International Journal of Statistics in Medical Research

A Double Truncated Binomial Model to Assess Psychiatric Health through Brief Psychiatric Rating Scale: When is Intervention Useful?

2024-01-18T11:02:30+00:00

A double truncated binomial distribution model with ‘u’ classes truncated on left and ‘v’ classes truncated on right is introduced. Its characteristics, namely, generating functions; and the measures of skewness and kurtosis have been obtained. The unknown parameter has been estimated using the method of maximum likelihood and the method of moments. The confidence interval of the estimate has been obtained through Fisher’s information matrix.

The model is applied on cross sectional data obtained through Brief Psychiatric Rating Scale (BPRS) administered on a group of school going adolescent students; and the above-mentioned characteristics have been evaluated. An expert, on the basis of the BPRS score values, suggested an intervention program. The BPRS scores of the students who could be administered the intervention program lied in a range (which was above the lowest and below the highest possible values) suggested by the expert. Whereas the complete data suggested the average number of problem areas is four (which was not in consonance with the observations given by the expert), the double truncated model suggested the number of such areas as five which was consistent with the observations made by the expert. This establishes the usefulness of double truncated models in such scenarios.

Analysis of Wide Modified Rankin Score Dataset using Markov Chain Monte Carlo Simulation

2024-01-18T11:02:27+00:00

Brain hemorrhage and strokes are serious medical conditions that can have devastating effects on a person's overall well-being and are influenced by several factors. We often encounter such scenarios specially in medical field where a single variable is associated with several other features. Visualizing such datasets with a higher number of features poses a challenge due to their complexity. Additionally, the presence of a strong correlation structure among the features makes it hard to determine the impactful variables with the usual statistical procedure. The present paper deals with analysing real life wide Modified Rankin Score dataset within a Bayesian framework using a logistic regression model by employing Markov chain Monte Carlo simulation. Latterly, multiple covariates in the model are subject to testing against zero in order to simplify the model by utilizing a model comparison tool based on Bayes Information Criterion.

Triglyceridemic Waist Phenotypes as Risk Factors for Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis

2024-02-19T14:23:03+00:00

Introduction: Triglyceride waist phenotypes, which combine high triglyceride levels and central obesity, have recently emerged as an area of interest in metabolic disease research.

Objective: To conduct a systematic review (SR) with meta-analysis to determine if triglyceride waist phenotypes are a risk factor for T2DM.

Materials: SR with meta-analysis of cohort studies. The search was conducted in four databases: PubMed/Medline, Scopus, Web of Science, and EMBASE. Participants were classified into four groups, based on triglyceride level and waist circumference (WC): 1) Normal WC and normalConduct triglyceride level (NWNT); 2) Normal WC and high triglyceride level (NWHT), 3) Altered WC and normal triglyceride level (EWNT) and 4) Altered WC and high triglyceride level (EWHT). For the meta-analysis, only studies whose measure of association were presented as Hazard ratio (HR) along with 95% confidence intervals (CI95%) were used.

Results: Compared to people with NWHT, a statistically significant association was found for those with NWHT (HR: 2.65; CI95% 1.77–3.95), EWNT (HR: 2.54; CI95% 2.05–3.16) and EWHT (HR: 4.41; CI95% 2.82–6.89).

Conclusions: There is a clear association between triglyceride waist phenotypes and diabetes, according to this SR and meta-analysis. Although central obesity and high triglyceride levels are associated with a higher risk of the aforementioned disease, their combination appears to pose an even greater risk. Therefore, in the clinical setting, it is important to consider this when assessing the risk of diabetes.

Adaptive Elastic Net on High-Dimensional Sparse Data with Multicollinearity: Application to Lipomatous Tumor Classification

2024-03-29T11:02:25+00:00

Predictive models can experience instabilities because of the combination of high-dimensional sparse data and multicollinearity problems. The adaptive Least Absolute Shrinkage and Selection Operator (adaptive Lasso) and adaptive elastic net were developed using the adaptive weight on penalty term. These adaptive weights are related to the power order of the estimators. Therefore, we concentrate on the power of adaptive weight on these penalty functions. This study purposed to compare the performances of the power of the adaptive Lasso and adaptive elastic net methods under high-dimensional sparse data with multicollinearity. Moreover, we compared the performances of the ridge, Lasso, elastic net, adaptive Lasso, and adaptive elastic net in terms of the mean of the predicted mean squared error (MPMSE) for the simulation study and the classification accuracy for a real-data application. The results of the simulation and the real-data application showed that the square root of the adaptive elastic net performed best on high-dimensional sparse data with multicollinearity.

