O. Hamidi, Seyed Reza Borzu, S. Maroufizadeh, Payam Amini
Introduction: One of the complications of hemodialysis treatment is hypotension, which can increase morbidity and mortality and compromise dialysis efficacy. Dialysate temperature is an important factor that contributes to hemodynamic stability during hemodialysis. This study investigated the effect of dialysate temperature on the patients' blood pressure and pulse rate. Model-based approaches were used to produce more reliable results compared with traditional methods. Methods: A total of 30 patients were studied during 9 dialysis sessions. Dialysate temperatures were 37° C, 36° C and 35° C. A joint longitudinal model was used to analyze both responses of blood pressure and pulse rate, simultaneously. Results: The results showed that low-dialysate temperature was not significantly associated with higher systolic blood pressure (p>0.05) or a higher pulse rate (p>0.05) either during or after dialysis. Pulse rate and blood pressure were higher for women during dialysate (p<0.001). However, increasing age was associated with higher blood pressure and a lower pulse rate (p<0.001). Conclusion: Using several separate, repeated measure analysis of variances may produce misleading results, when there is more than one response variable measured over time, Multivariate statistical methods (including joint longitudinal models), should be used.
{"title":"Application of Multivariate Generalized Linear Mixed Model to Identify Effect of Dialysate Temperature on Physiologic Indicators among Hemodialysis Patients","authors":"O. Hamidi, Seyed Reza Borzu, S. Maroufizadeh, Payam Amini","doi":"10.18502/jbe.v7i3.7298","DOIUrl":"https://doi.org/10.18502/jbe.v7i3.7298","url":null,"abstract":"Introduction: One of the complications of hemodialysis treatment is hypotension, which can increase morbidity and mortality and compromise dialysis efficacy. Dialysate temperature is an important factor that contributes to hemodynamic stability during hemodialysis. This study investigated the effect of dialysate temperature on the patients' blood pressure and pulse rate. Model-based approaches were used to produce more reliable results compared with traditional methods. \u0000Methods: A total of 30 patients were studied during 9 dialysis sessions. Dialysate temperatures were 37° C, 36° C and 35° C. A joint longitudinal model was used to analyze both responses of blood pressure and pulse rate, simultaneously. \u0000Results: The results showed that low-dialysate temperature was not significantly associated with higher systolic blood pressure (p>0.05) or a higher pulse rate (p>0.05) either during or after dialysis. Pulse rate and blood pressure were higher for women during dialysate (p<0.001). However, increasing age was associated with higher blood pressure and a lower pulse rate (p<0.001). \u0000Conclusion: Using several separate, repeated measure analysis of variances may produce misleading results, when there is more than one response variable measured over time, Multivariate statistical methods (including joint longitudinal models), should be used. \u0000 ","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45878273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Study of cancer incidence trends can provide better insight for decision-making and considering necessary interventions. The current study was focused on investigating the main patterns in the incidence of gynecological cancers among the provinces of Iran during the last decades. Methods: We carried out an applied longitudinal study through the growth mixture model (GMM), with a concentration on the trajectory of incidence rates. Information about the rate of gynecological cancer incidence (per 100,000) in 31 provinces of Iran during the 1990-2016 period was extracted from the Data Visualization System. Taking into account the p-value of the likelihood ratio test (LRT), the number of main patterns was estimated by Mplus 7.4 software. Results: Tehran province with the incidence of 2.00 per 100,000 was in the first rank in 1990, while in 2016 the highest rate was observed in Yazd province with 9.38 cases. Five main patterns were determined based on LRT. Tehran and Yazd provinces showed the sharpest rise, while Khuzestan, Fars, Esfahan, Semnan, East Azerbaijan, Razavi Khorasan, and Mazandaran provinces belonged to the pattern with a moderate-to-highrising trend. 10 provinces including Kerman, Kurdistan, Gilan, Lorestan, Alborz, Hamedan, Kermanshah, Markazi, Ardabil, and West Azerbaijan were on the other hand categorized in the moderate-rising trend. Sistan and Baluchestan and Hormozgan provinces had a slow-rising pattern, and finally, the remaining 10 provinces had the pattern with a slow-to-moderate upward trajectory. Conclusion: Due to the considerable rising trend in most provinces in Iran, taking urgent and effective preventive actions seems necessary
