Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors. Breast cancer (BC) is the world’s leading cause of death in women after lung cancer, with approximately 2,261,419 new cases and 684,996 new deaths in 2020 . In geneThese studies emphasize the diagnosis of tumors, and recently, it is a trending biomedical issue. The researchers are employing data mining (DM) and machine learning (ML) technologies for breast cancer prediction . ClassifIn the literature, a research study proved that breast cancer prediction with machine learning classifiers in the early phases does not just increase the survival chances but can control the diffusion of cancerous cells in the body . For insApart from these advantageous accuracies with different classifiers and methods, these studies mentioned above have not considered the data exploratory techniques, which enable the data mining techniques to be more robust to acquire efficient performance. Due to the absence of such essential techniques, various studies ,17,18,19https://github.com/abdul-rasool/Improved-machine-learning-based-Predictive-Models-for-Breast-Cancer-Diagnosis (accessed on 11 November 2021)) to assist data analysts and physicians in further advancement and apply it in real-time. As summarized, the following are the significant contributions of this study:We investigated four prediction models  with the WDBC and BCCD breast cancer datasets, which reached the next level of quality by diagnosing the tumor and classifying it into benign and malignant.It proposes four-layered data exploratory techniques before implementing four ML classifiers as prediction models. These techniques enable the predictive models to acquire peak accuracies for breast cancer diagnosis.We set up experiments to validate the models’ prediction and classification accuracy with regard to time complexity and deliver comparative analysis with state-of-the-art studies and various evaluation matrices.In this research, four different prediction models were formulated with four machine learning algorithms , and ensemble classifier (EC)) to deal with a massive volume of tumor features for the extraction of essential information for the diagnosis of breast cancer. The objective was to explore an accurate and efficient prediction model for tumor classification by using data mining techniques. It proposes four-layered significant data exploratory techniques (DET), including feature distribution, elimination, and constructing a hyperparameter for the practical analysis of Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD). These techniques enabled the machine learning predictive models to improve accuracy and enhance diagnostic efficiency. In the absence of these techniques, we observed some literature suffers from accuracy limitations. Although image data are more reasonable for breast cancer detection, we have not considered them in this work due to the targeted WDBC and BCCD datasets to apply the intelligent ML classifiers. It presents a framework by integrating DET and predictive models to explore the implementation method for breast cancer diagnosis. The tumor features can be presented in many details, which produces redundant information. Such features lead to tedious outcomes due to high computation times. As a result, our fundamental goal was not only to investigate the effective predictive model with attainable accuracy but also one with time complexity for the cancer diagnosis. The deliberation of time efficiency will enable our models to extract and mine vital information from a vast dataset by finding correlations and eliminating the features. The results presented satisfactory accuracy for the breast cancer diagnosis with the lowest computation time, which signifies the quality of our study as compared to others. This work will enable a data analyst to apply an intelligent machine learning model to analyze breast cancer data. Likewise, a physician would diagnose breast cancer precisely by the tumor classification. As the dataset is available publicly, we uploaded our code on GitHub  techniques assist in cancer-based data collection and evaluation to predict this deadly disease. Machine learning algorithms have been successfully applied to cancer-based data analysis among these DS technologies. For example, research  was condIn the last couple of decades, machine learning applications in the medical field have gradually increased. However, the data collected from the patients and evaluation by the medical expert are the essential factors for diagnosis. The machine learning classifiers have aided in minimizing human errors and delivered prompt analysis of medical data with greater depth . There aIn previous studies, SVM was a widely implemented machine learning algorithm in the diagnosis domain of breast cancer due to its highest prediction accuracy. For instance, Furey et al. (2000) presented SVM with a linear kernel for cancer tissue diagnosis and reached acceptable accuracy . SimilarSimilarly, Zheng et al. 2014) proposed a hybrid model combining K-mean and SVM classifiers. This model objective was to diagnose the tumor features from the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by employing the feature selection and extraction method. A K-mean classifier was employed to identify the benign and malignant tumor patterns. The generated patterns are computed and considered as new patterns for the training of the SVM model. Then, SVM is executed for the prediction of incoming tumors. The employment of their hybrid model improved the accuracy to 97%. However, the data exploratory techniques are the fundamental tasks for the data preparation, which have not been adequately addressed to train the proposed model [ proposedApart from the SVM, Lim and Sohn et al. (2013) performed logistic regression (LR) with optimal parameters on the Wisconsin Original Breast Cancer (WOBC) and WDBC datasets. It achieved 97.8% sufficient accuracy for the WOBC dataset and 93.8% accuracy for the WDBC dataset with optimized feature sets . SimilarPrevious literature reviews found numerous studies based on the SVM model for breast cancer detection; however, few were based on others. For example, A. Mert et al. (2015) delivered a feature reduction method with independent component analysis to predict breast cancer. It utilized the k-nearest neighbor (KNN) classifier to categorize the WDBC features efficiently with a reduced one feature (1C) and 30 features. It computed the performance with different matrices and attained 91% accuracy . Later, As for the fourth prediction model, named ensemble classifier (EC) with the voting technique, few studies consider this approach for breast cancer prediction. For instance, M. Abdar et al. (2020) proposed an ensemble method by vote/voting classifier to detect benign tumors from malignant breast cancer. It established a two-layer voting classifier for two or three different machine learning algorithms. The results of these voting techniques disclosed the adequate performance of the simple classification algorithm . From thThis section deliberates data information and evaluation matrices for this study.In this research, the experiments were performed on two different datasets: WDBC and BCCD. The selection reason for these datasets is it is extensively used in numerous studies ,28,29,30Wisconsin Diagnostic Breast Cancer (WDBC): The WDBC dataset consists of 10 features of breast tumor, and the result in the data were taken from 569 patients. Dr. William H. Wolberg distributed it at the General Surgery Department, University of Wisconsin-Madison, USA. It can be obtained via the file transfer protocol (FTP) from this link (https://ftp.cs.wisc.edu/math-prog/cpo-dataset/machine-learn/cancer/WDBC/ (accessed on 11 November 2021)). This dataset was created using fluid samples taken from patients’ solid breast masses. Then, software called Xcyt was used to perform cytological feature analysis based on the digital scan. This software applies a curve-fitting algorithm to calculate ten features by returning each feature’s mean value, worst value, and standard error (SE) value. Thus, there were 30 values in total for each sample, to which we have added an ID column to differentiate these samples. Finally, the diagnosis result of each sample, which consisted of malignant (M) and benign (B), was also added. In conclusion, the dataset contained 32 attributes  and 569 instances. Features of each sample were radius (mean of distances from the center to points on the perimeter), texture , perimeter, area, smoothness , compactness : This dataset consists of nine predictors and a binary dependent variable indicating the presence or absence of breast cancer. It can be downloaded from this link (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra (accessed on 11 November 2021)). The predictors are simple parameters that can be collected from routine blood analysis. The nine predictors are Age (years), BMI (kg/m2), Glucose (mg/dL), Insulin (μU/mL), Homeostasis Model Assessment (HOMA), Serum value of Leptin (ng/mL), Adiponectin (μg/mL), Resistin (ng/mL), and Chemokine Monocyte Chemoattractant Protein 1 (MCP-1) (pg/dL). The dataset was gathered by the Gynecology Department of the University Hospital Center of Coimbra in Portugal between 2009 and 2013. It was collected from naïve data (the data were collected before the treatment) of 64 women diagnosed with breast cancer and 52 healthy women .In this research, we compared four cross-validation matrices: precision, recall, F1 score, and accuracy. These matrices can be calculated by using the values in the confusion matrix, which are true positive (TP)—the prediction is yes, and the actual data is also yes; true negative (TN)—the prediction is no, and the actual data is also no; false positive (FP)—the prediction is yes, but the actual data is no; and false negative (FN)—the prediction is no, but the actual data is yes. Precision, recall, F1 score, and accuracy can be calculated as in the equations below :(1)preciThe proposed methodology, including data information, model architecture, ML models, and their assessment criteria, will be discussed in this section.In this work, we provide a solution to tackle the problems below for the breast cancer dataset, which we found from ,17,18,19How are the data exploratory techniques (DET) be used most efficiently utilized with the prediction models for breast cancer detection?How can the breast cancer features help the ML models detect cancer more precisely and more scalable?1.WDBC and BCCD datasets are downloaded from the machine learning repository.2.Execute the fundamental preprocessing tasks for individual data.3.Categorize the data into malignant and benign in WDBC and present and absent in BCCD.4.Distribute the features into positive, negative, and random (unrelated) by calculating their correlation with each other.5.Detect less significant features then eliminate such recursive features for effective results.6.After exploratory data analysis, distribute the dataset into training and testing datasets.7.Implementation of four predictive models  on the datasets.8.After the models’ execution, the classifier’s prediction is achieved with different matrices to evaluate the performance of the models, such as confusion matrices.9.Finally, analyze the results and compare each model’s accuracy and previous research studies.To solve these problems, a solution is proposed, illustrated in DE techniques, or DET, are the processes that help understand the nature of the dataset, which will identify the outliers or correlated variables that are more accessible. Our research applied feature distribution, correlation coefficient, and recursive feature elimination as our data exploratory techniques.Feature Distribution: First, the distribution of each feature was observed to find how these features are different from each other, i.e., benign and malignant in the WDBC dataset and the presence and absence of breast cancer in the BCCD dataset. The distribution was carried out by plotting the distribution plot for each feature. The data were separated by using binary code: benign (B) = 0, malignant (M) = 1, and absence = 0, presence = 1. Then, the distribution of each feature was plotted between 0 and 1.Feature Correlation: Next, the Pearson Correlation Coefficient (r) [r = +1) if the variables move in the same direction. In contrast, if these features move in the opposite