Opeyemi P. Ogundile, Hilary I. Okagbue, Akinwumi A. Akinpelu, Adedayo F. Adedotun, Toluwalase J. Akingbade
Background: HIV/AIDS is endemic in Nigeria since the first case was reported in 1986. Several risk factors contribute to its prevalence, and the successive government has devised different programs to halt the spread. Awareness is one of those programs that helps to promote voluntary testing and prevention of HIV. The aim of this paper is to assess the level of awareness of HIV/AIDS among private and public primary school pupils in Ado-Odo, Ota, Southwest Nigeria. Methods: Questionnaire was used as the tool for data collection and p-value < 0.05 was considered significant. Multistage sampling was used to select four primary schools divided into equal numbers of private and public schools. Thereafter, simple random sampling was used to administer the questionnaire to the pupils. The research was conducted in May 2019 and SPSS 23.0 was used in the data analysis. Mediation analysis was used to build the hierarchal models that describe the interrelationship among the variables that was used to measure the level of awareness. Results: Out of 400 questionnaires distributed, 354 representing 88.5% were used for the final analysis. 173 (48.9%) and 181 (51.1%) of the primary school pupils (respondents) were males and females, respectively. The main results are given as follows: The awareness of mode of transmission was the highest and followed by knowledge of preventive measures, general knowledge of HIV/AIDS and knowledge of non-risk factors in descending order. Hierarchical regression analysis yielded two mediation models. Firstly, knowledge of preventive measures mediate the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Secondly, knowledge of non-risk factors mediates the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Conclusion: Awareness of how the infection cannot be transmitted is low which connotes stigmatization. Attitudinal changes are needed and awareness campaigns should be channeled to private primary school. Also, the hierarchical models have provided the link through which possible preventive measures could be explored. Received: June 29, 2023Accepted: July 29, 2023
{"title":"MEDIATORS OF HIV/AIDS AWARENESS AMONG PRIMARY SCHOOL PUPILS IN NIGERIA","authors":"Opeyemi P. Ogundile, Hilary I. Okagbue, Akinwumi A. Akinpelu, Adedayo F. Adedotun, Toluwalase J. Akingbade","doi":"10.17654/0973514323017","DOIUrl":"https://doi.org/10.17654/0973514323017","url":null,"abstract":"Background: HIV/AIDS is endemic in Nigeria since the first case was reported in 1986. Several risk factors contribute to its prevalence, and the successive government has devised different programs to halt the spread. Awareness is one of those programs that helps to promote voluntary testing and prevention of HIV. The aim of this paper is to assess the level of awareness of HIV/AIDS among private and public primary school pupils in Ado-Odo, Ota, Southwest Nigeria. Methods: Questionnaire was used as the tool for data collection and p-value < 0.05 was considered significant. Multistage sampling was used to select four primary schools divided into equal numbers of private and public schools. Thereafter, simple random sampling was used to administer the questionnaire to the pupils. The research was conducted in May 2019 and SPSS 23.0 was used in the data analysis. Mediation analysis was used to build the hierarchal models that describe the interrelationship among the variables that was used to measure the level of awareness. Results: Out of 400 questionnaires distributed, 354 representing 88.5% were used for the final analysis. 173 (48.9%) and 181 (51.1%) of the primary school pupils (respondents) were males and females, respectively. The main results are given as follows: The awareness of mode of transmission was the highest and followed by knowledge of preventive measures, general knowledge of HIV/AIDS and knowledge of non-risk factors in descending order. Hierarchical regression analysis yielded two mediation models. Firstly, knowledge of preventive measures mediate the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Secondly, knowledge of non-risk factors mediates the relationship between knowledge of mode of transmission and general knowledge of HIV/AIDS. Conclusion: Awareness of how the infection cannot be transmitted is low which connotes stigmatization. Attitudinal changes are needed and awareness campaigns should be channeled to private primary school. Also, the hierarchical models have provided the link through which possible preventive measures could be explored. Received: June 29, 2023Accepted: July 29, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135740169","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}
Abolfazl Payandeh, Habibollah Esmaily, Masoud Salehi, Seyed Mahdi Amir Jahanshahi, Maryam Salari, Seyed Ali Alamdaran, Ahmad Bolouri
Today, there is a high demand for artificial intelligence (AI) applications in distinct areas of research. AI can be used in the medical context to help in clinical decision-making and limited resource allocation. The present study proposes the best model for the detection of COVID-19, the prediction of disease in new cases, and also determines the top significant features related to COVID-19, using DL algorithms as a subset of AI techniques. In this retrospective population-based study, 10862 individuals suspicious of COVID-19 participated. The information was collected from 35 different hospitals across Khorasan-Razavi province, Northeast of Iran, from 20 February 2020 to 21 June 2021. We employed artificial neural networks (ANN), random forests (RF), decision tree (DT), support vector machines (SVM), boosted trees (BT), and logistic regression (LR) DL algorithms. Our findings indicated that the RF model had higher performance than all other algorithms. The RF algorithm had a sensitivity of 66%, specificity of 95%, precision of 88%, accuracy of 85%, and AUC of 74%. Our study found that the common top predictors for detecting COVID-19 were: age, SpO2, reception season, CT result, contact history, sex, and fever. RF model can aid in clinical decision-making and limited resource allocation. This model needs to be externally validated in larger populations, more features, and multicenter settings. Received: August 1, 2023Accepted: September 4, 2023
