Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach

Twenty-Five Years of Progress-Lessons Learned From JMIR Publications to Address Gender Parity in Digital Health Authorships: Bibliometric Analysis

BACKGROUND

Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored.

OBJECTIVE

This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship.

METHODS

This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship.

RESULTS

The analysis revealed that 37% of first authors and 30% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49% of first authors and 38% of last authors being female, yielding odds ratios of 1.96 (95% CI 1.90-2.03; P<.001) and 1.78 (95% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field.

CONCLUSIONS

There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively.

Quantitative evaluation of factors influencing the 3 Hz repetitive nerve stimulation test in patients with amyotrophic lateral sclerosis

INTRODUCTION/AIMS

Previous studies have suggested that treatments targeting the neuromuscular junction (NMJ) may play a role in the treatment of amyotrophic lateral sclerosis (ALS). However, factors impacting repetitive nerve stimulation (RNS), a technique to evaluate NMJ function, have yet to be fully elucidated. We aimed to identify independent factors contributing to the decremental response of the accessory nerve and evaluated its value in ALS clinical practice.

METHODS

A total of 626 patients who were diagnosed with ALS and underwent 3 Hz RNS tests on the accessory nerve were enrolled. Data on their clinical and electrophysiological indicators were divided into a training set (collected from June 2016 to December 2022) and a test set (collected from January to August 2023). Stepwise regression was used in independent variable selection and model building.

RESULTS

Forty-two percent of patients had a decrement larger than 10% and 24% had a decrement larger than 15%. Onset age, sex, onset site, forced vital capacity (FVC) and motor unit potential (MUP) duration were independent factors contributing to the results of the RNS test. MUP duration had the greatest impact on decremental response, followed by FVC and onset age. The decremental response in females was larger than in males. Upper limb onset was found to contribute more to the decrement than lower limb or bulbar onset.

DISCUSSION

In patients with ALS, NMJ safety factor is reduced during re-innervation. Decremental response is affected by multiple factors, which needs to be considered in clinical trials targeting the NMJ in these patients.

Comparison of three sorbents for thin film solid phase microextraction of haloacetic acids from water

Water disinfection inevitably leads to disinfection byproduct formation, such as haloacetic acids. Many disinfection byproducts reportedly have adverse health effects and, in many instances, including four haloacetic acids, are classified as potential carcinogens. As the global awareness of these compounds increases, more regulatory bodies include certain disinfection byproduct groups in their regulations. Rugged, fast, and cheap analytical quantification methods are therefore crucial. In this paper, a thin film extraction method for haloacetic acids is outlined. Thin films were synthesized in-house using a spin coating procedure, which allowed for easy adjustment of the sorbent choice and film geometry. PDMS, Carboxen®, and HLB were of interest and their extraction potential for HAAs from spiked water was tested in three film variations. PDMS films impregnated with HLB or Carboxen® improved the extraction drastically compared to PDMS films. Specifically, HLB impregnated films achieved excellent extraction efficiencies for tri-substituted analytes (51% for BDCAA, 77% for CDBAA, and 92% TBAA), which are often present at extremely low concentrations in water. In addition to the extraction experiment, a computational model was applied to compare PDMS and HLB. Trends observed in the computational data reflected in the experimental results, showing the validity of the model and confirming that physisorption through hydrogen bonding was mainly responsible for successful extraction.

In silico exploration of 4(α-l-rhamnosyloxy)-benzyl isothiocyanate: A promising phytochemical-based drug discovery approach for combating multi-drug resistant Staphylococcus aureus

Multidrug-resistant (MDR) Staphylococcus aureus infections significantly threaten global health. With rising resistance to current antibiotics and limited solutions, the urgent discovery of new, effective, and affordable antibacterials with low toxicity is imperative to combat diverse MDR S. aureus strains. Hence, in this study, we introduce an in silico phytochemical-based approach for discovering novel antibacterial agents, underscoring the potential of computational approaches in therapeutic discovery. Glucomoringin Isothiocyanate (GMG-ITC) from Moringa oleifera Lam. is one of the phytochemical compounds with several biological activities, including antimicrobial, anti-inflammatory, and antioxidant activities, and is also effective against S. aureus. This study focuses on screening GMG-ITC as a potential drug candidate to combat MDR S. aureus infections through a molecular docking approach. Moreover, interaction amino acid analysis, in silico pharmacokinetics, compound target prediction, pathway enrichment analysis and molecular dynamics (MD) simulations were conducted for further investigation. Molecular docking and interaction analysis showed strong binding affinity towards S. aureus lipase, dihydrofolate reductase, and other MDR S. aureus proteins, including penicillin-binding protein 2a, MepR, D-Ala:D-Ala ligase, and RPP TetM, through hydrophilic and hydrophobic interactions. GMG-ITC also showed a strong binding affinity to cyclooxygenase-2 and FAD-dependent NAD(P)H oxidase, suggesting that it is a potential anti-inflammatory and antioxidant candidate that may eliminate inflammation and oxidative stress associated with S. aureus infections. MD simulations validated the stability of the GMG-ITC molecular interactions determined by molecular docking. In silico pharmacokinetic analysis highlights its potency as a drug candidate, showing strong absorption, distribution, and excretion properties in combination with low toxicity. It acts as an active protease and enzyme inhibitor with moderate activity against GPCR ligands, ion channels, nuclear receptor ligands, and kinases. Enrichment analysis further elucidated its involvement in important biological, molecular, and cellular functions with potential therapeutic applications in diseases like cancer, hepatitis B, and influenza. Results suggest that GMG-ITC is an effective antibacterial agent that could treat MDR S. aureus-associated infections.

Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study

INTRODUCTION

Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.

MATERIAL AND METHODS

Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.

RESULTS

The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.

CONCLUSION

Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.

RELEVANCE STATEMENT

Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.

KEY POINTS

• We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC.

Interplay between drought and plant viruses co-infecting melon plants

Drought affects crops directly, and indirectly by affecting the activity of insect pests and the transmitted pathogens. Here, we established an experiment with well-watered or water-stressed melon plants, later single infected with either cucumber mosaic virus (CMV: non-persistent), or cucurbit aphid-borne yellow virus (CABYV: persistent), or both CMV and CABYV, and mock-inoculated control. We tested whether i) the relation between CMV and CABYV is additive, and ii) the relationship between water stress and virus infection is antagonistic, i.e., water stress primes plants for enhanced tolerance to virus infection. Water stress increased leaf greenness and temperature, and reduced leaf water potential, shoot biomass, stem dimensions, rate of flowering, CABYV symptom severity, and marketable fruit yield. Virus infection reduced leaf water potential transiently in single infected plants and persistently until harvest in double-infected plants. Double-virus infection caused the largest and synergistic reduction of marketable fruit yield. The relationship between water regime and virus treatment was additive in 12 out of 15 traits at harvest, with interactions for leaf water content, leaf:stem ratio, and fruit set. We conclude that both virus-virus relations in double infection and virus-drought relations cannot be generalized because they vary with virus, trait, and plant ontogeny.