direction, they will negatively correlate (r = −1).ient (r)  calculatRecursive features elimination (RFE): RFE is one of the essential processes of machine learning. Since the dataset has many features, selecting the number of features that give the most optimal prediction result is important for improving model performance. Using fewer features that provide better understanding is the gist of doing RFE. It will recur the loop until it can find the optimal number of features. In this study, RFE was utilized to reduce the features from 30 to 15. It was conducted by a built-in function, selector.fit, of sklearn. The attributes support_, and ranking_ were passed to the ranking position of i − th feature and mask the selected features. RFE works by searching for a subset of features by starting with all features in the training dataset and successfully removing features until the desired number remains. This is achieved by fitting the given machine learning algorithm used in the core of the model, ranking features according to their relevance, discarding the least important features, and re-fitting the model. This process is repeated until a predetermined number of features is retained.Hyperparameter Optimization: Hyperparameters optimization is a process of machine learning used for tuning a set of optimal parameters. The values of these parameters are used to control the learning process. There are many approaches for hyperparameters optimization, such as grid search, random search, Bayesian optimization, gradient-based optimization, and evolutionary and population-based optimization. In this study, we used grid search optimization due to its effective results for optimization. It applies the brute-force method to generate candidates from the grid of parameter values specified with the parameter. The grid search goal is to get the highest cross-validation metric scores. In our case, we utilized scikit − learn based GridSearch K-fold CV due to the disease prediction datasets. In all prediction models, GridSearch CV was adopted to evaluate the hyperparameters. GridSearchCV uses a different combination of specified hyperparameters and their values to perform the analysis. We utilized estimator, param_grid, scoring, verbose, and jobsn parameters to calculate each combination’s performance.random_state. To tune the hyperparameter, this value can be any value, but split_size should be a particular value. In our scenario, we considered 20% testing sets and 80% training sets. The models were constructed on the training dataset, and then a test dataset was used to evaluate the model’s performance. We chose SVM due to its highest accuracy in the previous literature, and LR had the best performance by tuning the hyperparameter. Likewise, KNN was selected due to effective results with input features. Meanwhile, we experimented with the ensemble-based classifier using voting techniques to assess its performance and compared it with other classifiers. The precise details of these models are given below:In this study, four ML classifiers were utilized as predictive models (PM) to diagnose Y-variable in the data as malignant or benign in the WDBC dataset and as the presence or absence of breast cancer in the BCCD dataset. The data were distributed into training and test sets. In experiments, we conducted this distribution by setting an integer value for the PM1—SVM: The first model applied SVM as a predictive model. SVM is one of the robust supervised machine learning algorithms used to solve classification and regression tasks [on tasks . The ideon tasks .(5)minαIn this work, SVM is applied to predict whether the data are located in class 0 or 1 based on several features and then calculates its performance. SVM has many kernel functions. For the linear dataset, it is called linear kernel SVM. For nonlinear, there are many types, such as polynomial kernel SVM, radial kernel SVM, and hyperbolic tangent SVM. In this research, two different kinds of SVM kernels, the linear and polynomial kernel, were applied.PM2—LR: Our second model applied LR to predict the outcomes. LR is one of the most widespread machine learning techniques. It is mainly used to predict a binary variable with a large number of independent variables. It is efficient to forecast the probability of being 0 or 1 based on predictors [X is a vector that contains X; and edictors ,35,36. IThis study applied LR to predict whether the data are located in class 0 or 1 and then calculated the performance. LR is like an upgraded version of linear regression. However, by using linear regression to predict binary classification, some predictions will have values more than one or less than 0. A sigmoid function is employed in LR to normalize the prediction to be between 0 and 1.PM3—KNN: The third model applied KNN as a predictive model. The KNN algorithm used in our problem considered the output a target class. The problem was solved or classified by the majority vote of its neighbors, where the value of K was taken as a small and real-valued positive integer [ integer ,38. Ther integer . HoweverFrom PM4—EC: The fourth model applies the ensemble classifier method as a predictive model. It aims to maximize the precision and recall value to detect all malignant tumors in the WDBC dataset and detect all cancer presence in the BCCD dataset. Our research applied an ensemble classifier to optimize the logistic regression model [on model ,41. Enseon model . The kin1.pandas, NumPy, and sklearn and execute the preprocessing steps to drop out the missing values.Import the related Python libraries such as 2.Process and execute the four-layered data exploratory techniques on each dataset.3.Definitions and calling of all related functions, such as the confusion matrix, the precision–recall curve, the ROC curve, the learning curve, and cross-validation metrics, and assess the models’ performance.4.Starting with SVM, first, it needs to define variables and the number of test and training sets . Then, define the output results and run the model using Linear and Polynomial SVM. The results would be shown in cross-validation metrics.The following model is LR; after defining the variables and splitting the data, two methods were applied to find the best hyperparameter. The first one was to use GridSearchCV, and the second one was to use Recursive Feature Elimination (RFE). Then, plot the confusion matrix, ROC curve, and learning, and find cross-validation metrics were used for both methods.The 3rd prediction model was KNN; we used GridSearchCV to find the best hyperparameter to run KNN and showed the confusion matrix and cross-validation metrics.The final model is EC; it applied LR with EC and the voting classifier for this work. The execution steps are similar to the previous ones. The results are shown in the confusion matrix, learning curve, and cross-validation metrics.Implement the proposed prediction models:This work was implemented in Jupiter Notebook with the Python language. We processed the following key steps that can assist the data analyst or physician in implementing this work for the breast cancer prediction in real-time:The experimental environment and fundamental packages for implementing proposed prediction models and DE techniques are presented in This section will evaluate the findings of our proposed prediction models and DE techniques and compare them to prior research.Data exploratory techniques were discussed in the previous section, and the DE technique presents the following significant results. For simplicity, we presented feature distribution insights from the WDBC dataset in Furthermore, we delivered the rest of the feature’s curves in Note 01 in the Furthermore, The followings are the evaluations of given predictive models (PM):PM1—SVM: This work considered two kernels for employing support vector machines, i.e., linear and polynomial kernels. PM2—LR: This study utilized three types of experiments for the LR model, i.e., basic LR, LR with 100% recall, and LR with the RFE method on the WDBC dataset. However, PM3—KNN: The KNN predictive model has experimented on two methods, i.e., basic KNN and KNN with hyperparameter. From PM4—EC: The performance analysis of ensemble classifiers (EC) is presented in After the individual classifier performance analysis, Furthermore, The best-achieved outcomes with previous studies that used the same WDBC datasets were compared. Our results evaluations mostly analyzed our findings by considering the F1 score. As in real-world classification problems, large imbalanced class distributions happened in datasets. We find some observations with significant differences between the classes in the feature distribution results. For example, the concavity mean in Supplementary Note 01 had a significant difference between the distribution of benign and malignant classes. The resampling techniques, i.e., oversampling, undersampling, and cross-validation, were adopted to balance such features. The oversampling technique duplicates the minority classes, but it creates an overfitting issue for machine learning algorithms. In contrast, the undersampling technique deletes the majority classes that discard the potential data. These disadvantages can decrease machine learning accuracy for particular problems such as fraud detection, face recognition, disease detection, etc. Therefore, we omit the oversampling and undersampling techniques in our study due to the cancer detection problem. However, the author  suggesteIn the polynomial SVM implementation, we secured a 99.3% F1 score, which means our proposed prediction model successfully identified the tumor and classified the cancer features as malignant. Thus, a higher F1 score means a higher diagnostic efficiency of tumors. In In this study, the objective was completed for detecting breast cancer with the highest accuracy of machine learning models. However, we were unable to provide the precise reason for malignant features, which needs a domain expert. It should be noted that the BCCD dataset did not yield effective results with our prediction models except for SVM; thus, we ignored those results in this study. We provided the sources/links of the datasets in the “Data Description” subsection. As these datasets belong to American patients, the results may not be similar and effective with the Asian patients’ data. This is one of the limitations of this study, which could be extended in the future by a different dataset with neural network implementation.An accurate and timely diagnosis of various diseases, i.e., breast cancer, is still a major problem for proper treatment in the healthcare field. The precise analysis of cancer features is still a time-consuming and challenging task due to the availability of massive data and the lack of DM techniques with appropriate ML classifiers. In this study, four-layered essential data exploratory techniques were proposed with four different machine learning predictive models, including SVM, LR, KNN, and ensemble classifier, to detect breast cancer tumors and classify them into benign and malignant tumors. One of the primary objectives of this study was the implementation of DE techniques before the execution of ML classifiers on the WDBC and BCCD datasets. These mining techniques enabled us to improve the prediction model’s performance with a maximum F1 score and an accuracy score higher than before. The significant finding demonstrated that the first prediction model  had acquired the highest accuracy (99.3%). Meanwhile, logistic regression with recursive features elimination also secured 98.06% accuracy, which shows that DE techniques effectively detect higher accuracy. Our outcomes depict the competence of our prediction models for breast cancer diagnosis and provide adequate results by utilizing a short time for training the model. These sophisticated models, techniques, and results would help the physician and data analyst to apply a more intelligent classifier to diagnose breast cancer features.As the image data relating to breast cancer are available, we will use deep learning models to detect breast cancer with novel data augmentation strategies and data exploratory techniques to handle the data scarcity and diversity. In the future, we will conduct experiments on the datasets from other countries and try to answer whether or not the different area patient’s data affect the model’s performance.


