Identification Of Cases Research Articles

Measuring misclassification and sample bias in passive surveillance systems: Improving prevalence estimates of critical congenital heart defects in state-based passive surveillance systems.

We assessed reporting misclassification for 12 critical congenital heart defects (CCHDs) identified through administrative diagnosis codes within a passive surveillance system. We measured the effect of misclassification on prevalence estimation. Lastly, we investigated a sample-based review strategy to estimate surveillance misclassification resulting from administrative diagnosis codes for case detection. We received 419 reports of CCHDs between 2007 and 2018; 414 were clinically reviewed. We calculated confirmation probabilities to assess misclassification and adjust prevalence estimates. Random samples of reported cases were taken at proportions between 20% and 90% for each condition to assess sample bias. Sampling was repeated 1000 times to measure sample-estimate variability. Misclassification ranged from a low of 19% (n = 4/21) to a high of 84% (n = 21/25). Unconfirmed prevalence rates ranged between one and six cases per 10,000 live births, with some conditions significantly higher than national estimates. However, confirmed rates were either lower or comparable to national estimates. Passive birth defect surveillance programs that rely on administrative diagnosis codes for case identification of CCHDs are subject to misclassification that bias prevalence estimates. We showed that a sample-based review could improve the prevalence estimates of 12 cardiovascular conditions relative to their unconfirmed prevalence rates.

Synthetic cannabinoid identification in cases associated with blue lotus and valerian root vaping products.

Synthetic cannabinoids emerged in the early 21st century and have continued to evolve and flourish to present day. Like other novel psychoactive substances (NPS), synthetic cannabinoids have been sold under the guise of legitimate products. Some examples include "potpourri," "incense," and herbal material. Between May 2020 and December 2023, the United States Army Criminal Investigation Laboratory, Drug Chemistry Division (USACIL) received 29 seized drug cases mentioning "blue lotus" or "valerian root." In 90% of these cases, at least one exhibit contained one or more synthetic cannabinoids. During the same timeframe, the Armed Forces Medical Examiner System, Division of Forensic Toxicology received 65 toxicology cases that contained synthetic cannabinoids and/or their corresponding metabolites where case history mentioned "blue lotus." The most frequently observed synthetic cannabinoids between laboratories were 5F-MDMB-PICA, ADB-BUTINACA, and MDMB-4en-PINACA. Innocuous branding and marketing may deceive law enforcement, investigators, and healthcare providers into believing that the adverse effects of erratic behavior, sedation, slurred speech, and hallucinations are a result of toxicity from botanical extracts (e.g. apomorphine and nuciferine in blue lotus). Due to the dangerous nature of these NPS, synthetic cannabinoid screening is recommended for all cases where there is suspected use of vaping products suggested to contain "blue lotus" or "valerian root" as vendors continue to conceal the presence of these compounds.

Sosialisasi Pencegahan Stunting Di Wilayah Pesisir Lombok Bagian Barat: Kasus Stunting Di Desa Senggigi, Lombok Barat

The NTB Golden Generation Program (GEN NTB) is a strategy to improve the quality of human resources in West Nusa Tenggara Province since 2012. The target to be achieved from this program is the formation of a healthy, intelligent, devout and productive generation by 2045. In line with the GEN NTB program. Research has been conducted on GEN NTB children aged 4-6, to determine the relationship between OXTR gene mRNA expression and prosocial behavior in GEN NTB children. However, this research focused on the target areas of Central Lombok and East Lombok Regencies. Meanwhile, stunting cases also occur in other districts in NTB Province. To find out the condition of stunting in West Lombok Regency, especially in coastal areas, case identification and socialization are carried out. For this reason, through this community service, motorbikes will be carried out by socializing the causes of stunting, especially the target community is coastal areas. Specifically, the people of Senggigi Village are people who have been marginalized in terms of domicile and employment opportunities due to tourism development in the area. Thus, it is important to make efforts to reduce stunting rates in coastal areas, through socializing the stunting problem in Senggigi Village.