The Impact of the Risk Perception of COVID-19 PANDEMIC on College Students' Occupational Anxiety: The Moderating Effect of Career Adaptability

2024-05-21T10:23:41+00:00

In order to understand the changes in college students' risk perception and occupational emotion under major public health events and to explore the influencing factors of college graduates' employment guidance, 578 college students were surveyed by questionnaire to explore the impact of the risk perception of COVID-19 pandemic on college students' occupational anxiety and its internal mechanisms, and to analyze the mediating role of psychological resilience in the impact and the moderating role of career adaptability. The results showed that: (1) there is a significant positive correlation between the risk perception of COVID-19 pandemic and occupational anxiety; there is a significant negative correlation between risk perception and psychological resilience; there is a significant negative correlation between the psychological resilience and occupational anxiety. (2) Psychological resilience plays a mediating role between risk perception and occupational anxiety. (3) Career adaptability plays a negative moderating role between the risk perception of COVID-19 pandemic and occupational anxiety. These results showed that the risk perception of COVID-19 pandemic not only directly aggravates college students' occupational anxiety, but also indirectly affects occupational anxiety through psychological resilience; Career adaptability significantly alleviats the incremental effect of the risk perception of COVID-19 pandemic on college students' occupational anxiety. This paper has positive enlightenment on how to improve the employability of college students and alleviate their employment anxiety during major public health events.

Competing Risks Model to Evaluate Dropout Dynamics Among the Type 1 Diabetes Patients Registered with the Changing Diabetes in Children (CDiC) Program

2024-06-04T11:16:27+00:00

Understanding the survival dynamics of registered patients on a disease control program is a vital issue for the success of program objectives. Dropout of registered patients from such a program is a critical issue, hindering the effectiveness of the program. This study aimed to identify the risk factors of dropout of patients who were registered on the Changing Diabetes in Children (CDiC) program, taking a case of Uganda. Survival analysis was done by integrating competing risk of factors associated with attrition from the CDiC program. The data for the study was obtained from patients with type 1 diabetes mellitus (T1DM) registered during 2009-2018 at health units with specialized pediatric diabetes clinics from various regions in Uganda. The study considered follow-up data of 1132 children with T1DM. Our analysis revealed that the Body Mass Index (BMI) significantly influences dropout time, with patients classified as underweight showing higher hazards than those with normal BMI. Moreover, when considering competing risks, dropout hazards increased. Comparing the Cox model with the Fine and Gray model shows the latter exhibiting a smaller AIC value, which indicates its superiority in the time-to-dropout analysis. Thus, utilizing methods that integrate competing risks for CDiC dropout analysis is preferable and recommended for related studies. These findings provide actionable insights for enhancing CDiC program efficacy.

Automatic Diagnosis of Lung Diseases (Pneumonia, Cancer) with given Reliabilities on the Basis of an Irradiation Images of Patients

2024-06-10T12:11:08+00:00

The article proposes algorithms for the automatic diagnosis of human lung diseases pneumonia and cancer, based on images obtained by radiation irradiation, which allow us to make decisions with the necessary reliability, that is, to restrict the probabilities of making possible errors to a pre-planned level. Since the information obtained from the observation is random, Wald’s sequential analysis method and Constrained Bayesian Method (CBM) of statistical hypothesis testing are used for making a decision, which allow us to restrict both types of possible errors. Both methods have been investigated using statistical simulation and real data, which fully confirmed the correctness of theoretical reasoning and the ability to make decisions with the required reliability using artificial intelligence. The advantage of CBM compared to Wald’s method is shown, which is expressed in the relative scarcity of observation results needed to make a decision with the same reliability. The possibility of implementing the proposed method in modern computerized X-ray equipment due to its simplicity and promptness of decision-making is also shown.

Support of Characteristics, Physical Environmental and Psychological On Quality Of Life Of Patients With DM Type II

2024-07-15T12:55:53+00:00

Background: Diabetes mellitus is one of the diseases that ranks high in the list of non-communicable diseases in Indonesia. Factors that can affect quality of life such as physical environment and psychological.

Objective: This study aims to examine the relationship between characteristic, physical environment and psychological on quality of life of type II diabetes mellitus patients at the Barombong Public Health Center, Makkasar City.

Methodology: This study is a quantitative research with an analytical observational approach using a cross-sectional design. The population size in this study is 578 individuals, sample calculation using the WHO formula yielded a sample size of 385 individuals with predefined exclusion an inclusion criteria. The sampling technique employed is simple random sampling (SRS), and the hypothesis test used is chi-square.

Results: Bivariate statistical analysis shows that there is a relationship between quality of life and age (p=0,000), duration of illness (p=0,000), temperature (p=0,000), noise (p=0,000), positive feelings (p=0,000), thinking, learning, and concentration (p=0,000), self-esteem (p=0,000), while variables that are not associated with quality of life are gender (p=0,111), marital status (p=0,228) and social support (p=0,645). Based on logistic regression analysis, it was found that the factors that most influence quality of life are duration of illness (p=0,000) and positive feelings (p=0,000).

Conclusion: Length of suffering and positive feelings are the most dominant variables associated with quality of life with a probability level of 99.8%.

Body Mass Index and Metabolic Phenotypes in Breast Cancer Risk: A Meta-Analysis and Systematic Review

2024-08-01T14:30:08+00:00

Introduction: Numerous studies have established that obesity, often assessed through body mass index (BMI), is one of the most significant risk factors for the development of breast cancer (BC). However, not all individuals with obesity have the same risk of developing BC and vice versa.

Objective: To determine the association between metabolic states and the risk of BC.

