Qatar's National Mental Health Survey-The World Mental Health Qatar: Sampling design, instrument adaptation, quality control, and fielding procedures.

OBJECTIVES: The World Mental Health Qatar (WMHQ) study, the first national general population mental health survey in Qatar, was conducted as part of the World Health Organization (WHO) World Mental Health (WMH) Survey Initiative. It was one of the few WMH survey conducted during the COVID-19 pandemic. This paper presents the methodological advances and challenges encountered while conducting the survey by telephone during the pandemic. METHODS: Disproportionate stratified sampling using a national-level cellular telephone frame selected a representative sample of Arabic-speaking adults. Participants were initially contacted via Short Message Service text, followed by telephone interviews. WMH training materials supported a comprehensive training program, and data quality was ensured through a quality control indicator system and extensive monitoring. RESULTS: Over 234 days, 5195 interviews in Arabic were completed, averaging 77 min each. In line with Qatar's population, the majority of participants were non-Qatari residents living in Qatar (72.2%). CONCLUSIONS: A distributed remote Computer Assisted Telephone Interviewing system facilitated centralized quality monitoring and data security. However, the pandemic intensified challenges such as remote management of interviewer productivity, low response rates, and rising survey costs. The findings will inform Qatar's mental health policymakers, and the strategies used to address these challenges offer valuable insights for researchers worldwide.

Feasibility of replacing face-to-face with telephone interviews for the World Mental Health Qatar survey during the COVID-19 pandemic.

OBJECTIVES: We investigated the feasibility of replacing face-to-face with telephone interviews conducted as part of the World Mental Health Qatar (WMHQ) survey and discuss the main methodological changes across the two pilots that were subsequently implemented in the full-scale WMHQ telephone survey. METHODS: We assessed the net mode effect by comparing the lifetime prevalence estimates of the main mental disorder classes (mood and anxiety disorders) and a number of disorders across the two survey pilots conducted prior to and post-pandemic. RESULTS: The main differences in terms of methodology for both pilots stemmed from differences in the survey mode, including questionnaire length, study recruitment method, and fielding team size and structure. These factors influenced response rates and costs. However, the lifetime prevalence estimates and other key indicators of survey results did not differ across modes. CONCLUSIONS: Our findings confirm the comparability of data collected via telephone and face-to-face modes, supporting the adoption of telephone surveys for future mental health studies, particularly in the context of pandemics. They also confirm the feasibility of changing or mixing modes depending on field conditions in future psychiatric epidemiological research.

A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning.

OBJECTIVE: The main goal of this research is to use distinctive features in respiratory sounds for diagnosing Chronic Obstructive Pulmonary Disease (COPD). This study develops a classification method by utilizing inverse transforms to effectively identify COPD based on unique respiratory features while comparing the classification performance of various optimal algorithms. METHOD: Respiratory sounds are divided into individual breathing cycles. In the data standardization and augmentation phase, the CycleGAN model enhances data diversity. Comprehensive analyses for these segments are then implemented using various Wavelet families and different spectral transformations representing characteristic signals. Advanced convolutional neural networks, including VGG16, ResNet50, and InceptionV3, are used for the classification task. RESULTS: The results of this study demonstrate the effectiveness of the mentioned method. Notably, the best-performing method utilizes Wavelet Bior1.3 after standardization in combination with InceptionV3, achieving a remarkable 99.75% F1-score, the gold standard for classification accuracy. CONCLUSION: Inverse transformation techniques combined with deep learning models show significant accuracy in detecting COPD disease. These findings suggest the feasibility of early COPD diagnosis through AI-powered characterization of acoustic features. MOTIVATION AND SIGNIFICANCE: The motivation behind this research stems from the urgent need for early and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD). COPD is a respiratory disease that poses many difficulties when detected late, potentially causing severe harm to the patient's quality of life and increasing the healthcare burden. Timely identification and intervention are crucial to reduce the progression of the disease and improve patient outcomes.

Variational quantum metrology for multiparameter estimation under dephasing noise.

We present a hybrid quantum-classical variational scheme to enhance precision in quantum metrology. In the scheme, both the initial state and the measurement basis in the quantum part are parameterized and optimized via the classical part. It enables the maximization of information gained about the measured quantity. We discuss specific applications to 3D magnetic field sensing under several dephasing noise models. Indeed, we demonstrate its ability to simultaneously estimate all parameters and surpass the standard quantum limit, making it a powerful tool for metrological applications.

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.

Ultra-Deep Sequencing of Plasma-Circulating DNA for the Detection of Tumor- Derived Mutations in Patients with Nonmetastatic Colorectal Cancer.

Identification of tumor-derived mutation (TDM) in liquid biopsies (LB), especially in early-stage patients, faces several challenges, including low variant-allele frequencies, interference by white blood cell (WBC)-derived mutations (WDM), benign somatic mutations and tumor heterogeneity. Here, we addressed the above-mentioned challenges in a cohort of 50 nonmetastatic colorectal cancer patients, via a workflow involving parallel sequencing of paired WBC- and tumor-gDNA. After excluding potential false positive mutations, we detected at least one TDM in LB of 56% (28/50) of patients, with the majority showing low-patient coverage, except for one TDM mapped to KMT2D that recurred in 30% (15/30) of patients.

Conducting a state-of-the-art mental health survey in a traditional setting: Challenges and lessons from piloting the World Mental Health Survey in Qatar.