{"title":"Identification of Main Patterns in The Incidence of Gynecological Cancers Amongst the Provinces in Iran","authors":"Nafiseh Taei, H. Raeisi Shahraki","doi":"10.18502/jbe.v7i3.7296","DOIUrl":"https://doi.org/10.18502/jbe.v7i3.7296","url":null,"abstract":"Introduction: Study of cancer incidence trends can provide better insight for decision-making and considering necessary interventions. The current study was focused on investigating the main patterns in the incidence of gynecological cancers among the provinces of Iran during the last decades. \u0000Methods: We carried out an applied longitudinal study through the growth mixture model (GMM), with a concentration on the trajectory of incidence rates. Information about the rate of gynecological cancer incidence (per 100,000) in 31 provinces of Iran during the 1990-2016 period was extracted from the Data Visualization System. Taking into account the p-value of the likelihood ratio test (LRT), the number of main patterns was estimated by Mplus 7.4 software. \u0000Results: Tehran province with the incidence of 2.00 per 100,000 was in the first rank in 1990, while in 2016 the highest rate was observed in Yazd province with 9.38 cases. Five main patterns were determined based on LRT. Tehran and Yazd provinces showed the sharpest rise, while Khuzestan, Fars, Esfahan, Semnan, East Azerbaijan, Razavi Khorasan, and Mazandaran provinces belonged to the pattern with a moderate-to-highrising trend. 10 provinces including Kerman, Kurdistan, Gilan, Lorestan, Alborz, Hamedan, Kermanshah, Markazi, Ardabil, and West Azerbaijan were on the other hand categorized in the moderate-rising trend. Sistan and Baluchestan and Hormozgan provinces had a slow-rising pattern, and finally, the remaining 10 provinces had the pattern with a slow-to-moderate upward trajectory. \u0000Conclusion: Due to the considerable rising trend in most provinces in Iran, taking urgent and effective preventive actions seems necessary","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42138648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: The quantitative information on the risk of infection in the COVID-19 pandemic is calculated currently exclusively on the base of new infections per day, which only contribute 6.60%±1.34% to the 100% contagious acute infections and are, therefore, not proportional to the risk of infection. All methods and results presented here are shown for data in Germany, but can be transferred to any other region worldwide. Methods: More precise parameters as are used at present, are based on acute infections: stress index with information about the distance to the stress limit of the health system, the density of the sources of infection and the change in acute infections during the last 5 days are suggested here. Results: The comparison of the results of the current and the new assessment parameters shows that large daily fluctuations in new infections of up to ±22% lead to unnecessary uncertainties. The new assessment parameters are correspondingly more precise. The 7-days incidence warning thresholds introduced by German law in November 2020 and April 2021 are defined on the base of new infections. As a result, the real infection risks can be incorrectly assessed due to the large fluctuations of the 7-days incidence values up to ±23%, so that legal conflicts can arise if legally prescribed protective measures are objectively unjustified or introduced too late. Conclusion: By moving from new infections to acute infections as a base for calculation, infection risks can be described more precisely and even unjustified, expensive protective measures can be avoided.