Sample size calculation for data reliability and diagnostic performance: a go-to review

Sample size, namely the number of subjects that should be included in a study to reach the desired endpoint and statistical power, is a fundamental concept of scientific research. Indeed, sample size must be planned a priori, and tailored to the main endpoint of the study, to avoid including too many subjects, thus possibly exposing them to additional risks while also wasting time and resources, or too few subjects, failing to reach the desired purpose. We offer a simple, go-to review of methods for sample size calculation for studies concerning data reliability (repeatability/reproducibility) and diagnostic performance. For studies concerning data reliability, we considered Cohen's κ or intraclass correlation coefficient (ICC) for hypothesis testing, estimation of Cohen's κ or ICC, and Bland-Altman analyses. With regards to diagnostic performance, we considered accuracy or sensitivity/specificity versus reference standards, the comparison of diagnostic performances, and the comparisons of areas under the receiver operating characteristics curve. Finally, we considered the special cases of dropouts or retrospective case exclusions, multiple endpoints, lack of prior data estimates, and the selection of unusual thresholds for α and β errors. For the most frequent cases, we provide example of software freely available on the Internet.Relevance statement Sample size calculation is a fundamental factor influencing the quality of studies on repeatability/reproducibility and diagnostic performance in radiology.Key points• Sample size is a concept related to precision and statistical power.• It has ethical implications, especially when patients are exposed to risks.• Sample size should always be calculated before starting a study.• This review offers simple, go-to methods for sample size calculations.

Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

'Spin' in urology non-randomised studies comparing therapeutic interventions: a temporal analysis

OBJECTIVE

To determine the prevalence of 'spin' (i.e., reporting practices that distort the interpretation of results by positively reflecting negative findings or downplaying potential harms) strategies and level of spin in urological observational studies and whether the use of spin has changed over time.

MATERIALS AND METHODS

MEDLINE and Embase were searched to identify observational studies comparing therapeutic interventions in the top five urology journals and major urological subspecialty journals, published between 2000 and 2001, 2010 and 2011, and 2020 and 2021.

RESULTS

A total of 235 studies were included. Spin was identified in 81% of studies, with a median of two strategies per study. The most commonly used strategies were inadequate implication for clinical practice (30%), causal language or causal claim (29%), and use of linguistic spin (29%). Moderate to high levels of spin were found in 55% of conclusions. From 2000 to 2020, the average number of strategies used has significantly decreased each decade (H = 27.459, P < 0.001), and the median level of spin in conclusions was significantly lower in studies published in the 2020s and 2010s than in the 2000s (H = 11.649, P = 0.003).

CONCLUSIONS

Our results suggest that 81% of urological observational studies comparing therapeutic interventions contained spin. Over the past two decades, the use of spin has significantly declined, but this remains an area for improvement, with 70% of included studies published in the 2020s employing spin. Medical writing should scrupulously avoid words or phrases that are not supported by data in the manuscript.

Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

Outcomes of Laparoscopic Suture Rectopexy Versus Laparoscopic Mesh Rectopexy: A Systematic Review and Meta-Analysis

The contemporary literature provides conflicting evidence regarding the precedence of laparoscopic mesh rectopexy over laparoscopic suture rectopexy for full-thickness rectal prolapse. This study aimed to compare the clinical outcomes of mesh and suture rectopexy to improve the surgical management of complete rectal prolapse. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to extract studies based on mesh versus suture rectopexy and published from 2001 to 2023. The articles of interest were obtained from PubMed Central, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Journal Storage (JSTOR), Web of Science, Embase, Scopus, and the Cochrane Library. The primary outcomes included rectal prolapse recurrence, constipation improvement, and operative time. The secondary endpoints included the Cleveland Clinic Constipation Score, Cleveland Clinic Incontinence Score, intraoperative bleeding, hospital stay duration, mortality, overall postoperative complications, and surgical site infection. A statistically significant low recurrence of rectal prolapse (odds ratio: 0.41, 95% confidence interval (CI) 0.21-0.80; p=0.009) and longer mean operative duration (mean difference: 27.05, 95% CI 18.86-35.24; p<0.00001) were observed in patients with mesh rectopexy versus suture rectopexy. Both study groups, however, had no significant differences in constipation improvement and all secondary endpoints (all p>0.05). The laparoscopic mesh rectopexy was associated with a low postoperative rectal prolapse recurrence and a longer operative duration compared to laparoscopic suture rectopexy. Prospective randomized controlled trials should further evaluate mesh and suture rectopexy approaches for postoperative outcomes to inform the surgical management of complete rectal prolapse.

Praziquantel-related visual disorders among recipients in mass drug administration campaigns in schistosomiasis endemic settings: Systematic review and meta-analysis protocol

BACKGROUND

Hundreds of millions of doses of Praziquantel (PZQ) have been administered to persons with and without schistosomiasis living in schistosomiasis endemic settings, through the mass drug administration (MDA) strategy which started in the early 2000s. A recent publication suggested high risk of PZQ-related visual disorders, raising public health concerns. We aim to systematically synthesize evidence on the magnitude of PZQ-related visual disorders.

METHODS

We will search PubMed, Google Scholar, CINAHL, SCOPUS, CENTRAL and LILACS from 1977 (when the first human clinical trials on PZQ started) to 31st May 2024, with no language restrictions. The key search terms will include "Praziquantel", "PZQ", "visual disorder", "adverse events", "side effects", "blurry vision" and "visual impairment" together with alternative terms and synonyms. All the countries endemic for schistosomiasis will be included as search terms. We will also search HINARI, Africa Journals Online, Thesis Databases and Preprint Repositories. Where necessary, we will contact expert researchers working in the field of schistosomiasis, UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), pharmaceutical industries, country-specific Food and Drug Authorities (FDAs) and the European Medicines Agency databases. We will search Conference Proceedings and reference lists of relevant studies for additional studies. At least two authors will independently select studies, extract data and assess risk of bias in the included studies. Any disagreements or discrepancies will be resolved through discussion between the reviewers. Heterogeneity will be explored graphically, and statistically using the I2-statistic. We will conduct random-effects meta-analysis when heterogeneity is appreciable, and express dichotomous outcomes (visual adverse events including excessive lacrimation, blurry vision and visual impairments) as risk ratio (RR) or Odds Ratio (OR) with their 95% confidence interval (CI). We will perform subgroup analysis to assess the impact of heterogeneity, and sensitivity analyses to test the robustness of the effect estimates. The overall level of evidence will be assessed using GRADE.

EXPECTED OUTCOMES

The present review expects to identify and categorize visual disorders occurring after administration of PZQ, alone or in combination with other drugs. By synthesizing the data from multiple studies, the review aims to present a quantitative assessment of the risk or odds of experiencing a visual disorder in different populations after ingesting PZQ. The review will also generate insights into whether PZQ in combination with other drugs are associated with increased odds of visual disorders and whether the occurrence of visual disorders correlates with dosage or treatment duration. Policymakers, public health experts and stakeholders could rely on the review findings to deliver context-sensitive preventive chemotherapy programs by adjusting drug combinations or dosing schedules to reduce risk of visual adverse effects in populations treated with PZQ. The review aims to identify gaps in the current evidence regarding visual disorders following PZQ administration in schistosomiasis endemic settings which can serve as the basis for future research on important but unanswered questions.

DISSEMINATION AND PROTOCOL REGISTRATION

The findings of this study will be disseminated through stakeholder forums, conferences, and peer-review publications. The review protocol has been registered in the International Prospective Register for Systematic Reviews (PROSPERO)- CRD42023417963.