COVID-19 screening as an opportunity to find TB cases; experiences from an urban National Referral Hospital in Kampala, Uganda

BackgroundFollowing the World Health Organization’s declaration of COVID-19 as a global pandemic, several countries implemented population-wide lockdowns. However, these responses to COVID-19 caused severe healthcare system disruptions to service delivery. The TB case notification rate in Uganda decreased by 22% between January and April 2020, which coincided with a lockdown and an increase in COVID-19 cases. In this study, we tested the effect of screening all patients with both COVID-19-positive and negative symptom screen for TB at a National Referral Hospital.Design/MethodsFollowing our formative assessment, we identified potential barriers to and facilitators of integrating screening for COVID-19 and TB at Kiruddu National Referral Hospital. To address the barriers, in February 2021 we trained healthcare providers on integrated COVID-19-TB screening tools and provided COVID-19/TB screening tools/Standard operating procedures and personal protective equipment. From March 1, 2021, to June 30, 2021, we screened patients presenting to the emergency and outpatient departments for COVID-19 symptoms, and subsequently, we performed TB symptom screening for both patients with COVID-19 positive and negative symptom screen using the intensified tuberculosis case-finding (ICF) guide. We then compared the outcomes of TB symptom screening for patients initially with a positive COVID-19 symptom screen with those who initially had a negative COVID-19 symptom screen.ResultsFrom March 2021 to June 2021, we screened 1464 patients (44.3% male and 55.7% female) for COVID-19 symptoms. Out of these participants, 1252 (85.5%) screened positive for COVID-19 symptoms, while 212 (14.5%) screened negative. The majority of patients with a positive COVID-19 symptom screen, 717 (57.3%), also screened positive for TB symptoms compared to 19 (8.9%) among patients with a negative COVID-19 symptom screen. Out of the total 736 presumptive TB cases identified, 717 (97.4%) initially screened positive for COVID-19 symptoms. TB was diagnosed in 110 individuals including 104 who had positive COVID-19-symptom screen and six who had a negative COVID-19 symptom screen. All of the 110 newly diagnosed TB cases were linked to TB treatment.ConclusionsPatients who screen positive for COVID-19 symptoms should be routinely screened for TB to mitigate missed opportunities for TB case identification.

Notes from the Field: Health Monitoring, Testing, and Case Identification Among Persons Exposed to Influenza A(H5N1) - Michigan, 2024.

Notes from the Field: Health Monitoring, Testing, and Case Identification Among Persons Exposed to Influenza A(H5N1) - Michigan, 2024.

A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.

Tuberculosis (TB) stands as a paramount global health concern, contributing significantly to worldwide mortality rates. Effective containment of TB requires deployment of cost-efficient screening method with limited resources. To enhance the precision of resource allocation in the global fight against TB, this research proposed chest X-ray radiography (CXR) based machine learning screening algorithms with optimization, benchmarking and tuning for the best TB subclassification tasks for clinical application. This investigation delves into the development and evaluation of a robust ensemble deep learning framework, comprising 43 distinct models, tailored for the identification of active TB cases and the categorization of their clinical subtypes. The proposed framework is essentially an ensemble model with multiple feature extractors and one of three fusion strategies-voting, attention-based, or concatenation methods-in the fusion stage before a final classification. The comprised de-identified dataset contains records of 915 active TB patients alongside 1,276 healthy controls with subtype-specific information. Thus, the realizations of our framework are capable for diagnosis with subclass identification. The subclass tags include: secondary tuberculosis/tuberculous pleurisy; non-cavity/cavity; secondary tuberculosis only/secondary tuberculosis and tuberculous pleurisy; tuberculous pleurisy only/secondary tuberculosis and tuberculous pleurisy. Based on the dataset and model selection and tuning, ensemble models show their capability with self-correction capability of subclass identification with rendering robust clinical predictions. The best double-CNN-extractor model with concatenation/attention fusion strategies may potentially be the successful model for subclass tasks in real application. With visualization techniques, in-depth analysis of the ensemble model's performance across different fusion strategies are verified. The findings underscore the potential of such ensemble approaches in augmenting TB diagnostics with subclassification. Even with limited dataset, the self-correction within the ensemble models still guarantees the accuracies to some level for potential clinical decision-making processes in TB management. Ultimately, this study shows a direction for better TB screening in the future TB response strategy.

