Pub Date : 2021-02-23DOI: 10.11648/J.BSI.20210601.12
Justin Chu, Wen-Tse Yang, Tung-Han Hsieh, Fu-Liang Yang
Previous non-invasive Diabetes Mellitus (DM) prediction methods for rapid screening suffered from the trade-off between speed and accuracy. The accurate results of questionnaires rely on long and detailed questions thus sacrifice speed, meanwhile, photoplethysmography (PPG) offers convenient and fast testing but lacking accuracy. In this work, we developed a 5-grade model to accurately screen out non-DM subjects (low prediction grades) via one-minute PPG measurement. This efficient and effective rapid screening will practically reduce the loading for further invasive verification on the remaining DM-grade subjects. A total of 2538 subjects are recruited (DM: 1310, non-DM: 1228) with two 1-minute PPG samples taken from each subject. The model includes 8 features: 3 autonomic- and 3 vascular-related PPG features, heart rate, and waist circumference. All 8 features monotonically alter with increased DM prediction grade. The model provides users 5 DM risk grades. While defined grade 1 and grade 2 as non-DM grades, the prediction result shows a low false-negative rate of 13%. If only considering grade 1 as non-DM, the false-negative rate will be significantly reduced to 1.3%. Thus subjects predicted as grades 1 and 2 are substantially away from DM. The remaining subjects with higher DM risk grades such as grades 3, 4, and 5 (or unlikely grade 2) are recommended to take clinical-standard invasive DM test for corresponding therapeutic treatment. A table for assessing the risk index for each feature is also compiled. We have experimentally demonstrated a 1-minute pulsation measurement with PPG-based device (SpO 2 oximeter, smartphone, or wearable device) can be an efficient/effective DM rapid screening technique to filter out non-DM subjects. The resulted high-risk feature indexes also pose as warning signs of the degradation of either autonomic or vascular functions for personal healthcare management. The fast and convenient execution and useful results suggest that our approach is very simple and informative for quick DM risk assessment.
{"title":"One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate","authors":"Justin Chu, Wen-Tse Yang, Tung-Han Hsieh, Fu-Liang Yang","doi":"10.11648/J.BSI.20210601.12","DOIUrl":"https://doi.org/10.11648/J.BSI.20210601.12","url":null,"abstract":"Previous non-invasive Diabetes Mellitus (DM) prediction methods for rapid screening suffered from the trade-off between speed and accuracy. The accurate results of questionnaires rely on long and detailed questions thus sacrifice speed, meanwhile, photoplethysmography (PPG) offers convenient and fast testing but lacking accuracy. In this work, we developed a 5-grade model to accurately screen out non-DM subjects (low prediction grades) via one-minute PPG measurement. This efficient and effective rapid screening will practically reduce the loading for further invasive verification on the remaining DM-grade subjects. A total of 2538 subjects are recruited (DM: 1310, non-DM: 1228) with two 1-minute PPG samples taken from each subject. The model includes 8 features: 3 autonomic- and 3 vascular-related PPG features, heart rate, and waist circumference. All 8 features monotonically alter with increased DM prediction grade. The model provides users 5 DM risk grades. While defined grade 1 and grade 2 as non-DM grades, the prediction result shows a low false-negative rate of 13%. If only considering grade 1 as non-DM, the false-negative rate will be significantly reduced to 1.3%. Thus subjects predicted as grades 1 and 2 are substantially away from DM. The remaining subjects with higher DM risk grades such as grades 3, 4, and 5 (or unlikely grade 2) are recommended to take clinical-standard invasive DM test for corresponding therapeutic treatment. A table for assessing the risk index for each feature is also compiled. We have experimentally demonstrated a 1-minute pulsation measurement with PPG-based device (SpO 2 oximeter, smartphone, or wearable device) can be an efficient/effective DM rapid screening technique to filter out non-DM subjects. The resulted high-risk feature indexes also pose as warning signs of the degradation of either autonomic or vascular functions for personal healthcare management. The fast and convenient execution and useful results suggest that our approach is very simple and informative for quick DM risk assessment.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125677248","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 : 2021-01-22DOI: 10.11648/J.BSI.20210601.11
U. Eliyahu, Avi Magid
Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the "first wave" of the pandemic. Further study should evaluate the model accuracy based on data from the "second wave" and not only in continental Europe.