Male players showed higher values in the bone and muscle-related variables (p < 0.001–0.040), as well as in the strength and power production-related physical tests (p < 0.001–0.012), while the female showed higher values in the fat-related variables (p = 0.003–0.013), and performed better in the flexibility tests. Age, maturity offset, and birth quartile showed to have statistical influence in the differences found between sex groups; Conclusions: There is a clear influence of age and biological maturation on the differences found between sexes in adolescent volleyball players that could be taken into account regarding grouping in early stages.Background: Differences in kinanthropometric and physical fitness performance between boys and girls usually start during adolescence, as a result of the changes in the hormonal environment that occur with the advance of age and biological maturation; Methods: A total of 96 1st Regional Division players adolescent volleyball players, 48 males, (age = 14.17 ± 1.00 years-old) and 48 females (age = 14.41 ± 1.21 years-old) underwent a kinanthropometric assessment, were asked to perform different physical fitness test and to complete a questionnaire. Chronological age, maturity offset, age at peak height velocity (APHV), and birth quartile were calculated; Results: Statistical differences were observed between male and female players in the APHV ( Volleyball is a highly demanding sport due to the rules of the game, characterized by constant explosive actions ,2, whichHowever, this characterization of the requirements of athletes during puberty must be undertaken with caution due to the effect that biological maturation has been shown to have on kinanthropometric and physical fitness variables ,8. More Like biological maturation, age also exerts a determining influence on athletic performance in adolescent stages . ChangesDue to the importance of age and maturation in relation to adolescent sports, it has been observed that the traditional forms of grouping by chronological age may be a disadvantage for those players who mature later, or who were born in the last months of the year . For thiDespite the influence of age, biological maturation, and RAE on performance and kinanthropometric characteristics in adolescent athletes, little information has been found about the relationship of these variables with the differences found between sexes in volleyball players. Notwithstanding all the above, the aim of the present investigation was to analyze the differences between male and female adolescent volleyball players in kinanthropometric variables and physical fitness tests in relation to age, the maturity offset, and relative age.A total of 96 1st Regional Division adolescent volleyball players, 48 males (age = 14.17 ± 1.00 years old) and 48 females (age = 14.41 ± 1.21 years old), volunteered to take part in the study. Rstudio software , was used to perform the sample size calculations, setting the standard deviation (SD) based on the APHV reported in previous studies (SD = 0.65) . ClinicalTrials.gov (code: NCT04495595). The assessment was carried out in the players’ usual training hall. Sociodemographic and sporting information was collected from the players, and the coaches were asked to classify the players according to standardized criteria. Subsequently, a kinanthropometric assessment was carried out, followed by the implementation of physical fitness tests.A cross-sectional design was followed in the present study, in accordance with the STORBE guidelines . The meaThe players were asked to self-complete a questionnaire designed ad hoc, where they were asked their age and date of birth, days of training per week, years of experience playing volleyball in federated competition, days they had missed training in the last three months, and whether they had suffered any recent injury or were currently injured. A researcher explained each question in detail prior to the completion of the questionnaire and supervised the process.At the same time, according to previous research , coachesThe kinanthropometric measurements were performed in accordance with the protocols described by the International Society for the Advancement in Kinanthropometry (ISAK) . AccrediFour basic measurements ; eight skinfolds ; six girths ; five breadths ; three lengths ; and a height  were measured. A SECA 862 scale  with an accuracy of 100 g was used for measuring body mass; a SECA stadiometer  with an accuracy of 0.1 cm for measuring standing height and sitting height; an arm span meter  with an accuracy of 0.1 cm for measuring arm span; a skinfold caliper  with an accuracy of 0.2 mm accuracy for measuring skinfolds; an inextensible tape  with 0.1 cm accuracy for measuring perimeters; a segmometer  with 0.1 cm accuracy for measuring heights and lengths; an anthropometer  and a small girth sliding caliper  with 0.1 cm accuracy for measuring diameters. The final values of the kinanthropometric measurements were used to calculate the variables of the body mass index (BMI), fat mass , muscle 2 = 0.92–0.89 in the case of boys and an R2 = 0.91–0.88 in the case of girls.Mirwald et al. (2002) sex-specific formula was used to estimate the maturity offset of the players. From the maturity offset, the APHV of each subject was calculated using the formula: APHV = chronological age − maturity offset result. This method proved to be valid for estimating the maturity offset with respect to the gold standard using regression equations with an RAll physical fitness tests were performed by two investigators familiar with the technique and with previous experience in the evaluation of physical fitness in the adolescent population. Each investigator was in charge of the same tests during the measurement sessions, in order to avoid an inter-rater error. The intraclass correlation coefficient (ICC) was 0.995 , and the coefficient of variation (CV) was 2.3%. The sit-and-reach and back scratch tests were performed before the warm-up because an unequal effect of the warm-up on performance in flexibility tests has been observed . The parThe sit-and-reach test was performed with the Acuflex Tester III ; the back scratch test with a millimeter ruler ; the long jump and medicine ball throw tests with a tape measure  with a 0.1 cm accuracy; the CMJ with a force platform with a sampling frequency of 200 Hz ; the sprint test (20 m) with MySprint  ; and the2p). Bonferroni’s post hoc was used to analyze differences between groups. Chi2 test was used to analyze differences in discrete quantitative variables and qualitative variables. The significance level was set a priori at p < 0.05. All statistical analyses were performed with SPSS v.23 software .The distribution of the sample was analyzed using the Kolmogorov-Smirnov test. The kurtosis and skewness of the variables were calculated, as well as homogeneity using Levene’s test. A descriptive analysis of the variables measured was carried out, including the mean and standard deviation (SD). To analyze the differences between boys and girls in the continuous quantitative variables, a MANCOVA analysis was performed, introducing sex as a grouping variable and age, maturity offset and birth quartile as covariates. Both main effects and interactions between variables were tested to determine their influence on the differences found according to sex. The effect size was calculated with partial eta squared (Ƞp < 0.001), with an effect of the covariates age (p < 0.001) and maturity offset (p = 0.048) in the model, but not of the covariate birth quartile. However, the analysis of the intersections between sex and the covariates  did show an influence of these covariates on the differences found between groups (p < 0.001).The results of the comparison between sexes with respect to the APHV and the main effects of the covariates can be seen in p < 0.001), with an earlier APHV in the female group.p < 0.001–0.042), except in body mass; in the bone and muscle variables (p < 0.001–0.004); in the somatotype components (p < 0.001–0.040); in the skinfolds sum (p = 0.003–0.013); in the body composition components and in the muscle-bone index (p < 0.001–0.030) (p = 0.001–0.038). The covariate maturity offset showed main effects in all the kinanthropometric variables (p < 0.001–0.003), except for the somatotype components and the fat and muscle percentages. The covariate birth quartile did not demonstrate statistical significance in terms of main effects on the model (p < 0.05).Regarding the kinanthropometric variables, significant differences were observed between sexes in the basic measures (1–0.030) . The covp < 0.001–0.007); upper limb length, biacromial, biiliocrestal, and humerus breadths (p < 0.001–0.031); corrected leg girth and muscle and bone masses (p = 0.001–0.005) (p < 0.001–0.039), except somatotype, skinfolds sum, and muscle and fat percentages (p < 0.001–0.015), except for biiliocrestal breadth, mesomorphy, fat mass, and BMI.The analysis of the interactions between sex and age showed a significant influence of age on the differences found between groups in the basic measures (1–0.005) . The anacentages . Analysip < 0.001–0.030), except for the biiliocrestal breadth and the BMI, the mesomorphic component in the interaction models and the skinfolds sums, bone percentage and fat mass in the sex*age interaction  . The covariate age showed a significant effect on the model in the long jump, medicine ball throw, CMJ height and power, 20 m sprint, and agility tests (p = 0.001–0.007). The covariate maturity offset showed statistical effects over the long jump, medicine ball thrown CMJ height, and power tests (p < 0.001–0.007). The covariate birth quartile showed no main effects on the model.Physical fitness differences between male and female players and covariable main effects can be observed in p < 0.001–0.026), and also in sprint and agility tests in the case of birth quartile (p < 0.001–0.009)  showed that the three covariables had significant influence in the differences observed between groups in long jump, medicine ball throw, CMJ height and power (1–0.009) .p < 0.001–0.012). Statistical differences were also found in the sit-and-reach test in the sex model (p < 0.004), and in the agility test in sex*maturity offset and sex*birth quartile model (p = 0.002)  in back scratch test, long jump, medicine ball throw, CMJ height and power, and 20 m sprint (= 0.002) .2 analysis can be seen in The results of the ChiOne of the main objectives of the present study was to analyze the differences between boys and girls in terms of kinanthropometric variables. In this sense, significant differences were observed between groups in basic measurements, somatotype components, bone and muscle variables, and body composition components. These results are in line with previous studies that found differences between sexes in the variables studied in track and field athletes of similar age to the present study . In addiWhen analyzing the results of the Bonferroni adjustment, it was observed that the group of boys showed higher values in both bone and muscle variables, as well as in the components of muscle and bone body composition, while the girls obtained higher results in the adipose related variables and the fat component of body composition, with no differences observed in body mass or BMI. In the case of boys, circulating testosterone increases up to 30 times with respect to values measured before the growth spurt . In girlRegarding the physical fitness tests, significant differences between sexes were observed without the influence of any of the covariates in the sit-and-reach and back scratch tests, where the group of girls obtained better results. These results are in line with other studies that have analyzed flexibility and range of motion (ROM) comparing between sexes ,42. FlexOn the other side, differences between males and females were observed in the long jump, medicine ball throw, CMJ height and power, sprint, and agility tests, observing an interaction of age, maturity offset and birth quartile in the differences. All the tests where the differences were found are related to the ability to produce power and strength ,34. AmonIn relation to the birth quartile, it did not show statistical main effect in the MANCOVA model with respect to the kinanthropometric and physical fitness variables, although it did show influence on the differences between groups in the interaction with sex. REA has been well documented in team sports ,12,46. TThe differences found between boys and girls could be altered by differences in training, due to the influence of systematic physical exercise on kinanthropometric variables and variables related to physical fitness tests ,50. It hThe present study is not free of limitations. Among the most important limitations are the descriptive