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Approximation of van Genuchten Parameter Ranges from Hydraulic Conductivity Data

The use of retention function and relative conductivity function is essential for the calculation of flow in a variably saturated media using the Richards equation. A widely used mathematical model for this is the Mualem-van Genuchten model which requires the shape parameters α and n . These, however, are difficult to obtain. When data is scarce, α and n are often taken from literature and may deviate largely from actual values. The current study presents a novel mathematical model for the approximation of α and n and for the further estimation of realistic value ranges, which may be used as parameter space, for example, for the calibration of a numerical model. The model was developed for cases where data is scarce and only values of saturated hydraulic conductivity are available. It is based on a large data set from literature and it is demonstrated that the model estimates mean values from that data set with a good accuracy. In order to show the applicability of the model, a second data set has been compiled anew (provided as Supporting Information). The model is incorporated into the current version of the freeware computer program HYPAGS, which enables easy usage.

Exploring the Association between Elevated Anxiety Symptoms and Low Skeletal Muscle Mass among Asymptomatic Adults: A Population-Based Study in Republic of Korea

Individuals with mental health problems are at higher risk of musculoskeletal diseases. However, the association between low muscle mass (LMM) and anxiety symptoms remains uninvestigated. This cross-sectional study enrolled 174,262 adults (73,833 women, 100,429 men), aged 18 to 89, who completed the anxiety scale and body composition analyses. Using bio-electrical impedance analysis, skeletal muscle mass index (SMI) was calculated based on appendicular skeletal muscle mass (ASM) (kg)/height (m2). LMM was defined as SMI < 7.0 kg/m2 in men and <5.4 kg/m2 in women. Anxiety symptoms were screened using the Clinical Useful Anxiety Outcome Scale (CUXOS) with cut-off scores of 20, 30, and 40. Multivariable logistic regression analyses were performed. LMM prevalence was 20.17% in women, 3.86% in men (p < 0.001). The prevalence of anxiety symptoms in LMM group decreased from mild (CUXOS > 20: women, 32.74%, men, 21.17%) to moderate (CUXOS > 30: 13.34%, 7.32%), to severe anxiety symptoms (CUXOS > 40: 4.00%, 1.73%). In multivariable-adjusted models, LMM was associated with mild (aOR (95% confidence interval)), women, 1.13 (1.08-1.17); men, 1.17 (1.08-1.27)), moderate (1.17 (1.11-1.24); 1.35 (1.19-1.53) and severe anxiety symptoms (1.18 (1.07-1.3), 1.36 (1.06-1.74)), demonstrating an increased risk of ORs with escalating anxiety severity. LMM was independently associated with a higher prevalence of anxiety symptoms.

Telehealth Utilization and Associations in the United States During the Third Year of the COVID-19 Pandemic: Population-Based Survey Study in 2022

BACKGROUND

The COVID-19 pandemic rapidly changed the landscape of clinical practice in the United States; telehealth became an essential mode of health care delivery, yet many components of telehealth use remain unknown years after the disease's emergence.

OBJECTIVE

We aim to comprehensively assess telehealth use and its associated factors in the United States.

METHODS

This cross-sectional study used a nationally representative survey (Health Information National Trends Survey) administered to US adults (≥18 years) from March 2022 through November 2022. To assess telehealth adoption, perceptions of telehealth, satisfaction with telehealth, and the telehealth care purpose, we conducted weighted descriptive analyses. To identify the subpopulations with low adoption of telehealth, we developed a weighted multivariable logistic regression model.

RESULTS

Among a total of 6252 survey participants, 39.3% (2517/6252) reported telehealth use in the past 12 months (video: 1110/6252, 17.8%; audio: 876/6252, 11.6%). The most prominent reason for not using telehealth was due to telehealth providers failing to offer this option (2200/3529, 63%). The most common reason for respondents not using offered telehealth services was a preference for in-person care (527/578, 84.4%). Primary motivations to use telehealth were providers' recommendations (1716/2517, 72.7%) and convenience (1516/2517, 65.6%), mainly for acute minor illness (600/2397, 29.7%) and chronic condition management (583/2397, 21.4%), yet care purposes differed by age, race/ethnicity, and income. The satisfaction rate was predominately high, with no technical problems (1829/2517, 80.5%), comparable care quality to that of in-person care (1779/2517, 75%), and no privacy concerns (1958/2517, 83.7%). Younger individuals (odd ratios [ORs] 1.48-2.23; 18-64 years vs ≥75 years), women (OR 1.33, 95% CI 1.09-1.61), Hispanic individuals (OR 1.37, 95% CI 1.05-1.80; vs non-Hispanic White), those with more education (OR 1.72, 95% CI 1.03-2.87; at least a college graduate vs less than high school), unemployed individuals (OR 1.25, 95% CI 1.02-1.54), insured individuals (OR 1.83, 95% CI 1.25-2.69), or those with poor general health status (OR 1.66, 95% CI 1.30-2.13) had higher odds of using telehealth.

CONCLUSIONS

To our best knowledge, this is among the first studies to examine patient factors around telehealth use, including motivations to use, perceptions of, satisfaction with, and care purpose of telehealth, as well as sociodemographic factors associated with telehealth adoption using a nationally representative survey. The wide array of descriptive findings and identified associations will help providers and health systems understand the factors that drive patients toward or away from telehealth visits as the technology becomes more routinely available across the United States, providing future directions for telehealth use and telehealth research.

Health and health behaviours in adolescence as predictors of education and socioeconomic status in adulthood - a longitudinal study

BACKGROUND

The positive association of health with education level and socioeconomic status (SES) is well-established. Two theoretical frameworks have been delineated to understand main mechanisms leading to socioeconomic health inequalities: social causation and health selection but how these work in adolescence is poorly known. We studied if adolescent health and health behaviours predict higher education and higher SES in adulthood and if family background and school performance in adolescence explain these associations.

METHODS

Surveys on health and health behaviours were sent to representative samples of 12-18-year-old Finns in 1981-1997 every second year (response rate 77.8%, N = 55,682). The survey data were linked with the respondents' and their parents' socioeconomic data from the Finnish national registries. Both latent variables, namely, health (perceived health, health complaints, chronic disease), health-compromising behaviours (smoking status, drunkenness frequency), and family background (parents' occupation-based SES, education, family type) and variables directly measuring health-enhancing behaviours (toothbrushing, physical activity) and school performance were used to predict higher education and higher occupation-based SES at age 34. Logistic regression analysis and structural equation models (SEM) were used.

RESULTS

In logistic regression analyses, good health, health-enhancing behaviours, and lack of health-compromising behaviours were related to higher education and SES, also after controlling for family background and school performance. In the SEM analyses, good health, health-enhancing behaviours, and lack of health-compromising behaviours directly predicted higher SES and higher education, although the standardised coefficients were low (from 0.034 to 0.12). In all models, health, lack of health-compromising behaviours, and health-enhancing behaviours predicted school performance, which in turn, predicted the outcomes, suggesting indirect routes to these. Good socioeconomic prospects in terms of family background predicted good health, healthy behaviours, and good school performance in adolescence and higher SES and higher education in adulthood.

CONCLUSION

Health and health behaviours in adolescence predicted education and SES in adulthood. Even though the relationships were modest, they support the health selection hypotheses and emphasise the importance of adolescence for health inequalities during the life-course. Health and health behaviours were strongly associated with school performance and family background which together modified the paths from health and health behaviours to the outcomes.

MRI-derived extracellular volume as a biomarker of cancer therapy cardiotoxicity: systematic review and meta-analysis

OBJECTIVES

MRI-derived extracellular volume (ECV) allows characterization of myocardial changes before the onset of overt pathology, which may be caused by cancer therapy cardiotoxicity. Our purpose was to review studies exploring the role of MRI-derived ECV as an early cardiotoxicity biomarker to guide timely intervention.