背景:2019冠状病毒病(COVID-19)疫情的爆发和全球范围内因COVID-19导致的死亡率过高，提出了开发一种简单适用的数学模型来预测不同国家死亡率的必要性，并指出COVID-19死亡率的危险因素，特别是人口危险因素。方法:根据随机选择的10个人口稠密的欧洲国家(奥地利、匈牙利、葡萄牙、瑞典、捷克、比利时、荷兰、罗马尼亚、意大利、法国)的人口统计数据(人口密度、65岁以上人口比例和城市化程度)和临床数据(每个国家确诊首例病例的天数)建立线性模型。采用IBM SPSS version 20软件建立线性回归模型。结果:提出的模型预测了所选国家的死亡率。研究发现，该模型与各国实际(报告)死亡人数高度相关(R2=0.821, p=0.042)。65岁以上人口比例、人口密度和各州出现首例病例的天数与COVID-19死亡率呈正相关，而城市化程度与死亡率呈负相关。结论:65岁以上人口比例、人口密度和暴露天数是导致死亡的潜在危险因素，城市化程度是保护因素。然而，应该记住，这个模型是基于来自中等到大量人口的数据，而且只在欧洲大陆。此外，它是根据大流行“第一波”的死亡率数据编制的。进一步的研究应该基于“第二次浪潮”的数据来评估模型的准确性，而不仅仅是在欧洲大陆。
{"title":"Estimation of Risk Factor’s Contribution to mortality from COVID-19 in Highly Populated European Countries","authors":"U. Eliyahu, Avi Magid","doi":"10.11648/J.BSI.20210601.11","DOIUrl":"https://doi.org/10.11648/J.BSI.20210601.11","url":null,"abstract":"Background: The outbreak of the COVID-19 epidemic and the excess of mortality attributed to COVID-19 worldwide raised the need to develop a simple and applicable mathematical model for predicting mortality in different countries, as well as to point out the risk factors for COVID-19 mortality, and, in particular, demographic risk factors. Methods: A linear model was developed based on demographic data (population density, percentage of population over age 65 and degree of urbanity) as well as a clinical data (number of days since the first case was diagnosed in each country) from 10 highly populated (over 8.5 million people) randomly selected European countries (Austria, Hungary, Portugal, Sweden, Czech Republic, Belgium, the Netherlands, Romania, Italy, France). A linear regression model was applied, using IBM SPSS version 20 software. Results: The proposed model predicts mortality among the selected countries. This model is found to be highly correlated (R2=0.821, p=0.042) with the actual (reported) number of deaths in each country. Percentage of population above age 65, population density and number of days since the first case appear at each state were found to be positively correlated with COVID-19 mortality, whereas urbanity were negatively correlated with mortality. Conclusions: Percentage of population above age 65 and population’s density and the number of days of exposure to COVID 19 are potential risk factors for dying from the pandemic, whereas, urbanity is considered a protective factor. However, it should be remembered that this model is based on data from medium to large populations and only in continental Europe. Moreover, it is based on mortality data of the \"first wave\" of the pandemic. Further study should evaluate the model accuracy based on data from the \"second wave\" and not only in continental Europe.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126033669","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 : 2020-12-31DOI: 10.11648/J.BSI.20200504.13
L. Jibril, O. Odetunde
Cardiovascular diseases (CVDs) have remained the leading causes of global death in the last 16 years which is the cause of mortality of 17.7 million people every year. Nowadays, people live in a time where sitting takes up the majority of their daily affairs. The sedentary behavior for prolonged periods of time can leads to a problem of deadly disease such as heart disease, obesity, and diabetes. In this paper a deterministic model for the effects of prolonged sitting is designed. The model, which consists of three ordinary differentials equations is developed and analyzed to study the optimal control analysis on sedentary behavior, physical activity in relation to cardiovascular disease (CVD) in a community. The solutions of the model uniquely exist, nonnegative for all t ≥ 0 with nonnegative initial conditions in R3+, and bounded in a region ΩN. The basic reproduction number which measures the relationship threshold is presented. The model was extended and optimal control theory was applied to examine optimal strategies for controlling or eradicating the new cases of CVD that may be borne due to a life of inactivity. The control measures comprises of education or sensitization u1, living a healthy lifestyle (good nutrition, weight management) u2, and getting plenty of physical activity u3. The impact of using possible combinations of the three intervention strategies was investigated and analyzed. The results of the optimal control model using Pontryagin maximum principle (PMP) revealed that combination of education or sensitization with any other control strategy yields better result to reduce or eradicate the risk of new cases of CVD from sedentary lifestyle.
