Pub Date : 2021-08-30DOI: 10.15406/bbij.2021.10.00336
J. Tallon, Paulo Gomes, L. Bacelar-Nicolau, Sérgio Bacelar
Introduction: About a year and a half after the declaration of the COVID-19 pandemic, almost the entire planet has been affected by SARS-CoV-2 coronavirus and its variants, with serious public health consequences and other repercussions not yet thoroughly evaluated or foreseen in terms of economic, financial and social disruption throughout communities. Therefore, it is of utmost importance to understand the geography of the evolution of successive pandemic waves. Particularly in European countries, where, in recent decades, more advanced models for cohesion and competitiveness of a whole with more than 400 million inhabitants have been achieved, with ambitious challenges for horizon 2030 regarding this vast territory’s economic, social, and environmental sustainability. Objective: The main objective of this research is to describe the multivariate trajectories of COVID-19 incidence, mortality, hospital admissions, ICU admissions and testing, over three successive waves, covering all European Union (EU) countries with more than two million inhabitants, over 14-days periods before May 4 2020, until February 22 2021. Methods: This research includes 22 European countries representing about 98.8% of the EU population, described by six epidemiological variables over 43 time periods from the ECDC database: the 14-day notification rate Biometrics & Biostatistics International Journal Research Article Open Access of new cases reported for 100,000 inhabitants; the 14-day notification rate of reported deaths per one million inhabitants; the mean and the rate for 100,000 population of hospital occupancy and ICU occupancy; the testing rate per 100,000 population; and the 14-days percentage of test positivity An exploratory data analysis of each epidemiological variable identified a typology of countries profiles evolution. Multivariate exploratory statistical methods, namely a 3-way data analysis (double principal components and rank principal components analyses), were applied with software R version 4.1.0. Results: The multivariate evolution profile of the COVID-19 pandemic in the EU over the studied period highlighted 3 phases: the first phase over 24 time periods, with a relatively low COVID-19 incidence, hitting only part of EU countries; a second phase at the beginning of the second wave, when COVID-19 spread to most countries, with a higher impact on national health systems; lastly, a third phase coincident with the peak of the second wave and the onset of the third wave, a particularly reactive phase from the public authorities, with intensified testing of the population. These results are clear from the principal component analysis of the centres of gravity of the 43 time periods (interstructure). The multivariate statistical analysis of the global dataset of all countries over the 43 time periods additionally provides the main factorial representation of the trajectories of COVID-19 for each country in direct comparison with the global average rank
{"title":"A three-way multivariate data analysis: comparison of EU countries’ COVID-19 incidence trajectories from May 2020 to February 2021","authors":"J. Tallon, Paulo Gomes, L. Bacelar-Nicolau, Sérgio Bacelar","doi":"10.15406/bbij.2021.10.00336","DOIUrl":"https://doi.org/10.15406/bbij.2021.10.00336","url":null,"abstract":"Introduction: About a year and a half after the declaration of the COVID-19 pandemic, almost the entire planet has been affected by SARS-CoV-2 coronavirus and its variants, with serious public health consequences and other repercussions not yet thoroughly evaluated or foreseen in terms of economic, financial and social disruption throughout communities. Therefore, it is of utmost importance to understand the geography of the evolution of successive pandemic waves. Particularly in European countries, where, in recent decades, more advanced models for cohesion and competitiveness of a whole with more than 400 million inhabitants have been achieved, with ambitious challenges for horizon 2030 regarding this vast territory’s economic, social, and environmental sustainability. Objective: The main objective of this research is to describe the multivariate trajectories of COVID-19 incidence, mortality, hospital admissions, ICU admissions and testing, over three successive waves, covering all European Union (EU) countries with more than two million inhabitants, over 14-days periods before May 4 2020, until February 22 2021. Methods: This research includes 22 European countries representing about 98.8% of the EU population, described by six epidemiological variables over 43 time periods from the ECDC database: the 14-day notification rate Biometrics & Biostatistics International Journal Research Article Open Access of new cases reported for 100,000 inhabitants; the 14-day notification rate of reported deaths per one million inhabitants; the mean and the rate for 100,000 population of hospital occupancy and ICU occupancy; the testing rate per 100,000 population; and the 14-days percentage of test positivity An exploratory data analysis of each epidemiological variable identified a typology of countries profiles evolution. Multivariate exploratory statistical methods, namely a 3-way data analysis (double principal components and rank principal components analyses), were applied with software R version 4.1.0. Results: The multivariate evolution profile of the COVID-19 pandemic in the EU over the studied period highlighted 3 phases: the first phase over 24 time periods, with a relatively low COVID-19 incidence, hitting only part of EU countries; a second phase at the