MATERIALS AND METHODS

In April 2022, we performed a systematic search on EMBASE and PubMed for articles on MRI-derived ECV as a biomarker of cancer therapy cardiotoxicity. Two blinded researchers screened the retrieved articles, including those reporting ECV values at least 3 months from cardiotoxic treatment. Data extraction was performed for each article, including clinical and technical data, and ECV values. Pooled ECV was calculated using the random effects model and compared among different treatment regimens and among those who did or did not experience overt cardiac dysfunction. Meta-regression analyses were conducted to appraise which clinical or technical variables yielded a significant impact on ECV.

RESULTS

Overall, 19 studies were included. Study populations ranged from 9 to 236 patients, for a total of 1123 individuals, with an average age ranging from 12.5 to 74 years. Most studies included patients with breast or esophageal cancer, treated with anthracyclines and chest radiotherapy. Pooled ECV was 28.44% (95% confidence interval, CI, 26.85-30.03%) among subjects who had undergone cardiotoxic cancer therapy, versus 25.23% (95%CI 23.31-27.14%) among those who had not (p = .003).

CONCLUSION

A higher ECV in patients who underwent cardiotoxic treatment could imply subclinical changes in the myocardium, present even before overt cardiac pathology is detectable.

CLINICAL RELEVANCE STATEMENT

The ability to detect subclinical changes in the myocardium displayed by ECV suggests its use as an early biomarker of cancer therapy-related cardiotoxicity.

KEY POINTS

• Cardiotoxicity is a common adverse effect of cancer therapy; therefore, its prompt detection could improve patient outcomes. • Pooled MRI-derived myocardial extracellular volume was higher in patients who underwent cardiotoxic cancer therapy than in those who did not (28.44% versus 25.23%, p = .003). • MRI-derived myocardial extracellular volume represents a potential early biomarker of cancer therapy cardiotoxicity.

Farit NA. The Efficacy of Elonide Nasal Corticosteroids in Managing Allergic Rhinitis: A Randomized, Double-Blinded Trial

Background: Mometasone furoate nasal spray is efficacious in relieving allergic rhinitis symptoms. The objectives of this study were, firstly, to compare the efficacy of Elonide to Nasonex® and a placebo and secondly, to investigate the side effects of Elonide. Method: This was a prospective, single-centered, double blinded, randomized, placebo-controlled, non-inferiority trial. A total of 163 participants from the Otorhinolaryngology Clinic, Hospital Canselor Tuanku Muhriz (HCTM), were randomized into three treatment groups receiving Elonide (n = 56), Nasonex® (n = 54), and placebo (n = 53) nasal sprays using an online randomizer (Random.org). Treatment was administered for 4 weeks. The primary outcome measure was the Total Nasal Resistance (TNR), and the secondary outcomes were the Visual Analogue Score (VAS) and the Rhinoconjunctivitis Quality of Life Questionnaire (RQOLQ) score. Side effects were recorded. Results: There were significant improvements for all groups from baseline. The Elonide group had the greatest mean difference for all primary and secondary outcomes compared to Nasonex® and the placebo (0.77 ± 2.44 vs. 0.35 ± 1.16, p = 1.00 vs. 0.17 ± 0.82, p = 0.01). Elonide is non-inferior to Nasonex (p = 1.00) and superior to the placebo (p < 0.05). The highest side effects reported were for Nasonex (n = 14, 26%), followed by the placebo (n = 8, 16%) and Elonide (n = 6, 12%); headaches (n = 9, 17%) and sore throat (n = 9, 17%) were the most common. Conclusions: Elonide has similar efficacy to Nasonex® when compared to a placebo in the treatment of AR in adults. Elonide is safe and tolerable, with fewer side effects and no adverse side effects.

Trans-omic analysis reveals opposite metabolic dysregulation between feeding and fasting in liver associated with obesity

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese (ob/ob) mice at ad libitum feeding and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob/ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob/ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations and activating enzyme protein regulations in ob/ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity.

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models. These difficulties motivate us to propose an efficient and accurate system based on deep learning and evolutionary optimization for the classification of four types of brain modalities (t1 tumor, t1ce tumor, t2 tumor, and flair tumor) on a large-scale MRI database. Thus, a CNN architecture is modified based on domain knowledge and connected with an evolutionary optimization algorithm to select hyperparameters. In parallel, a Stack Encoder-Decoder network is designed with ten convolutional layers. The features of both models are extracted and optimized using an improved version of Grey Wolf with updated criteria of the Jaya algorithm. The improved version speeds up the learning process and improves the accuracy. Finally, the selected features are fused using a novel parallel pooling approach that is classified using machine learning and neural networks. Two datasets, BraTS2020 and BraTS2021, have been employed for the experimental tasks and obtained an improved average accuracy of 98% and a maximum single-classifier accuracy of 99%. Comparison is also conducted with several classifiers, techniques, and neural nets; the proposed method achieved improved performance.

Causal Influence of Linguistic Learning on Perceptual and Conceptual Processing: A Brain-Constrained Deep Neural Network Study of Proper Names and Category Terms

Language influences cognitive and conceptual processing, but the mechanisms through which such causal effects are realized in the human brain remain unknown. Here, we use a brain-constrained deep neural network model of category formation and symbol learning and analyze the emergent model's internal mechanisms at the neural circuit level. In one set of simulations, the network was presented with similar patterns of neural activity indexing instances of objects and actions belonging to the same categories. Biologically realistic Hebbian learning led to the formation of instance-specific neurons distributed across multiple areas of the network, and, in addition, to cell assembly circuits of "shared" neurons responding to all category instances-the network correlates of conceptual categories. In two separate sets of simulations, the network learned the same patterns together with symbols for individual instances ["proper names" (PN)] or symbols related to classes of instances sharing common features ["category terms" (CT)]. Learning CT remarkably increased the number of shared neurons in the network, thereby making category representations more robust while reducing the number of neurons of instance-specific ones. In contrast, proper name learning prevented a substantial reduction of instance-specific neurons and blocked the overgrowth of category general cells. Representational similarity analysis further confirmed that the neural activity patterns of category instances became more similar to each other after category-term learning, relative to both learning with PN and without any symbols. These network-based mechanisms for concepts, PN, and CT explain why and how symbol learning changes object perception and memory, as revealed by experimental studies.

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks' best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work.

Assumption-checking rather than (just) testing: The importance of visualization and effect size in statistical diagnostics

Statistical methods generally have assumptions (e.g., normality in linear regression models). Violations of these assumptions can cause various issues, like statistical errors and biased estimates, whose impact can range from inconsequential to critical. Accordingly, it is important to check these assumptions, but this is often done in a flawed way. Here, I first present a prevalent but problematic approach to diagnostics-testing assumptions using null hypothesis significance tests (e.g., the Shapiro-Wilk test of normality). Then, I consolidate and illustrate the issues with this approach, primarily using simulations. These issues include statistical errors (i.e., false positives, especially with large samples, and false negatives, especially with small samples), false binarity, limited descriptiveness, misinterpretation (e.g., of p-value as an effect size), and potential testing failure due to unmet test assumptions. Finally, I synthesize the implications of these issues for statistical diagnostics, and provide practical recommendations for improving such diagnostics. Key recommendations include maintaining awareness of the issues with assumption tests (while recognizing they can be useful), using appropriate combinations of diagnostic methods (including visualization and effect sizes) while recognizing their limitations, and distinguishing between testing and checking assumptions. Additional recommendations include judging assumption violations as a complex spectrum (rather than a simplistic binary), using programmatic tools that increase replicability and decrease researcher degrees of freedom, and sharing the material and rationale involved in the diagnostics.