{"title":"Mathematical Modeling and Optimal Control Analysis on Sedentary Behavior and Physical Activity in Relation to Cardiovascular Disease (CVD)","authors":"L. Jibril, O. Odetunde","doi":"10.11648/J.BSI.20200504.13","DOIUrl":"https://doi.org/10.11648/J.BSI.20200504.13","url":null,"abstract":"Cardiovascular diseases (CVDs) have remained the leading causes of global death in the last 16 years which is the cause of mortality of 17.7 million people every year. Nowadays, people live in a time where sitting takes up the majority of their daily affairs. The sedentary behavior for prolonged periods of time can leads to a problem of deadly disease such as heart disease, obesity, and diabetes. In this paper a deterministic model for the effects of prolonged sitting is designed. The model, which consists of three ordinary differentials equations is developed and analyzed to study the optimal control analysis on sedentary behavior, physical activity in relation to cardiovascular disease (CVD) in a community. The solutions of the model uniquely exist, nonnegative for all t ≥ 0 with nonnegative initial conditions in R3+, and bounded in a region ΩN. The basic reproduction number which measures the relationship threshold is presented. The model was extended and optimal control theory was applied to examine optimal strategies for controlling or eradicating the new cases of CVD that may be borne due to a life of inactivity. The control measures comprises of education or sensitization u1, living a healthy lifestyle (good nutrition, weight management) u2, and getting plenty of physical activity u3. The impact of using possible combinations of the three intervention strategies was investigated and analyzed. The results of the optimal control model using Pontryagin maximum principle (PMP) revealed that combination of education or sensitization with any other control strategy yields better result to reduce or eradicate the risk of new cases of CVD from sedentary lifestyle.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403351","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 : 2020-03-10DOI: 10.21203/rs.3.rs-16507/v1
Endale Alemayehu, T. Tilahun, Eshetu Mebrate
Introductions: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. According to updated global burden of cholera estimate 2019 in Ethiopia 68,805,272 populations are at risk of cholera with incidence rate of 4 per 1000 population and case fatality of 3.8% estimated annual number of cases 275,221.Methods: The main objective of this study is to identify the significant risk factors of dehydration status of cholera outbreak in Oromia regional state of Ethiopia. Ordinal logistic regression was used to model the data by incorporating the assumption behind this novel model. Results: The results of the study indicated that of the total 965 cholera patients, most of them 560(58%) were severely dehydrated by cholera. The overall goodness of model (p-valu=0.07) shows that the model fits the data well. Besides, the proportional odds assumption also revealed that the slop coefficients in the model are the same across dehydration status (p-value=0.094). For those have history of travel, the odds of severely dehydrated versus the combined some dehydrated and no dehydrated was exp(1.133804)=3.11 times higher than those have no history of travel (p-value<0.01). All the other factors like history of contact with other patients, other sick patients in the family, Intravenous and Antibiotics drugs are statistically significant with 5% level of significance to determine the status of dehydration. Conclusions: The ordinal logistic regression was fitted the data well and most of the included factors were significant for the dehydration status of cholera outbreak.