beginning of the second wave, when COVID-19 spread to most countries, with a higher impact on national health systems; lastly, a third phase coincident with the peak of the second wave and the onset of the third wave, a particularly reactive phase from the public authorities, with intensified testing of the population. These results are clear from the principal component analysis of the centres of gravity of the 43 time periods (interstructure). The multivariate statistical analysis of the global dataset of all countries over the 43 time periods additionally provides the main factorial representation of the trajectories of COVID-19 for each country in direct comparison with the global average rank","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86636887","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 : 2021-02-25DOI: 10.15406/bbij.2021.10.00326
Rufai Iliyasu, I. Etikan
The possibility that researchers should be able to obtain data from all cases is questionable. There is a need; therefore, this article provides a probability and non-probability sampling. In this paper we studied the differences and similarities of the two with approach that is more of fritter away time, cost sufficient with energy required throughout the sample observed. The pair shows the differences and similarities between them, different articles were reviewed to compare the two. Quota sampling and Stratified sampling are close to each other. Both require the division into groups of the target population. The main goal of both methods is to select a representative sample and facilitate sub-group research. There are major variations, however. Stratified sampling uses simple random sampling when the categories are generated; sampling of the quota uses sampling of availability. For stratified sampling, a sampling frame is necessary, but not needed for quota sampling. More specifically, stratified sampling is a method of probability sampling which enables the calculation of the sampling error. For quota samples, this is not possible. Quota sampling is therefore primarily used by market analysts rather than stratified sampling, as it is mostly cost-effective and easy to conduct and has the appealing equity of satisfying population reach. However, it disguises potentially significant bias.
{"title":"Comparison of quota sampling and stratified random sampling","authors":"Rufai Iliyasu, I. Etikan","doi":"10.15406/bbij.2021.10.00326","DOIUrl":"https://doi.org/10.15406/bbij.2021.10.00326","url":null,"abstract":"The possibility that researchers should be able to obtain data from all cases is questionable. There is a need; therefore, this article provides a probability and non-probability sampling. In this paper we studied the differences and similarities of the two with approach that is more of fritter away time, cost sufficient with energy required throughout the sample observed. The pair shows the differences and similarities between them, different articles were reviewed to compare the two. Quota sampling and Stratified sampling are close to each other. Both require the division into groups of the target population. The main goal of both methods is to select a representative sample and facilitate sub-group research. There are major variations, however. Stratified sampling uses simple random sampling when the categories are generated; sampling of the quota uses sampling of availability. For stratified sampling, a sampling frame is necessary, but not needed for quota sampling. More specifically, stratified sampling is a method of probability sampling which enables the calculation of the sampling error. For quota samples, this is not possible. Quota sampling is therefore primarily used by market analysts rather than stratified sampling, as it is mostly cost-effective and easy to conduct and has the appealing equity of satisfying population reach. However, it disguises potentially significant bias.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84144877","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 : 2021-02-05DOI: 10.15406/BBIJ.2021.10.00324
Othmar W. Winkler
This study explores the correlation between two variables and to demonstrate a simple graphic method to assess their degree of correlation. Following the lead of early English biometricians, it has been tacitly assumed that the studied variables develop in the same direction: when variable A’s measurements are higher from one object to another, the measurements of variable B, also are higher. The customary measure of co-relation relies on a least squares fitted trend line, then assuming that the trend is more real than, and has priority over the individually recorded data. The situation changes when measurements of variables develop in opposite directions: The very first data set I used to perform a correlation analysis was a study of student grades achieved and the percentage of their having missed classes: the more a student was absent from class, the lower were his achieved grades. In that situation the accepted model of correlation analysis – the mathematically fitted straight line and the squared distance of each student’s record from that line - was not appropriate. The usual correlation coefficient contradicted visual evidence of those data because the model underlying that situation treats the individual data as having more reality value than the general trend, but not as deviations or errors. The visual appearance, the graph of that situation, resembles a rectangular triangle, formed by the horizontal and vertical axis as its catheters, and the hypotenuse formed by a line through and representing the highest data points. This image justifies the expression “Triangular correlation”.
