Missing Value Analysis Research Articles

Cognitive flexibility as a predictor of optimism in university students

The aim of this study is to investigate the predictive power of cognitive flexibility levels of university students on optimism. In addition, it is aimed to reveal whether the relationship between optimism and cognitive flexibility differs according to gender. The participants of the study consisted of a total of 835 university students, 632 females (75.7%) and 203 males (24.3%), studying at Ondokuz Mayıs University, Turkey. The ages of the university students participating in the research range from 17 to 43, and the average age of the students was determined as 20.58. In the data collection process, the Personal Information Form was used to determine the demographic information of the participants, the Cognitive Flexibility Inventory to determine their cognitive flexibility levels, and the Life Orientation Test to determine the level of positive expectations that individuals generally have about their experiences. To achieve the research purposes, missing value analysis, outlier analysis, normal distribution, linearity and homogeneity of variance-covariance matrices were examined, and it was found that the assumptions were met. Pearson Moment Correlation Coefficients were used to determine the relationships between optimism and cognitive flexibility. According to the correlation analysis results, positive and meaningful relationships were found between optimism and cognitive flexibility, and it was found that university students with high optimism scores also have high cognitive flexibility levels. Simple Linear Regression Analysis was performed to determine how much cognitive flexibility predicts optimism. As a result of the regression analysis, it was concluded that cognitive flexibility explains 19.4% of the total variance of optimism. Considering the research results, universities can organize programs and workshops aimed at developing the cognitive flexibility of students. These programs can increase students' optimism by teaching them positive thinking skills.

The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes Patients.

Diabetes Mellitus is a long-term, multifaceted metabolic condition that necessitates ongoing medical management. Hypogonadism is a syndrome that is a clinical and/or biochemical indicator of testosterone deficiency. Cross-sectional studies have reported that 20-80.4% of all men with Type 2 diabetes have hypogonadism, and Type 2 diabetes is related to low testosterone. This study presents an analysis of the use of ML and EL classifiers in predicting testosterone deficiency. In our study, we compared optimized traditional ML classifiers and three EL classifiers using grid search and stratified k-fold cross-validation. We used the SMOTE method for the class imbalance problem. This database contains 3397 patients for the assessment of testosterone deficiency. Among these patients, 1886 patients with Type 2 diabetes were included in the study. In the data preprocessing stage, firstly, outlier/excessive observation analyses were performed with LOF and missing value analyses were performed with random forest. The SMOTE is a method for generating synthetic samples of the minority class. Four basic classifiers, namely MLP, RF, ELM and LR, were used as first-level classifiers. Tree ensemble classifiers, namely ADA, XGBoost and SGB, were used as second-level classifiers. After the SMOTE, while the diagnostic accuracy decreased in all base classifiers except ELM, sensitivity values increased in all classifiers. Similarly, while the specificity values decreased in all classifiers, F1 score increased. The RF classifier gave more successful results on the base-training dataset. The most successful ensemble classifier in the training dataset was the ADA classifier in the original data and in the SMOTE data. In terms of the testing data, XGBoost is the most suitable model for your intended use in evaluating model performance. XGBoost, which exhibits a balanced performance especially when the SMOTE is used, can be preferred to correct class imbalance. The SMOTE is used to correct the class imbalance in the original data. However, as seen in this study, when the SMOTE was applied, the diagnostic accuracy decreased in some models but the sensitivity increased significantly. This shows the positive effects of the SMOTE in terms of better predicting the minority class.

Effect of Brand Elements on Organizational Success: A Moderating Approach to the Brand Experience of Muslim and Non-Muslim Customers of Islamic financial Products in Pakistan

The primary purpose of this research is to analyze the impact of brand elements on the achievement of an organization. In addition, the study also examined the moderating effect of brand experience on Muslim and non-Muslim customers' perception of Islamic products in Pakistan. This research examines the significance of brand elements in the success of organizations and compares the reactions of Muslim and Non-Muslim customers of Islamic Products in Pakistan. The researchers used Keller's renowned CBBE model to examine the impact of brand elements on organizational success. This research used the convenience sampling technique to gather data from customers of Islamic products. Following the completion of the missing value analysis, only 124 out of 150 respondents provided their replies. The study analysis indicates that the brand resonance of both Muslim and non-Muslim customers of Islamic products has a positive effect on the success of the organization. Additionally, the relationship between these consumers and the organization is also crucial for achieving success or improved performance. The study shows that both Muslim and non-Muslim consumers in Pakistan have positive responses towards Islamic products offered by Islamic banks. This information can be used by managers to implement this strategy for the success of their organizations.