Accuracy of plant identification applications to identify plants in suspected poisoning cases referred to the Queensland Poisons Information Centre

Accidental exposure to potentially poisonous plants is a common reason for contacting poisons information centres. Although most exposures are mild, early identification can be critical for severe cases. Smartphone apps that use image recognition can serve as an alternative to traditional botanist identification. This study investigates the accuracy of smartphone applications compared to botanist identification in real-life plant exposure cases referred to the Queensland Poisons Information Centre. This prospective observational study analysed data from 25 plant exposure cases between February 2022 and January 2023. In these cases, images were referred to a botanist for identification and reviewed by six researchers using six different plant identification applications (Google Lens™, PictureThis, PlantSnap, LeafSnap, Plant Identifier, and Pl@ntnet™). The researchers’ identifications were then compared to the botanist’s identification. The accuracy of the applications varied considerably, PictureThis was the best, with 74% correct identification per exposure, followed by Plant Identifier and Pl@ntnet™ (both 72%). PlantSnap had the lowest accuracy (38%). Agreement between researchers using the same application also showed variation. Current smartphone plant identification applications are not reliable enough for poison information centres to use in guiding advice for plant exposure calls. Botanist identification remains the gold standard. Clinicians should be wary of their use in real-world scenarios.

Thoracic computed tomography (CT) image-based identification and severity classification of COVID-19 cases using vision transformer (ViT)

In this research, we developed a two-stage deep learning (DL) model using Vision Transformer (ViT) to detect COVID-19 and assess its severity from thoracic CT images. In the first stage, we utilized a pre-trained ViT model (ViT_B/32) and a custom CNN model to classify CT images as COVID-19 or non-COVID-19. The ViT model achieved superior performance with a fivefold cross-validated accuracy of 99.7%, compared to the custom CNN’s 98%. In the second stage, we employed a ViT-based U-Net model (Vision Transformer for Biomedical Image Segmentation, VITBIS) to segment lung and infection regions in COVID-19 positive CT images, determining the infection severity. This model uses transformers with attention mechanisms in both the encoder and decoder. The lung segmentation network achieved an Intersection Over Union (IOU) of 95.8% and a sensitivity of 99.67%, while the lesion segmentation network attained an IOU of 94% and a sensitivity of 98.3%.

Improving Children’s Services Engagement of Fathers in Child Protection: Logic Model for an Organisational Development and Staff Training Intervention

There is a long-standing and ongoing problem of practice with at-risk families in child welfare work focusing primarily on mothers and failing to properly engage fathers. The article describes a child welfare innovation from the UK designed to tackle this issue—the ISAFE (Improving Safeguarding through Audited Father-Engagement) intervention, developed by The Fatherhood Institute and The Children’s Social Care Research and Development Centre (CASCADE) at Cardiff University and based on two previous separate interventions which had positive initial evaluations. ISAFE combines in-service social work practitioner training with other elements of organisational development to improve the engagement of fathers. Activities targeting organisational culture are case file audits, identification and training of team champions, and a webinar for service leaders. The practitioner training involves both awareness raising about the importance of engaging men and skills development via an introduction to motivational interviewing. ISAFE’s theory of change is summarised in the form of a logic model. Limitations of the intervention and its evaluation are discussed.