Dual Energy X-ray Absorptiometry: Radiographer'S Role in Assessing Fracture Risk Assessment Tool (FRAX) Questionnaire Variables

BACKGROUND

The FRAX® algorithm is a tool used to calculate the 10-year probability of fracture in patients with osteoporosis and is based the assessment of several risk factors. We assessed the performance and accuracy of the completion of the FRAX® anamnestic questionnaire by the radiographer without impact on the clinical workflow.

METHODOLOGY

We evaluated the accuracy of fracture risk calculation by the radiographer using the FRAX® algorithm before and after specific training. A total of 100 women were enrolled in the study. The radiographer preliminarily administered the FRAX® questionnaire to all subjects before the execution of the DXA examination. After the end of the examination, a radiologist administered the questionnaire to the patient. Women were divided into two groups: group A (pre-training) and group B (post-training). The radiographer in group A completed the FRAX® questionnaire for the patients before training. For group B, the same radiographer completed the FRAX® questionnaire after training. The results of the FRAX® questionnaire completed by radiographer were compared with that completed by the referring physician.

RESULTS

Before training, radiographer's accuracy ranged from 92% (question 7, alcohol consumption) to 36% (question 6, secondary osteoporosis). After training, accuracy values improved substantially, ranging from 100% to 92%. Analysis of the absolute values of FRAX® showed that in the pre-training group data tended to be overestimated by the radiographer, with both major and fractures probabilities being significantly higher when assessed by the radiographer (12% and 5.8%, respectively). After the training, there was a marked decrease in the variation between the FRAX® data calculated by the radiographer and the radiologist.

CONCLUSIONS

The accuracy of fracture risk calculation by the radiographer using the FRAX® algorithm is significantly improved after a specific training period. This study demonstrates the importance of dedicated training radiographers on the FRAX® algorithm.

Community Exercise Program Participation and Mental Well-Being in the U.S. Texas-Mexico Border Region

Hispanics are disproportionately affected by low rates of physical activity and high rates of chronic diseases. Hispanics generally and Mexican Americans specifically are underrepresented in research on physical activity and its impact on mental well-being. Some community-based interventions have been effective in increasing physical activity among Hispanics. This study examined data from a sample of low-income Hispanic participants in free community exercise classes to characterize the association between self-reported frequency of exercise class attendance, intensity of physical activity, and participant well-being. As part of two cross-sectional samples recruited from a stratified random sample of community exercise classes, 302 participants completed a questionnaire consisting of a modified version of the Godin-Shephard Leisure-Time Exercise Questionnaire (LTEQ) and the Mental Health Continuum Short Form (MHC-SF). Adjusted logistic regression analyses indicated that those who achieve mild, moderate, and strenuous self-reported physical activity have 130% higher odds (p = 0.0422) of positive mental well-being after adjustment for age, frequency of attendance, and self-reported health. This study provides evidence that the intensity of physical activity is associated with flourishing mental well-being among Hispanic adults. The association between physical activity and mental well-being is more pronounced when considering participants engaged in mild levels of physical activity. The study further provides insight into the planning and development of community-based physical activity programming tailored to low-income populations.

The interplay between self-regulation, learning flow, academic stress and learning engagement as predictors for academic performance in a blended learning environment: A cross-sectional survey

Aim

To examine the correlations between self-regulation, learning flow, academic stress and learning engagement as predicting variables for academic achievement in a blended learning environment in Namibia.

Design

Cross-sectional survey.

Methods

Data were collected from 166 randomly selected undergraduate nursing students through an online survey between January and February 2023, and were analysed using IBM SPSS AMOS version 28.0. The data were explored through factor, parallel and confirmatory factor analyses. The relationship between the study factors and the total score of the scale was analysed using the Pearson correlation coefficient.

Results

The results indicate that the two factors identified in the factor analysis are consistent with the theoretical proposition in this research. Factor 1 comprises items C1 to C24, which pertain to self-regulation (SR), while factor 2 consists of items D1 to D9, which relate to learning flow (LR). The findings demonstrate that self-regulation significantly predicts both flow and stress, as well as learning engagement. Additionally, there is a significant relationship between stress and self-regulated learning, as well as between stress and learning flow (r = 0.23-0.26; p= < .05). However, none of the study constructs were found to predict academic achievement.

Conclusion

Although self-regulation significantly predicted flow, stress and learning engagement, a non-significant association exists between all the study constructs and academic achievement. The results of this study have significant implications for improving the development of a positive learning environment that fosters active student engagement. Future studies should investigate correlation by conducting large-scale studies.

Impact

This study makes a valuable contribution to the current body of literature concerning academic achievement within the context of undergraduate nursing education. The insignificant relationship between the study variables and academic achievement indicate that these elements are not of considerable significance in enhancing educational achievements in blended learning surroundings in Namibia.

Patient or public contribution

One hundred and sixty-six undergraduate nursing students participated in the survey. The data collected were analysed and interpreted by a skilled statistician.

Kynurenine pathway dynamics in patients with schizophrenia spectrum disorders across the disease trajectory

The aim of this study was to evaluate how schizophrenia spectrum disorders (SSD) and applied long-term (5.1 years) antipsychotic (AP) treatment affect the serum levels of tryptophan (Trp) metabolites. A total of 112 adults (54 first-episode psychosis [FEP] patients and 58 control subjects [CSs]) participated in the study. The investigated changes in the metabolite levels appeared against a background of persistent increase in BMI and waist circumference among the patients. Regarding the kynurenine (KYN) pathway, the strongest changes were seen in AP-naïve FEP patients. Trp, KYN, kynurenic acid (KYNA), and anthranilic acid (ANT) levels were significantly reduced in blood samples from patients in the early stage of the disease. Furthermore, 3-OH-kynurenine (3-HK) and quinolinic acid (QUIN) levels were somewhat lower in these patients. Most of these changes in the KYN pathway became weaker with AP treatment. The levels of serotonin and its metabolite 5-HIAA tended to be higher at 5.1 years in patients showing the relation of elevated serotonin turnover to increased BMI and waist circumference. The similar trend was evident for the ratio between xanthurenic acid (XA) and KYNA with strong link to the elevated BMI. Altogether, the present study supports the role of Trp-metabolites in the development of obesity and metabolic syndrome in SSD patients.

The Perception of Health Care Practitioners Regarding Telemedicine During COVID-19 in Saudi Arabia: Mixed Methods Study

BACKGROUND

Telemedicine is a rapidly evolving field that uses information and communication technology to provide remote health care services, such as diagnosis, treatment, consultation, patient monitoring, and medication delivery. With advancements in technology, telemedicine has become increasingly popular during the COVID-19 lockdown and has expanded beyond remote consultations via telephone or video to include comprehensive and reliable services. The integration of telemedicine platforms can enable patients and health care providers to communicate more efficiently and effectively.

OBJECTIVE

This study aims to investigate the awareness, knowledge, requirements, and perceptions of health care practitioners in Saudi Arabia during the pandemic health crisis from the end-user perspective. The findings of this study will inform policy makers regarding the sustainability of telemedicine and how it affects the process of provision of health care and improves the patients' journey.