{"title":"Determinants of Dehydration Status and Associated Risk Factors of Cholera Outbreak in Oromia, Ethiopia","authors":"Endale Alemayehu, T. Tilahun, Eshetu Mebrate","doi":"10.21203/rs.3.rs-16507/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-16507/v1","url":null,"abstract":"\u0000 Introductions: Cholera is a diarrheal disease caused by infection of the intestine with the gram-negative bacteria Vibrio cholera. According to updated global burden of cholera estimate 2019 in Ethiopia 68,805,272 populations are at risk of cholera with incidence rate of 4 per 1000 population and case fatality of 3.8% estimated annual number of cases 275,221.Methods: The main objective of this study is to identify the significant risk factors of dehydration status of cholera outbreak in Oromia regional state of Ethiopia. Ordinal logistic regression was used to model the data by incorporating the assumption behind this novel model. Results: The results of the study indicated that of the total 965 cholera patients, most of them 560(58%) were severely dehydrated by cholera. The overall goodness of model (p-valu=0.07) shows that the model fits the data well. Besides, the proportional odds assumption also revealed that the slop coefficients in the model are the same across dehydration status (p-value=0.094). For those have history of travel, the odds of severely dehydrated versus the combined some dehydrated and no dehydrated was exp(1.133804)=3.11 times higher than those have no history of travel (p-value<0.01). All the other factors like history of contact with other patients, other sick patients in the family, Intravenous and Antibiotics drugs are statistically significant with 5% level of significance to determine the status of dehydration. Conclusions: The ordinal logistic regression was fitted the data well and most of the included factors were significant for the dehydration status of cholera outbreak.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131684144","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 : 2019-12-27DOI: 10.11648/J.BSI.20190404.12
B. Gegbe, B. Kandeh, Andrew Baimba
Sierra Leone needs strong information systems to adequately track progress made and to inform decisions about the implementation of health care programmes as it implements its recovery and resilience plans. A challenge observed with the national health management information system (HMIS) is the quality of routine reports from health facilities and districts. The objective of this research is to assess the service availability and readiness of health facilities in Moyamba district. This research was facility based cross sectional survey. A representative sample of 87 health facilities was selected for the assessment, with an oversampling of hospitals. In this sampling procedure 86% of the health facilities considered for this research were Government/Public owned facilities and 1.1% mission/faith owned facilities. Stat graphic 18 was used to do the data analysis. The district has 55% General Service Index (GSI) for all categories of health facilities. Readiness scores in preventive curative, antenatal care service and malaria services were above 90% in the district. The least readiness score was high level diagnostic equipment with 1%. Blood transmission services had the least specific readiness score of 4.3%. Government of Sierra Leone to Strengthening capacity of District Health Management Teams to plan, supervise and monitor all health facility programs at district levels.