and cross-sectional nature of the study, a relatively small sample size, the age of the participants, which together with the design of the study does not allow us to analyze the evolution of the phenomenon studied throughout the entire maturation stage, and the method of estimating biological maturation, which although it is a widely used method in sports science , is not There is a clear influence of age and biological maturation in the differences found between sexes in adolescent volleyball players, while the birth quartile seems to have less influence as a main effect. The male volleyball players showed higher values in the basic kinanthropometric variables; bone breadths, except biiliocrestal; corrected muscle girths; bone and muscle masses, and muscle-bone index, while female players showed higher values in adipose related variables. In addition, significant differences were observed in the physical condition tests dependent on muscle strength and power, with boys showing higher values, while girls showed better performance in the flexibility tests. The differences found in both anthropometric variables and physical fitness test performance, and the relationship of age and maturation on the results could affect volleyball performance in both girls and boys after the APHV and become more marked as adolescence progresses. The practical implications of these results, together with the rules of the sport in youth categories, could be related to the creation of mixed teams and competitions between boys and girls until differences in kinanthropometric variables and physical performance are present around the APHV, moving at later stages to splitting the competition between boys and girls when differences in these variables may be determinant in volleyball sports performance. It would also be interesting during these stages of growth to include the evaluation of biological maturation when categorizing players to establish in which category they compete, and not to use chronological age as the only criterion, as is usually done, since those with more advanced maturation will have a competitive advantage as their anthropometric variables and physical condition performance variables will be affected.


Depression is one of the leading causes of disability worldwide. Given the socioeconomic burden of depression, appropriate depression screening for community dwellers is necessary. We used data from the 2014 and 2016 Korea National Health and Nutrition Examination Surveys. The 2014 dataset was used as a training set, whereas the 2016 dataset was used as the hold-out test set. The synthetic minority oversampling technique (SMOTE) was used to control for class imbalances between the depression and non-depression groups in the 2014 dataset. The least absolute shrinkage and selection operator (LASSO) was used for feature reduction and classifiers in the final model. Data obtained from 9488 participants were used for the machine learning process. The depression group had poorer socioeconomic, health, functional, and biological measures than the non-depression group. From the initial 37 variables, 13 were selected using LASSO. All performance measures were calculated based on the raw 2016 dataset without the SMOTE. The area under the receiver operating characteristic curve and overall accuracy in the hold-out test set were 0.903 and 0.828, respectively. Perceived stress had the strongest influence on the classifying model for depression. LASSO can be practically applied for depression screening of community dwellers with a few variables. Future studies are needed to develop a more efficient and accurate classification model for depression. Depression causes emotional, cognitive, vegetative, and somatic symptoms, which lead to functional impairment in everyday activities . The preThus far, increasing evidence indicates that genetic , neurogeTreatment of individuals with depression is often unsatisfactory. For example, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study showed that only one third of the total sample entered remission following initial treatment. In that study, less than 30% of patients achieved remission throughout four consecutive therapeutic regimens . The STAHowever, most studies have focused on diagnosing and predicting the prognosis of depression in clinical samples ,18. In aSome studies have investigated depression in non-clinical samples using modalities other than machine learning. For example, social media has been widely used, particularly in non-clinical adolescents and youths ,20,21. TRecent reviews have suggested that machine learning-based approaches have shown some promise in the diagnosis and treatment of depression ,18,23. OIn the present study, we built a predictive model for depression using a machine learning algorithm based on national survey data. Moreover, we identified which variables were the most important for predicting depression.n = 7550) and 2016 (n = 8150).The Korea National Health and Nutrition Examination Survey (KNHANES) is an annual nationwide survey that collects a variety of data on health behaviors, the prevalence of chronic diseases, and food and nutrition status. A detailed description of the KNHANES can be found in Kweon et al. . AccordiOnly participants who responded to questions that focused on depression and its predictive factors were included in this study. All participants received a full explanation of the aims and protocol of the KNHANES and provided written informed consent. All data processing procedures were approved by the Institutional Review Board of the KCDC (2013-12EXP-03-5C).The nine-item version of the Patient Health Questionnaire (PHQ-9) was used to measure depression . As suggOther variables included sociodemographic characteristics , health , quality of life (EuroQol EQ-5D), and laboratory findings .All machine learning processes were conducted using the scikit-learn library implemented in Python 3.7. The 2014 dataset was used as the training and validation sets. Given the unbalanced ratio of depression and non-depression, a synthetic minority oversampling technique (SMOTE) was used . To tuneRegularizing the logistic regression model attenuated the overfitting and allowed the classifying model to learn from the training data, not just copy it. Both L1 regularization ) and L2 regularization  provide a practical solution for overfitting. In a linear regression model, k=1l|ωk| . As the The regularized logistic regression model has low computing costs and easy-to-understand algorithms, contrary to most other machine learning algorithms that have high computing costs with the black box model.λ, was set to 0.0076. As we used LASSO, the penalty option was set to “l1.” Other hyperparameters were set to default in the LogisticRegression scikit-learn library.In this study, we first applied LASSO with the initial 37 contributing variables for feature reduction. Subsequently, we re-entered the resultant 13 variables with non-zero coefficients in the final model. The hyperparameter C, which inversely reflects the strength of the regularization parameter TP, TN, FP, and FN) of the confusion matrix. As the MCC is a discretized form of Pearson’s correlational analysis, the value can also be interpreted on the basis of Pearson’s correlational coefficient r [The area under the receiver operating characteristic curve (AUC) was used as the primary performance metric. Generally, an AUC of 0.8 to 0.9 is considered good, and that >0.9 is regarded as excellent . Other picient r . Hence, After excluding missing cases from the initial 37 variables, 4186 of 7550 (55.4%) participants in 2014 and 5302 of 8150 (65.1%) participants in 2016 were included in the machine learning . Table 2The prevalence of the minority class  was 6.16% (584 out of 9488) in the total sample, 6.45% (270 out of 4186) in the 2014 dataset, and 5.92% (314 out of 5302) in the 2016 dataset.The number (%) of the older adults  was 2074 (21.86%). There were significantly higher rates of divorce or separated marital status, older age, and females in the depression group than in the non-depression group. The depression group had significantly lower values than the non-depression group in the socioeconomic domain, such as the number of houses, the number of private insurance policies, receiving a basic living allowance, and household income. The depression group also had a significantly higher prevalence of chronic diseases such as hypertension, dyslipidemia, cerebrovascular disease, cardiovascular disease, thyroid disease, diabetes mellitus, and arthritis compared to the non-depression group. Regarding the quality of life, the depression group had lower scores than the non-depression group on all five domains of the EQ-5D.As shown in Feature importance was obtained from the magnitude of the coefficients. The variables with the greatest importance were perceived stress, subjective health, anxiety/depression in the EQ-5D, and divorced/separated status .We built a machine learning-based model for predicting future depression. The AUC (0.903), overall accuracy (0.828), sensitivity (0.828), and specificity (0.828) showed that this model could be practically used for screening community-dwelling individuals who may develop depression.In the final set of variables, perceived stress was the strongest predictor of depression. Stress is generally categorized as either eustress or distress. Eustress represents positive aspects of stress, whereas distress refers to its negative aspects. Perceived stress measures distress by using questions such as “In the last month, how often have you felt nervous and stressed?” The negative effects of stress have a well-documented relationship with the pathophysiology of psychiatric disorders, such as depression ,33. As mOur study had several strengths. First, we built a model to classify depression among community dwellers. Although depression causes substantial disability, the treatment of clinical depression is difficult . Hence, Second, we used various types of variables. As depression is based on a complex interaction among biopsychosocial variables ,40,41, cThird, we used LASSO to reduce features and build a final model to classify depression. We found that a model with fewer variables resulted in a performance comparable to one with more variables. We believe that practicality is necessary for such a machine learning model, and from a practical perspective, a questionnaire with too many questions might not be suitable for use in routine screening settings. If the performance between the two models is not substantially different, one with fewer variables could be practically used with the benefits of a short screening time and effort. As we developed this model for use in community health institutions, rather than higher-level facilities, we presumed that low computing costs with fewer variables are an important point. The reasonable computing costs of LASSO facilitate its deployment in community health institutions.p-value decreases [n = 9488), negligible differences were statistically significant (p < 0.05). Moreover, as the standard deviation  increases, the p-value increases [Fourth, it is noteworthy to discuss why we used the 2014 dataset for the training set and the 2016 dataset for the test set, rather than randomly selecting training and test sets. First, we wanted to test whether the algorithm made with past data  could be applied to future data . There will be some changes in the frequency or severity of the variables by reflecting the number of times the dataset was collected. If an algorithm should be useful in the real world over time, it should be robust for future data. In addition, there were statistical differences in many of the variables between the 2014 and 2016 datasets, whereas there was no statistical difference in the severity of depression between the two datasets. We interpreted the results mainly in terms of sample size and standard deviation. Generally, as the total sample size increases, the ecreases . As the ncreases ; thus, tn = 683), and the outcome measure was only moderately predictive (AUC = 0.69 to 0.72), such an approach should be used to supplement future surveys and help construct a more comprehensive dataset.This study had several limitations. First, although we included biopsychosocial factors for depression, neuroimaging and genetic variables were not available. Neuroimaging markers, such as structural volumes and functional activity, have long been used to classify depression ,45. GeneIn summary, we successfully built a model for classifying depression using the LASSO algorithm and sociodemographic, psychosocial, and laboratory data obtained from community dwellers. We believe that this model may help improve the accuracy of depression screening among community-dwelling individuals.