{"title":"How to Reduce Misinterpretation of Quantitative Infection Risk by Assessment Parameters Associated with the COVID-19 Pandemic","authors":"J. Rassow","doi":"10.18502/jbe.v7i3.7302","DOIUrl":"https://doi.org/10.18502/jbe.v7i3.7302","url":null,"abstract":"Introduction: The quantitative information on the risk of infection in the COVID-19 pandemic is calculated currently exclusively on the base of new infections per day, which only contribute 6.60%±1.34% to the 100% contagious acute infections and are, therefore, not proportional to the risk of infection. All methods and results presented here are shown for data in Germany, but can be transferred to any other region worldwide. \u0000Methods: More precise parameters as are used at present, are based on acute infections: stress index with information about the distance to the stress limit of the health system, the density of the sources of infection and the change in acute infections during the last 5 days are suggested here. \u0000Results: The comparison of the results of the current and the new assessment parameters shows that large daily fluctuations in new infections of up to ±22% lead to unnecessary uncertainties. The new assessment parameters are correspondingly more precise. The 7-days incidence warning thresholds introduced by German law in November 2020 and April 2021 are defined on the base of new infections. As a result, the real infection risks can be incorrectly assessed due to the large fluctuations of the 7-days incidence values up to ±23%, so that legal conflicts can arise if legally prescribed protective measures are objectively unjustified or introduced too late. \u0000Conclusion: By moving from new infections to acute infections as a base for calculation, infection risks can be described more precisely and even unjustified, expensive protective measures can be avoided.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44958880","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}
Sara Sabbaghian Tousi, H. Tabesh, A. Saki, A. Tagipour, M. Tajfard
Introduction: Propensity score matching (PSM) is a method to reduce the impact of essential and confounders. When the number of confounders is high, there may be a problem of matching, in which, finding matched pairs for the case group is difficult, or impossible. The propensity score (PS) minimizes the effect of the confounders, and it is reduced to one dimension. There are various algorithms in the field of PSM. This study aimed to compared the nearest neighbor and caliper algorithms. Methods: Data obtained in this study were from patients undergoing angiography at Ghaem Hospital in Mashhad, between 2011-12. The study was a retrospective case-control using PSM. In total, 604 patients were included in the case and control groups. A logistic regression model was used to calculate the propensity score and adjust the variables, such as age, gender, Body Mass Index (BMI), systolic blood pressure, smoking status, and triglyceride. Then, the Odds Ratios (ORs) with 95% Confidence Intervals (CIs) for the raw data and two matching algorithms were determined to examine the relationship between type 2 diabetes and coronary artery disease (CAD). Results: Propensity score in the nearest neighbor and caliper algorithms matched the total number of 604 samples, 200 and 178 pairs, respectively. All variables were significantly different between the two groups before matching (P<0.05). The gender was significantly different between the two groups after matching using the nearest neighbor algorithm (P=0.002). No variables created a significant difference between the two groups after matching with the caliper algorithm. Conclusion: Bias reduction in the caliper algorithm was greater than for the nearest neighbor algorithm for all variables except the triglyceride variable.
{"title":"Comparison of Nearest Neighbor and Caliper Algorithms in Outcome Propensity Score Matching to Study the Relationship between Type 2 Diabetes and Coronary Artery Disease","authors":"Sara Sabbaghian Tousi, H. Tabesh, A. Saki, A. Tagipour, M. Tajfard","doi":"10.18502/jbe.v7i3.7297","DOIUrl":"https://doi.org/10.18502/jbe.v7i3.7297","url":null,"abstract":"Introduction: Propensity score matching (PSM) is a method to reduce the impact of essential and confounders. When the number of confounders is high, there may be a problem of matching, in which, finding matched pairs for the case group is difficult, or impossible. The propensity score (PS) minimizes the effect of the confounders, and it is reduced to one dimension. There are various algorithms in the field of PSM. This study aimed to compared the nearest neighbor and caliper algorithms. \u0000Methods: Data obtained in this study were from patients undergoing angiography at Ghaem Hospital in Mashhad, between 2011-12. The study was a retrospective case-control using PSM. In total, 604 patients were included in the case and control groups. A logistic regression model was used to calculate the propensity score and adjust the variables, such as age, gender, Body Mass Index (BMI), systolic blood pressure, smoking status, and triglyceride. Then, the Odds Ratios (ORs) with 95% Confidence Intervals (CIs) for the raw data and two matching algorithms were determined to examine the relationship between type 2 diabetes and coronary artery disease (CAD). \u0000Results: Propensity score in the nearest neighbor and caliper algorithms matched the total number of 604 samples, 200 and 178 pairs, respectively. All variables were significantly different between the two groups before matching (P<0.05). The gender was significantly different between the two groups after matching using the nearest neighbor algorithm (P=0.002). No variables created a significant difference between the two groups after matching with the caliper algorithm. \u0000Conclusion: Bias reduction in the caliper algorithm was greater than for the nearest neighbor algorithm for all variables except the triglyceride variable.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43768841","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}