STRATEGIC ORGANIZATIONAL CHALLENGES AND SUSTAINABLE MANAGING HUMAN RESOURCES IN JORDAN SME LOGISTICS COMPANIES

Organizational development is a natural phenomenon in the world of administrative organizations. As it plays a significant role in enhancing the effectiveness and functionality of job performance. The human and organizational side is regarded as one of the most crucial components of a successful renaissance in the life of administration in general and human resources management. However, this paper aims to investigate the impact of strategic organizational challenges on managing human resources in the light of entrepreneurship as a moderate variable in Jordanian SME logistics companies. Smart PLS4 was used to analyze the data that collected via questionnaire. Probability sampling were used. A total of 527 questionnaires were collected from the respondents. The result of missing value analysis showed that there are 193 responses are missing. Moreover, the findings indicate that strategic organizational challenges positively and significantly affect the entrepreneurship capabilities and competitive advantage. In addition, entrepreneurship capabilities positively and significantly affect competitive advantage and human resources performance. Entrepreneurship capabilities have a mediator effect between variables. However, this study contributes to providing decision makers and policy makers; where adopting sustainable HR practices can benefit decision makers in Jordan SME logistics companies by improving employee retention, productivity, efficiency, reputation, and talent acquisition, while mitigating risks associated with legal, regulatory, and ethical issues.

Determinants of comprehensive knowledge on mother-to-child transmission of HIV and its prevention among childbearing women in Rwanda: insights from the 2020 Rwandan Demographic and Health Survey

BackgroundMaternal knowledge on mother-to-child transmission (MTCT) and its prevention has been identified to enhance maternal testing and adherence to antiretroviral therapy (ART) regimen. Examining prevalence and associated factors on MTCT and its prevention among women provides empirical evidence for design and implementation of health strategies aimed at increasing MTCT knowledge and its elimination. This study therefore examined women’s comprehensive knowledge and associated factors on MTCT and its prevention among childbearing women in Rwanda.MethodsAnalysis was conducted on a weighted sample of 14,634 women from the 2020 Rwanda Demographic and Health Survey (RDHS). Dataset cleaning and missing value analysis was conducted. Chi square, bivariate and multivariable regression was then conducted in complex samples in SPSS. Alpha level set at p < 0.05 and at 95% Confidence Interval (95% CI). All analysis were adjusted for unequal probability sampling using survey weights. Bivariate and multivariable results were reported with crude and adjusted odds ratios.ResultsThe mean age was 29.2 years, SD-9.1. Prevalence of HIV testing and comprehensive knowledge on MTCT and its prevention among women in Rwanda was 79.6% and 65.1% respectively. Findings from this study showed that married women have higher odds (aOR = 1.18, 95% CI = 1.04–1.35) of comprehensive knowledge on MTCT and its prevention compared to those unmarried. Women who were living in southern (aOR = 1.23, 95%CI = 1.02–1.48) and eastern (aOR = 1.37, 95% CI = 1.13–1.66) parts of Rwanda were more likely to have adequate knowledge on MTCT of HIV and its prevention than those in Kigali. Also, women who received post-test counselling (aOR = 1.26, 95% CI = 1.01–2.11) have increased knowledge on MTCT than those who did not. Women with access to radio (aOR = 1.18, 95% CI = 1.06–1.32) and television (aOR = 1.25, 95% CI = 1.07–1.45) at least once a week were more likely to have adequate knowledge on MTCT and its prevention compared to those who do not in Rwanda.ConclusionThere is inadequate knowledge on MTCT and its elimination among women of reproductive age in Rwanda. Strategies to enhance knowledge on MTCT and its prevention among childbearing women should be adopted through rigorous educational sensitization campaigns using local media such as radio and television. Health services that focus on prevention of MTCT must emphasize post-test counselling.