{"title":"A simple graphic method to assess correlation","authors":"Othmar W. Winkler","doi":"10.15406/BBIJ.2021.10.00324","DOIUrl":"https://doi.org/10.15406/BBIJ.2021.10.00324","url":null,"abstract":"This study explores the correlation between two variables and to demonstrate a simple graphic method to assess their degree of correlation. Following the lead of early English biometricians, it has been tacitly assumed that the studied variables develop in the same direction: when variable A’s measurements are higher from one object to another, the measurements of variable B, also are higher. The customary measure of co-relation relies on a least squares fitted trend line, then assuming that the trend is more real than, and has priority over the individually recorded data. The situation changes when measurements of variables develop in opposite directions: The very first data set I used to perform a correlation analysis was a study of student grades achieved and the percentage of their having missed classes: the more a student was absent from class, the lower were his achieved grades. In that situation the accepted model of correlation analysis – the mathematically fitted straight line and the squared distance of each student’s record from that line - was not appropriate. The usual correlation coefficient contradicted visual evidence of those data because the model underlying that situation treats the individual data as having more reality value than the general trend, but not as deviations or errors. The visual appearance, the graph of that situation, resembles a rectangular triangle, formed by the horizontal and vertical axis as its catheters, and the hypotenuse formed by a line through and representing the highest data points. This image justifies the expression “Triangular correlation”.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77629004","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: 10.15406/BBIJ.2020.09.00322
Chathura B. Wickrama, Ruwan D. Nawarathna, Lakshika S. Nawarathna
Crimes have been disturbing threats to all the Sri Lankans all over the country. Finding the main variables associated with crimes are very vital for policymakers. Our main goal in this study is to forecast of homicides, rapes and counterfeiting currency from 2013 to 2020 using auto-regressive conditional Poisson (ACP) and auto-regressive integrated moving average (ARIMA) models. All the predictions are made assuming that the prevailing conditions in the country affecting crime rates remain unchanged during the period. Moreover, multiple linear regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to identify the key variables associated with crimes. Profiling of districts as safe or unsafe was performed based on the overall total crime rate of Sri Lanka which is to compare with individual district’s crime rates. Data were collected from the Department of Police and Department of Census and Statistics, Sri Lanka. It is observed that there are 14 safe and 11 unsafe districts in Sri Lanka. Moreover, it is found that the total migrant population and percentage of urban population is positively correlated with total crime. Besides, total migrant population, unemployment rate, mean household income and percentage of the urban population are significant variables for total crimes, and total migrant population, Gini index, mean household income and percentage of the urban population are significant variables for homicides. Random K-nearest neighbour (RKNN) algorithm classified districts as safe and unsafe with 84% of prediction accuracy.
{"title":"Forecasting homicides, rapes and counterfeiting currency: A case study in Sri Lanka","authors":"Chathura B. Wickrama, Ruwan D. Nawarathna, Lakshika S. Nawarathna","doi":"10.15406/BBIJ.2020.09.00322","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00322","url":null,"abstract":"Crimes have been disturbing threats to all the Sri Lankans all over the country. Finding the main variables associated with crimes are very vital for policymakers. Our main goal in this study is to forecast of homicides, rapes and counterfeiting currency from 2013 to 2020 using auto-regressive conditional Poisson (ACP) and auto-regressive integrated moving average (ARIMA) models. All the predictions are made assuming that the prevailing conditions in the country affecting crime rates remain unchanged during the period. Moreover, multiple linear regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to identify the key variables associated with crimes. Profiling of districts as safe or unsafe was performed based on the overall total crime rate of Sri Lanka which is to compare with individual district’s crime rates. Data were collected from the Department of Police and Department of Census and Statistics, Sri Lanka. It is observed that there are 14 safe and 11 unsafe districts in Sri Lanka. Moreover, it is found that the total migrant population and percentage of urban population is positively correlated with total crime. Besides, total migrant population, unemployment rate, mean household income and percentage of the urban population are significant variables for total crimes, and total migrant population, Gini index, mean household income and percentage of the urban population are significant variables for homicides. Random K-nearest neighbour (RKNN) algorithm classified districts as safe and unsafe with 84% of prediction accuracy.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83438694","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 : 2020-12-30DOI: 10.15406/BBIJ.2020.09.00323
F. Tasnim, M. Kamrujjaman
Choristoneura Fumiferana is perilous defoliators of forest lands in North America and many countries in Europe. In this study, we consider mathematical models in ecology, epidemiology and bifurcation studies; the spruce budworm model and the population model with harvesting. The study is designed based on bifurcation analysis. In particular, the results support population thresholds necessary for survival in certain cases. In a series of numerical examples, the outcomes are presented graphically to compare with bifurcation results.