Seyedeh Solmaz Taheri, A. Baghestani, F. Minoo, A. Saeedi
Introduction: Chronic Kidney Disease (CKD) is a disease in which damaged kidneys could not remove waste material from the blood which could result in other health problems. The aim of this analysis was to identify significant laboratory prognostic factors on death hazard due to CKD. Methods: There were 109 patients with end-stage renal disease (ESRD) treated at Helal pharmaceutical and clinical complex. The survival time was set as the time interval from starting dialysis until death due to CKD. Age, gender and factors such as creatinine, cholesterol, uric acid, SGOT, SGPT, bilirubin, hemoglobin, potassium, ALP, HbA1C, ferritin, calcium, phosphorus, PTH and albumin were employed in this study. Weibull Distribution with non-Constant Shape Parameter versus constant Shape Parameter for the analysis were used. Results: Death due to CKD occurred in 29 (26.6%) of the patients. Sixty-seven (61.5%) had uric acid higher than 6.8 (mg/dl) and 39(35%) had phosphorus higher than 4.7 (mg/dl) which were poor prognoses. The incidence of death was 48.4%. Calcium<8.5 (mg/dl) (p=0.002), Calcium > 9.5 (mg/dl) (p=0.003), Albumin 4-6.3 (g/dl) (p=0.034), Phosphorus (p=0.022), hemoglobin<10 (g/dl) (p=0.043), hemoglobin>12.5 (g/dl) (p=0.006) and iPTH (p<0.001) were significant variables which had an effect on death hazard rates. Conclusion: The Weibull model with Non-Constant shape parameter was suggested to be more accurate for identifying risk factors, leading to more precise results, compared to constant shape parameter. Investigators mostly emphasize on the importance of Calcium, Albumin, Phosphorus, hemoglobin and iPTH for reducing hazard rates in CKD patients.
{"title":"Identifying Influential Prognostic Factors of Death Hazard Rates in Patients with Chronic Kidney Disease (CKD) Using Weibull Model with Non-Constant Shape Parameter","authors":"Seyedeh Solmaz Taheri, A. Baghestani, F. Minoo, A. Saeedi","doi":"10.18502/jbe.v7i3.7299","DOIUrl":"https://doi.org/10.18502/jbe.v7i3.7299","url":null,"abstract":"Introduction: Chronic Kidney Disease (CKD) is a disease in which damaged kidneys could not remove waste material from the blood which could result in other health problems. The aim of this analysis was to identify significant laboratory prognostic factors on death hazard due to CKD. \u0000Methods: There were 109 patients with end-stage renal disease (ESRD) treated at Helal pharmaceutical and clinical complex. The survival time was set as the time interval from starting dialysis until death due to CKD. Age, gender and factors such as creatinine, cholesterol, uric acid, SGOT, SGPT, bilirubin, hemoglobin, potassium, ALP, HbA1C, ferritin, calcium, phosphorus, PTH and albumin were employed in this study. Weibull Distribution with non-Constant Shape Parameter versus constant Shape Parameter for the analysis were used. \u0000Results: Death due to CKD occurred in 29 (26.6%) of the patients. Sixty-seven (61.5%) had uric acid higher than 6.8 (mg/dl) and 39(35%) had phosphorus higher than 4.7 (mg/dl) which were poor prognoses. The incidence of death was 48.4%. Calcium<8.5 (mg/dl) (p=0.002), Calcium > 9.5 (mg/dl) (p=0.003), Albumin 4-6.3 (g/dl) (p=0.034), Phosphorus (p=0.022), hemoglobin<10 (g/dl) (p=0.043), hemoglobin>12.5 (g/dl) (p=0.006) and iPTH (p<0.001) were significant variables which had an effect on death hazard rates. \u0000Conclusion: The Weibull model with Non-Constant shape parameter was suggested to be more accurate for identifying risk factors, leading to more precise results, compared to constant shape parameter. Investigators mostly emphasize on the importance of Calcium, Albumin, Phosphorus, hemoglobin and iPTH for reducing hazard rates in CKD patients. \u0000 ","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46336063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Reliability is an integral part of measuring the reproducibility of research information. Intra-cluster correlation coefficient (ICC) is one of the necessary indicators for reliability reporting, which can be misleading in terms of its diversity. The main purpose of this study was to introduce the types of reliability and appropriate ICC indices. Methods: In this tutorial article, useful information about the types of reliability and indicators needed to report the results, as well as the types of ICC and its applications were explained for dummies. Results: Three general types of reliability include inter-rater reliability, test-retest reliability, and intra-rater reliability was presented. 10 different types of ICC were also introduced and explained. Conclusion: The research results may be misleading if any of the reliability types and calculation criteria types are chosen incorrectly. Therefore, to make the results of the study more accurate and valuable. Medical researchers must seek help from relevant guidelines such as this study before conducting reliability analysis.