Materials: AS systematic review (SR) with a meta-analysis of cohort studies was conducted. The search was performed in four databases: PubMed/Medline, SCOPUS, Web of Science, and EMBASE. Metabolic states were classified as Metabolically Healthy Normal Weight (MHNW), Metabolically Unhealthy Normal Weight (MUNW), Metabolically Healthy Obesity (MHO), and Metabolically Unhealthy Obesity (MUO). Association measures were presented as hazard ratios (HR) with their 95% confidence intervals (CI95%).

Results: A total of four studies were evaluated. The meta-analysis found a statistically significant association between the development of BC and the MHO state (HR: 1.14; CI95% 1.02, 1.28) and MUO state (HR: 1.37; CI95% 1.16, 1.62) compared to individuals with MHNW. No association was found with the MUNW state.

Conclusions: The findings suggest that obesity, as determined by BMI, is significantly associated with an increased risk of BC, regardless of metabolic state. Additionally, metabolically unhealthy states, especially in obese individuals, appear to increase the risk of BC. Proposed mechanisms include systemic inflammation, metabolic dysfunction, and altered hormone production. These results have important public health implications, emphasizing the need for prevention strategies focused on obesity management and awareness of its associated BC risks.

Development of New Methods and Materials for the Restoration of Tooth Pulp

2024-08-01T14:30:04+00:00

Nowadays, the latest treatment technologies are actively developing in dental practice, namely for the restoration of tooth pulp.

Aim: to evaluate the advantages of using modern materials in the treatment of tooth pulps.

Materials and Methods: We examined 33 patients with pulp diseases: 18 women (54.5%) and 15 men (45.5%) with an average age of (33.2±2.3) years. 18 patients (group I) had conservative treatment; 15 patients (group II) got pulp restoration using Biodentin.

Results: In 33 (100 %) patients of both groups, inflammation of tooth pulps was found; in 5 of 18 (27.8 %) patients of group I and 6 of 15 (40.0 %) patients of group II, the presence of fibrous pulpitis without signs of periodontitis was determined, in patients of group II, 4 of 15 (26.7 %) - acute diffuse pulpitis. Streptococci with α-haemolytic activity, staphylococci and fungi of the genus Candida albicans were detected in the plaque. In 93.3% of patients, both clinical and overall success was achieved with Biodentin, and the frequency of isolation of microorganisms of the genus Streptococcus spp. with α-haemolytic activity and Candida albicans decreased.

Conclusions: Effective pulp restoration, inflammatory process reduction, and conditionally pathogenic microflora suppression were found in patients treated with Biodentin.

Body Mass Index as a Risk Factor for Diffuse Large B-Cell Lymphoma: A Systematic Review and Meta-Analysis

2024-08-01T14:29:59+00:00

Introduction: Given the increasing prevalence of obesity, as measured by the Body Mass Index (BMI), and the significant impact of Diffuse Large B-cell lymphoma (DLBCL) on global health, it is crucial to update our understanding of the relationship between BMI and DLBCL.

Objective: to carry out a systematic review (SR) with meta-analysis to determine the association between DLBCL and BMI

Methods: This study is a systematic review and meta-analysis following the PRISMA guidelines. It searched PubMed/Medline, SCOPUS, Web of Science, and EMBASE. The inclusion criteria were primary observational studies involving adults with DLBCL confirmed by biopsy. Exclusions were non-peer-reviewed materials and studies without inferential statistics. The findings of the study are presented as association measures such as relative risks (RR), accompanied by their corresponding 95% confidence intervals (95% CI).

Results: From 451 articles, 13 met the criteria for inclusion. The included studies, conducted between 2002 and 2013 in various countries, showed varied follow-up periods and sample sizes. The meta-analysis revealed that individuals with high BMI have a 1.31 times higher risk of developing DLBCL than those with lower BMI (RR: 1.31; 95% CI 1.07, 1.61). The heterogeneity among studies was moderate.

Conclusions: The study confirms an association between higher BMI and the increased risk of developing DLBCL. This finding underscores the need to explore further how obesity, chronic inflammation, and the development and progression of DLBCL are interconnected. Understanding this area could significantly reduce DLBCL incidence and improve patient outcomes.

Compliance with the NATO Standards in the Field of Psychological Assistance for the Servicemen with Post-Traumatic Stress Disorder

2024-08-13T11:45:59+00:00

The increasing number of the servicemen with post-traumatic stress disorder (PTSD) as a result of the war raises the problem of providing them with psychological assistance among the top priorities at the national level. The aim of the study is to identify the effectiveness of using NATO standards in the system of psychological assistance to the servicemen with PTSD.

Methods: The study employed the Mississippi Scale, Impact of Event Scale (IES-R), Hospital Anxiety and Depression Scale (HADS). The statistics were processed using descriptive statistics, analysis of variance, Student’s t-test for independent samples.

Results: The study showed that 20.28% of the servicemen had a high level of PTSD. They have pronounced symptoms of intrusion, avoidance, hyperarousal, high anxiety, and depression. It was established that the use of NATO standards of psychological assistance to the servicemen with PTSD is reliably effective in reducing symptoms of intrusion (t=2.53, p≤0.01), avoidance (t=3.22, p≤0.01), excitability (t =4.18, p≤0.01), anxiety (t=4.56, p≤0.01), and depression (t=3.87, p≤0.01).Conclusions. The problem of the occurrence of PTSD and symptoms of the disorder among the servicemen participating in military operations was confirmed. It was proved that the use of NATO standards in the training and psychological assistance to service men is effective in reducing the PTSD level and its symptoms.