METHODS

This study adopted a mixed methods design with a quantitative-based cross-sectional design and qualitative interviews to assess the perceptions of various health care professionals working in outpatient departments that have a telemedicine system that was used during the COVID-19 pandemic. For both approaches, ethics approval was obtained, and informed consent forms were signed. In total, 81 completed questionnaires were used in this study. In the second phase, general interviews were conducted with managerial staff and health care professionals to obtain their view of telemedicine services in their hospitals.

RESULTS

The study revealed that most participants (67/81, 83%) were familiar with telemedicine technology, and the study proved to be statistically significant at P<.05 with a proportion of the participants (52/81, 64%) believing that continuous training was essential for its effective use. The study also found that consultations (55/153, 35.9%) and monitoring patients (35/153, 22.9%) were the major components of telemedicine used by health care professionals, with telephones being the most commonly used mode of interaction with patients (74/117, 63.2%). In addition, 54% (44/81) of the respondents expressed concerns about patient privacy and confidentiality, highlighting this as a major issue. Furthermore, the majority of participants (58/81, 72%) reported the necessity of implementing national standards essential for telemedicine technology in Saudi Arabia. The interviews conducted as part of the study revealed 5 major themes: culture, barriers and difficulties, communication, implementation, and evaluation. These themes highlighted the importance of a culture of acceptance and flexibility, effective communication, and ongoing evaluation of telemedicine technologies in health care systems.

CONCLUSIONS

This study provides a crucial message with insights into the perceptions and experiences of health care professionals with telemedicine during the COVID-19 pandemic in Saudi Arabia.

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.

Epworth sleepiness scale: A meta-analytic study on the internal consistency

OBJECTIVE/BACKGROUND

The Epworth Sleepiness Scale (ESS) is one of the most used self-reported instruments to assess sleepiness. Thus, several adaptations into different Languages have been performed worldwide over the years. The scale has produced disparate psychometric properties when applied in different settings. In the current study, our aim was to perform a Reliability Generalization meta-analysis of the Cronbach᾽s alphas of all published studies on ESS, specifically with a psychometric focus.

PATIENTS/METHODS

Three reference databases (Scopus, PubMed and Web of Science) were searched since 1991 to October 2022 and all the records on psychometric or validation studies that reported Cronbach's alphas, from clinical and nonclinical groups, were included. In total, data from 46 publications (63 estimates) were extracted, comprising 92,503 participants.

RESULTS

Using a Random-Effects Model, the cumulative Cronbach's alpha for the 63 estimates was about 0.82 (CI: 0.798, 0.832) which can be considered as a good measure. However, and as expected, it was observed a high level of heterogeneity (I2 = 98.96%). Moderation analyses considering setting, date, continent, risk of bias, sex, age and language were performed in order to account for the heterogeneity. Even so, only the variables study setting and continent were significant, and had little importance in explaining the heterogeneity.

CONCLUSIONS

The ESS is a reliable tool to measure sleepiness; however, further studies are needed to investigate what variables might explain the observed variability. Moreover, it will be important to include empirical studies beyond psychometric ones.

Comparison of Percutaneous Coronary Intervention-Related Adverse Cardiac Outcomes in Patients With in-stent vs de novo Chronic Total Occlusion: A Systematic Review and Meta-Analysis

Contemporary literature reveals a range of cardiac complications in patients who receive the percutaneous coronary intervention (PCI) for chronic total occlusion (CTO). This study compared the adverse cardiac outcomes and procedural/technical success rates between the patients groups of in-stent (IS) CTO PCI and de novo CTO PCI. This systematic review and meta-analysis compared odds for primary (all-cause mortality, MACE, cardiac death post PCI, stroke) and secondary (bleeding requiring blood transfusion, ischemia-driven target-vessel revascularization, PCI procedural success, PCI technical success, and target-vessel MI) endpoints between 2734 patients who received PCI for IS CTO and 17,808 for de novo CTO. Odds ratios for outcome variables were calculated within 95% confidence intervals (CIs) via the Mantel-Haenszel method. The pooled analysis was undertaken for observational (retrospective/prospective) single- and multicentered studies published between January 2005 and December 2021. We found 57% higher, 166% higher, 129% higher, and 57% lower odds for MACE (OR: 1.57, 95% CI 1.31, 1.89, P < 0.001), ischemia-driven target-vessel revascularization (OR: 2.66, 95% CI 2.01, 3.53, P < 0.001), target-vessel myocardial infarction (MI) (OR: 2.29, 95% CI 1.70, 3.10, P < 0.001), and bleeding requiring blood transfusion (OR: 0.43, 95% CI 0.19, 1.00, P = 0.05), respectively, in patients with IS CTO PCI as compared to that of the de novo CTO PCI. No statistically significant differences between the study groups were recorded for the other primary/secondary outcome variables. The findings from this study indicated a high predisposition for MACE, ischemia-driven target-vessel revascularization, target vessel MI, and a lower incidence of bleeding episodes among IS CTO PCI patients as compared to those with de novo CTO PCI. The prognostic outcomes in CTO PCI cases require further investigation with randomized controlled trials.

Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective

Attention Deficit Hyperactivity Disorder (ADHD) is an increasingly prevalent neuropsychiatric disorder characterized by hyperactivity, inattention, and impulsivity. Symptoms emerge from underlying deficiencies in neurocircuitry, and recent research has suggested a role played by the gut microbiome. The gut microbiome is an ecosystem of interdependent taxa involved in an exponentially complex web of interactions, plus host gene and reaction pathways, some of which involve neurotransmitters with roles in ADHD neurocircuitry. Studies have analyzed the ADHD gut microbiome using macroscale metrics such as diversity and differential abundance, and have proposed several taxa as elevated or reduced in ADHD compared to Control. Few studies have delved into the complex underlying dynamics ultimately responsible for the emergence of such metrics, leaving a largely incomplete, sometimes contradictory, and ultimately inconclusive picture. We aim to help complete this picture by venturing beyond taxa abundances and into taxa relationships (i.e. cooperation and competition), using a publicly available gut microbiome dataset (targeted 16S, v3-4 region, qPCR) from an observational, case-control study of 30 Control (15 female, 15 male) and 28 ADHD (15 female, 13 male) undergraduate students. We first perform the same macroscale analyses prevalent in ADHD gut microbiome literature (diversity, differential abundance, and composition) to observe the degree of correspondence, or any new trends. We then estimate two-way ecological relationships by producing Control and ADHD Microbial Co-occurrence Networks (MCNs), using SparCC correlations (p ≤ 0.01). We perform community detection to find clusters of taxa estimated to mutually cooperate along with their centroids, and centrality calculations to estimate taxa most vital to overall gut ecology. We finally summarize our results, providing conjectures on how they can guide future experiments, some methods for improving our experiments, and general implications for the field.