{"title":"Service Availability and Readiness Assessment (SARA) of Health Facilities in Moyamba District Southern Province of Sierra Leone","authors":"B. Gegbe, B. Kandeh, Andrew Baimba","doi":"10.11648/J.BSI.20190404.12","DOIUrl":"https://doi.org/10.11648/J.BSI.20190404.12","url":null,"abstract":"Sierra Leone needs strong information systems to adequately track progress made and to inform decisions about the implementation of health care programmes as it implements its recovery and resilience plans. A challenge observed with the national health management information system (HMIS) is the quality of routine reports from health facilities and districts. The objective of this research is to assess the service availability and readiness of health facilities in Moyamba district. This research was facility based cross sectional survey. A representative sample of 87 health facilities was selected for the assessment, with an oversampling of hospitals. In this sampling procedure 86% of the health facilities considered for this research were Government/Public owned facilities and 1.1% mission/faith owned facilities. Stat graphic 18 was used to do the data analysis. The district has 55% General Service Index (GSI) for all categories of health facilities. Readiness scores in preventive curative, antenatal care service and malaria services were above 90% in the district. The least readiness score was high level diagnostic equipment with 1%. Blood transmission services had the least specific readiness score of 4.3%. Government of Sierra Leone to Strengthening capacity of District Health Management Teams to plan, supervise and monitor all health facility programs at district levels.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123667497","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 : 2019-12-23DOI: 10.11648/J.BSI.20190404.11
Mikias Alayu, Fikirte Girma, Mengistu Biru, T. Teshome, Desalegn Belay
Dengue fever is caused by dengue virus (DENV), a member of the genus Flavivirus, family Flaviviridae. The virus is transmitted by the infected female mosquito called Aedes aegypti. There are four serotypes, DENV1 through DENV4. Dengue fever is one of the most important re-emerging arboviral disease, more than half of the world’s population are at risk of this disease. Starting from 2013 over 12,000 cases were reported from Ethiopia. Descriptive cross-sectional study design was applied to describe dengue fever outbreak data from Kebridhar District reported to Ethiopian Public Health Institute from May to June 2017. Ratios, proportions and rates were analyzed by using Microsoft excel and findings were presented by narrations, frequency distributions and graphs. A total of 101 dengue fever cases were reported from Kebridhar District of Somali Region. Sixty-eight-point three percent (69/101) were males and 9.9% (10/101) cases were hospitalized. The positivity rate of dengue virus was 76.9% (10/13). The median age of cases was 27 years (IQR: 22 – 38). The case fatality rate was zero and the attack rate was 86 cases per 100,000 population. Eighteen-point eight percent (19/101) cases had bleeding. All cases reported that, they had open water containers, no spraying of houses for six months prior to the onset of the fever and bed net utilization rate was 30.7%. Males and 50 – 54 years old individuals were highly affected groups. Ministry of Health Regional Health Bureau and District Health Office should work on vector and environmental control activities.
{"title":"Epidemiological Description of Dengue Fever Outbreak in Kebridhar District, Somali Region, Ethiopia – 2017","authors":"Mikias Alayu, Fikirte Girma, Mengistu Biru, T. Teshome, Desalegn Belay","doi":"10.11648/J.BSI.20190404.11","DOIUrl":"https://doi.org/10.11648/J.BSI.20190404.11","url":null,"abstract":"Dengue fever is caused by dengue virus (DENV), a member of the genus Flavivirus, family Flaviviridae. The virus is transmitted by the infected female mosquito called Aedes aegypti. There are four serotypes, DENV1 through DENV4. Dengue fever is one of the most important re-emerging arboviral disease, more than half of the world’s population are at risk of this disease. Starting from 2013 over 12,000 cases were reported from Ethiopia. Descriptive cross-sectional study design was applied to describe dengue fever outbreak data from Kebridhar District reported to Ethiopian Public Health Institute from May to June 2017. Ratios, proportions and rates were analyzed by using Microsoft excel and findings were presented by narrations, frequency distributions and graphs. A total of 101 dengue fever cases were reported from Kebridhar District of Somali Region. Sixty-eight-point three percent (69/101) were males and 9.9% (10/101) cases were hospitalized. The positivity rate of dengue virus was 76.9% (10/13). The median age of cases was 27 years (IQR: 22 – 38). The case fatality rate was zero and the attack rate was 86 cases per 100,000 population. Eighteen-point eight percent (19/101) cases had bleeding. All cases reported that, they had open water containers, no spraying of houses for six months prior to the onset of the fever and bed net utilization rate was 30.7%. Males and 50 – 54 years old individuals were highly affected groups. Ministry of Health Regional Health Bureau and District Health Office should work on vector and environmental control activities.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267028","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 : 2019-08-15DOI: 10.11648/J.BSI.20190402.11
Eyiah-Bediako Francis, Bosson-Amedenu Senyefia, B. Sena
Age is a very important variable that guides clinicians to carryout diagnosis, treatment, as well as administering medical procedures to patients. Misreporting of age by patients to clinicians can have dire consequences on the patients’ health. This retrospective study used a 10 year demographic data involving the ages reported by 906,383 patients. Demographic indexes such as Whipples, Myers Blended and Joint Score were employed to analyse reported ages among insured and uninsured patients at the Cape Coast Teaching Hospital. The computed joint score values of 76.88 and 85.60 respectively for uninsured and insured patients qualified the data as highly inaccurate by the standards of interpretation of UN index. The summary of the digit preference of the uninsured and insured patients by Myers blended index approach were 29.34 and 29.87 respectively. The blended sum at the digits 0, 1, 2 and 5 exceeded 10% of the total blended population, an indication of over selection of ages ending in those digits by the insured and uninsured patients. Whipple’s index for uninsured and insured patients was 149.3 and 287.1 respectively. These values respectively show that the reliability of the ages reported were rough and very rough, by the Whipple’s index interpretation standards. The insured were found to have higher tendency of concentrating on ages ending in 0 and 5 than the uninsured. The study concluded that age data in Cape Coast Teaching Hospital is misreported and inaccurate and if not adjusted may result in wrong age-dependent medical procedures undertaken by clinicians. It was recommended among others for hospitals to institute innovative ways of recording ages such as using calendar of historical events technique where the patients could not recall their correct age.