Untimely Death of a Nursing Mother during Bus Travel in a Remote Area of Nepal: A Case Study

Despite a transformation in health services, Nepal continues to endure health system challenges at all levels. Several contributing factors include lack of transportation, poor quality roads, unavailability of quality healthcare facilities, low health literacy, and lack of communication which all play significant roles in the delay of diagnosis and treatment. This study aims to investigate the unexpected death of a nursing mother traveling on the bus in the remote hilly region of Nepal to highlight deficits in the healthcare system and the possibility this death could have been prevented. The starting point of the investigation was done through secondary sources of information such as online media. Also, an in-depth face-to-face interview was conducted with the victim’s husband. The main finding of this study showed the woman who died on the bus while nursing her child highlighted a serious health system challenge. The exact cause of the mother’s death was unrecognized. Early case identification and proper management at the local level were deemed to be a context-specific corrective measure to prevent death. This paper aims to demonstrate the unexpected and untimely death of a nursing mother, due to multiple factors including gaping deficits in the healthcare system across Nepal.

Real-world economic burden associated with disease progression from metastatic castration-sensitive to castration-resistant prostate cancer on treatment in the United States.

The advent of next-generation imaging will likely reduce nonmetastatic prostate cancer (PC) prevalence and increase identification of metastatic prostate cancer cases, resulting in two predominant advanced stages in the metastatic setting. There is a need to characterize changes in health care resource utilization (HRU) and costs when metastatic castration-sensitive PC (mCSPC) progresses to metastatic castration-resistant PC (mCRPC) to identify value drivers from current and new treatments. To describe treatment patterns, HRU, and total health care costs among patients with mCSPC, before and after progression to mCRPC. Clinical data from the Flatiron Metastatic PC Core Registry (January 1, 2013, to December 1, 2021) and linked claims from Komodo Health (January 1, 2014, to December 1, 2021) were used to identify patients with progression from mCSPC to mCRPC (date of progression was the index date) and subsequently initiated first-line mCRPC therapy on/after January 1, 2017. Treatment patterns and all-cause/PC-related HRU and health care costs were described per-patient-per-month (PPPM), separately for no more than 12 months pre-index (mCSPC disease state) and post-index (mCRPC disease state). Costs (payer's perspective) included those for services/procedures from medical claims and costs from pharmacy claims. Continuous HRU and costs were compared between the mCSPC and mCRPC disease states using Wilcoxon signed rank tests. Among 296 patients with mCSPC progressing to mCRPC (median age 69.0 years, 60.5% White, 15.9% Black), use of systemic therapies with androgen deprivation therapy increased dramatically from 35.1% in the mCSPC disease state to 92.9% in the mCRPC disease state, and use of androgen deprivation therapy monotherapy decreased from 25.7% to 2.4%, respectively. Although 39.2% received none of these therapies in the mCSPC disease state, this proportion decreased to 4.7% after transition to mCRPC. The mean number of days with PC-related outpatient visits increased from 1.57 to 2.16 PPPM in the mCSPC and mCRPC disease states (P < 0.001). From the mCSPC to mCRPC disease states, mean all-cause total health care costs PPPM increased from $4,424 (medical costs: $2,846) to $9,717 (medical costs: $4,654), and mean PC-related total health care costs PPPM increased from $2,859 (medical costs: $1,626) to $8,012 (medical costs: $3,285; all P < 0.001). In this real-world study of patients with disease progression from mCSPC to mCRPC in US clinical practice, nearly 2-in-3 patients did not receive treatment with additional systemic therapies before progression to castration resistance. Post-progression, mean PC-related total costs increased nearly 3-fold, with a more than 2-fold increase in PC-related medical costs. Use of additional systemic therapies may delay the time and cost associated with disease progression to castration resistance.