Prospects: The obtained results are significant for military psychologists who work directly with the servicemen in warfare and can use the obtained data for early intervention to prevent the occurrence of PTSD.

Assessment of the Awareness and Oral Hygiene Practices among Patients with Gum and Periodontal Diseases

2024-08-13T11:45:55+00:00

The level of awareness and practice of hygiene of the average person with periodontal and periodontal tissue diseases remains low and needs to be strengthened by receiving constant information, monitoring and some training from the dentist. The aim of the research is to assess awareness and practice of oral hygiene in patients with gum and periodontal diseases.

Methods: 140 people were divided into two groups: I - control group (CG), relatively healthy individuals (n=40), II - patients with gum and periodontal diseases (n=100), who were interviewed, determined the Simplified Oral Health Index (OHI-S) and periodontal index at the first visit to the dentist and 6 months later.

Results: Correct responses too ralhy giene questions differed significantly between groups, with the highest meanin group I and the lowest in group II. There was no statistically significant difference between the groups in termsof frequency of brushing, duration, care products, or brushing techniques. Ingroup I, the OHI-S index corresponded to a satisfactory state of hygiene (1.53 ± 0.08) compared to the control group (0.17 ± 0.01) (Table 4), while the period on talindex corresponded to the presence of gingivitis (0.81 ± 0.03) compared to the control group (0.21 ± 0.09).

Conclusions: The importance of careful attention to oral hygiene, the use of various means to reduce inflammation and bleeding gums, and the reduction of dental plaque are important parameters in the prevention of gum and periodontal diseases.

Prospects: Further study of awareness and oral hygiene practice with therapy in patients who have gum and periodontal diseases is required.

A Study on the Effects of Biodiversity and Conservation Efforts on Community Health in the Sunderban Area of Eastern India

2024-08-23T10:54:57+00:00

The Sunderbans, located at the southernmost tip of the Bay of Bengal, is a UNESCO World Heritage site renowned for its mangrove extent encompassing tidal rivers, mudflats, and islands. As the home of the Royal Bengal Tiger and countless humans, it represents the ecological centre of Eastern India. The primary objective of this study is to analyse community participation in Sunderbans conservation strategies. We are in a position to identify the primary catalysts and inhibitors of such community engagement by understanding the correlation between active conservation participation and health outcomes. The essence of the study emphasises the community's awareness of environmental factors that affect the health. Our ultimate objective is to design a framework that clarifies the connections between conservation and health initiatives in areas of high biodiversity. Using a mixed-methods approach, quantitative biodiversity metrics were derived using species richness, evenness, and Simpson's Diversity Index, and health data were gathered using standardised community health surveys that focused on disease prevalence, nutrition status, and sanitation practises. Twenty sites with differing degrees of community-based conservation activities provided the data. Using sophisticated statistical methods, such as multivariate regression analyses and non-metric multidimensional scaling, patterns and correlations between biodiversity and health indicators were identified. Preliminary results indicated a correlation between biodiversity metrics and specific health indicators. There was a 16.8% decrease in waterborne maladies and a 12.1% increase in nutritional diversity among community members in areas with greater biodiversity. Additionally, areas with robust community-based conservation activities demonstrated a 19.8% increase in biodiversity and community health metrics in comparison to areas with minimal to no conservation activities. Our findings highlight the necessity of merging conservation and health agendas, arguing for an integrative strategy in biodiverse regions. It is in the best interest of global stakeholders to recognise and exploit such potential in comparable ecologies.

Influence of Stress Factors on the Development of Post-Traumatic Stress Disorder in Children: Risk Factors

2024-09-09T12:22:43+00:00

In the current conditions of the ongoing war in Ukraine, which began in February 2022, Ukrainian children might develop post-traumatic stress disorder (PTSD) due to stress factors caused by the conflict between Ukraine and Russian-backed separatist forces, along with the Russian military. In particular, the relationship between reduced emotional intelligence and the development of post-traumatic stress disorder in children has become more relevant. This study aimed to assess structural and cognitive changes in children with PTSD and their relationship to depression, anxiety, and event segmentation.

The study methods included clinical interviews (CAPS-CA-5 scale), neuropsychological tests (short-term and long-term memory tests), self-assessment questionnaires (standardised CDI, RCADS and SCAS-Child scales), and a single-shotMRI. The results showed that patients with post-traumatic stress disorder had reduced hippocampal volume (p=0.018) and the volume of cingulate cortex isthmus (p=0.026). Diffusion in the cerebellum-hippocampal tract was reduced (p=0.014). The level of depression was positively correlated with hippocampal volume (r=0.32, p=0.021) and anxiety with the volume of cingulate cortex isthmus (r=0.26, p=0.048).

These results emphasise the importance of the relationship between structural changes and levels of depression and anxiety in patients with PTSD.