{"title":"Guideline for Selecting Types of Reliability and Suitable Intra-class Correlation Coefficients in Clinical Research","authors":"Kh Taherzadeh Chenani, F. Madadizadeh","doi":"10.18502/jbe.v7i3.7301","DOIUrl":"https://doi.org/10.18502/jbe.v7i3.7301","url":null,"abstract":"Introduction: Reliability is an integral part of measuring the reproducibility of research information. Intra-cluster correlation coefficient (ICC) is one of the necessary indicators for reliability reporting, which can be misleading in terms of its diversity. The main purpose of this study was to introduce the types of reliability and appropriate ICC indices. \u0000Methods: In this tutorial article, useful information about the types of reliability and indicators needed to report the results, as well as the types of ICC and its applications were explained for dummies. \u0000Results: Three general types of reliability include inter-rater reliability, test-retest reliability, and intra-rater reliability was presented. 10 different types of ICC were also introduced and explained. \u0000Conclusion: The research results may be misleading if any of the reliability types and calculation criteria types are chosen incorrectly. Therefore, to make the results of the study more accurate and valuable. Medical researchers must seek help from relevant guidelines such as this study before conducting reliability analysis. \u0000 ","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44844954","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}
Y. Alimohamadi, M. Sepandi, Anís Dadgar, Homeira Sedighi Nezhad, R. Mosaed, S. Zargar Balaye Jame
Introduction: In the present study, the goal was to estimate the hospital length of stay among patients admitted with COVID-19 in a hospital in Tehran. Methods: We used retrospective data on 446 hospitalized patients with COVID-19 who admitted from 7 March to 8 Oct 2020 in a referral hospital in Tehran, Iran. The prognostic effects of variables, including age, gender, comorbidity status, and symptoms were analyzed by using Kaplan-Meier methods and a competing risk analysis. Length of stay in hospital was calculated using time of last status minus time of admission. All analyses performed using SPSS version 22.0 and STATA version 15. Results: The mean age of cases was 57.09±16.85 years old. The median (IQR) of hospital length of stay among all patients was 7 (11-5) days. The length of Hospital stay, for >80 years’ patients (9days (15-5)) and females (7days (11-5)) was the longest. The most of cases (94 (21.1%)) were in 60–69 age group. In overall 267 (59.9%) of all cases were males and 179 (40.1%) were females. The most common symptom among patients was Respiratory distress 249 (55.8), Cough 233 (52.2) and fever 209 (46.9) respectively. Regarding having any comorbidities, 106 (23.8%) of COVID-19 cases had Cardiovascular disease, 114 (25.6%) had diabetes and 100 (22.4%) had hypertension. Most of deaths (21 (32.3%)) occurred in 70-79 years’ age group. The overall Case Fatality Rate (CFR) in under-studied cases was 14.6%. Conclusion: Although the result of the present study showed that hospital length of stay in Iran is not higher than in other countries, but by applying some measures including the early detection of suspected cases and timely treatment and necessary funding on preparing required facilities, medicine and equipment, it could be shortened or at least prevented from increasing.