A retrospective study of 29 fatal cases of insulin overdose

PurposeTo summarize recent cases of fatal insulin poisoning both domestically and internationally, thereby offering valuable insights for the forensic identification of insulin overdose cases. MethodsLiterature published since 2000 on fatal insulin overdose were systematically searched and screened. Data encompassing variables such as year, age, sex, cause of death, scene conditions, occupations, medical histories of victims and perpetrators, autopsy timing, dosage and administration methods, forensic pathology, and toxicological analysis, were compiled for rigorous statistical analysis. ResultsAmong the 29 fatal cases of insulin poisoning, suicides and homicides accounted for 55.2 % and 41.4 %, respectively. Precisely 34.5 % of victims or perpetrators were associated with the medical industry, 27.6 % had diabetes, and 24.1 % had mental illnesses such as depression. Intravenous injection resulted in quicker death than did subcutaneous injection. In some cases, immunohistochemical staining of insulin and protamine at injection sites yielded positive results. The average molar ratio of insulin to C-peptide in post-mortem blood was 13.76 ± 5.167, indicating a significant diagnostic value for insulin poisoning. ConclusionAssessment of cases of fatal insulin overdose should be thorough, incorporating case investigation, scene examination, medical records review, autopsy findings, pathological examinations, and laboratory tests, alongside considering the condition of the body and timing of death autopsy. Using mass spectrometry to detect insulin proves valuable, particularly in cases of poor body preservation.

Carbapenemase-Producing Enterobacteriaceae detected in a Large Canadian Tertiary Care Hospital: Five-year retrospective study

Background: The prevalence of carbapenemase-producing Enterobacteriaceae (CPE) is increasing worldwide. In Canada, where rates of healthcare-associated (HA) transmission of CPE remains relatively low, there is a need to share early experience of universal screening programs and risk factors for HA acquisition. Method: In 2018, universal screening was introduced throughout our large Canadian tertiary care hospital across, all critical care and oncology units. Additionally, risk-factor based screening was applied in all other inpatient units, with further targeted screening of roommate exposures or all inpatients on unit following identification of a single HA case. A retrospective cohort study was carried out on CPE cases detected between January 2018 and December 2023. We assessed the proportion of HA CPE cases, defined as CPE identified in patients with prior admission to our facility or after &gt;72 hours after admission. HA cases were examined for relevant risk factors, including known roommate with CPE, the presence of other CPE on the unit, exposure to outbreak units, prior travel history, travel by a family member, and antibiotic exposure within the past 90 days. Result: A total of 150 CPE cases were identified, with 66 (44%) classified as HA. Among these HA cases, 14 (21%) were associated with presence of known case on the unit. The remaining 52 (79%) represented sporadic nosocomial cases without a known exposure or further transmission on the unit. Upon further retrospective review, 6 (9.2%) HA cases had documented travel history or exposure to a family member with recent travel to China, India, Sri Lanka, or the United States within the past year. Nearly all HA cases (62, 95.4%) had antibiotic exposure within 90 days of CPE detection; specifically, 47 (72.3%) received beta-lactams, 42 (64.6%) cephalosporin, 25 (38.5%) glycopeptide, 20 (30.8%) carbapenem, and 8 (12.3%) macrolide. Conclusion: HA CPE acquisition identified during the first 5-years of universal screening were mostly sporadic and not associated with known exposures or other risk factors. Receipt of prior antibiotics was present in nearly all cases.

Exploring diverse aspects of celiac disease - A review

Celiac disease is a chronic inflammatory disorder triggered by the ingestion of gluten-containing foods in genetically susceptible individuals. This multifactorial disease has both genetic and environmental components, with gluten serving as the environmental trigger and specific genetic variations in the major histocompatibility complex region contributing to the genetic predisposition. Contrary to previous assumptions, celiac disease is not a rare disorder, with a global prevalence estimated at approximately 1%. However, it often remains under-recognized because many affected individuals do not exhibit the classic gastrointestinal symptoms associated with the condition. Instead, they may present with nonspecific manifestations of nutritional deficiencies or may be entirely asymptomatic. Celiac disease is associated with an increased incidence of autoimmune disorders, and its identification in asymptomatic or silent cases is crucial for managing associated conditions and preventing long-term complications such as lymphoma. The relationship between celiac disease and other autoimmune disorders, particularly type 1 diabetes, suggests a potential trigger role for gluten. In this article we review the development of celiac disease, including its types, pathogenesis, clinical features, and associated diseases or complications. We will also discuss available tests, treatments, and preventive approaches related to this subject.

Using machine learning to improve anaphylaxis case identification in medical claims data.

Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes. This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets. Then a variety of unsupervised and supervised methods were used (eg, Sammon mapping and eXtreme Gradient Boosting) to train models on datasets of differing data quality, which reflects the varying availability and potential rarity of ground truth data in medical databases. Resulting machine learning model accuracies ranged from 47.7% to 94.4% when tested on ground truth data. Finally, we found new features to help experts enhance existing case-finding algorithms. Developing precise algorithms to detect medical outcomes in claims can be a laborious and expensive process, particularly for conditions presented and coded diversely. We found it beneficial to filter out highly potent codes used for data curation to identify underlying patterns and features. To improve rule-based algorithms where necessary, researchers could use model explainers to determine noteworthy features, which could then be shared with experts and included in the algorithm. Our work suggests machine learning models can perform at similar levels as a previously published expert case-finding algorithm, while also having the potential to improve performance or streamline algorithm construction processes by identifying new relevant features for algorithm construction.

Validating new coding algorithms to improve identification of alcohol-associated and nonalcohol-associated cirrhosis hospitalizations in administrative databases.

Alcohol (AC) and nonalcohol-associated cirrhosis (NAC) epidemiology studies are limited by available case definitions. We compared the diagnostic accuracy of previous and newly developed case definitions to identify AC and NAC hospitalizations. We randomly selected 700 hospitalizations from the 2008 to 2022 Canadian Discharge Abstract Database with alcohol-associated and cirrhosis-related International Classification of Diseases 10th revision codes. We compared standard approaches for AC (ie, AC code alone and alcohol use disorder and nonspecific cirrhosis codes together) and NAC (ie, NAC codes alone) case identification to newly developed approaches that combine standard approaches with new code combinations. Using electronic medical record review as the reference standard, we calculated case definition positive and negative predictive values, sensitivity, specificity, and AUROC. Electronic medical records were available for 671 admissions; 252 had confirmed AC and 195 NAC. Compared to previous AC definitions, the newly developed algorithm selecting for the AC code, alcohol-associated hepatic failure code, or alcohol use disorder code with a decompensated cirrhosis-related condition or NAC code provided the best overall positive predictive value (91%, 95% CI: 87-95), negative predictive value (89%, CI: 86-92), sensitivity (81%, CI: 76-86), specificity (96%, CI: 93-97), and AUROC (0.88, CI: 0.85-0.91). Comparing all evaluated NAC definitions, high sensitivity (92%, CI: 87-95), specificity (82%, CI: 79-86), negative predictive value (96%, CI: 94-98), AUROC (0.87, CI: 0.84-0.90), but relatively low positive predictive value (68%, CI: 62-74) were obtained by excluding alcohol use disorder codes and using either a NAC code in any diagnostic position or a primary diagnostic code for HCC, unspecified/chronic hepatic failure, esophageal varices without bleeding, or hepatorenal syndrome. New case definitions show enhanced accuracy for identifying hospitalizations for AC and NAC compared to previously used approaches.

Retraction and replacement of: Using machine learning to improve anaphylaxis case identification in medical claims data.

[This retracts the article DOI: 10.1093/jamiaopen/ooad090.].

Development and Validation of a Diagnosis System for Lung Infection Using Hybrid Deep-Learning Techniques

A fast and accurate test is necessary to detect COVID-19. A computed tomography (CT) scan has shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. We proposed a deep learning system for COVID-19 detection using CT images, including using and comparing transfer-learning, fine-tuning, and the embedding process. This paper presents the development of a COVID-19 case identification model using deep learning techniques. The suggested model utilized a modified visual geometry group (VGG16) architecture as the deep learning framework. The model was trained and validated using a chest CT image dataset. The SARS-COV-2 dataset contains 2482 CT scans of 210 patients from publicly available sources. The modified model demonstrated encouraging outcomes by greatly enhancing the sensitivity measure (95.82±1.75)%, which is an essential criterion for accurately detecting instances of COVID-19 infection. In addition, the model achieved higher values for the accuracy metric (91.67±1.68)%, the specificity meter (88.08±3.72)%, the precision metric (87.51±3.27)%, the F1 score (91.43±1.55)%, and the area under the curve (91.98±1.55)%. Deep learning effectively detects COVID-19 in chest CT scan images. Clinical practitioners may employ the suggested approach to study, identify, and effectively mitigate a greater number of pandemics.