A New Institutional Sustainability Index Regarding Local Governments: The Case of Istanbul

The literature on institutional sustainability has largely focused on global-based analysis, and it has generally ignored to examine local-based analyses. The main purpose of this study was to measure the institutional sustainability of local governments. In this research, the intention was to develop an institutional sustainability index based on the district municipalities in the province of Istanbul, which is one of the most significant metropolises in the world. A total of 48 variables were used in the calculation of the General Institutional Sustainability Index (social dimension with 26 variables, economic dimension with 15 variables, and environmental dimension with 7 variables). The index consists of 3 sub-indexes comprising the general institutional sustainability index. The social dimension was examined in 6 sub-dimensions, the economic dimension in 5 sub-dimensions, and the environmental dimension in 4 sub-dimensions. The results indicate that Istanbul municipalities achieved low economic and environmental sustainability index scores. Decision-makers will be able to assess the general institutional sustainability index results for their own municipalities and to observe strong and weak aspects with the sub-indexes. In the process of developing the index, missing value analysis, cluster analysis, principal component analysis, and the composite index calculation methods were applied to data from 39 district municipalities in the province of Istanbul.

The effect of ICU diaries on the occurrence of impaired mental health and quality of life of ICU SARS-CoV-2 patients and their families: a protocol for an observational cohort study

Background: The SARS-CoV-2 pandemic puts patients and their relatives at risk of developing post-intensive care syndrome (PICS and PICS-family). The use of an intensive care unit (ICU) diary for these patients could help fill in memory gaps and complete their fragmented illness narrative. Objectives: The aim of this study is to evaluate the effect of an ICU diary on the occurrence of impaired mental health in patients admitted with a SARS-CoV-2 infection and their relatives. Methods: An observational cohort study of SARS-CoV-2 patients treated in the ICU of Elizabeth-Tweesteden Hospital (ETZ) in Tilburg, The Netherlands will be conducted. Adult patients admitted at the ICU because of respiratory insufficiency due to SARS-CoV-2, who are mechanically ventilated, are included. During treatment in the ICU a diary is kept of the patient. Anxiety, depression and post-traumatic stress disorder, cognitive dysfunction and quality of life will be screened in patients and their relatives by using the psychosocial screening instrument for adult trauma patients (PSIT), the Cognitive Failure Questionnaire (CFQ) and the short-form health survey 36 (SF-36). The recollection of patients’ memory of the ICU stay will be evaluated by the ICU memory tool. The appreciation of the ICU diary will be assessed using a questionnaire. Results: Participants’ characteristics including how they valued the diary, and their memory of ICU treatment will be reported. Multiple logistic regression analysis will be performed for analyzing the relation between mental health outcome and the use of ICU diaries. Change in PICS symptoms over time will be analyzed using longitudinal data analysis. Missing values in questionnaires will be analyzed using missing value analysis and multiple imputations at item level will be performed if necessary. Conclusions: This study will contribute to the knowledge of mental health outcome in SARS-CoV-2 patients and the use of ICU diaries in this group.

Predictive Model for Type II Diabetes: Deep Learning with Probability Density Function

Abstract: Diabetes mellitus is a scientific ailment defined by hyperglycemia caused by a lack of absolute or relative insulin efficiency in the human body. Diabetes prediction is one of the newest and fastest-growing technologies in medical data analysis. The clustering method for grouping diabetes data based on cluster head properties is the focus of this study. This study proposes a new BG prediction method called RNN, which is based on recurrent neural networks (RNN). The probability of values of the variable is calculated using the probability density function (PDF). For pre-processing and missing value analysis, we used an enhanced Decision Tree and a weighted K-means method. The proposed method uses the weighted Binary Bat Optimization algorithm for feature selection. In terms of diabetic categorization, numerical findings reveal that when compared to other existing methods, the DP-RNN approach with PDF produces the way PDF-DPRNN produces the more accurate classification result. Keywords: PDF, RNN, Decision Tree, Bat Optimization, Diabetes mellitus