Prospects for further research are based on the study of the long-term effects of psychotherapeutic interventions aimed at improving cognitive function in patients with posttraumatic stress disorder.

The Relationship between Traumatic Experiences, the Prevalence of Social Anxiety and Insecure Attachment among University Students

2024-09-09T12:22:36+00:00

University students face unique challenges and are considered a vulnerable population, making it crucial to understand the impact of trauma on their mental health. This study aimed to investigate the associations between traumatic experiences, the prevalence of social anxiety, and insecure attachment among MSU students. The present study adopted a quantitative research approach using the Trauma Screening Questionnaire (TSQ), the DSM-5 Severity Rating of Social Anxiety Disorder (SAD-D), moreover, for the purpose of assessing PTSD, the Vulnerable Attachment Styles Questionnaire (VASQ), Social Anxiety Disorder Severity, and Insecure Attachment, respectively. A total of 406 respondents participated in the research. Through descriptive analysis, data were collected using three different assessments, revealing that 67% of the students were identified as having a high risk of post-traumatic stress disorder (PTSD), while 6.9% experienced severe social anxiety, which was relatively low compared to the total number. Additionally, 87% of the students displayed a high level of insecure attachment. In order to test the research hypotheses, Pearson correlation analysis, linear regression analysis and path analysis were conducted in this study. The study's findings demonstrated that there was a significant correlation between traumatic experiences and insecure attachment and a non-significant correlation between traumatic experiences and social anxiety. Additionally, traumatic experiences had a significant positive effect on insecure attachment but did not significantly affect social anxiety. Lastly, traumatic experiences did not significantly affect insecure attachment through social anxiety or traumatic experiences through social anxiety.

The Chronic Progressive Repeated Measures (CPRM) Model for Clinical Trials Comparing Change Over Time in Quantitative Trait Outcomes

2024-09-09T12:22:32+00:00

Repeated measures analysis is a common analysis plan for clinical trials comparing change over time in quantitative trait outcomes in treatment versus control. Mixed model for repeated measures (MMRM) assuming an unstructured covariance of repeated measures is the default statistical analysis plan, with alternative covariance structures specified in the event that the MMRM model with unstructured covariance does not converge. We here describe a parsimonious covariance structure for repeated measures analysis that is specifically appropriate for longitudinal repeated measures of chronic progressive conditions. This model has the parsimonious features of the mixed effects model with random slopes and intercepts, but without restricting the repeated measure means to be linear with time. We demonstrate with data from completed trials that this pattern of longitudinal trajectories spreading apart over time is typical of Alzheimer’s disease. We further demonstrate that alternative covariance structures typically specified in statistical analysis plans using MMRM perform poorly for chronic progressive conditions, with the compound symmetry model being anticonservative, and the autoregressive model being poorly powered. Finally, we derive power calculation formulas for the chronic progressive repeated measures model that have the advantage of being independent of the design of the pilot studies informing the power calculations. When data follow the pattern of a chronic progressive condition. These power formulas are also appropriate for sizing clinical trials using MMRM analysis with unstructured covariance of repeated measures.

The Impact of Practical Skills on Improving the Servicemen’s Preparedness to Act in Case of Radiation Contamination of the Area

2024-09-25T11:08:24+00:00

The servicemen’s practical skills to respond to threats of chemical, biological, radiological and nuclear attacks, as well as the ability to make effective decisions are necessary for the implementation of effective targeted actions in the face of military threats. The aim of the article is to identify the impact of servicemen’s decision-making skills on their preparedness to act in case of radiation contamination of the area as well as an analysis of the opportunities of skills development in the educational simulation environment. The research employed such empirical methods as: educational experiment, testing, survey, quantitative assessment, and qualitative analysis. The study of causal relationships between servicemen’s decision-making skills under Contaminated Remains Mitigation System CRMS conditions and their preparedness to act under conditions of radiation contamination made it possible to identify a set of decision-making skills that affect high, medium and low servicemen’s preparedness to act under the chemical, biological, radiological, and nuclear (CBRN) attacks. The authors developed and tested a virtual reality training simulator for training decision-making skills in a simulated environment of potential threats using the Zaporizhzhia Nuclear Power Plant (NPP) situation as an example. The results of the assessment of students’ knowledge after the educational experiment showed that simulation training in virtual reality was more effective than training using educational video content. The students of the experimental group (EG) showed a 13.2 points better result (90.6 points) in decision-making accuracy than the students of the control group (CG) (77.4 points).

Process Capability Indices for Processes when the Underlying Data are Interval-Valued

2024-10-09T09:00:54+00:00

One of the important activities of process quality management is to see that the processes of interest are, in fact, stable and capable. In this paper, the problem of obtaining process capability indices (PCIs) for the processes when the underlying data are interval-valued is considered. Since interval-valued data such as systolic and diastolic readings have specifications for both lower and upper values, drawing PCIs cannot be straightforward. In this paper, we attempted to build connections between the lower and upper specifications limits based on which the resulting PCIs are drawn. This is done by considering the coefficients of inflation and the mean shift values of distributions of both lower and upper values of the interval-valued data. The new expressions for the proposed PCIs are determined. We have considered the systolic and diastolic data to demonstrate the computations of PCIs.