{"title":"Hospital length of Stay among COVID-19 Patients: An Application of Competing Risk Analysis","authors":"Y. Alimohamadi, M. Sepandi, Anís Dadgar, Homeira Sedighi Nezhad, R. Mosaed, S. Zargar Balaye Jame","doi":"10.18502/jbe.v7i3.7294","DOIUrl":"https://doi.org/10.18502/jbe.v7i3.7294","url":null,"abstract":"Introduction: In the present study, the goal was to estimate the hospital length of stay among patients admitted with COVID-19 in a hospital in Tehran. \u0000Methods: We used retrospective data on 446 hospitalized patients with COVID-19 who admitted from 7 March to 8 Oct 2020 in a referral hospital in Tehran, Iran. The prognostic effects of variables, including age, gender, comorbidity status, and symptoms were analyzed by using Kaplan-Meier methods and a competing risk analysis. Length of stay in hospital was calculated using time of last status minus time of admission. All analyses performed using SPSS version 22.0 and STATA version 15. \u0000Results: The mean age of cases was 57.09±16.85 years old. The median (IQR) of hospital length of stay among all patients was 7 (11-5) days. The length of Hospital stay, for >80 years’ patients (9days (15-5)) and females (7days (11-5)) was the longest. The most of cases (94 (21.1%)) were in 60–69 age group. In overall 267 (59.9%) of all cases were males and 179 (40.1%) were females. The most common symptom among patients was Respiratory distress 249 (55.8), Cough 233 (52.2) and fever 209 (46.9) respectively. Regarding having any comorbidities, 106 (23.8%) of COVID-19 cases had Cardiovascular disease, 114 (25.6%) had diabetes and 100 (22.4%) had hypertension. Most of deaths (21 (32.3%)) occurred in 70-79 years’ age group. The overall Case Fatality Rate (CFR) in under-studied cases was 14.6%. \u0000Conclusion: Although the result of the present study showed that hospital length of stay in Iran is not higher than in other countries, but by applying some measures including the early detection of suspected cases and timely treatment and necessary funding on preparing required facilities, medicine and equipment, it could be shortened or at least prevented from increasing.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45804625","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}
Elham Nazari, Rizwana Biviji, Amir Hossein Farzin, Parnian Asgari, H. Tabesh
Introduction: Recently, with the surge in the availability of relevant data in various industries, the use of Information Fusion technique for data analysis is increasing. This method has several advantages, such as increased accuracy, and the use of meaningful information. In addition, there are certain challenges, including the impact of data type and analytical method on results. The goal of this study is to propose a framework for introducing the advantages and classifying the challenges of this technique. Method: We conducted a review of articles published between January 1960 and December 2017 for the design stage and from January 2018 to December 2018 for the evaluation stage. Articles were identified from various databases such as Science Direct, IEEE, Scopus, Web of Science, and Google Scholar, using the keywords decision fusion, information fusion, and symbolic fusion. We report the advantages and challenges of the methodologies described in these articles. Analysis was conducted in accordance with PRISMA guidelines. Results: A total of 132 articles were identified in the design stage and 90 articles were identified in the evaluation stage. Categories within the framework for challenges include “hardware and software requirements for processing and maintaining the process”, “data” and “data analysis method”. The categories for advantages include “value modeling”, “preferable management of uncertainty and variability”, “excellent decision making”, “comprehensive interpretation and representation”, “data management” and “simplicity of infrastructure”. Our results indicate using these two frameworks with 95% Confidence interval. Conclusion: An overall understanding of the advantages and challenges of the information fusion technique could act as a guide for the researcher for the correct usage of this technique.