Baf155 regulates skeletal muscle metabolism via HIF-1a signaling

During exercise, skeletal muscle is exposed to a low oxygen condition, hypoxia. Under hypoxia, the transcription factor hypoxia-inducible factor-1α (HIF-1α) is stabilized and induces expressions of its target genes regulating glycolytic metabolism. Here, using a skeletal muscle-specific gene ablation mouse model, we show that Brg1/Brm-associated factor 155 (Baf155), a core subunit of the switch/sucrose non-fermentable (SWI/SNF) complex, is essential for HIF-1α signaling in skeletal muscle. Muscle-specific ablation of Baf155 increases oxidative metabolism by reducing HIF-1α function, which accompanies the decreased lactate production during exercise. Furthermore, the augmented oxidation leads to high intramuscular adenosine triphosphate (ATP) level and results in the enhancement of endurance exercise capacity. Mechanistically, our chromatin immunoprecipitation (ChIP) analysis reveals that Baf155 modulates DNA-binding activity of HIF-1α to the promoters of its target genes. In addition, for this regulatory function, Baf155 requires a phospho-signal transducer and activator of transcription 3 (pSTAT3), which forms a coactivator complex with HIF-1α, to activate HIF-1α signaling. Our findings reveal the crucial role of Baf155 in energy metabolism of skeletal muscle and the interaction between Baf155 and hypoxia signaling.

Strategies of Modelling Incident Outcomes Using Cox Regression to Estimate the Population Attributable Risk

When the Cox model is applied, some recommendations about the choice of the time metric and the model's structure are often disregarded along with the proportionality of risk assumption. Moreover, most of the published studies fail to frame the real impact of a risk factor in the target population. Our aim was to show how modelling strategies affected Cox model assumptions. Furthermore, we showed how the Cox modelling strategies affected the population attributable risk (PAR). Our work is based on data collected in the North-West Province, one of the two PURE study centres in South Africa. The Cox model was used to estimate the hazard ratio (HR) of mortality for all causes in relation to smoking, alcohol use, physical inactivity, and hypertension. Firstly, we used a Cox model with time to event as the underlying time variable. Secondly, we used a Cox model with age to event as the underlying time variable. Finally, the second model was implemented with age classes and sex as strata variables. Mutually adjusted models were also investigated. A statistical test to the multiplicative interaction term the exposures and the log transformed time to event metric was used to assess the proportionality of risk assumption. The model's fitting was investigated by means of the Akaike Information Criteria (AIC). Models with age as the underlying time variable with age and sex as strata variables had enhanced validity of the risk proportionality assumption and better fitting. The PAR for a specific modifiable risk factor can be defined more accurately in mutually adjusted models allowing better public health decisions. This is not necessarily true when correlated modifiable risk factors are considered.

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research.

Teaching students to R3eason, not merely to solve problem sets: The role of philosophy and visual data communication in accessible data science education

Much guidance on statistical training in STEM fields has been focused largely on the undergraduate cohort, with graduate education often being absent from the equation. Training in quantitative methods and reasoning is critical for graduate students in biomedical and science programs to foster reproducible and responsible research practices. We argue that graduate student education should more center around fundamental reasoning and integration skills rather than mainly on listing 1 statistical test method after the other without conveying the bigger context picture or critical argumentation skills that will enable student to improve research integrity through rigorous practice. Herein, we describe the approach we take in a quantitative reasoning course in the R3 program at the Johns Hopkins Bloomberg School of Public Health, with an error-focused lens, based on visualization and communication competencies. Specifically, we take this perspective stemming from the discussed causes of irreproducibility and apply it specifically to the many aspects of good statistical practice in science, ranging from experimental design to data collection and analysis, and conclusions drawn from the data. We also provide tips and guidelines for the implementation and adaptation of our course material to various graduate biomedical and STEM science programs.

Investigation of chalcopyrite removal from low-grade molybdenite using response surface methodology and its effect on molybdenum trioxide morphology by roasting

In this research, purification of molybdenite concentrate (MoS₂) using a nitric acid leaching process was employed for the improvement of molybdenum trioxide morphology during oxidative roasting in an air atmosphere. These experiments were performed using 19 trials designed with response surface methodology and three effective parameters being temperature, time, and acid molarity. It was found that the leaching process reduced the chalcopyrite content in the concentrate by more than 95%. The influence of chalcopyrite elimination and roasting temperature on the morphology and fiber growth of the MoO₃ was also investigated by SEM images. Copper plays an important role in controlling the morphology of MoO₃ and its decrease led to enhancing the length of quasi-rectangular microfibers from less than 30 μm for impure MoO₃ up to several centimeters for purified MoO₃.

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model

BACKGROUND

Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality).

METHODS

This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate.

RESULTS

A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6.

CONCLUSION

The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition

Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (<150 kΩ) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver-silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10-20 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 kΩ. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol.

Derivation of Corrosion Depth Formula According to Corrosion Factors in District Heating Water through Regression Analysis

In order to predict the corrosion depth of a district heating pipeline, it is necessary to analyze various corrosion factors. In this study, the relationship between corrosion factors such as pH, dissolved oxygen, and operating time and corrosion depth was investigated using the Box-Behnken method within the response surface methodology. To accelerate the corrosion process, galvanostatic tests were conducted in synthetic district heating water. Subsequently, a multiple regression analysis was performed using the measured corrosion depth to derive a formula for predicting the corrosion depth as a function of the corrosion factors. As a result, the following regression formula was derived for predicting the corrosion depth: "corrosion depth (μm) = -133 + 17.1 pH + 0.00072 DO + 125.2 Time - 7.95 pH × Time + 0.002921 DO × Time".

Myocardial extracellular volume assessment at CT in hospitalized COVID-19 patients with regards to pulmonary embolism

Purpose

To evaluate myocardial status through the assessment of extracellular volume (ECV) calculated at computed tomography (CT) in patients hospitalized for novel coronavirus disease (COVID-19), with regards to the presence of pulmonary embolism (PE) as a risk factor for cardiac dysfunction.

Method

Hospitalized patients with COVID-19 who underwent contrast-enhanced CT at our institution were retrospectively included in this study and grouped with regards to the presence of PE. Unenhanced and portal venous phase scans were used to calculate ECV by placing regions of interest in the myocardial septum and left ventricular blood pool. ECV values were compared between patients with and without PE, and correlations between ECV values and clinical or technical variables were subsequently appraised.

Results

Ninety-four patients were included, 63/94 of whom males (67%), with a median age of 70 (IQR 56−76 years); 28/94 (30%) patients presented with PE. Patients with PE had a higher myocardial ECV than those without (33.5%, IQR 29.4−37.5% versus 29.8%, IQR 25.1−34.0%; p = 0.010). There were no correlations between ECV and patients’ age (p = 0.870) or sex (p = 0.122), unenhanced scan voltage (p = 0.822), portal phase scan voltage (p = 0.631), overall radiation dose (p = 0.569), portal phase scan timing (p = 0.460), and contrast agent dose (p = 0.563).

Conclusions

CT-derived ECV could help identify COVID-19 patients at higher risk of cardiac dysfunction, especially when related to PE, to potentially plan a dedicated, patient-tailored clinical approach.

Is the excellent air quality a protective factor of health problems for Taitung County in eastern Taiwan? Perspectives from visual analytics

Taitung, an agricultural country in Eastern Taiwan, was famous for its fresh air with less industrial and petrochemical pollution. Air pollution may induce cardiovascular disease, chronic obstructive pulmonary disease (COPD), asthma, and stroke, poor air quality also resulted in a higher depression rate and less feeling of happiness; therefore, our study aims to use visualization tools to demonstrate the association between air quality index (AQI) and the among negative factors and try to find that whether Taitung got the benefit of good air quality on health issues. We retrieved data from the government of Taiwan and other open sources in the year 2019, then visual maps and generalized association plots with clusters demonstrated the relationship between each factor and each county/city. Taitung had the lowest AQI and asthma attack rate, but AQI had a negative relationship to air pollution-caused death (R = -0.379), happiness index (R = -0.358), and income (R = -0.251). The GAP analysis revealed that smoke and overweight were the nearest to air pollution causing death, also counties and cities were divided into two major clusters initially based on the air pollution-related variables. In conclusion, the World Health Organization (WHO) definition and the weight of each air pollution cause death may not be suitable for Taiwan due to too many confounding factors.