Estimating Optimum Length of Stay in a Hospital to Control the Infection Spread during an Epidemic Using Left-Right Truncated Poisson Model

2024-10-10T08:38:07+00:00

Background: The unprecedented havoc of COVID-19 pandemic stressed medical infrastructure of every affected country. The developing countries were more affected as their already inadequate medical resources were strained further.

Material and Methods: In order to estimate the time of onset of recovery through the period of hospitalisation stay, the retrospective data on the number of days that 83 COVID-19 patients stayed in a hospital in New Delhi, India was obtained. A Left-Right Truncated Poisson Distribution Model (LRTPD) was developed to estimate the average number of days that patients had to spend in the hospital before the onset of recovery and they were no longer infected. Left truncation is on the ‘u’ left most classes of the random variable and right truncation is after ‘v’ classes. The parametric estimates of the LOS were validated using the Monte-Carlo method.

Results and Conclusion: The models suggested that if appropriate truncation limits (both the data-specific and as per expert advice) are used in case of critical medical emergencies, approximately 90 percent of the patients will be able to get hospital admission, without over-burdening the hospital infrastructure. The median recovery onset time/ Length of stay (LOS) obtained using the Kaplan-Meier estimator was consistent with the results of the parametric modeling and simulation. However, the Kaplan-Meier method overestimated the mean LOS as compared to the parametric methods.

Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data

2024-10-14T09:34:55+00:00

Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suitable approach. We analysed the performance of six different variable selection methods, which includes stepwise, forward and backward selection using p-value and AIC, LASSO, and Elastic Net. Health-related surveillance data on behaviors, health status, and medical service usage were used across ten databases, with sizes ranging from 10% to 100%, maintaining consistent outcome proportions. Varying database sizes were utilized to assess their impact on prediction models, as they can significantly influence accuracy, overfitting, generalizability, statistical power, parameter estimation reliability, computational complexity, and variable selection. The stepwise and backward AIC model showed the highest accuracy with an Area under the ROC Curve (AUC) of 0.889. Despite its sparsity, the Lasso and Elastic Net model also performed well. The study also found that binary variables were considered more crucial by the Lasso and Elastic Net model. Importantly, the significance of variables remained consistent across different database sizes. The study shows that no major variations in results between the fitness metric of the model and the number of variables in stepwise and backward p-value models, irrespective of the database's size. LASSO and Elastic Net models surpassed other models throughout various database sizes, and with fewer variables.

The Effect of Educational Media Website and Surveillance on Risk Behavior for Prevention of Premarital Sex and Sexual Violence in Adolescents in Gorontalo Regency High School

2024-10-17T10:27:43+00:00

Objective: This study aims to assess the effect of website media and surveillance on risk behaviors to prevent premarital sex and sexual violence among high school adolescents in Gorontalo Regency.

Materials and Methods: Quasi-experimental research with non-equivalent control group design. Involving 158 adolescent students who were divided into two, namely the intervention group was given education through the Si Waspada Diri website media and the control group was given G'sites, both of which contained material modified from the Comprehensive Sexuality and Reproductive Health Education (CSHE) guidelines. The sampling technique was proportional systematic random sampling with sample criteria aged 15-18 years who were already dating. The research instrument was a knowledge, attitude and practice questionnaire that had previously been tested for validity and reliability

Results: Knowledge, attitudes and practices of preventing premarital sex and sexual violence were significantly better using the Si Waspada Diri website and G'Sites. With a p value of less than 0.005, the mean difference after education showed that using the website was more beneficial than using G'Sites in terms of knowledge and attitude. In addition, for practices, p = 1.000 showed no significant difference between the two groups.

Conclusion: Comprehensive sexual and reproductive health education materials such as Si Waspada Diri websites and G'sites help lower teenage sexual behavior practices, promote good attitudes, and raise awareness. Nonetheless, G'sites are less successful than websites in raising awareness and attitudes.

The Impact of Group Psychotherapy on the Mental Health of Servicemen with Post-Traumatic Stress Disorder

2024-11-25T08:33:09+00:00

Being in a combat zone negatively affects the psycho-emotional state of servicemen, which can lead to post-traumatic stress disorder (PTSD). The purpose of the study is to determine whether group psychotherapy is effective in reducing symptoms of post-traumatic stress disorder in military personnel. The research methodology is presented by the Post-Traumatic Stress Disorder Checklist for DSM-5, the World Health Organization Quality of Life Instrument, and statistical methods (ANOVA, Levene’s test, and the Mann-Whitney U-test). The results showed that group therapy effectively reduced symptoms of posttraumatic stress disorder from 42.5 (SD = 6.1) to 31.2 (SD = 5.7). The practical significance of the results obtained lies in the possibility of their use in the treatment of post-traumatic stress disorder in military personnel. Prospects for further research lie in studying effective methods of treating other mental disorders in people affected by war.

Sample Size and Statistical Power Calculation in Multivariable Analyses: Development and Implementation of "SampleSizeMulti" Packages in R

2024-11-25T08:33:08+00:00

This paper presents advanced methodological approaches and practical tools for sample size calculation in epidemiological studies involving multivariable analyses. Traditional sample size calculation methods often fail to account for the complexity of modern statistical analyses, particularly regarding the correlation between covariates in multivariable models.

