Journal of Biostatistics and Epidemiology
Volume:8 Issue: 1, Winter 2022

An essential concept in assessing the extent to which an infectious outbreak spread is the concept of basic reproductive number (R0). The current systematic review and meta-analysis aimed to estimate the R0 of the Delta variant of SARS-CoV-2 based on studies published from 1 January 2021 to 23 September 2021.

International databases (including Google Scholar, Science Direct, PubMed, and Scopus) were searched using keywords: "Basic reproduction number, R0, COVID-19, SARS-COV-2, Severe Acute Respiratory Syndrome Coronavirus, NCOV, 2019 NCOV, coronavirus, Delta variant, B.1.617.2". Due to significant heterogeneity, DerSimonian-Laird random-effects model was used to estimate the pooled value of R0.

A total of 245 reports were identified. After assessing the inclusion criteria, three studies were selected. The pooled R0 for the Delta variant was estimated as 5.10 (95% CI, 3.04 to 7.17). (I2 =86.77%, T2:2.68, p-value from the chi-square test for heterogeneity was<0.001).

Considering the estimated value of R0 for the Delta variant of SARS-CoV-2, the amount of vaccine coverage required to achieve herd immunity appears to be higher than previous variants of the virus.

Early arrival of patients with acute ischemic stroke to start of treatment by recombinant tissue plasminogen activator (rt-PA) within 4.5 hours after onset of stroke. We aimed to develop a machine learning method to predict effective factors on arrival time of patients with stroke to hospital after symptom onset.

We included 676 patients with ischemic stroke who referred to Ardabil city hospital a province in northwest of Iran at year 2018. Classification models such as Random forest (RF), Gradient Boosting Classifier (GB), Decision Tree Classifier (DT), Support-Vector Machines (SVM), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) with 10-fold cross-validation were developed to predict effective factors on arrival time of patient with stroke to hospital. The performances were evaluated with accuracy, sensitivity, specificity, positive prophetical worth, and negative prophetical worth.  

Of all patients, 25.3% arrived to the hospital in less than 4.5 hours. The accuracy of RF, NB, ANN, GB, DT, SVM, LR and suggest method (Stacking) were 0.98, 0.72, 0.73, 0.79, 0.98, 0.73, 0.74, and 0.99.

In this study, the Stacking technique provide a better result (Accuracy 99.51%, sensitivity 100%, and specificity 99.40%) among all other techniques and this model could be used as a valuable tool for clinical decision making.

In developing country like Ethiopia as a whole and globally Anemia is the most common public problem caused by nutritional deficiency diseases among women at reproductive age. Hence, this study was determining the regional variation and associated factors of anemia status among women at reproductive age in Ethiopia using multilevel model.

A cross-sectional study was conducted among 14489 women enrolled in Ethiopia demographic and health survey of 2016 which nested under nine regions and 2 administrative city .The data was entered into Stata version-14 for cleaning and data analysis. Binary and multilevel logistic regression was carried out for variables to determine associated factors with anemia status of women and its regional variations at ascertained of 5% level.

The random intercept with fixed effect of the covariates multilevel model was considered as best fit of the data set based on Akaike information criteria when compared with other candidates models. In determining the potential factors associated with anemia status of women, the study indicated that women who use improved source of drinking water (OR=1.98, 95%CI=1.05, 3.72), being in middle wealth index (OR=0.25, 95%CI=0.10, 0.63), being in rich wealth index (OR=0.42, 95%CI=0.19, 0.94), having age at 1st birth in 20-24 years(OR=0.24, 95%CI=0.11, 0.53), having number of living children 1-2(OR=3.68, 95%CI=3.48, 4.98), having number of living children 3-4(OR=3.03, 95%CI=2.48, 4.05)and used government place of delivery(OR=0.96, 95%CI=0.22, 1.70) were significantly related to anemia status of women at 5 % level of significance.

The findings of this study showed that wealth index, age of women at 1st birth, number of living children, source of drinking water and place of delivery were potential covariates associated to anemia status of women and there were regional variability.  It is recommended that health workers should be give attention to these proximate determinants on anemia at regional level.