The predictive role of right ventricular late gadolinium enhancement in patients with tetralogy of Fallot undergoing pulmonary valve replacement

BACKGROUND

Our purpose was to evaluate the correlations between right ventricular (RV) late gadolinium enhancement (LGE) at cardiac magnetic resonance (CMR) in patients with tetralogy of Fallot (ToF) scheduled for pulmonary valve replacement (PVR) and post-PVR functional data.

METHODS

We retrospectively reviewed ToF patients scheduled for PVR who underwent two CMR examinations at our institution, one before the procedure (CMR-0), including contrast-enhanced sequences, and one after the procedure (CMR-1). Functional left and RV data were obtained by segmenting short-axis stacks on both CMR examinations, and normalised variations were calculated by dividing differences between CMR-1 and CMR-0 by the intercurring time interval, whereas the RV scar burden was assessed on CMR-0 LGE sequences both semiquantitatively and quantitatively. Data were reported as median and interquartile range, differences were appraised with the Mann-Whitney U test, while correlations were assessed with Spearman's ρ.

RESULTS

Fifteen patients with a median age of 25 years (16-29), including 9 (60%) males, with a median time interval between CMR-0 and CMR-1 of 17 months (12-23), were retrospectively reviewed. The semiquantitative LGE score at CMR-0 was 7 (6-9), and LGE volume was 4.49 mL (3.70-5.78), covering 5.63% (4.92-7.00) of the RV. RV LGE score showed a moderate positive correlation with the normalised variation of RV stroke volume (ρ = 0.662, p = 0.007) and a borderline moderate positive correlation with the normalised variation of RV end-diastolic indexed volume (ρ = 0.513, p = 0.050).

CONCLUSIONS

The assessment of RV LGE before PVR may provide insights on post-PVR functional data, potentially facilitating a patient-tailored treatment pathway.

Design, power, and alpha levels in randomized phase II oncology trials

BACKGROUND

The statistical plan of a phase II trial should balance minimizing the premature termination of potentially beneficial therapies (i.e. false negatives) and the further, costly testing of ineffective drugs (i.e. false positives). We sought to examine the methodology, reporting, and bias in the interpretation of outcomes of phase II oncology trials in recent years.

MATERIALS AND METHODS

In a retrospective cross-sectional analysis, we reviewed all full-length articles published on PubMed from 1 January 2021 to 20 June 2022. We searched for data regarding the sample size calculation (number, α value, power, and expected effect size), the primary and secondary outcomes and results, and the authors' conclusion of the study.

RESULTS

About 5.4% of studies (n = 10) used a statistical power that was inferior to 80%, and 16.7% (n = 34) did not indicate the level of power for the sample size calculation. Approximately 16.7% (n = 31) of studies used a one-sided α level of ≤0.025; 17.7% (n = 33) of studies used a predefined threshold (no comparator effect size or difference between groups) to determine the sample size for efficacy. The percentage of studies with a positive authors' conclusion but not meeting the primary endpoint, or the endpoint was equivocal, was 27.4% (n = 51).

CONCLUSION

Many randomized phase II studies in oncology failed to report essential data for determining sample size calculations, many did not actually use a comparator to determine efficacy even though the studies were randomized, and many had positive conclusions even though the results were indeterminate or the primary endpoint was not met.

Service Needs of Clients Before and After Short Term Community Mental Health Case Management

This study evaluated level of service need before and after a short-term community mental health case management intervention from the perspective of both clients and case managers. Ontario Common Assessment of Need data were used to describe client needs. McNemar's test was applied to assess differences in pre- and post- need scores. Psychological distress, company, daytime activities, and physical health were most commonly rated by clients as unmet needs at enrolment. At discharge, there was a significant change in psychological distress from the perspective of clients, and in psychological distress and daytime activities from the perspective of case managers. Statistically significant changes were observed for Total Need, Total Unmet Need and Met Need scores from the perspective of case managers. While both clients and case managers reported changes in total service needs between admission and discharge from short-term case management, clients were less likely to report a difference in needs.

Information literacy at journalists’ workplace in Pakistan

This research investigated the development of information literacy skills and their self-perceived assessment among journalists in Pakistan. A cross-sectional survey using an online questionnaire was conducted in four Provinces and the Federal Capital of Pakistan with the consent of relevant authorities for data collection. A total of 1089 responses were received. The data were analyzed in SPSS by applying descriptive as well as inferential statistics. A large majority of the surveyed respondents received information literacy instruction of short duration during their careers while being mainly in practice. The most covered topics included research literacy, communication skills, information discovery, critical literacy, tools literacy, and information handling. These participants perceived information literacy as invaluable in their practical work context. The survey participants perceived themselves as information literate not only for basic levels of information literacy but also for advanced levels. In addition, journalists’ gender, age, academic qualification, job experience, information literacy instructions received, nature of work, and region predicted their levels of information literacy. The results generated pragmatic insight for educators and information professionals in designing a need-based information literacy instruction program for existing as well as prospective journalists. This research would make a worthy contribution to the existing information literacy research in the context of the workplace as no such comprehensive study on journalists’ workplace had appeared so far.

Marital dissolution and associated factors in Hosanna, Southwest Ethiopia: a community-based cross-sectional study

BACKGROUND

Marriage dissolution, divorce, or separation from a spouse or common-law partner is a serious public health concern due to its increasing prevalence and devastating health and socio-economic consequences. Evidence suggests an increased risk of marital instability in Ethiopia. In addition, the extent of marital dissolution and other related factors have increased in the study area. Despite these, the prevalence of marital dissolution and the influence of associated factors (main reason for marriage, and parental history of marital dissolution) on marital dissolution has not been assessed in the study area. Therefore, the aim of this study was to investigate the prevalence of marital dissolution and its associated factors among residents of Hosanna town in southwestern Ethiopia in 2022.

METHODS

We conducted a community-based cross-sectional study among 459 randomly selected Hosanna Township residents. We used structured questionnaires to collect data. Descriptive statistics and logistic regression were performed to describe the data and test-associated factors, respectively. A p-value less than 0.05 was used to define statistical significance. We used STATA 14 and IBM SPSS 25.0 computer packages to process data.

RESULTS

Out of the 459 potentially eligible individuals, 450 participants properly responded to the questionnaires yielding a response rate of 98.04%. Of these, 218 (52.9%) were female. The commonly reported reason for marriage was to have children 150 (36.9%). The prevalence rate of marital dissolution was 26.0% (95% CI: (21.7%, 30.3%)). The participant's level of education and the primary reasons (motives) why they get married were statistically significantly associated with marital dissolution. The odds of marital dissolution was higher among participants who completed secondary education (AOR = 3.2, 95% CI = 1.26-8.17) compared to those having no formal education. The participants who married for companionship reasons (AOR = 0.31, 95% CI = 0.11-0.83) had significantly lower odds of marriage dissolution compared with those who married for financial security.

CONCLUSIONS

In this study, the prevalence of marital dissolution was high. The participant's level of education and the primary reasons (motives) why they getting married were significantly associated with marital dissolution. Therefore, an integrated, community-based approach should be developed to prevent marital dissolution.