{"title":"A DEEP LEARNING APPROACH FOR DIAGNOSIS OF COVID-19 INFECTION AND ITS RELATED FACTORS: A POPULATION-BASED STUDY","authors":"Abolfazl Payandeh, Habibollah Esmaily, Masoud Salehi, Seyed Mahdi Amir Jahanshahi, Maryam Salari, Seyed Ali Alamdaran, Ahmad Bolouri","doi":"10.17654/0973514323016","DOIUrl":"https://doi.org/10.17654/0973514323016","url":null,"abstract":"Today, there is a high demand for artificial intelligence (AI) applications in distinct areas of research. AI can be used in the medical context to help in clinical decision-making and limited resource allocation. The present study proposes the best model for the detection of COVID-19, the prediction of disease in new cases, and also determines the top significant features related to COVID-19, using DL algorithms as a subset of AI techniques. In this retrospective population-based study, 10862 individuals suspicious of COVID-19 participated. The information was collected from 35 different hospitals across Khorasan-Razavi province, Northeast of Iran, from 20 February 2020 to 21 June 2021. We employed artificial neural networks (ANN), random forests (RF), decision tree (DT), support vector machines (SVM), boosted trees (BT), and logistic regression (LR) DL algorithms. Our findings indicated that the RF model had higher performance than all other algorithms. The RF algorithm had a sensitivity of 66%, specificity of 95%, precision of 88%, accuracy of 85%, and AUC of 74%. Our study found that the common top predictors for detecting COVID-19 were: age, SpO2, reception season, CT result, contact history, sex, and fever. RF model can aid in clinical decision-making and limited resource allocation. This model needs to be externally validated in larger populations, more features, and multicenter settings. Received: August 1, 2023Accepted: September 4, 2023","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135831139","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}
{"title":"COMPUTATIONAL STATISTICS AND DATA ANALYSIS TO DETERMINE FINANCIAL AND ECONOMICAL IMPACTS OF COVID-19","authors":"A. T. Abdulrahman","doi":"10.17654/0973514323014","DOIUrl":"https://doi.org/10.17654/0973514323014","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44021649","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}
Charles K. Mutai, P. McSharry, I. Ngaruye, E. Musabanganji
{"title":"TEMPORAL TRENDS OF HIV PREVALENCE IN SUB-SAHARAN AFRICA","authors":"Charles K. Mutai, P. McSharry, I. Ngaruye, E. Musabanganji","doi":"10.17654/0973514323015","DOIUrl":"https://doi.org/10.17654/0973514323015","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45491404","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}
I. Elbatal, Safar M. Alghamdi, A. Ghorbal, A. W. Shawki
{"title":"EXPONENTIATED KAVYA-MANOHARAN BURR X DISTRIBUTION: ESTIMATION UNDER CENSORED TYPE II WITH APPLICATIONS IN MEDICAL DATA","authors":"I. Elbatal, Safar M. Alghamdi, A. Ghorbal, A. W. Shawki","doi":"10.17654/0973514323013","DOIUrl":"https://doi.org/10.17654/0973514323013","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"1 1","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67808126","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}
{"title":"APPLICATION OF ROBUST REGRESSION ON SEA SURFACE TEMPERATURE DATA IN THE INDIAN OCEAN","authors":"N. Mohamed","doi":"10.17654/0973514323012","DOIUrl":"https://doi.org/10.17654/0973514323012","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42867468","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}
{"title":"THREE-STATE MARKOV MODEL FOR CONGESTIVE HEART FAILURE","authors":"P. T. Sakkeel, Tirupathi Rao Padi, V. Kanimozhi","doi":"10.17654/0973514323011","DOIUrl":"https://doi.org/10.17654/0973514323011","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47702923","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}
{"title":"AUTOMATED EVALUATION OF SUPERVISED LEARNING ALGORITHM FOR ENDOMETRIOSIS PREDICTION","authors":"S. Visalaxi, T. Sudalaimuthu, K. Hemapriya","doi":"10.17654/0973514323009","DOIUrl":"https://doi.org/10.17654/0973514323009","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47630186","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}
{"title":"AUTOMATED EVALUATION OF SUPERVISED LEARNING ALGORITHM FOR ENDOMETRIOSIS PREDICTION","authors":"V. Suriya, R. Geetha","doi":"10.17654/0973514323010","DOIUrl":"https://doi.org/10.17654/0973514323010","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44066714","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}
{"title":"MODELING THE MEDICAL DATA USING A NEW THREE-PARAMETER DISTRIBUTION WITH STATISTICAL PROPERTIES","authors":"Mohammed N. Alshahrani","doi":"10.17654/0973514323008","DOIUrl":"https://doi.org/10.17654/0973514323008","url":null,"abstract":"","PeriodicalId":40703,"journal":{"name":"JP Journal of Biostatistics","volume":"1 1","pages":""},"PeriodicalIF":0.1,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42492396","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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