We introduce a series of R packages (SampleSizeMulti) designed to address these limitations. These packages offer two distinct calculation approaches: one based on the multiple correlation coefficient between covariates (rho-based method) and another utilizing standard errors from previous studies (SE-based method). These complementary approaches provide comprehensive solutions for different association measures commonly used in epidemiological research: prevalence ratios, odds ratios, risk ratios, and hazard ratios.

The rho-based method innovatively incorporates the explicit consideration of the multiple correlation coefficient between covariates, significantly impacting required sample sizes in multivariable analyses. The SE-based method leverages information from previous studies through their confidence intervals, offering an alternative when correlation estimates are unavailable but published results exist. Furthermore, both approaches integrate crucial logistical considerations, including rejection rates, eligibility criteria, and expected losses to follow-up, providing researchers with realistic estimates of recruitment requirements and timelines.

Seven detailed case studies covering various epidemiological study designs and analytical scenarios demonstrate the practical application of these methods. These examples illustrate how correlation values, standard errors, and logistical factors influence sample size calculations and study planning.

The implementation in R ensures accessibility and reproducibility, while the incorporation of logistical planning tools bridges the gap between theoretical calculations and practical research requirements. These methods represent a significant advancement in study design methodology, potentially improving the quality and efficiency of epidemiological research by ensuring adequate statistical power while optimizing resource utilization.

A Novel RPCA Method Using Log-Weighted Nuclear and L_(2,1) Norms Combined with Contrast-Limited Adaptive Histogram Equalization (CLAHE) for High Dimensional Natural and Medical Image Data

2024-11-26T11:42:37+00:00

Estimating the true underlying images from distorted high-dimensional data is crucial for applications in high-profile fields such as crime detection in security, clinical settings and medical diagnosis in healthcare, and radar imaging in signal processing. Existing statistical methods often struggle with robustness and image reconstruction quality when processing high-dimensional image data. While Robust Principal Component Analysis (RPCA) is widely used for image recovery, its reliance on uniform weights with singular value decomposition (SVD) weakens performance, especially in noisy environments. The L₁ norm also fails to capture image details and recovery under high noise levels, a critical limitation for applications like medical diagnoses, where detail is essential. These challenges emphasize the need for improved methods to handle noise and enhance image quality in sensitive fields. Therefore, this paper proposes a novel RPCA method that integrates CLAHE with Log weighted nuclear norm (LWNN) and the L_2,1 norm for high-dimensional natural and medical imaging. To reduce the computational load, our novel method is formulated into a new optimization problem and solved using the Alternating Direction Method of Multipliers (ADMM). This method leverages LWNN for enhanced low-rank approximation to drastically prune out the anomalies in images and the norm for improved sparse component recovery. Our approach has superior performance in image reconstruction compared to other state-of-the-art methods (SOTAs), showing significant advancements with real-world datasets. An interesting finding of this research is that combining the LWNN with the L_2,1 norm is highly effective at removing noise from images. Furthermore, when the CLAHE technique is combined with LWNN and the L_2,1 norm, it significantly enhances the extraction of previously unseen features, making blood vessels in medical images much clearer and more distinguishable. This combination proves to be a powerful approach for medical image analysis, revealing details that are otherwise difficult to detect. This method will be used for crime detection in security intelligence, and clinical settings and medical diagnosis in human retinal eyes.

Elimination Diet Guided by Food-Specific IgG Antibodies Measurements in Chronic Adult Acne in Thailand: A Prospective RCT Study

2024-11-29T15:19:40+00:00

Background: Adult female acne is increasingly prevalent and significantly affects quality of life. Current treatments, including antibiotics and hormonal therapies, often yield varied efficacy and risks. While adult acne shares similarities with adolescent acne, its pathogenesis involves genetic, hormonal, immune, and dietary factors. Emerging research implicates high glycemic load diets, milk proteins, and insulin/IGF1 signaling in exacerbating acne. Despite growing understanding, dietary influences remain poorly understood, overshadowed by persistent myths. Inflammation, preceding follicular plugging and hyperkeratinization, suggests a potential shift towards addressing gut inflammation and digestive issues. IgG-mediated food reactions, linked to conditions like IBS and migraines, are controversial in diagnosing acne but warrant investigation, especially in Thai patients.

Objective: Evaluate the efficacy of elimination diets guided by food-specific IgG antibodies measurement in chronic adult acne.

Patients and Methods: This randomized controlled trial and prospective cohort study enrolled 75 participants with chronic acne aged 19 to 45 years, meeting stringent inclusion criteria. Participants were randomized into three groups: one receiving elimination diets based on reversed IgG antibody measurements, another on common food-specific IgG antibodies, and a third on individual IgG antibody measurements. Elimination diets were informed by Genarrayt™ 200+ Food IgG kit results, with compliance monitored through dietary checklists. Primary outcomes were assessed using the Global Acne Grading System (GAGS) Score, with secondary outcomes including the Thai version of the Dermatology Life Quality Index Questionnaire and a Multiorgan-Symptoms Checklist. Follow-ups were conducted at 2, 4, 8, and 12 weeks.