Benefits of Missing Value Analyses for Clinical Research using a Tele-Psychology Clinical Trial for Individuals with Spinal Cord Injury

<h3>Research Objectives</h3> Understand and account for missing data in a clinical research study. <h3>Design</h3> Randomized controlled clinical trial. <h3>Setting</h3> Santa Clara Valley Medical Center acute inpatient rehabilitation and general community setting. <h3>Participants</h3> Twenty-six individuals with spinal cord injury who presented with mild to moderate depression within 1-year post-injury. <h3>Interventions</h3> Cognitive Behavioral Therapy-based tele-psychology delivered via iPad FaceTime (10 sessions over 12 weeks). <h3>Main Outcome Measures</h3> Missing values across study variables, including demographics, Patient Health Questionnaire-9, Generalized Anxiety Disorder–7, and Satisfaction with Life Scale. <h3>Results</h3> The whole group Missing Value Analysis (MVA) revealed ∼17% of values were missing (125/754), 9/26 participants had at least one missing value, and 27/29 variables had at least one missing value. Out of 2^29 possible missing value patterns, only 10 were apparent and the most common missingness pattern was no missing data. Little's Missing Completely At Random (MCAR) test (ꭕ2[df=136, N=26]=134.2,p=.528) showed data were MCAR, with no relationship between missing data and any values in the dataset. Principal component analysis suggested four principal components that remained consistent after imputing missing data (mean substitution). Within-arm MVA showed data in both groups was MCAR (Group 1:ꭕ2[df=101, N=15]=63.2,p=.999;Group 2:ꭕ2[df=35, N=11]=10.8,p=1.00) with similar missingness patterns. <h3>Conclusions</h3> Missing data are an inevitable consequence of clinical research. Although often overlooked, MVA provides a mechanism to account for missing data in either the interpretations (e.g., skewed results) or analytics (e.g., imputing missing data). In this study, whole group and within arm MVA showed the data was MCAR and the data remained consistent after imputing missing values. MVA has the benefit of 1) quality control and assurance of data, 2) understanding and accounting for the missingness of data, and 3) reporting patterns in missing data to inform future studies. <h3>Author(s) Disclosures</h3> The authors have no conflicts of interest or financial ties to disclose.

FAIR Data Reuse in Traumatic Brain Injury: Exploring Inflammation and Age as Moderators of Recovery in the TRACK-TBI Pilot.

The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients.

The interplay of personal values, relational mobile usage and organizational citizenship behavior

PurposeThis paper aims to examine the mediating role of relational mobile usage on the relationship between personal values (individualism, collectivism and academic self-efficacy) and organizational citizenship behavior (OCB) among the faculty members of higher education/degree-awarding institutes (DAIs) in Pakistan. The paper also examines the moderating role of gender on the relationship between relational mobile usage and OCB.Design/methodology/approachCross-sectional data from 217 faculty members of higher education/DAIs in Pakistan were collected through an online questionnaire. Structural equation modeling technique using SmartPLS was used to assess the measurement and structural model. Statistical Package for the Social Sciences (SPSS) version 21 was used for data normality analysis, missing value analysis and variable calculation. Response and non-response bias were also examined.FindingsThe findings confirmed that individualism was not significantly related to relational mobile usage; however, collectivism and academic self-efficacy were significantly related to relational mobile usage. The relationship of relational mobile usage and OCB was also significant. The mediating role of relational mobile usage on the relationship between individualism and OCB was not significant; however, the mediating role of relational mobile usage between collectivism and OCB, and academic self-efficacy and OCB was significant. The moderating role of gender on the relationship between relational mobile usage and OCB was also significant.Practical implicationsDAIs’ administration should strive to encourage individuals to value group priorities over the individualistic interests. Valuing group interest over the individualistic interest would promote the usage of mobile technology for relational purpose. This relational perspective of mobile usage will ultimately develop OCB among individuals. Also, academic self-efficacy through relational mobile usage can lead to OCB; therefore, another important policy implication for the administration of DAIs is to develop academic self-efficacy among the stakeholders of the institutions.Originality/valueAlthough OCB has become a theoretically mature concept, the framework proposed for this study has rarely been discussed in previous scholarships, particularly from the perspective of relational mobile usage. Arguably, this study is the first in testing the mediating role of relational mobile usage between the personal values and OCB within the context of academia in Pakistan.