In 2020, the Governments of many countries maintained different levels of confinement of the population due to the pandemic that produced the COVID-19. There are few studies published on the psychological impact in the child and adolescent population diagnosed with mental disorders, especially during the home confinement stage. Explanatory models based on socio-demographic and clinical variables provide an approximation to level changes in different dimensions of behavioural difficulties. A categorical-response logistic ordinal regression model, based on a cross-sectional study with 139 children and adolescents diagnosed with mental disorders is performed for each dimension under analysis. Most of the socio-demographic and clinical explanatory variables considered (24 of 26) were significant at population level for at least one of the four dimensions of behavioural difficulties (15 response variables) under analysis. Odds-ratios were interpreted to identify risk or protective factors increasing or decreasing severity in the response variable. This analysis provides useful information, making it possible to more readily anticipate critical situations due to extreme events, such as a confinement, in this population. Public and private authorities have a growing research interest related to mental health, looking for, among others, the benefit of the community from an epidemiological point of view, as well as reducing the costs of, for example, health systems, etc. Particularly, mental health in children and adolescents has always had a special interest due to its impact in adult life. The child-adolescent population with mental disorders is especially vulnerable to extreme or extraordinary events, such as those experienced during the COVID-19 pandemic, particularly in relation to home confinement. Since the beginning of the pandemic, many countries implemented public health measures, such as home confinement, with the main goal of minimizing the spread of this virus . For exaIn general, mental health involves anxiety, depression, suicide intentions, etc. There is wide and recent literature regarding the mental health consequences of these extreme public health measures ,4,5. ManThere are few studies published on the psychological impact of COVID-19 in the child and adolescent population diagnosed with mental disorders, especially during the home confinement stage. The most studied diagnosed are Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) ,19,20. IIn this sense, this study has the purpose to identify factors increasing or decreasing severity in different dimensions of behavioural difficulties due to extreme situations, in patients diagnosed with internalizing and externalizing disorders according to DSM-5 classification .The sample comprised 139 children and adolescents who regularly attend the Child and Adolescent Mental Health Unit (CHAMHU) in Granada. All these subjects were diagnosed as affected by internalizing  or externalizing disorders . It is important to mention that the main diagnosis of each participant in the case of comorbidities, for example autism + ADHD, was established based on giving PRIORITY to the SEVERITY criterion (which diagnosis causes the main disability) and TEMPORALITY (diagnosis is stable over time in the event that transitory diagnoses such as conduct disorders converge). The diagnosis of the participants was collected from the digitized clinical history of the CHAMHU of Granada and was included in the database using a labelling system that guaranteed anonymity. The mean age of the subjects was 11.4 years (SD = 3.9), 71.9% of them were male and 55.4% were diagnosed with internalizing disorders, while 44.6% with externalizing disorders.A cross-sectional study comprising all the subjects was carried out. Data were obtained from an online survey by means of a standardized form model, Google Forms. The anonymous survey was sent via email (prior telephone authorization to parents) between 12 April and 25 May 2020 to a total of 328 parents of children and adolescents from the CHAMHU of Granada, who met the criteria to be accepted in this study. The inclusion criteria were an age range between 2 and 18 years and having an active follow-up (at least 12 previous months) in the CHAMHU of Granada. Participants who did not meet the two previous requirements were excluded. Those in whom, even meeting them, the reliability of the survey results could not be guaranteed , or those in whom the context of the child was not that of a home confinement  were also excluded. A total of 139 participants (the parents) responded to the survey. A random telephone survey was conducted among those who did not respond to the form, to determine the causes. Most of the parents reported having problems receiving e-mail, difficulties in completing the questionnaire, or lack of time.The explanatory variables considered were selected according to the previous bibliography ,24 and wThe form consists of five sections. The information reported by means of this survey is collected in statistical variables, measured in an ordinal scale (range 0–2) when it refers to responses variables. All the statistical variables, explanatory and response, are described in detail in One of the best statistical tools, because of its capacity for data analysis in clinical and epidemiological research, is logistic regression, hence its wide use . Indeed,A cumulative ordinal logistic regression model with categorical ordinal response and qualitative explanatory variables was used ,28. Fifthttps://www.r-project.org/, accessed on 19 October 2021).Statistical analyses were performed using the R Statistical Computing software 4.0.3 , and approved by the Ethics Committee of the Andalusian Health Service (TFG-IECS-2021).Difficulties due to confinement comprised of four categories: physiological disorders, social difficulties, behavioural disorders, and emotional disorders. These categories were described by means of 15 variables (response variables), whilst the considered explanatory variables (risk factors) were defined in 26 variables. The descriptive analysis of the response variables are shown in The 15 estimated models related to physiological, behavioural, and emotional disorders, as well as social difficulties, include 24 of the 26 explanatory variables, at least for one of the response variables. The odds-ratios estimated for each explanatory variable in the cumulative ordinal logistic regression model, as well as their results of the Wald’s test, are shown in having a pet (p = 0.041), the mother suffers from a physical disease (p = 0.045), a brother or sister suffers from a physical disease (p = 0.040), global assessment during confinement (p = 0.009), behavioural problems during confinement (p = 0.029), maintains communication with other families (p = 0.016), and maintains communication with friends (p = 0.044) are significant at the population level for at least one of the dimensions considered for difficulties related to physiological disorders. The only dimension of social difficulties is influenced by having a pet (p < 0.001) and behavioural problems during confinement (p = 0.002). Regarding behavioural disorders, the variables age (p = 0.006), gender (p = 0.006), use Information and Communication Technology (p = 0.008), level of conflict before confinement (p = 0.001), level of conflict during confinement (p = 0.020), a close family member has died of COVID-19 (p = 0.011), some family member is affected by COVID-19 (p = 0.006), the mother suffers from a physical disease (p = 0.037), a brother or sister suffers from a physical disease (p = 0.006), behavioural problems during confinement (p = 0.006), maintains communication with other family (p = 0.003), and maintains other types of contacts (p = 0.009) are significant for at least one of the dimensions of this category of difficulties. Finally, the variables economic situation (p = 0.012), having children (p = 0.026), having a pet (p = 0.011), type of family home (p = 0.007), use Information and Communication Technology (p < 0.001), level of conflict before confinement (p < 0.001), level of conflict during confinement (p < 0.001), the mother suffers from a physical disease (p = 0.023), behavioural problems during confinement (p = 0.003), participates in household chores (p = 0.001), physical activity (p = 0.004), maintains communication with other families (p = 0.011), maintains communication with friends (p = 0.022), and maintains other types of contacts (p = 0.002) are significant at the population level for at least one of the dimensions within the category related to emotional disorders.According to the results of the Wald test, the variables economic situation, having children, type of family home, participates in household chores, and physical activity are only significant in relation to emotional disorders. Age, gender, a close family member has died of COVID-19, some family member is affected by COVID-19, and a brother or sister suffers from a physical disease are significant at the population level only for behavioural disorders. Having a pet is significant for physiological disorders, social difficulties and emotional disorders. Use Information and Communication Technology, level of conflict before confinement, level of conflict during confinement, and maintains other types of contacts are relevant for both, behavioural and emotional disorders. The variable maintains communication with friends is relevant for physiological and emotional disorders, whilst global assessment during confinement is only significant for physiological disorders. The variables the mother suffers from a physical disease and maintains communication with other family are significant regarding physiological, behavioural, and emotional disorders. Behavioural problems during confinement is the only variable that is significant for the four groups of difficulties. Finally, only the variables coexistence in the family nucleus, the father suffers from a physical disease, and maintains school work schedule are not relevant for inference according to this sample.The previous paragraph identifies risk factors for prognosis increasing or decreasing severity for the dimensions considered at each one of the four categories of difficulties due to confinement. Taking into account the double purpose of age induces an improvement in the levels of aggressiveness (objects) since for older children or adolescents the possibility of being at the same level or at a better level is double  that of the youngest, whilst it induces a worsening for self-injury because the possibility of being at the same level or on a worse level is double  for youngest child or adolescents; gender also induce an improvement for aggressiveness (objects) because women multiply by three  the possibility of being at a better level for this response variable with respect to the men’s group; etc. p < 0.05); when OR > 1, it will be a protection factor , whilst for OR < 1, one interprets conversely as a real risk factor  compared with respect to the last level of the response variable. In this sense, for instance having a pet is a risk factor for Physiological Disorders and Social Difficulties but a protection factor for Emotional Disorders; economic situation and type of family home are protection factors for Emotional