Results: The study cohort, predominantly female (74.7%), exhibited a mean age of 29.3 ± 5.3 years and an average GAGS Score indicating moderate acne severity. Significant differences in GAGS Scores were found among groups (P<0.001), indicating reduced acne severity in the elimination diet groups. Statistically significant reductions in weight and BMI were observed in one group (p-values = 0.048), but not in the other groups. Compliance with follow-up criteria was high.

Conclusion: Elimination diets guided by food-specific IgG antibodies measurement show promise in reducing chronic adult acne severity, as indicated by GAGS Scores. This study sheds light on the relationship between diet and adult acne pathophysiology, emphasizing the potential of personalized treatment approaches. Further research is warranted to validate these findings and elucidate underlying mechanisms.

Evaluating the Psychological Impact of Forest Bathing: A Meta-Analysis of Emotional State Outcomes

2024-11-29T15:19:28+00:00

Background: Forest bathing, a therapeutic practice involving immersion in natural forest environments, has gained attention for its potential mental health benefits. This meta-analysis evaluates the impact of forest bathing on psychological parameters such as tension-anxiety, depression, anger-hostility, fatigue, confusion, and vigor.

Methods: A meta-analysis was conducted on studies assessing forest bathing's effects on psychological states. Six studies were included, analyzing data using fixed and random effects models.

Results: The analysis of six studies with 296 participants revealed a strong positive correlation between forest bathing and reduced tension-anxiety, with correlation coefficients of 0.634 (fixed effects) and 0.613 (random effects). Both models were statistically significant (p < 0.001), despite moderate to high heterogeneity (I² = 67.57%). For depression, five studies (277 participants) showed a significant reduction, with a stronger correlation in the random effects model (0.557) compared to the fixed effects model (0.432). Anger-hostility was similarly reduced, with high heterogeneity (I² = 90.12%) and correlation coefficients of 0.741 (fixed) and 0.767 (random). Fatigue, assessed in six studies (296 participants), also showed significant reductions, with moderate heterogeneity (I² = 45.16%). Confusion was moderately reduced (I² = 29.52%), with correlation coefficients of 0.339 (fixed) and 0.323 (random). Lastly, vigor showed a weak positive association, with a correlation coefficient of 0.269.

Conclusion: The findings confirm the therapeutic potential of forest environments in promoting mental health. Given the observed positive effects, forest bathing could be integrated into public health strategies as a non-pharmacological intervention for stress and mood disorders.

The Association between Sweet Sugar Beverage Intakes and the Quality of Sleep in Working Age Adults

2024-11-29T15:19:25+00:00

Background: This cross-sectional study investigates the relationship between daily sugar intake from sugar-sweetened beverages (SSBs) and sleep quality in working adults aged 18–45 years who do not have chronic illnesses or diagnosed sleep disorders. The primary objective is to determine whether high sugar consumption increases the risk of poor sleep when other risk factors are considered simultaneously. This study is significant as it adds to the growing body of evidence regarding the impact of dietary factors on sleep quality, highlighting the potential health implications of sugar consumption.

Methods: Participants were selected using consecutive sampling, and the study was conducted at Jomtien Hospital in Chonburi Province, Thailand. Data collection involved three key components: general demographic and health information records, a validated Thai questionnaire assessing sweetened beverage consumption, and the Thai version of the Pittsburgh Sleep Quality Index (PSQI). Sugar intake from SSBs was categorized into two groups: high (≥ 50 grams/day) and low (< 50 grams/day). Sleep quality was classified as poor (PSQI > 5) or good (PSQI ≤ 5). The relationship between sugar intake and sleep quality was analyzed using logistic regression, and the results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs).

Results: The study included 300 participants, 68% of whom were women, with an average age of 30 ± 7.6 years. A total of 58% of participants were found to have poor sleep quality. Those with poor sleep quality consumed an average of 131.9 ± 102.9 grams of sugar per day, compared to 99.8 ± 86.3 grams for those with good sleep quality, a difference that was statistically significant (p = 0.005). When sugar consumption was categorized, 79.3% of individuals in the high sugar consumption group (≥ 50 grams/day) had poor sleep quality, compared to 65.1% with good sleep quality in the same group. In contrast, 20.7% of individuals in the low sugar consumption group (< 50 grams/day) had poor sleep quality, compared to 34.9% with good sleep quality, also showing a statistically significant difference (p = 0.008). The crude odds ratio for high sugar consumption associated with poor sleep quality was 2.06 (95% CI: 1.22-3.45, p = 0.006). After adjusting for other variables, the odds ratio remained significant, with an adjusted OR of 2.02 (95% CI: 1.05-3.92, p = 0.036).

Conclusion: The findings indicate that high sugar consumption from sugar-sweetened beverages is significantly associated with an increased risk of poor sleep quality (adjusted OR: 2.02, 95% CI: 1.05–3.92, p = 0.036). Based on these results, it is recommended that individuals experiencing sleep issues consider reducing their sugar intake. Future research should explore the mechanisms underlying this relationship and evaluate public health strategies aimed at reducing high sugar consumption.