Avian Influenza A(H5N1) Neuraminidase Inhibition Antibodies in Healthy Adults after Exposure to Influenza A(H1N1)pdm09.

We detected high titers of cross-reactive neuraminidase inhibition antibodies to influenza A(H5N1) virus clade 2.3.4.4b in 96.8% (61/63) of serum samples from healthy adults in Hong Kong in 2020. In contrast, antibodies at low titers were detected in 42% (21/50) of serum samples collected in 2009. Influenza A(H1N1)pdm09 and A(H5N1) titers were correlated.

Social Vulnerability and Compliance With World Health Organization Advice on Protective Behaviors Against COVID-19 in African and Asia Pacific Countries: Factor Analysis to Develop a Social Vulnerability Index.

BACKGROUND: COVID-19 protective behaviors are key interventions advised by the World Health Organization (WHO) to prevent COVID-19 transmission. However, achieving compliance with this advice is often challenging, particularly among socially vulnerable groups. OBJECTIVE: We developed a social vulnerability index (SVI) to predict individuals' propensity to adhere to the WHO advice on protective behaviors against COVID-19 and identify changes in social vulnerability as Omicron evolved in African countries between January 2022 and August 2022 and Asia Pacific countries between August 2021 and June 2022. METHODS: In African countries, baseline data were collected from 14 countries (n=15,375) during the first Omicron wave, and follow-up data were collected from 7 countries (n=7179) after the wave. In Asia Pacific countries, baseline data were collected from 14 countries (n=12,866) before the first Omicron wave, and follow-up data were collected from 9 countries (n=8737) after the wave. Countries' socioeconomic and health profiles were retrieved from relevant databases. To construct the SVI for each of the 4 data sets, variables associated with COVID-19 protective behaviors were included in a factor analysis using polychoric correlation with varimax rotation. Influential factors were adjusted for cardinality, summed, and min-max normalized from 0 to 1 (most to least vulnerable). Scores for compliance with the WHO advice were calculated using individuals' self-reported protective behaviors against COVID-19. Multiple linear regression analyses were used to assess the associations between the SVI and scores for compliance to WHO advice to validate the index. RESULTS: In Africa, factors contributing to social vulnerability included literacy and media use, trust in health care workers and government, and country income and infrastructure. In Asia Pacific, social vulnerability was determined by literacy, country income and infrastructure, and population density. The index was associated with compliance with the WHO advice in both time points in African countries but only during the follow-up period in Asia Pacific countries. At baseline, the index values in African countries ranged from 0.00 to 0.31 in 13 countries, with 1 country having an index value of 1.00. The index values in Asia Pacific countries ranged from 0.00 to 0.23 in 12 countries, with 2 countries having index values of 0.79 and 1.00. During the follow-up phase, the index values decreased in 6 of 7 African countries and the 2 most vulnerable Asia Pacific countries. The index values of the least vulnerable countries remained unchanged in both regions. CONCLUSIONS: In both regions, significant inequalities in social vulnerability to compliance with WHO advice were observed at baseline, and the gaps became larger after the first Omicron wave. Understanding the dimensions that influence social vulnerability to protective behaviors against COVID-19 may underpin targeted interventions to enhance compliance with WHO recommendations and mitigate the impact of future pandemics among vulnerable groups.

Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study.

BACKGROUND: Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures. OBJECTIVE: This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses. METHODS: We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020. RESULTS: The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong. CONCLUSIONS: Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.

Exploring Offline Large Language Models for Clinical Information Extraction: A Study of Renal Histopathological Reports of Lupus Nephritis Patients.

Open source, lightweight and offline generative large language models (LLMs) hold promise for clinical information extraction due to their suitability to operate in secured environments using commodity hardware without token cost. By creating a simple lupus nephritis (LN) renal histopathology annotation schema and generating gold standard data, this study investigates prompt-based strategies using three state-of-the-art lightweight LLMs, namely BioMistral-DARE-7B (BioMistral), Llama-2-13B (Llama 2), and Mistral-7B-instruct-v0.2 (Mistral). We examine the performance of these LLMs within a zero-shot learning environment for renal histopathology report information extraction. Incorporating four prompting strategies, including combinations of batch prompt (BP), single task prompt (SP), chain of thought (CoT) and standard simple prompt (SSP), our findings indicate that both Mistral and BioMistral consistently demonstrated higher performance compared to Llama 2. Mistral recorded the highest performance, achieving an F1-score of 0.996 [95% CI: 0.993, 0.999] for extracting the numbers of various subtypes of glomeruli across all BP settings and 0.898 [95% CI: 0.871, 0.921] in extracting relational values of immune markers under the BP+SSP setting. This study underscores the capability of offline LLMs to provide accurate and secure clinical information extraction, which can serve as a promising alternative to their heavy-weight online counterparts.

Tracking diarrhea viruses and mpox virus using the wastewater surveillance network in Hong Kong.