Disorders; use Information and Communication Technology and level of conflict before confinement are protection factors for Behavioural and Emotional Disorders whilst level of conflict during confinement is a risk factor for these dimensions; behavioural problems during confinement is a risk factor for the four dimensions under analysis; the mother suffers from a physical disease is a risk factor for Physiological and Emotional Disorders but a protection factor for Behavioural Disorders; a brother or sister suffers from a physical disease is a protection factor for Physiological and Behavioural Disorders; global assessment during confinement is a risk factor for Physiological Disorders; participates in household chores, physical activity, maintains communication with other family, and maintains other types of contacts are protection factors for Emotional Disorders, maintains communication with other family is also a protection factor for Physiological and Behavioural Disorders, maintains communication with friends for Physiological Disorders but a protection factor for Humor Changes and maintains other types of contacts for Behavioural Disorders. Here, it is also noted that a close family member has died of COVID-19 has a high strength as risk factor for self-injury since the possibility of worsening is 100-fold  if a close family member has died of COVID-19, whilst some family member is affected by COVID-19 is a protection factor for this response and the possibility of improvement is 20-fold  if some family member is affected by COVID-19, and for No Collaboration the possibility of improvement is 6-fold  in this case.On the other hand, among all the significant explanatory variables, their odds ratio identify the presence of protection factors, i.e., they induce decreasing severity in the level of the response variable, as well as the presence of real risk factors since they produce an increasing severity for the response variable. Indeed, regarding this fact, for example: economic situation, having children, type of family home, participating in household chores and physical activity are only significant in relation to emotional disorders. Age, gender, a close family member has died by COVID-19, a close family member is affected by COVID-19 and a brother or sister suffers from a physical disease are significant at the population level only with regard to behavioural disorders. Epidemiological studies show that manifestations of mental distress in the form of behavioural alterations are more frequent in younger children and in boys [The aim of this work was to identify factors increasing or decreasing in severity in different dimensions of behavioural difficulties due to extreme situations, in patients diagnosed with internalizing and externalizing disorders. With regard to this objective, 15 explanatory models based on socio-demographic and clinical variables provided an approximation to level changes in the dimensions of physiological disorders, social difficulties, and behavioural and emotional disorders. These models were used to identify risk or protective factors increasing or decreasing in severity in the response variable. The results obtained showed that some of these variables were significant for the prognosis of potential change to a higher or more severe level in each of the dimensions of behavioural difficulties under analysis. In this sense,  in boys . Our stu in boys  replicat in boys ,30. The  in boys ,21.From the results obtained, we propose a friendly-reading tabulation of the response dimensions and the socio-demographic and clinical variables, providing useful information for physicians. Indeed, it can be interpreted by rows (left to right) for quick identification of the dimensions affected by one of the socio-demographic or clinical characteristics. It can also be read by column (top to bottom) for quick identification of all the variables that influence a response characteristic or in the whole dimension of interest. Accordingly, we assessed the possible inclusion of these variables in a profile associated with child and adolescents with a high probability of experiencing increasing severity in behavioural difficulties under extreme situations. This conclusion was drawn on the basis of having found the influence, as risk or protective factor, of multiple socio-demographic and clinical variables for the four dimensions under analysis.In view of the above considerations, we conclude than in order to establish a risk profile for behavioural difficulties among children and adolescents diagnosed as affected by internalizing or externalizing disorders, socio-demographic and clinical variables should be taken into account. This finding could be of great interest, and can be used as a basis for designing measures to prevent extreme situations among this population. Due to the considerable weight of these variables as risk or protective factors with respect to behavioural difficulties, we believe further research should be conducted, among populations of children and adolescents with these diagnoses and other children, in order to ratify the results in this paper. Furthermore, this would better facilitate the creation of risk profiles for each dimension of behavioural difficulties under extreme situations.One of the strengths of this work, conversely to other studies which are focused on isolated risk factors influencing a particular response variable ,32, is tChildren and adolescents diagnosed as affected by internalizing or externalizing disorders are a risk group for behavioural difficulties under extreme situations because of the special nature of their disorder. This study identifies changes in these dimensions and determines which variables bear most weight, thus enabling us to predict and characterize changes in the severity of these behavioural difficulties. The analysis performed provides valuable information of risk and protective factors, facilitating the consideration of risk profiles for these difficulties, making it more readily identifiable and its evolution predictable.


Background: Colic is a common condition in infants <4 months of age. Attempts to treat infantile colic with probiotics have shown variable efficacy and overall low evidence of success. In this work, we tested the hypothesis that oral administration of Bifidobacterium longum CECT7894 (KABP042) and Pediococcus pentosaceus CECT8330 (KABP041) mix (1 × 109 colony forming units) would improve the symptoms of infantile colic.Methods: A total of 112 exclusively breastfed or mixed fed infants aged <2 months and meeting the ROME IV criteria for infantile colic were recruited. The infants were randomized in a double-blind, placebo-controlled trial to receive orally administered probiotics  or placebo  daily for 21 days.Results: Infants in the IG had significantly shorter crying time (p < 0.001) on day 7 , day 14 [IG vs. PG: 20 (0–40) vs. 50 (30–75) min/day], and day 21 [IG vs. PG: 14 (0–33) vs. 40 (28–62) min/day]. Higher responder ratio and fewer crying/fussing episodes on days 7, 14, and 21 and better stool consistency on day 21 were observed in the IG (p < 0.01) as compared to the PG. Conversely, no significant effects on stool frequency or quality of life were observed.Conclusions: In summary, daily oral administration of B. longum CECT7894 (KABP042) and P. pentosaceus CECT8330 (KABP041) was an effective treatment for shortening crying time due to infantile colic and for improving fecal consistency. This trial was registered retrospectively in December 2019 with a trial number of ISRCTN92431452. Infantile colic is a behavioral condition in early infancy that involves long crying bouts and hard-to-soothe behavior for no apparent cause. This distressing condition affects up to 20% of infants under 3 months of age . AlthougLactobacilli and Bifidobacteria and higher proportions of opportunistic proteobacteria    as compala spp.) , 11.Pediococcus pentosaceus CECT8330 (KABP041) can induce anti-inflammatory interleukin 10 (IL-10) production. Its combination with Bifidobacterium longum CECT7894 (KABP042) has shown a broad spectrum of inhibitory activities against gas-producing enteropathogens. Both strains have been isolated from fecal samples of healthy infants (B. longum CECT7894 (KABP042) and P. pentosaceus CECT8330 (KABP041), as found in a currently marketed product, would reduce daily crying or fussing time (primary outcome), decrease daily episodes of crying or fussing, and improve parental quality of life (secondary outcomes). infants . In thishttp://www.isrctn.com/ISRCTN92431452).This randomized, double-blinded, placebo-controlled parallel-group study was performed in Qingbaijiang, Jinniu, and Wuhou Districts of Chengdu City and Huili County of Xichang City, Sichuan Province, China from June 1st, 2018 to June 1st, 2019. Infants diagnosed with colic by pediatricians in the outpatient care department at Qingbaijiang Maternal and Child Health Hospital, Caojiaxiang Community Healthcare Center, Huili Maternity and Child Care Center, and Angel Children's Hospital Chengdu were recruited. Nursing advices were provided to caregivers of the infants by nurses in each hospital. The enrollment and research plan were reviewed and approved by the institutional ethics committee of Angel Children's Hospital Chengdu . Written informed consent was obtained from parents of each infant. The present study complied with the code of ethics of the World Medical Association (Declaration of Helsinki). No important changes to methodology was made after trial commencement and all planned outcomes were reported. This trial was registered retrospectively in December 2019 with a trial number of ISRCTN92431452   were recZ test. The significant level of the test was targeted at 0.05 with a power of 80%. Statistical power was calculated using the G*Power software  a baseline visit, which corresponded to the day when parents were referred to the pediatrician with the main complaint of the baby's crying and/or fussing without a definite reason; (ii) day 1 visit, when the ROME IV colic diagnostic criteria were verified and when participants were randomized for intervention; and (iii) day 7, 14, and 21 visits, that corresponded to the weekly assessments during the 3-week intervention phase from day 1. Caregivers were reminded of the study instructions on each visit.A completely randomized schedule that maintains balance between treatment arms in a 1:1 proportion was prepared by an independent statistician, who was not directly involved in the analysis of the study results. The RAND function of Excel  was used to generate randomly permutated codes. The physicians responsible for enrollment of patients allocated the next available number to the subject. At enrollment, the parents of the patient received a closed envelope containing a written usage guidance for the oil drops. To minimize potential biases, the study was double-blinded whereby treatment allocation was concealed from all study investigators and participants. Drug containers prepared by an independent pharmacy were sequentially numbered and opened sequentially. Containers were of identical appearance, tamper-proof, and equal in weight. All the caregivers have been well-trained by using a standardized protocol.B. longum CECT7894 (KABP042) and P. pentosaceus CECT8330 (KABP041). Each