Predictive classification of ICU readmission using weight decay random forest

Intensive care unit (ICU) readmissions of critically ill patients result in significant increases in mortality rates and costs, but most readmissions could be avoided. Therefore, the medical management community has devoted considerable effort to developing predictive classifications for ICU readmissions. However, the existing classification methods lack effective feature engineering and are dependent on large quantity of imbalanced and sparse data. In this paper, we use an objective quantitative data set to estimate the probability of ICU readmission for patients who have been transferred from the ICU to the general ward at various risk levels. To implement valuable feature selection for imbalanced time series data, we integrate the missing value analysis and the likelihood ratio test for the distribution characteristics of time series indicators and introduce a weight decay random forest model to achieve ICU readmission classification based on sparse data. Using these approaches, we can rank the most relevant factors that affect the probability of ICU readmission and identify the missing indicators that have the greatest impact on ICU readmission classification. Comprehensive experimental results show that our proposed method can outperform other traditional methods according to seven different performance indicators.

Rice husk ash as an indigenous construction material in building process, a statistical evaluation

The research described the rice husk ash as an indigenous construction material used in building production process of the concrete design mix. The production process is analyzed statistically. The quality of concrete mixture is of inevitable concern to all stakeholders in the construction industry and on building production process in the zone when the climatic conditions of the zone are considered. The mix design ratio is investigated and all the prevailing construction/production practices are considered statistically to portray the experimental results in the system. The statistical tools applied in this research for clarity of the results are descriptive, normality, missing value analysis, process statistical summary and confidence estimation methods of statistics. The experimental matrix was designed using three level four factors. Twenty five (25) experimental runs was conducted the M-Estimator was used to obtain the missing value analysis, the estimate of the output parameter at each selected factor levels. The results show that all the factors selected are fit for the experimental analysis. The factors in M-estimators show that the response (Slump) can be as low as 68.8924mm and as high as 145.5352mm. In descriptive statistic, the mean for the parameters: cement, water, fine husk, coarse aggregate and slump are 242.56 kg/m3, 6.00 kg/m3, 568.56 kg/m3, 111544 kg/m3 and 110.84mm respectively. The tools portray the necessary information in the data to understand what the data information for further experimental process analysis.

Imputation of Missing Values for Solar Irradiance Data under Different Weathers using Univariate Methods

Significant investment risks of large photovoltaic (PV) systems are uncertainties of energy yield predictions from a PV power plant. Unfortunately, solar irradiance weather datasets often have missing values due to operational issues; hence techniques of imputing or recovering the missing values become essential. This paper examined five statistical imputation methods that are frequently adopted in missing values analysis for daily solar irradiance series based on two different weather conditions in the tropical climate with 10% to 50% of missing values and the methods were evaluated using performance indicators such as normalised root-mean-square at standard test conditions (nRMSESTC). The results show that during Sunny weather, the Bezier curve and Stineman interpolation give the lowest nRMSESTC for 10% and 20% to 50% of daily missing values, respectively. Meanwhile, during Largely Cloudy weather, three methods share the best estimations, which are Linear interpolation for 10% missing values, Stineman interpolation for 20%, 30% and 50% missing values, and Spline interpolation for 40% of missing values.

Pre-Clinical Common Data Elements for Traumatic Brain Injury Research: Progress and Use Cases.