{"title":"Dynamics of Spruce budworms and single species competition models with bifurcation analysis","authors":"F. Tasnim, M. Kamrujjaman","doi":"10.15406/BBIJ.2020.09.00323","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00323","url":null,"abstract":"Choristoneura Fumiferana is perilous defoliators of forest lands in North America and many countries in Europe. In this study, we consider mathematical models in ecology, epidemiology and bifurcation studies; the spruce budworm model and the population model with harvesting. The study is designed based on bifurcation analysis. In particular, the results support population thresholds necessary for survival in certain cases. In a series of numerical examples, the outcomes are presented graphically to compare with bifurcation results.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88679679","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 : 2020-12-21DOI: 10.15406/BBIJ.2020.09.00321
J. Tallon, Paulo Gomes, L. Bacelar-Nicolau
Introduction: The pandemic generated by COVID–19 completely changed people's daily lives, their relationship with family and friends, unexpectedly disrupted their working conditions and enhanced the need for an enduring resilience to face yet a second wave of the disease. It is crucial to keep continuously updating our knowledge about COVID–19 prevalence and incidence evolutions over large connected territories, where the disease is striking in alarming proportions. Objective: The main objective of this research is to identify and describe COVID–19 prevalence, incidence and mortality profiles in EU and EEE/EFTA countries, seven months after the start of the pandemic in Europe, and more recent tendencies, probably associated to the beginning of a second wave. Methods: This COVID–19 study covers thirty–one European countries. Six epidemiological variables where analyzed per 100 000 inhabitants on October 25 2020, two of them evaluated over the seven previous days. A multivariate statistical exploratory analysis based on rank principal components and cluster analysis was applied. Results: A COVID–19 prevalence typology of six country clusters was identified regarding 31 countries (EU, UK and three EEE/EFTA countries). The five epidemiological variables and number of tests revealed a wider dispersion with outlier observations. The rank transformation of data and their multivariate statistical analysis allowed us to construct a rational to better discriminate and describe these clusters, identifying specific behaviours related to the global prevalence from March until the end of October or highlight recent evolutions of COVID–19 incidence in the context of a second wave of pandemic. In fact we pinpointed country clusters where COVID–19 reached alarming levels which persist, or have even worsen, at the beginning of the second wave. Additionally, two other clusters were identified: one with countries that seems to be evolving into a situation under control, and another cluster of countries very weakly struck on the first wave, but are now facing a very complex surge, that will test their health systems capacity and timely response regarding covid and non–covid patients. Finally, the worst and more dramatic situation occurred in countries where the number of deaths per 100 000 inhabitants attained an impressive cumulative score.
{"title":"Comparative prevalence of COVID–19 in european countries: a time window at second wave","authors":"J. Tallon, Paulo Gomes, L. Bacelar-Nicolau","doi":"10.15406/BBIJ.2020.09.00321","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00321","url":null,"abstract":"Introduction: The pandemic generated by COVID–19 completely changed people's daily lives, their relationship with family and friends, unexpectedly disrupted their working conditions and enhanced the need for an enduring resilience to face yet a second wave of the disease. It is crucial to keep continuously updating our knowledge about COVID–19 prevalence and incidence evolutions over large connected territories, where the disease is striking in alarming proportions. Objective: The main objective of this research is to identify and describe COVID–19 prevalence, incidence and mortality profiles in EU and EEE/EFTA countries, seven months after the start of the pandemic in Europe, and more recent tendencies, probably associated to the beginning of a second wave. Methods: This COVID–19 study covers thirty–one European countries. Six epidemiological variables where analyzed per 100 000 inhabitants on October 25 2020, two of them evaluated over the seven previous days. A multivariate statistical exploratory analysis based on rank principal components and cluster analysis was applied. Results: A COVID–19 prevalence typology of six country clusters was identified regarding 31 countries (EU, UK and three EEE/EFTA countries). The five epidemiological variables and number of tests revealed a wider dispersion with outlier observations. The rank transformation of data and their multivariate statistical analysis allowed us to construct a rational to better discriminate and describe these clusters, identifying specific behaviours related to the global prevalence from March until the end of October or highlight recent evolutions of COVID–19 incidence in the context of a second wave of pandemic. In fact we pinpointed country clusters where COVID–19 reached alarming levels which persist, or have even worsen, at the beginning of the second wave. Additionally, two other clusters were identified: one with countries that seems to be evolving into a situation under control, and another cluster of countries very weakly struck on the first wave, but are now facing a very complex surge, that will test their health systems capacity and timely response regarding covid and non–covid patients. Finally, the worst and more dramatic situation occurred in countries where the number of deaths per 100 000 inhabitants attained an impressive cumulative score.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87982501","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 : 2020-11-27DOI: 10.15406/BBIJ.2020.09.00320
Masoud Amiri
Statins are commonly prescribed to prevent or treat of cardiovascular diseases (CVD) worldwide, preventing about 80,000 stroke and heart attack cases annually. Various statins have been initiated with different biologic properties, chemical structure, safety, efficacy and side effects, with no similar prescription pattern in different countries. One of the most common reasons of the changes among different countries, might be due to the behavior of physicians in various continents.