OBJECTIVES: A small country in the Arabian Peninsula, Qatar experienced rapid economic growth in the last 3 decades accompanied by major socio-demographic shifts towards a younger and more highly educated population. To date, no national epidemiological study has examined the prevalence, associated factors, or sequelae of mental disorders in Qatar's general population. METHODS: The World Mental Health Qatar (WMHQ) is a national mental health needs assessment survey and is the first carried out in collaboration with the World Mental Health Survey initiative to assess the prevalence and burden of psychiatric illnesses among the full Arabic speaking population (nationals and non-nationals) within the same country. RESULTS: Standard translation and harmonization procedures were used to develop the WMHQ instrument. A survey quality control system with standard performance indicators was developed to ensure interviewer adherence to standard practices. A pilot study was then carried out just prior to the COVID-19 pandemic. Endorsement from public health authorities and sequential revision of the interview schedule led to full survey completion (as opposed to partial completion) and good overall response rate. CONCLUSIONS: The WMHQ survey will provide timely and actionable information based on quality enhancement procedures put in place during the development and piloting of the study.

Prevalence and Potential Determinants of COVID-19 Vaccine Hesitancy and Resistance in Qatar: Results from a Nationally Representative Survey of Qatari Nationals and Migrants between December 2020 and January 2021.

Global COVID-19 pandemic containment necessitates understanding the risk of hesitance or resistance to vaccine uptake in different populations. The Middle East and North Africa currently lack vital representative vaccine hesitancy data. We conducted the first representative national phone survey among the adult population of Qatar, between December 2020 and January 2021, to estimate the prevalence and identify potential determinants of vaccine willingness: acceptance (strongly agree), resistance (strongly disagree), and hesitance (somewhat agree, neutral, somewhat disagree). Bivariate and multinomial logistic regression models estimated associations between willingness groups and fifteen variables. In the total sample, 42.7% (95% CI: 39.5-46.1) were accepting, 45.2% (95% CI: 41.9-48.4) hesitant, and 12.1% (95% CI: 10.1-14.4) resistant. Vaccine resistant compared with hesistant and accepting groups reported no endorsement source will increase vaccine confidence (58.9% vs. 5.6% vs. 0.2%, respectively). Female gender, Arab ethnicity, migrant status/type, and vaccine side-effects concerns were associated with hesitancy and resistance. COVID-19 related bereavement, infection, and quarantine status were not significantly associated with any willingness group. Absence of or lack of concern about contracting the virus was solely associated with resistance. COVID-19 vaccine resistance, hesitance, and side-effects concerns are high in Qatar's population compared with those globally. Urgent public health engagement should focus on women, Qataris (non-migrants), and those of Arab ethnicity.

Evaluation of a Liquid Biopsy Protocol using Ultra-Deep Massive Parallel Sequencing for Detecting and Quantifying Circulation Tumor DNA in Colorectal Cancer Patients.

The identification and quantification of actionable mutations are critical for guiding targeted therapy and monitoring drug response in colorectal cancer. Liquid biopsy (LB) based on plasma cell-free DNA analysis has emerged as a noninvasive approach with many clinical advantages over conventional tissue sampling. Here, we developed a LB protocol using ultra-deep massive parallel sequencing and validated its clinical performance for detection and quantification of actionable mutations in three major driver genes (KRAS, NRAS and BRAF). The assay showed a 92% concordance for mutation detection between plasma and paired tissues and great reliability in quantification of variant allele frequency.

Assessing the factor structure and measurement invariance of the eating attitude test (EAT-26) across language and BMI in young Arab women.

BACKGROUND: The objective of the study was to determine the factorial structure and test the measurement invariance of the EAT-26 in a large probability sample of young female university students in Qatar (n = 2692), a Muslim country in the Middle East. METHODS: The maximum number of factors was derived based on results from initial exploratory factor analysis (EFA) in the first-half of the randomly split sample (Sample 1). A subsequent EFA and Exploratory Structural Equation Models (ESEM) were conducted to identify the number of valid factors. A five-factor model with 19 items was identified as the optimal factor structure. This structure was further replicated using ESEM in the second-half of the sample (Sample 2). Multi-group Confirmatory Factor Analyses (CFAs) were conducted at this stage and their fit was evaluated with and without further sub-grouping by language (Arabic and English) and BMI (underweight, normal weight, and overweight/obese). Finally, measurement invariance tests were conducted in the entire sample assessing equivalence across language and BMI within the final five-factor model. RESULTS: The five-factor structure of the new EAT-19 [fear of getting fat (FGF), eating-related control (ERC), food preoccupation (FP), vomiting-purging behavior (VPB), and social pressure to gain weight (SP)] provided the best fit: CFI = 0.976, TLI = 0.952, RMSEA = 0.045 (90%CI 0.039-0.051), SRMR = 0.018, CD =1.000. CFAs supported metric invariance for language and for BMI. Language and BMI-based population heterogeneity comparisons provided modest and small-to-moderate evidence for differential factor means, respectively. CONCLUSION: Although the five-factor model of the EAT-19 demonstrated good item characteristics and reliability in this young female population, the lack of scalar invariance across language and BMI-categories pose measurement challenges for use of this scale for screening purposes. Future studies should develop culture- and BMI-specific cut-offs when using the EAT as a screening instrument for disordered eating in non-clinical populations.