Logistic regression is one of the most common models used to predict and classify binary and multiple state responses in medicine. Genetic algorithms search techniques inspired by biology have recently been used successfully as a predictive model.

The aim of this study was to use the genetic algorithm and logistic regression models in diagnosing and predicting factors affecting breast cancer mortality.

In this study, data of 2836 people with breast cancer during the years 2014-2018 was examined; their information was recorded in the cancer registration system of Kerman University of Medical Sciences. Death status was considered a dependent variable, while age, morphology, tumor differentiation (grad), residence status, and place of residence were considered independent variables. Sensitivity, specificity, accuracy, and area under the ROC curve were used to compare the models.

the logistic regression model determined factors affecting the breast cancer mortality rate, (with sensitivity (0.62), specificity (0.81), area under the ROC curve (0.74), and accuracy (0.84)), and genetic algorithm model (, with sensitivity (0.19), specificity (0.97), area under the ROC curve (0.58) and accuracy (0.87)).

The sensitivity and area under the ROC curve of the logistic regression model were higher than those of the genetic algorithm, but the specificity and accuracy of the genetic algorithm were higher than those of the logistic regression. According to the purpose of the study, two models can be used simultaneously.

The multidimensional item response theory (MIRT) model provides an ideal foundation for assessing the psychological properties of a questionnaire designed with multidimensional structure. This study aimed to present the first use of MIRT models to investigate the psychometric properties of general health questionnaire (GHQ-12) in parents of school-aged children.

A total of 1104 parents of school children-aged completed the Persian version of GHQ-12 questionnaire. The unidimensional IRT model and MIRT models with two and three factors were applied to model the observed scores for each GHQ-12 item as a function of the subject’s latent traits while taking the correlation among dimensions of the questionnaire into account. Goodness of fit indices were reported for the three models, and the fits of items were assessed for the best model. Individual items were described in detail through item characteristic curves, and the amount of information carried by different items was presented using information curves.

The MIRT analysis with three factors corresponding with anxiety depression, social dysfunction and loss of confidence provided the best account of the GHQ-12 data. The model showed that all items were fitted adequately. Items varied in their discrimination ranged from 0.94 to 2.13, 1.31 to 2.74, and 2.87 to 3.57 for social dysfunction, anxiety depression, and loss of confidence, respectively. Moreover, items 8 and 2 provided the least information in social dysfunction and anxiety depression dimensions, respectively. Items in the loss of confidence dimension carried the most information among all items of the GHQ-12.

The developed framework for evaluating the psychometric properties of GHQ-12 can be a suitable alternative to traditional approaches as well as unidimensional IRT models, the use of which has been restricted due to the multidimensional structure of the questionnaire.

An important part of preventing major common diseases is identifying genetic factors that contribute to their occurrence. For the first time in our knowledge, we investigated the association between polymorphisms of five vitamin D receptor (VDR) genes (ApaI, BsmI, FokI, EcoRV, and TaqI) and low bone density/osteopenia/osteoporosis in individuals with type 2 diabetes using classification and regression tree (CART) algorithms.

Data from 158 participants with T2D were used to develop the CART analysis. The binary output variable was "bone state" with low or normal values. Age and BMI (continuous variables), vitamin D deficiency (yes/no), and gender (binary variables), as well as polymorphisms of the five VDR genes (categorical variables) all played a role in the explanatory model. A 10-fold cross-validation process was used for model validation.

Participants were divided into three groups based on their sex. In all groups, age was the major factor predicting the low state in the final obtained tree model. The second most significant predictor in each model was BMI in both sexes (accuracy:75.32% and, AUC:0.748), EcoRV polymorphism in women (accuracy:78.79 %, AUC: 0.794), and TaqI polymorphism in men (accuracy:71.19%, AUC: 0.651).

Model validation of the final tree models demonstrated that the use of CART algorithms could be a valuable technique for identifying individuals with T2D who are at risk for early-onset osteoporosis based on their polymorphism of the studied VDR genes. Our recommendation is to conduct more population-based studies. We hope this study will serve as a basis for future research.

 Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary measures to deliver the best possible care for patients and, accordingly, diminish the pressure on the health care industries. The main purpose of the present paper was to devise practical solutions based on Machine Learning (ML) techniques to ease the COVID-19 screening in routine blood test data. We came up with different algorithms for the early detection of COVID-19 and finally succeeded to opt for the best performing algorithm.

 In this developmental study,  the laboratory data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms which included, K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, and HistGradient Boosting Classifier. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms. Using the best ML-developed model, a Clinical Decision Support System (CDSS) was implemented with C# programming language.

The results indicated that the best performance belongs to the AdaBoost classifier with mean accuracy, specificity, sensitivity, F-measure, KAPA rate, and ROC of 87.1%, 85.3%, 87.3%, 87.1 %, 89.4%, and 87.3 % respectively

The ML makes a reasonable level of accuracy possible for an early diagnosis and screening of COVID-19. The empirical results reveal that the Adaboost model yielded higher performance compared with other classification models and was used for developing our CDSS interface in discriminates positive COVID-19 from negative cases.

recent medical and health advances have reduced mortality, consequently a relative increase in life expectancy and aging of population. One of the indices that properly indicate the status of elderly is the quality of life index.

objective of the present study was to identify the factors affecting the quality of life of the elderly in Zahedan, Iran.

This cross-sectional study was performed on 600 elderly people referring to the Zahedan health centers. In this two-stage cluster random sampling method, the data were collected in the  check list  and  the quality of life questionnaire SF12 through interview and  analyzed using independent t-test, one-way ANOVA, Pearson correlation coefficient and  multiple linear regression

Of the 600 elderly men and women over 60 years, 472 subjects participated in the study, of whom 291 (61%) were male and 182 (39%) female. The mean age of the study subjects was 66.2(4.04), and the mean overall quality of life scores in males and females were 28.4(3.7) and 29.07(3.7), respectively. The mean and standard deviation of PCS and MCS scores in males and females were 12.3(2.2) and 16.6(2.5), respectively. Age had inverse correlation with QOL and MS and had a direct and significant relationship with PCS. In multiple linear regression, significant relation was observed between chronic illness, hypertension, skeletal disease, diabetes, gastrointestinal disease, marital status, hookah use and smoking with PCS and also between marital status, Hypertension and mental illness with MCS.

What is obtained from this study and the other relevant studies indicate that QOL is a multifactorial phenomenon that is influenced by demographic, clinical and behavioral factors, but the role of chronic diseases is more obvious. Therefore, it seems necessary to adopt health policies to correct the lifestyle of society

 There is pandemic of HIV/AIDS all over the world and a new trend is seen in the management of this disease with the advent of highly active antiretroviral therapy, and more advancement in medicine, patients now live longer with the disease. These facts have brought about the challenges of managing chronic complications of this condition. The chronic complication of interest in this study is Depression.

 This study thus looked into the effects of caregiver counselling and follow up on Depression among People Living with HIV/AIDS (PLWHA), assess the pattern of depression among them, determine the relationship between CD4 count and depression; and between BMI and Depression among PLWHA attending Federal Teaching Hospital Ido-Ekiti.

 A total of 351 patients were considered for the study. An experimental study was performed on 64 Depressed HIV patients (32 intervention group and 32 in the control group). Bar chart and descriptive statistics was employed to explain the data. Yate’s Chi-squared statistics was employed to find out statistical associations between the groups while the p-values were consequently reported. 

 The result shows that there is a  statistically significant effect of caregiver counselling on depression among PLWHA. The percentage of the intervention group that suffered severe depression reduced from 40.6% to 6.2% after the intervention as opposed to a marginal reduction of 34.4% to 31.2% in the control group without intervention. A very strong statistically significant effect with  p-value of 0.001. This shows that the effect of caregiver intervention was statistically significant in the management of Depression among PLWHA. The intervention programme of caregiver in this study resulted in significant improvement in management of depression in the study group. This is likely the only reason that accounts for why more than half of the respondents experienced resolution of depression, because no other intervention was given to that group.

 The attending physician could do well by involving caregivers of PLWHA in the management of these patients.