{"title":"Worldwide statins prescription pattern: is it similar?","authors":"Masoud Amiri","doi":"10.15406/BBIJ.2020.09.00320","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00320","url":null,"abstract":"Statins are commonly prescribed to prevent or treat of cardiovascular diseases (CVD) worldwide, preventing about 80,000 stroke and heart attack cases annually. Various statins have been initiated with different biologic properties, chemical structure, safety, efficacy and side effects, with no similar prescription pattern in different countries. One of the most common reasons of the changes among different countries, might be due to the behavior of physicians in various continents.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82002372","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 : 2020-11-23DOI: 10.15406/BBIJ.2020.09.00319
Charles Masih, Kanwal Parveen, Samreen Brohi, Shehar Bano Siyal, Fatima Zia, Shabnam Pari Bhutto, Muhammad Faisal Fahim
Objective: To determine the visual outcome in Diabetic Macular Edema patients after 3rd Avastin injections attending a tertiary eye care hospital. Materials and methods: This was a cross sectional study with Non probability convenient sampling technique. The study was carried out at Diabetic clinic of Al-Ibrahim Eye Hospital, Isra Postgraduate Institute of Ophthalmology, Karachi-Pakistan. Ethical approval was taken from the institutional review board of Institute. Data collection were done retrospectively from January 2017 to June 2019. Data were retrieved for DME patients who have completed three follow-ups with Avastin injection. Inclusion Criteria were patients with age 30 to 60 years, Patient with PDR and NPDR with diabetic macular edema after 3rd injection. Data Analysis was done using SPSS version 23.0. Results: A total of 40 eyes of 40 patients were included in this study after getting information from the record sheet. Analysis were done in 30 eyes of 30 patients because 10 patients were missed their follow-up due to certain reason which were observed from record sheet. Mean age of patients was found to be 41.25±10.24.Pre-operative Avastin injection best corrected visual acuity (BCVA) was noticed by using Log MAR without glasses was 0.49 and with glasses was 0.40. Post-operative best corrected visual acuity Log MAR without glasses 0.51 and with glasses 0.42 after Avastin injection. Improvement of visual acuity was classified as Improved, worsen and Stable. There were 22 (73.33%) patients observed with improvement in visual acuity, 5 (16.66%) patients retained their vision stable and only 3 (10%) patients worsen their visual acuity after all three Avastin injections. Conclusion: The most common cause of diabetic macular edema is non-proliferative diabetic retinopathy and proliferative Diabetic Retinopathy. The Intravitreal injection play vital role, the timely treatment would improve prognosis of visual outcomes in Diabetic macular edema. So the study significantly shows the improvement in best corrected visual acuity before and after three visits.