five drops of the formula contained one billion total colony-forming units (CFU) in a 1:1 proportion for the two strains, as stated by the manufacturer under ICH zone-II stability conditions. However, the exact counts in the clinical batch were not independently verified in this study. The reference product (placebo) contained an identical sunflower oil, without the probiotics. Both the probiotic and the placebo were manufactured by PROCEMSA srl  for AB-Biotics SA (Spain), the company owning IP rights of the strains. PROCEMSA has GMP and ISO9001 certifications and tests the quality of each batch using the European pharmacopeia-compliant methods for total yeasts and molds, total Gram-negative bile-tolerant bacteria, E. coli, Salmonella, Staphylococcus aureus, Listeria monocytogenes, and peroxides. Additionally, PROCEMSA tests for heavy metals using ICP-OES. Products are routinely shipped from Spain to China in containers controlled for temperature with data loggers. Shelf life of the product is 2 years from manufacturing when stored at room temperature (25°C) or below. Expiry date is noted on boxes. The products were used before reaching the expiry date.All eligible colicky infants were randomized to receive either the probiotics  or a reference product without the probiotics . There was no delay between randomization and the initiation of the intervention. The probiotic formula comprised a pure sunflower oil suspension (not an emulsion) of strains Caregivers, who received the standardized operation training from nurses at the time of recruitment, administered five drops of the study product orally to each infant, daily for 21 days. Caregivers were instructed to give five more drops if regurgitation occurred right after the first administration. The dose was not required to be given at a fixed time or given with feeds. However, for compliance and ease of administration, each family has been recommended to give the dose with the same feed each day. When the administration was completed, the empty bottle was retrieved to assess the adherence.The probiotics and the placebo products were indistinguishable in smell, taste, and appearance. Both products were only labeled with the randomization number, batch number, expiry date, and the statement “For clinical trial use only.” The random allocation sequence was generated by an independent statistician. Caregivers of each participant received a numbered treatment bottle based on the sequence. Parents of the subjects, the clinical team, statisticians, and representatives from AB-Biotics S.A were blinded during the entire study until the database was unlocked.TM, a 15-item validated questionnaire, was used to assess family functioning (The primary outcome (daily crying or fussing time) and secondary outcome  assessments were adapted from Sung et al. with minor modifications  and werectioning , each itt test was used to compare the mean values of continuous variables approximating a normal distribution. For non-normally distributed variables, the Kruskal-Wallis or chi-square test was used. Relative risk based on the proportion of responders on the 7th, 14th, and 21st days during the intervention and its 95% confidence interval were calculated. Data were analyzed using the SAS for Windows statistical software package .The distribution of the data was tested for normality prior to analysis using the Shapiro-Wilks normality test. Baseline characteristics and study outcomes were presented as mean and standard deviation (SD) for normally distributed variables or median and 25th and 75th percentiles  for variables with a skewed distribution. All tests of significance were two-tailed with an α = 0.05. The Student's n = 48 for the IG and n = 42 for the PG) retained 80.9% power for crying time, 99.1% power for number of episodes, and 81.6% power for responder rate on day 21, when the differences between the IG and the PG were the smallest during the 7–21 day period. Even higher statistical powers were reached on days 7 and 14.A total of 112 infants met the inclusion criteria (56 for each group). Five infants were excluded due to parental rejection. Of the remaining 107 infants, 17 (15.9%) dropped out during the study. Three of these infants were withdrawn for using other probiotics, seven for loss to follow-up, three for formula-related allergy , and four for diarrhea. Of note, antacid medication is normally not used in colicky infants in China. No infant in the study was taking antacid medication. Thus, the primary and secondary outcome measures were obtained from 90 infants (48 for the IG and 42 for the PG) . Despitep > 0.05) , and feeding method during the intervention between the two groups ( > 0.05) . Most inp > 0.05, p < 0.001, The Shapiro-Wilks normality test showed that crying/fussing time and frequency were both non-normally distributed variables. Therefore, the Kruskal-Wallis test was used to compare the difference between the two groups at different time points. There were no significant differences in crying/fussing time and frequency between the IG and the PG at baseline , 14th , and 21st  days  days .p > 0.05). Infants in the IG had lower fecal frequency/day than infants in the PG. The differences were already significant on the 1st day and further increased on the 7th, 14th, and 21st days  and a similar result was found for the Social Function Subscale on the 14th day  and P. pentosaceus CECT8330 (KABP041) at a dose of 109 CFU per day to exclusively breastfed or mixed fed infants was superior to the placebo for the management of infantile colic. The use of the two strains significantly reduced the crying/fussing time and the frequency of episodes. No differences were observed on stool consistency until day 21, when a small but significant increase in consistency was observed in the IG group. Conversely, differences in the stool frequency were already apparent on day 1 and became more significant during the study. However, the fact those differences were already present on day 1 precludes attributing the effect to the probiotic intervention. Besides, no significant effects were observed on family quality of life. No adverse events and unintended effects were recorded during the intervention. Pediococcus strains have been used as probiotics in other clinical trials to treat several diseases, including obesity  reduced the duration of crying by almost 35 min compared to the placebo  had no significant effect on crying of colicky infants  and P. pentosaceus CECT8330 (KABP041) might exert this action have yet to be elucidated. Intestinal inflammation has been discussed as one of the possible causes of infantile colic, together with dysbiosis, production of gas (such as H2), and hypersensitivity to some nutrients (P. pentosaceus CECT8330 has been reported to induce the anti-inflammatory cytokine IL-10 (P. pentosaceus CECT8330 (KABP041) and especially B. longum CECT7894 (KABP042) were able to inhibit the growth of a wide spectrum of opportunistic gas-producing enterobacteria of the Escherichia and Klebsiella genera, known to be abnormally abundant in colicky infants  is effective in inhibiting the growth of Ent. aerogenes. Future studies should aim at elucidating this effect by exploring microbiome, metabolome, and inflammation markers .The exact mechanisms by which utrients . In thisne IL-10 , 38. In  infants , 40. Thi330 KABP0 might ex894 KABP0 and P. p strains . AdditioThe strengths of our study include adequate sample size for the predefined outcomes, proper blinding maintained throughout the treatment, data management, and analyses. In addition, high retention and reported adherence rates allowed the achievement of the predetermined statistical power and significance. Moreover, a generally accepted definition of colic was used for diagnosis (ROME-IV criteria), and infants were recruited at a similar, early age. Similar to previous studies mentioned above, a potential limitation of this study is the assessment of the duration and frequency of colicky full force crying and fussing in infants with colic relied solely on the caregivers' report. Thus, we used a placebo-controlled, blinded design to minimize this potential shortcoming. Moreover, the design of our study did not allow for a better description of the crying . Another limitation was that only a non-significant effect could be observed in parental quality of life despite a significant effect on crying/fussing. This could be due to an inadequate sample size for this outcome or other factors having a stronger effect on parental quality of life than the baby's crying . Another limitation of this study is that the compliance with the study products was not objectively assessed. A potential approach to assessing compliance is to weigh the study bottles both before and after dispensing; however, this method has reportedly produced highly variable results . FinallyB. longum CECT7894 (KABP042) and P. pentosaceus CECT8330 (KABP041) mix in comparison to the placebo. We recommend these probiotics for reducing crying times of colicky infants. Future studies should clarify the mechanism of the mix of probiotics in the management of infantile colic.In summary, exclusively breastfed or mixed fed infants with colic benefited from the treatment with The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Institutional ethics committee of Angel Children's Hospital Chengdu. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.KC and FS designed the research. KC, HL, YL, CZ, SX, and JL were responsible for the collection and assembly of the data. KC and CL analyzed the data. KC, FS, and CL wrote the first draft of the manuscript. All authors critically reviewed the manuscript, contributed to its revision, and approved the final manuscript.This work was supported by the Maternal and Infant Health and Care Science Laboratory, Shanghai, China (MIHC/2017/10/AKO2018).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. AB-Biotics SA  is the company owning worldwide IP rights of the probiotic strains used in this research and PROCEMSA Spa  is a toll manufacturer employed by AB-BIOTICS SA. These two companies did not participate in study funding, design, data collection, analysis, and interpretation, or in writing the manuscript. Maternal and Infant Health and Care Science Laboratory  holds an interest in the distribution of the probiotic used in this study in China, but its contribution was limited to funding the study and publication, and did not participate in study design, data collection, analysis, and interpretation, or in writing the manuscript. The authors are not employed by any of the aforementioned companies nor hold their shares. The companies did not compensate for time and efforts of the authors in cash or kind for conducting this study.