{"title":"Statistical Analysis of Age Reporting Errors by Insured and Uninsured Patients in Cape Coast Teaching Hospital of Ghana","authors":"Eyiah-Bediako Francis, Bosson-Amedenu Senyefia, B. Sena","doi":"10.11648/J.BSI.20190402.11","DOIUrl":"https://doi.org/10.11648/J.BSI.20190402.11","url":null,"abstract":"Age is a very important variable that guides clinicians to carryout diagnosis, treatment, as well as administering medical procedures to patients. Misreporting of age by patients to clinicians can have dire consequences on the patients’ health. This retrospective study used a 10 year demographic data involving the ages reported by 906,383 patients. Demographic indexes such as Whipples, Myers Blended and Joint Score were employed to analyse reported ages among insured and uninsured patients at the Cape Coast Teaching Hospital. The computed joint score values of 76.88 and 85.60 respectively for uninsured and insured patients qualified the data as highly inaccurate by the standards of interpretation of UN index. The summary of the digit preference of the uninsured and insured patients by Myers blended index approach were 29.34 and 29.87 respectively. The blended sum at the digits 0, 1, 2 and 5 exceeded 10% of the total blended population, an indication of over selection of ages ending in those digits by the insured and uninsured patients. Whipple’s index for uninsured and insured patients was 149.3 and 287.1 respectively. These values respectively show that the reliability of the ages reported were rough and very rough, by the Whipple’s index interpretation standards. The insured were found to have higher tendency of concentrating on ages ending in 0 and 5 than the uninsured. The study concluded that age data in Cape Coast Teaching Hospital is misreported and inaccurate and if not adjusted may result in wrong age-dependent medical procedures undertaken by clinicians. It was recommended among others for hospitals to institute innovative ways of recording ages such as using calendar of historical events technique where the patients could not recall their correct age.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134456642","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 : 2019-07-31DOI: 10.11648/J.BSI.20190401.11
Bosson-Amedenu Senyefia, Joseph Otoo., Eyiah-Bediako Francis
Caesarian Section (CS) rates have been known to have geographical varaitions. The purpose of this paper was to determine Ghana’s situation (regional trend) and also to provide a two- year forcast estimates for the ten (10) regions of Ghana. The data was longitudinal and comprised monthly CS records of women from 2008 to 2017. The dataset was divided into training and testing dataset. A total of eighty four (84) months were used as the training dataset and the remaining thirty six (36) months were used as testing dataset. The ARIMA methodology was applied in the analysis. Augmented Dicker-Fuller (ADF), KPSS and the Philips-Perron (PP) unit root tests were employed to test for stationarity of the series plot. KPSS (which is known to give more robust results) and PP test consistently showed that the series was stationary (p ＜ 0.05) for all ten (10) regions, although there were some conflicting results with the ADF test for some regions. Tentative models were formulated for each region and the model with the lowest AIC was selected as the “Best” model fit for respective regions of Ghana. The “best” Model fit for Greater Accra, Central and Eastern regions were respectively SARIMA (2, 0, 0) (0, 1, 1)12, SARIMA (2, 0, 0) (0, 1, 1)12 with a Drift and SARIMA (1, 1, 1) (0, 1, 1)12. Additionally, the best model fit for Northern and Volta regions were SARIMA (3,0,2) (0,1,1)12 with drift and SARIMA (0,1,1) (0,1,1)12. Ashanti, Upper East and Western regions failed the JB test or the normality test for the residuals. Upper West and Brong Ahafo Regions were not suitable for forecasting due failure to depict white noise and ARCH test failure, respectively. The best models fit were used to forecast for 2019 and 2020. The results showed that regional variations of CS exist in Ghana. The study recommended for future studies to apply methods that will allow for forecasting for regions which failed the test under the methods used in this study.