Traumatic brain injury (TBI) is an extremely complex condition due to heterogeneity in injury mechanism, underlying conditions, and secondary injury. Pre-clinical and clinical researchers face challenges with reproducibility that negatively impact translation and therapeutic development for improved TBI patient outcomes. To address this challenge, TBI Pre-clinical Working Groups expanded upon previous efforts and developed common data elements (CDEs) to describe the most frequently used experimental parameters. The working groups created 913 CDEs to describe study metadata, animal characteristics, animal history, injury models, and behavioral tests. Use cases applied a set of commonly used CDEs to address and evaluate the degree of missing data resulting from combining legacy data from different laboratories for two different outcome measures (Morris water maze [MWM]; RotorRod/Rotarod). Data were cleaned and harmonized to Form Structures containing the relevant CDEs and subjected to missing value analysis. For the MWM dataset (358 animals from five studies, 44 CDEs), 50% of the CDEs contained at least one missing value, while for the Rotarod dataset (97 animals from three studies, 48 CDEs), over 60% of CDEs contained at least one missing value. Overall, 35% of values were missing across the MWM dataset, and 33% of values were missing for the Rotarod dataset, demonstrating both the feasibility and the challenge of combining legacy datasets using CDEs. The CDEs and the associated forms created here are available to the broader pre-clinical research community to promote consistent and comprehensive data acquisition, as well as to facilitate data sharing and formation of data repositories. In addition to addressing the challenge of standardization in TBI pre-clinical studies, this effort is intended to bring attention to the discrepancies in assessment and outcome metrics among pre-clinical laboratories and ultimately accelerate translation to clinical research.

Impact of Technology on Taxi Industry: Determinants on Ridesharing in Kuala Lumpur

The aim of this study is to identify the determinants of ridesharing services adoption in Kuala Lumpur. This study employed descriptive and correlational study of ridesharing services adoption in Kuala Lumpur. Statistical Package for Social Science (SPSS) version 20. Nonresponse Bias, Normality Test, Means and Standard Deviation, Common Method Variance, Missing Value Analysis, Frequency Analysis, Cronbach’s Alpha, Hierarchical Multiple Regression Analysis, Spearman’s Correlation and Hayes’s Mediation PROCESS Analysis were the statistical tools used to produce representative results. The correlation results found attitude, operational characteristics and knowledge have a strong correlation with the intention to adopt ridesharing services.

Statistical Guidelines for Handling Missing Data in Traumatic Brain Injury Clinical Research.

Missing data is a persistent and unavoidable problem in even the most carefully designed traumatic brain injury (TBI) clinical research. Missing data patterns may result from participant dropout, non-compliance, technical issues, or even death. This review describes the types of missing data that are common in TBI research, and assesses the strengths and weaknesses of the statistical approaches used to draw conclusions and make clinical decisions from these data. We review recent innovations in missing values analysis (MVA), a relatively new branch of statistics, as applied to clinical TBI data. Our discussion focuses on studies from the International Traumatic Brain Injury Research (InTBIR) initiative project: Transforming Research and Clinical Knowledge in TBI (TRACK-TBI), Collaborative Research on Acute TBI in Intensive Care Medicine in Europe (CREACTIVE), and Approaches and Decisions in Acute Pediatric TBI Trial (ADAPT). In addition, using data from the TRACK-TBI pilot study (n = 586) and the completed clinical trial assessing valproate (VPA) for the treatment of post-traumatic epilepsy (n = 379) we present real-world examples of typical missing data patterns and the application of statistical techniques to mitigate the impact of missing data in order to draw sound conclusions from ongoing clinical studies.

Analysis of Rainfall Trend in Selected States with in the Coastal Region of Nigeria using non-Parametric Mann-Kendall Test Statistics

Trend in rainfall data have a great impact on the hydrological cycle and thus involve both the character and quantity of water resources. Analysis of trend in rainfall data also aids to see the result of rainfall variability on the occurrence of drought and flood. The aim of this research is to detect and estimate the magnitude of trend associated with rainfall data from Akure and Calabar which are located within the coastal region of Nigeria using non-parametric Mann-Kendall test statistics. Monthly data for thirty six (36) years spanning from 1980 to 2016 was used as input parameters for the analysis. Infilling of the missing records was done with the aid of expectation maximization algorithm which is unarguably one of the best missing value analysis techniques. Preprocessing of the rainfall data was done by conducting numerous time series validation test such as test of homogeneity, test of normality and outlier detection. Homogeneity test was aimed at testing the assumption of same population distribution; outlier detection was to detect the presence of bias in the data while test of normality was done to validate the claim that climatic data are not always normally distributed. In addition to testing the normality assumption of the data, normality test was also employed to select the most suitable trend detection and estimation technique. Results of the analysis revealed that the rainfall data from Akure and Calabar are statistically homogeneous. The records did not contain outliers and they are not normally distributed as expected for most climatic variables. The non-parametric trend detection and estimation analysis revealed that the rainfall data from Akure shows statistical significant evidence of a decreasing trend with a computed M-K trend value of -129. Although, the rainfall records from Calabar do not have sufficient statistical evidence of a significant trend, the computed M-K trend value was +50 which is; evidence of an increasing trend.