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sensitivity to the vaccine profile was also stronger in August compared to November 2020, in younger age groups, among those with lower income; and in those that are vaccine hesitant. Uptake of COVID-19 vaccines could vary in a country based upon effectiveness and availability. Effective communication tools will need to be developed for certain sensitive groups, including young adults, those with lower income, and those more vaccine hesitant.Controlling the spread of SARS-CoV-2 will require high vaccination coverage, but acceptance of the vaccine could be impacted by perceptions of vaccine safety and effectiveness. The aim of this study was to characterize how vaccine safety and effectiveness impact acceptance of a vaccine, and whether this impact varied over time or across socioeconomic and demographic groups. Repeated cross-sectional surveys of an opt-in internet sample were conducted in 2020 in the US, mainland China, Taiwan, Malaysia, Indonesia, and India. Individuals were randomized into receiving information about a hypothetical COVID-19 vaccine with different safety and effectiveness profiles (risk of fever 5% vs. 20% and vaccine effectiveness 50% vs. 95%). We examined the effect of the vaccine profile on vaccine acceptance in a logistic regression model, and included interaction terms between vaccine profile and socioeconomic/demographic variables to examine the differences in sensitivity to the vaccine profile. In total, 12,915 participants were enrolled in the six-country study, including the US (4054), China (2797), Taiwan (1278), Malaysia (1497), Indonesia (1527), and India (1762). Across time and countries, respondents had stronger preferences for a safer and more effective vaccine. For example, in the US in November 2020, acceptance was 3.10 times higher for a 95% effective vaccine with a 5% risk of fever, vs a vaccine 50% effective, with a 20% risk of fever . Across all countries, there was an increase in the effect of the vaccine profile over time ( The WHO  declared SARS-CoV-2 to be a pandemic on 11 March 2020 [Although the outbreak has had a differential impact across countries , controlSince the pandemic outbreak of SARS-CoV-2, many efforts have been made by governments, medical personnel and other institutions/organizations for stopping the spread of infections and limiting the burden of disease . As SARSOverall, the acceptance for these vaccines depends not only on their widespread availability and convenience of access but also people’s confidence in vaccination . VaccineThere may be worldwide variations in COVID-19 vaccine acceptance, particularly as roll-out of the vaccine is uneven across the globe. In a survey conducted in China, scientists observed that vaccine acceptancy was very high, especially among health care workers and the main reason was the high trust in the central government ,21. AmonThe differential availability across countries of different COVID-19 vaccine types, which have varying safety and effectiveness profiles, may make it difficult to quickly attain a high level of vaccine uptake, even when the vaccine is available. The aim of this study was to characterize how vaccine safety and effectiveness impacts acceptance of a vaccine, and whether this impact varied over time or across socioeconomic groups. We hypothesize that individuals will prefer safer and more effective vaccines, but that this degree of preference could vary significantly across countries. This information contributes to our understanding of COVID-19 vaccine decision-making on a global scale and can identify potential pitfalls in the roll-out of COVID-19 vaccines with lower effectiveness or safety.https://doi.org/10.3886/E130422V2 (accessed on 15 June 2021). Questionnaires, and details of the sampling scheme, are available at: https://doi.org/10.6084/m9.figshare.14792058.v2 (accessed on 17 June 2021).This study is part of a larger project looking at resiliency and adherence to public health countermeasures during the COVID-19 pandemic. We conducted several waves of cross-sectional surveys  between March and November 2020 , with siWe attempted to obtain a sample size of 800 for each country for each wave of data collection. With an alpha of 0.05 and a power of 80%, and a proportion of 50% , the margin of error will be 4%. This margin of error would allow us to assess substantial trends over time. Respondents received this question: “A vaccine is currently not available for the new coronavirus strain . Imagine that a new coronavirus vaccine has just been developed. It has received the same testing as the adult influenza vaccine. The government is offering it as a free and optional vaccine. Would you accept a coronavirus vaccine, which is (95%) effective, with a (5%) chance of a side effect like fever? (95%) effective means that there is a (95%) reduction in disease among those vaccinated compared to those unvaccinated.” Participants were randomized to one of four groups, with effectiveness varying between 95% and 50%, and risk of side effect varying between 5% and 20%. These experimental groups are the main independent variable, and acceptance of a COVID-19 vaccine is the main outcome.We measured general vaccine hesitancy on a 10-item scale, whose properties were previously evaluated in Chinese and US studies . BrieflyWe explored vaccine acceptance across three socioeconomic and demographic factors: age, gender, and income. For age, we split the sample into three groups: 18–34, 35–54, and 55 and above. For gender, individuals could self-report male, female, or other, with others being excluded from the analysis. For monthly household income, individuals responded to several categories. We categorized these into “higher” and “lower” income based roughly on a cut-off of $2000 per month, based on a purchasing power parity currency conversion. The lower category was ≤$2000 in the US, ≤7500 CNY in China, ≤30,000 TWD in Taiwan, ≤40,000 INR in India, and ≤3000 MYR in Malaysia.Our initial analysis was a test of the experimental effect of vaccine profile attributes on vaccine acceptance. In our first set of models, we constructed separate logistic regression models for each country and for each wave of data collection. The second set of models tested changes between August and November. For this, we combined data from all countries and waves, and we placed an interaction term between an indicator variable for wave and independent variables, including the vaccine profile attributes. In the third set of models, we examined differences in the effect of vaccine profile attributes by specifying an interaction term between sociodemographic variables and the vaccine profile attributes. In this third set of models, we report marginal estimates , which are predicted margins on the logit scale, balanced over the other covariates in the model. The p-value for the interaction term between the experimental vaccine profile and other variables thus indicates whether the effect of the experimental vaccine profile differed by group.https://doi.org/10.3886/E130422V2 (accessed on 15 June 2021). The models output odds ratios (OR) and 95% confidence intervals (CI). We used survey procedures that accounted for clustering between countries, and incorporated weights. We assessed significance at an alpha = 0.05 level, and used SAS version 9.4 .The first set of models only included the experimental conditions (the vaccine profile attributes) in the model. The second model also adjusted for age, gender, income, and vaccine hesitancy. All models used weights so that the survey respondents matched the general population’s distribution by age, gender, and region of country. In the US, model weights were also based on race/ethnicity. Details about the weight construction, including sources of population information, are available at: In total, 12,886 participants were enrolled in the studies from six countries in five waves from March, June, Aug, October, and November in 2020, including the United States (4050), China (2797), Indonesia (1507), India (1762), Malaysia (1492), and Taiwan (1278). Participants were divided into lower and higher income levels based on the $2000 monthly salary by PPP currency conversion. Most of the respondents from the U.S. (77.2%), China (89.5%), Malaysia (71.4%), and Taiwan (89.1%) were from the higher income level. More than half of the participants from Indonesia (61.7%) and India (53.6%) were from the lower-income group.Preferences of the COVID-19 vaccine with different effectiveness and safety in different waves were shown in p < 0.0001), such that were reduced preferences for the least effective and safe vaccine in November 2020 compared to August 2020. Patterns of acceptance by age, gender, and income did not vary between August and November 2020. Changes in vaccine acceptance over time are shown in Overall, vaccine acceptance increases from 75% in the 50% effective vaccine with a 20% risk of fever, to 88% in the 95% effective vaccine with a 5% risk of fever . The impThis study has investigated how vaccine safety and effectiveness influences vaccine acceptancy among different socioeconomic groups over time. Individuals’ preferences for a more effective and a safer vaccine are obvious and well documented previously ,28, but Other studies have pointed out stubborn rates of vaccine refusal despite the availability of COVID-19 vaccines ,31. TherWe found that socioeconomic status could moderate vaccine decision-making. For example, individuals with a lower income were less accepting of a COVID-19 vaccination, but this was most readily apparent with the safest and most effective vaccine , and the lower income group was 83% accepting . This could be due to lower health literacy in lower income groups. Studies examining education have found mixed relationships between educational attainment and vaccination status. For example, in one study there was no specific association between economic hardship, education and vaccine hesitancy , but othGender and age played an important role in vaccine hesitancy. According to one study, men are less hesitant in receiving a vaccine then women and older people are more willing to receive the vaccine . In contThere are several limitations to this study. We use opt-in samples, and so the study population is biased to a more affluent, internet-accessing population. This study did not contain probability samples, which are very difficult to obtain in many low- and middle-income countries. Therefore, the results should not be considered representative of any one country and should be confirmed in additional studies with more robust samples. Additionally, we did not ascertain certain important variables, including educational status, marital status, size of the family unit, or numbers and age of children. It is possible that social desirability bias could have affected some responses. We measured vaccine hesitancy dichotomously, but it is a complex phenomenon that may not be fully captured with our variable. We also note that our study’s time frame  occurred during rapidly changing epidemiological circumstances and vaccine development. Opinion about a vaccine could have changed across this time. In contrast to a longitudinal study, we were not able to look at time-varying changes within an individual. The strength of our study was a robust experimental design and the inclusion of various countries.Our study provides additional support for understanding vaccine acceptancy worldwide. When examining acceptance of COVID-19 vaccination by general vaccine hesitancy, we found that those who were more vaccine hesitant were more sensitive to the vaccine safety and effectiveness profile. This means that increased general concerns about vaccines, a trend that seems to be intensifying in recent years, could mean that the population is more particular about what vaccine they want to receive . However