{"title":"Time Series Analysis and Forecasting of Caesarian Section Births in Ghana","authors":"Bosson-Amedenu Senyefia, Joseph Otoo., Eyiah-Bediako Francis","doi":"10.11648/J.BSI.20190401.11","DOIUrl":"https://doi.org/10.11648/J.BSI.20190401.11","url":null,"abstract":"Caesarian Section (CS) rates have been known to have geographical varaitions. The purpose of this paper was to determine Ghana’s situation (regional trend) and also to provide a two- year forcast estimates for the ten (10) regions of Ghana. The data was longitudinal and comprised monthly CS records of women from 2008 to 2017. The dataset was divided into training and testing dataset. A total of eighty four (84) months were used as the training dataset and the remaining thirty six (36) months were used as testing dataset. The ARIMA methodology was applied in the analysis. Augmented Dicker-Fuller (ADF), KPSS and the Philips-Perron (PP) unit root tests were employed to test for stationarity of the series plot. KPSS (which is known to give more robust results) and PP test consistently showed that the series was stationary (p ＜ 0.05) for all ten (10) regions, although there were some conflicting results with the ADF test for some regions. Tentative models were formulated for each region and the model with the lowest AIC was selected as the “Best” model fit for respective regions of Ghana. The “best” Model fit for Greater Accra, Central and Eastern regions were respectively SARIMA (2, 0, 0) (0, 1, 1)12, SARIMA (2, 0, 0) (0, 1, 1)12 with a Drift and SARIMA (1, 1, 1) (0, 1, 1)12. Additionally, the best model fit for Northern and Volta regions were SARIMA (3,0,2) (0,1,1)12 with drift and SARIMA (0,1,1) (0,1,1)12. Ashanti, Upper East and Western regions failed the JB test or the normality test for the residuals. Upper West and Brong Ahafo Regions were not suitable for forecasting due failure to depict white noise and ARCH test failure, respectively. The best models fit were used to forecast for 2019 and 2020. The results showed that regional variations of CS exist in Ghana. The study recommended for future studies to apply methods that will allow for forecasting for regions which failed the test under the methods used in this study.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122090582","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 : 1900-01-01DOI: 10.11648/j.bsi.20220701.13
Nyoni Hamis Salum, Katakweba Abdul
{"title":"Prevalence of Subclinical Mastitis from Milking Dairy Goat Species Reared in Different Climatic Conditions in Morogoro Region","authors":"Nyoni Hamis Salum, Katakweba Abdul","doi":"10.11648/j.bsi.20220701.13","DOIUrl":"https://doi.org/10.11648/j.bsi.20220701.13","url":null,"abstract":"","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134354203","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 : 1900-01-01DOI: 10.11648/j.bsi.20210604.13
S. Bansal
{"title":"Determining Disease Using Machine Learning Algorithm in Medical Image Processing: A Gentle Review","authors":"S. Bansal","doi":"10.11648/j.bsi.20210604.13","DOIUrl":"https://doi.org/10.11648/j.bsi.20210604.13","url":null,"abstract":"","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116650703","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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