Empirical clustering to identify individuals for whom insomnia is more closely related to suicidal ideation

BackgroundAlthough the effect sizes are modest, insomnia is consistently associated with suicidal thoughts and behaviors. Subgroup analyses can efficiently identify for whom insomnia is most relevant to suicidal ideation. To improve clinical case identification, the present study sought to identify subclusters of lifetime suicidal ideators for whom insomnia was most closely related to current suicidal ideation. MethodsData on N = 4750 lifetime suicidal ideators were extracted from the Military Suicide Research Consortium's Common Data Elements. Data on sociodemographic characteristics, severity and history of suicidal thoughts and behaviors, and related clinical characteristics were clustered by unsupervised machine learning algorithms. Robust Poisson regression estimated cluster by insomnia associations with current suicidal ideation. ResultsThree clusters were identified: a modest symptom severity cluster (N = 1757, 37.0 %), an elevated severity cluster (N = 1444 30.4 %), and a high severity cluster (N = 1549 32.6 %). In Cluster 1, insomnia was associated with current suicidal ideation (PRR 1.29 [1.13–1.46]) and remained significant after adjusting for sociodemographic and clinical covariates. In Cluster 2, insomnia was associated with current suicidal ideation (PRR 1.14 [1.01–1.30]), but not after adjusting for sociodemographic and clinical covariates. In Cluster 3, insomnia was associated with current suicidal ideation (PRR 1.12 [1.03–1.21]) and remained significant after adjusting for sociodemographic covariates, but not clinical covariates. LimitationsCross-sectional design, lack of diagnostic data, non-representative sample. ConclusionInsomnia appears more closely related to current suicidal ideation among modest severity individuals than other subgroups. Future work should use prospective designs and more comprehensive risk factor measures to confirm these findings.

The role of hematological parameters in asymptomatic and non-severe cases of Omicron variant infection

BackgroundOmicron variants are currently the predominant circulating lineage worldwide and most cases are mild or asymptomatic. The Omicron variant is characterized by high transmissibility and immune evasion. Early identification of Omicron cases in clinical settings is crucial for controlling its spread. Previous studies have indicated that changes in hematological parameters can be used to predict the severity of coronavirus disease 2019 (COVID-19). However, the role of hematological parameters in non-severe and asymptomatic cases remains unknown. This study aimed to investigate the role of hematological parameters in non-severe and asymptomatic Omicron variant infections.MethodsHematological parameters and results were analyzed and compared in symptomatic (n = 356) and asymptomatic (n = 171) groups respectively, and between these two groups with positive COVID-19 tests. The utility of hematological parameters for predicting positive COVID-19 tests was analyzed using receiver operating characteristic curves.ResultsIndividuals with non-severe cases exhibited decreased levels of platelets, lymphocytes, eosinophils, basophils, lymphocytes (%), eosinophils (%), and basophils (%), while exhibiting elevated counts of monocytes, neutrophils (%), monocytes (%), neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio (PLR), and C-reactive protein (CRP) when compared to suspected cases or asymptomatic carriers. In asymptomatic patients, positive carriers had lower leukocyte, neutrophil, and lymphocyte counts but higher monocyte, monocyte (%), PLR, and CRP levels than negative carriers. Basophil counts combined with lymphocytes or the PLR demonstrated a more significant predictive value in screening non-severe cases earlier compared to other parameters. The combined assessment of the monocyte (%) and the PLR had the highest area under the curve for diagnosing asymptomatic carriers.ConclusionsCirculating basophils, alone or in combination with other hematological parameters, may be used as efficient biomarkers for early screening of non-severe Omicron cases.