The wastewater surveillance network successfully established for COVID-19 showed great potential to monitor other infectious viruses, such as norovirus, rotavirus and mpox virus. In this study, we established and validated detection methods for these viruses in wastewater. We developed a supernatant-based method to detect RNA viruses from wastewater samples and applied it to the monthly diarrhea viruses (norovirus genogroup I & II, and rotavirus) surveillance in wastewater treatment plants (WWTPs) at a city-wide level for 16 months. Significant correlations were observed between the diarrhea viruses concentrations in wastewater and detection rates in faecal specimens by clinical surveillance. The highest norovirus concentration in wastewater was obtained in winter, consistent with the seasonal pattern of norovirus outbreak in Hong Kong. Additionally, we established a pellet-based method to monitor DNA viruses in wastewater and detected weak signals for mpox virus in wastewater from a WWTP serving approximately 16,700 people, when the first mpox patient in Hong Kong was admitted to the hospital within the catchment area. Genomic sequencing provided confirmatory evidence for the validity of the results. Our findings emphasized the efficacy of the wastewater surveillance network in WWTPs as a cost-effective tool to track the transmission trend of diarrhea viruses and to provide sensitive detection of novel emerging viruses such as mpox virus in low-prevalence areas. The developed methods and surveillance results provide confidence for establishing robust wastewater surveillance programs to control infectious diseases in the post-pandemic era.

Report from the World Health Organization's immunization and vaccines-related implementation research advisory committee (IVIR-AC) meeting, virtual gathering, 26 February-1 March 2024.

The Immunization and Vaccine-related Implementation Research Advisory Committee (IVIR-AC) is the World Health Organization's key standing advisory body to conduct an independent review of research, particularly of transmission and economic modeling analyses that estimate the impact and value of vaccines. From 26th February-1st March 2024, at its first of two semi-annual meetings, IVIR-AC provided feedback and recommendations across four sessions; this report summarizes the proceedings and recommendations from that meeting. Session topics included modeling of the impact and cost-effectiveness of the R21/Matrix-M malaria vaccine, meta-analysis of economic evaluations of vaccines, a global analysis estimating the impact of vaccination over the last 50 years, and modeling the impact of different RTS,S malaria vaccine dose schedules in seasonal settings.

Enhanced prediction of abnormal glucose tolerance using an extended non-invasive risk score incorporating routine renal biochemistry.

INTRODUCTION: Type 2 diabetes is preventable in subjects with impaired glucose tolerance based on 2-hour plasma glucose (2hPG) during 75 g oral glucose tolerance test (OGTT). We incorporated routine biochemistry to improve the performance of a non-invasive diabetes risk score to identify individuals with abnormal glucose tolerance (AGT) defined by 2hPG≥7.8 mmol/L during OGTT. RESEARCH DESIGN AND METHODS: We used baseline data of 1938 individuals from the community-based "Better Health for Better Hong Kong - Hong Kong Family Diabetes Study (BHBHK-HKFDS) Cohort" recruited in 1998-2003. We incorporated routine biochemistry in a validated non-invasive diabetes risk score, and evaluated its performance using area under receiver operating characteristics (AUROC) with internal and external validation. RESULTS: The AUROC of the original non-invasive risk score to predict AGT was 0.698 (95% CI, 0.662 to 0.733). Following additional inclusion of fasting plasma glucose, serum potassium, creatinine, and urea, the AUROC increased to 0.778 (95% CI, 0.744 to 0.809, p<0.001). Net reclassification improved by 31.9% (p<0.001) overall, by 30.8% among people with AGT and 1.1% among people without AGT. The extended model showed good calibration (χ2=11.315, p=0.1845) and performance on external validation using an independent data set (AUROC=0.722, 95% CI, 0.680 to 0.764). CONCLUSIONS: The extended risk score incorporating clinical and routine biochemistry can be integrated into an electronic health records system to select high-risk subjects for evaluation of AGT using OGTT for prevention of diabetes.

Higher Short-Chain Fermentable Carbohydrates Are Associated with Lower Body Fat and Higher Insulin Sensitivity in People with Prediabetes.

The quality of carbohydrates has metabolic consequences in people with prediabetes. However, the causality of short-chain fermentable carbohydrate intakes and metabolic parameters has not been explored in the prediabetic or diabetic population. We investigated associations between different types of carbohydrates, including fermentable oligosaccharides, disaccharides, monosaccharides, polyols (FODMAPs), and polysaccharides (dietary fibre), and body composition and glucose/insulin responses in subjects with prediabetes. In this prospective cross-sectional study, 177 subjects with impaired glucose tolerance (IGT) (mean age: 60 (54-62) years, 41% men) underwent an assessment of body composition and completed six-point oral glucose tolerance tests (OGTT), Homeostatic Model Assessment of Insulin Resistance (HOMA2-IR), insulin sensitivity, detailed 3-day food records, and physical activity questionnaire. Daily habitual FODMAP intake decreased progressively with increasing BMI, ranging from 7.9 (6.2-12.7) g/d in subjects with normal BMI and 6.6 (4.6-9.9) g/d in subjects with overweight to 5.8 (3.8-9.0) g/d in subjects with obesity (p = 0.038). After adjustment for age and gender, galactooligosaccharides (GOSs) were negatively correlated with body fat (Standardised Beta coefficient ß = -0.156, p = 0.006) and positively associated with insulin sensitivity (ß = 0.243, p = 0.001). This remained significant after adjustment for macronutrients, fibre, and physical activity (p = 0.035 and p = 0.010, respectively). In individuals with IGT, higher dietary GOS intake was associated with lower body fat and higher insulin sensitivity independent of macronutrients and fibre intake, calling for interventional studies to evaluate the effect of FODMAP intake in prediabetes.