简介：最近，随着各行业相关数据的可用性激增，信息融合技术在数据分析中的应用越来越多。这种方法有几个优点，例如提高了准确性，并使用了有意义的信息。此外，还有一些挑战，包括数据类型和分析方法对结果的影响。本研究的目的是提出一个框架，介绍这项技术的优势并对其挑战进行分类。方法：我们对1960年1月至2017年12月期间发表的设计阶段和2018年1月到2018年12月之间发表的评估阶段的文章进行了回顾。文章使用决策融合、信息融合和符号融合等关键词，从Science Direct、IEEE、Scopus、Web of Science和Google Scholar等各种数据库中进行识别。我们报告了这些文章中描述的方法的优势和挑战。根据PRISMA指南进行分析。结果：共有132篇文章在设计阶段被鉴定，90篇文章在评估阶段被鉴定。挑战框架内的类别包括“处理和维护过程的硬件和软件要求”、“数据”和“数据分析方法”。优势类别包括“价值建模”、“更好地管理不确定性和可变性”、“卓越的决策”、“全面的解释和表示”、“数据管理”和“基础设施的简单性”。我们的结果表明，使用这两个框架的置信区间为95%。结论：全面了解信息融合技术的优势和挑战，可以为研究人员正确使用该技术提供指导。
{"title":"Advantages and Challenges of Information Fusion Technique for Big Data Analysis: Proposed Framework","authors":"Elham Nazari, Rizwana Biviji, Amir Hossein Farzin, Parnian Asgari, H. Tabesh","doi":"10.18502/JBE.V7I2.6737","DOIUrl":"https://doi.org/10.18502/JBE.V7I2.6737","url":null,"abstract":"Introduction: Recently, with the surge in the availability of relevant data in various industries, the use of Information Fusion technique for data analysis is increasing. This method has several advantages, such as increased accuracy, and the use of meaningful information. In addition, there are certain challenges, including the impact of data type and analytical method on results. The goal of this study is to propose a framework for introducing the advantages and classifying the challenges of this technique. \u0000 Method: We conducted a review of articles published between January 1960 and December 2017 for the design stage and from January 2018 to December 2018 for the evaluation stage. Articles were identified from various databases such as Science Direct, IEEE, Scopus, Web of Science, and Google Scholar, using the keywords decision fusion, information fusion, and symbolic fusion. We report the advantages and challenges of the methodologies described in these articles. Analysis was conducted in accordance with PRISMA guidelines. \u0000 Results: A total of 132 articles were identified in the design stage and 90 articles were identified in the evaluation stage. Categories within the framework for challenges include “hardware and software requirements for processing and maintaining the process”, “data” and “data analysis method”. The categories for advantages include “value modeling”, “preferable management of uncertainty and variability”, “excellent decision making”, “comprehensive interpretation and representation”, “data management” and “simplicity of infrastructure”. Our results indicate using these two frameworks with 95% Confidence interval. \u0000 Conclusion: An overall understanding of the advantages and challenges of the information fusion technique could act as a guide for the researcher for the correct usage of this technique.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45872141","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}
Y. Alimohamadi, M. Sepandi, Taher Teymouri, Hadiseh Hosamirudsari
Introduction: Epidemic curves are a type of time series data consisting of the number of events that occur over a period of time. The time unit in this data can be a day, a week, or a month, etc. Methods: In the current letter, the authors tried to explain the growth factor and its effect on epidemic curves by using some literature. Results: In the outbreaks setting, the number of cases can increase with different patterns. When the number of cases is increasing exponentially, it means that the number of cases is increasing at a certain speed, which is determined by a factor called an exponential growth factor. When this factor is greater than one, it means that the cases are increasing exponentially, and when this coefficient is equal to 1, it means that we have reached an inflection point that we will face a change in the growth rate of the cases. Conclusion: Some factors such as reducing the contact between infected and healthy people, run the social distancing program, and so on can have an effective role in decreasing epidemic growth factor and controlling the epidemic.