{"title":"Visual outcomes in diabetic macular edema patients after avastin injection","authors":"Charles Masih, Kanwal Parveen, Samreen Brohi, Shehar Bano Siyal, Fatima Zia, Shabnam Pari Bhutto, Muhammad Faisal Fahim","doi":"10.15406/BBIJ.2020.09.00319","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00319","url":null,"abstract":"Objective: To determine the visual outcome in Diabetic Macular Edema patients after 3rd Avastin injections attending a tertiary eye care hospital. Materials and methods: This was a cross sectional study with Non probability convenient sampling technique. The study was carried out at Diabetic clinic of Al-Ibrahim Eye Hospital, Isra Postgraduate Institute of Ophthalmology, Karachi-Pakistan. Ethical approval was taken from the institutional review board of Institute. Data collection were done retrospectively from January 2017 to June 2019. Data were retrieved for DME patients who have completed three follow-ups with Avastin injection. Inclusion Criteria were patients with age 30 to 60 years, Patient with PDR and NPDR with diabetic macular edema after 3rd injection. Data Analysis was done using SPSS version 23.0. Results: A total of 40 eyes of 40 patients were included in this study after getting information from the record sheet. Analysis were done in 30 eyes of 30 patients because 10 patients were missed their follow-up due to certain reason which were observed from record sheet. Mean age of patients was found to be 41.25±10.24.Pre-operative Avastin injection best corrected visual acuity (BCVA) was noticed by using Log MAR without glasses was 0.49 and with glasses was 0.40. Post-operative best corrected visual acuity Log MAR without glasses 0.51 and with glasses 0.42 after Avastin injection. Improvement of visual acuity was classified as Improved, worsen and Stable. There were 22 (73.33%) patients observed with improvement in visual acuity, 5 (16.66%) patients retained their vision stable and only 3 (10%) patients worsen their visual acuity after all three Avastin injections. Conclusion: The most common cause of diabetic macular edema is non-proliferative diabetic retinopathy and proliferative Diabetic Retinopathy. The Intravitreal injection play vital role, the timely treatment would improve prognosis of visual outcomes in Diabetic macular edema. So the study significantly shows the improvement in best corrected visual acuity before and after three visits.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85784518","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 : 2020-10-31DOI: 10.15406/BBIJ.2020.09.00318
S. Zheng
where nij are sometimes replaced by 0.5 ij n + , when some of the ij n are zero. This result can be derived under the assumption of multinomial sampling by using the Delta Method. When RR is used to compare two quantities, the log transformation is conducted, that is ( ) 1|1 1|2 ˆ ˆ log / π π is often considered instead of ( ) 1|1 1|2 ˆ ˆ / π π , since the former has a sampling distribution which is closer to normal than that of the latter. The estimated asymptotic standard error (ASE) of log(RR):
{"title":"Comparing two quantities by using a ratio","authors":"S. Zheng","doi":"10.15406/BBIJ.2020.09.00318","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00318","url":null,"abstract":"where nij are sometimes replaced by 0.5 ij n + , when some of the ij n are zero. This result can be derived under the assumption of multinomial sampling by using the Delta Method. When RR is used to compare two quantities, the log transformation is conducted, that is ( ) 1|1 1|2 ˆ ˆ log / π π is often considered instead of ( ) 1|1 1|2 ˆ ˆ / π π , since the former has a sampling distribution which is closer to normal than that of the latter. The estimated asymptotic standard error (ASE) of log(RR):","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74294831","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 : 2020-10-26DOI: 10.15406/BBIJ.2020.09.00317
K. Shukla
In this paper, Truncated Akash distribution has been proposed. Its mean and variance have been derived. Nature of cumulative distribution and hazard rate functions have been derived and presented graphically. Its moments including Coefficient of Variation, Skenwness, Kurtosis and Index of dispersion have been derived. Maximum likelihood method of estimation has been used to estimate the parameter of proposed model. It has been applied on three data sets and compares its superiority over one parameter exponential, Lindley, Akash, Ishita and truncated Lindley distribution.
{"title":"Truncated Akash distribution: properties and applications","authors":"K. Shukla","doi":"10.15406/BBIJ.2020.09.00317","DOIUrl":"https://doi.org/10.15406/BBIJ.2020.09.00317","url":null,"abstract":"In this paper, Truncated Akash distribution has been proposed. Its mean and variance have been derived. Nature of cumulative distribution and hazard rate functions have been derived and presented graphically. Its moments including Coefficient of Variation, Skenwness, Kurtosis and Index of dispersion have been derived. Maximum likelihood method of estimation has been used to estimate the parameter of proposed model. It has been applied on three data sets and compares its superiority over one parameter exponential, Lindley, Akash, Ishita and truncated Lindley distribution.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86152549","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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