-4(OR=3.03, 95%CI=2.48, 4.05) and women who used government health center for place of delivery(OR=0.96, 95%CI=0.22, 1.70) were significantly related to anemia status of women. Conclusion: This finding concluded that there is a significant variation of anemia status of women between regions in Ethiopia. Women in the middle and rich wealth index was less likely to be anemic than poor. Women having age at 1st birth in 20-24 years and women who used government health center for place of delivery were less likely to be anemic. But women having number of living children 1-2 and 3-4 were more likely to be more anemic than no child. Likewise, women who use improved source of drinking water were more likely to be anemic as compared to an unimproved source of drinking water. It is recommended that health workers should begive attention to these proximate determinants on anemia at regional level.
{"title":"Multilevel Modeling on the Anemia status of Women in Ethiopia","authors":"Kindu Kebede Gebre, M. Demissie","doi":"10.18502/jbe.v8i1.10402","DOIUrl":"https://doi.org/10.18502/jbe.v8i1.10402","url":null,"abstract":"Introduction: Anemia is the most common public problem caused by nutritional deficiency diseases among women of reproductive age. The main objective of this study was determining the regional variation and associated factors of anemia status among women of reproductive age in Ethiopia. \u0000Methods: A cross-sectional study was conducted among 14,489 women who enrolled in Ethiopia demographic and health survey data of 2016. Binary and multilevel logistic regression was carried out for variables to determine associated factors with anemia status of women and its regional variations at ascertained of 5% level. This study was used information criteria to compared candidates models. \u0000Results: This finding shows that women who use improved source of drinking water (OR=1.98, 95%CI=1.05, 3.72), being in middle wealth index (OR=0.25, 95%CI=0.10, 0.63), being in rich wealth index (OR=0.42, 95%CI=0.19, 0.94), having age at 1st birth in 20-24 years(OR=0.24, 95%CI=0.11, 0.53), having number of living children 1-2(OR=3.68, 95%CI=3.48, 4.98), having number of living children 3-4(OR=3.03, 95%CI=2.48, 4.05) and women who used government health center for place of delivery(OR=0.96, 95%CI=0.22, 1.70) were significantly related to anemia status of women. \u0000Conclusion: This finding concluded that there is a significant variation of anemia status of women between regions in Ethiopia. Women in the middle and rich wealth index was less likely to be anemic than poor. Women having age at 1st birth in 20-24 years and women who used government health center for place of delivery were less likely to be anemic. But women having number of living children 1-2 and 3-4 were more likely to be more anemic than no child. Likewise, women who use improved source of drinking water were more likely to be anemic as compared to an unimproved source of drinking water. It is recommended that health workers should begive attention to these proximate determinants on anemia at regional level.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42866333","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}
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