The association of posttraumatic stress disorder risk with help-seeking behavior: A cross-sectional study of earthquake and tsunami survivors in Japan.

The Great East Japan Earthquake in March 2011 and subsequent tsunami affected the northeastern coast of Japan. We investigated the association between the risk of posttraumatic stress disorder (PTSD) and help-seeking behaviors of survivors in a landlocked city in Japan’s Tohoku area at 1 year after this disaster. The participants were 902 residents of Ichinoseki City and 218 evacuees from coastal areas to the city. We used sociodemographic characteristics, the Trauma Screening Questionnaire with a score ≥6 defined as predicted PTSD, and the General Help-Seeking Questionnaire. We examined the whole sample and the stratified samples between the Ichinoseki residents and the evacuees. Missing values were complemented using a multiple imputation method after a missing-value analysis. Data from the final sample of 1,101 respondents were analyzed using logistic regression and sensitivity analyses. Compared with the Ichinoseki residents, the evacuees were older, showed higher Trauma Screening Questionnaire scores, and had fewer house occupants. However, the prevalence of predicted PTSD was not different between the 2 groups. In the whole sample and among the Ichinoseki residents, predicted PTSD was associated with an active help-seeking behavior, possibly from mental health professionals. In the evacuees, predicted PTSD was not associated with any specific help-seeking behavior. Although survivors with predicted PTSD in the whole sample and among the Ichinoseki residents showed an active help-seeking behavior, the evacuees with predicted PTSD showed little active help-seeking behavior. Traumatized survivors’ help-seeking activity may also vary according to the type of disaster and/or evacuation situation.

Primary repair for pediatric colonic injury: Are there differences among adult and pediatric trauma centers?

BackgroundManagement of colonic injuries (colostomy [CO] versus primary anastomosis [PA]) among pediatric patients remains controversial. The aim of this study was to assess outcomes in pediatric trauma patient with colonic injury undergoing operative intervention. MethodsThe National Trauma Data Bank (2011-2012) was queried including patients with isolated colonic injury undergoing exploratory laparotomy with PA or CO with age ≤18 y. Missing value analysis was performed. Patients were stratified into two groups: PA and CO. Outcome measures were mortality, in-hospital complications, and hospital length of stay. Multivariate regression analysis was performed. ResultsA total of 1151 patients included. Mean ± standard deviation age was 11.61 ± 2.8 y, and median [IQR] Injury Severity Score was 12 [8-16]; 39% (n = 449) of the patients had CO, and 35.6% (n = 410) were managed in pediatric trauma centers (PC). Patients with CO had a higher Injury Severity Score (P < 0.001), a trend toward lower blood pressure (P = 0.40), and an older age (P < 0.001). There was no difference in mortality between the PA and CO groups. However, patients who underwent PA had a shorter length of stay (P < 0.001) and lower in-hospital complications (P < 0.001). A subanalysis shows that, after controlling for all confounding factors, patients managed in PC were 1.2 times (1.2 [1.1-2.1], P = 0.04) more likely to receive a CO than those patients managed in adult trauma centers (AC). Moreover, there was no difference in mortality between the AC and the PC (P = 0.79). ConclusionsOur data demonstrate no difference in mortality in pediatric trauma patients with colonic injury who undergo primary repair or CO. However, adult trauma centers had lower rates of CO performed as compared to a similar cohort of patients managed in pediatric trauma centers. Further assessment of the reasons underlying such differences will help improve patient outcomes.