{"title":"Growth Factor: an Important Factor in Determining the Fate of Outbreaks","authors":"Y. Alimohamadi, M. Sepandi, Taher Teymouri, Hadiseh Hosamirudsari","doi":"10.18502/JBE.V7I2.6738","DOIUrl":"https://doi.org/10.18502/JBE.V7I2.6738","url":null,"abstract":"Introduction: Epidemic curves are a type of time series data consisting of the number of events that occur over a period of time. The time unit in this data can be a day, a week, or a month, etc. \u0000 Methods: In the current letter, the authors tried to explain the growth factor and its effect on epidemic curves by using some literature. \u0000 Results: In the outbreaks setting, the number of cases can increase with different patterns. When the number of cases is increasing exponentially, it means that the number of cases is increasing at a certain speed, which is determined by a factor called an exponential growth factor. When this factor is greater than one, it means that the cases are increasing exponentially, and when this coefficient is equal to 1, it means that we have reached an inflection point that we will face a change in the growth rate of the cases. \u0000 Conclusion: Some factors such as reducing the contact between infected and healthy people, run the social distancing program, and so on can have an effective role in decreasing epidemic growth factor and controlling the epidemic.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42634379","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}
Soraya Moradi, E. Maraghi, Azar Babaahmadi, S. Younespour
Introduction: Anxiety in students is a challenge of educational systems. The present study was conducted to investigate the efficiency of Pop Quiz (unannounced formative tests) in teaching biostatistics to postgraduate midwifery students and its effects on their statistics anxiety, test anxiety and statistical analysis skills. Methods: This quasi-experimental study conducted during the first semester of the academic year of 2019-2020 in the Faculty of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences. The MSc midwifery students were divided into two separate classes. One of the classes was randomly selected for educational intervention (Pop Quiz). Teaching via the lecture method considered as control method. Test anxiety and statistical anxiety questionnaires were completed by the students in both groups before the educational intervention, during and at the end of semester. The final exam score considered as the statistical skills score. Data were analyzed in SPSS 22 using Fisher's exact test and GEE model. Results: Thirty eight MSC midwifery students (12 in intervention group and 26 in comparison group) were recruited in this study. The mean and standard deviation of the exam score of students in lecture and Pop Quiz groups were respectively 14.43 ± 3.80 and 15.95 ± 2.79 (P=0.182). The patterns of change in test anxiety score differed significantly over time between the two teaching methods (P = 0.003). Although, there was a decreasing trend in mean score of statistics anxiety scores in Pop Quiz group in comparison with lecture based group, but there were not statistically significant differences. Conclusion: Applying Pop Quiz to teaching biostatistics reduces test anxiety and statistics anxiety and increases statistical analysis skills in postgraduate midwifery students.
{"title":"Application of Pop Quiz Method in Teaching Biostatistics to Postgraduate Midwifery Students and Its Effect on Their Statistics Anxiety, Test Anxiety and Academic Achievement: A QuasiExperimental Study with Control Group","authors":"Soraya Moradi, E. Maraghi, Azar Babaahmadi, S. Younespour","doi":"10.18502/JBE.V7I2.6736","DOIUrl":"https://doi.org/10.18502/JBE.V7I2.6736","url":null,"abstract":"Introduction: Anxiety in students is a challenge of educational systems. The present study was conducted to investigate the efficiency of Pop Quiz (unannounced formative tests) in teaching biostatistics to postgraduate midwifery students and its effects on their statistics anxiety, test anxiety and statistical analysis skills. \u0000Methods: This quasi-experimental study conducted during the first semester of the academic year of 2019-2020 in the Faculty of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences. The MSc midwifery students were divided into two separate classes. One of the classes was randomly selected for educational intervention (Pop Quiz). Teaching via the lecture method considered as control method. Test anxiety and statistical anxiety questionnaires were completed by the students in both groups before the educational intervention, during and at the end of semester. The final exam score considered as the statistical skills score. Data were analyzed in SPSS 22 using Fisher's exact test and GEE model. \u0000Results: Thirty eight MSC midwifery students (12 in intervention group and 26 in comparison group) were recruited in this study. The mean and standard deviation of the exam score of students in lecture and Pop Quiz groups were respectively 14.43 ± 3.80 and 15.95 ± 2.79 (P=0.182). The patterns of change in test anxiety score differed significantly over time between the two teaching methods (P = 0.003). Although, there was a decreasing trend in mean score of statistics anxiety scores in Pop Quiz group in comparison with lecture based group, but there were not statistically significant differences. \u0000Conclusion: Applying Pop Quiz to teaching biostatistics reduces test anxiety and statistics anxiety and increases statistical analysis skills in postgraduate midwifery students.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47224237","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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