Trend of HPV Molecular Epidemiology in the Post-Vaccine Era: A 10-Year Study.

Cervical cancer, a major health concern among women worldwide, is closely linked to human papillomavirus (HPV) infection. This study explores the evolving landscape of HPV molecular epidemiology in Taiwan over a decade (2010-2020), where prophylactic HPV vaccination has been implemented since 2007. Analyzing data from 40,561 vaginal swab samples, with 42.0% testing positive for HPV, we reveal shifting trends in HPV genotype distribution and infection patterns. The 12 high-risk genotypes, in order of decreasing percentage, were HPV 52, 58, 16, 18, 51, 56, 39, 59, 33, 31, 45, and 35. The predominant genotypes were HPV 52, 58, and 16, accounting for over 70% of cases annually. The proportions of high-risk and non-high-risk HPV infections varied across age groups. High-risk infections predominated in sexually active individuals aged 30-50 and were mixed-type infections. The composition of high-risk HPV genotypes was generally stable over time; however, HPV31, 33, 39, and 51 significantly decreased over the decade. Of the strains, HPV31 and 33 are shielded by the nonavalent HPV vaccine. However, no reduction was noted for the other seven genotypes. This study offers valuable insights into the post-vaccine HPV epidemiology. Future investigations should delve into HPV vaccines' effects and their implications for cervical cancer prevention strategies. These findings underscore the need for continued surveillance and research to guide effective public health interventions targeting HPV-associated diseases.

Alternate Antimicrobial Therapies and Their Companion Tests.

New antimicrobial approaches are essential to counter antimicrobial resistance. The drug development pipeline is exhausted with the emergence of resistance, resulting in unsuccessful trials. The lack of an effective drug developed from the conventional drug portfolio has mandated the introspection into the list of potentially effective unconventional alternate antimicrobial molecules. Alternate therapies with clinically explicable forms include monoclonal antibodies, antimicrobial peptides, aptamers, and phages. Clinical diagnostics optimize the drug delivery. In the era of diagnostic-based applications, it is logical to draw diagnostic-based treatment for infectious diseases. Selection criteria of alternate therapeutics in infectious diseases include detection, monitoring of response, and resistance mechanism identification. Integrating these diagnostic applications is disruptive to the traditional therapeutic development. The challenges and mitigation methods need to be noted. Applying the goals of clinical pharmacokinetics that include enhancing efficacy and decreasing toxicity of drug therapy, this review analyses the strong correlation of alternate antimicrobial therapeutics in infectious diseases. The relationship between drug concentration and the resulting effect defined by the pharmacodynamic parameters are also analyzed. This review analyzes the perspectives of aligning diagnostic initiatives with the use of alternate therapeutics, with a particular focus on companion diagnostic applications in infectious diseases.

Risk Stratification for Herpes Simplex Virus Pneumonia Using Elastic Net Penalized Cox Proportional Hazard Algorithm with Enhanced Explainability.

Herpes simplex virus (HSV) pneumonia is a serious and often fatal respiratory tract infection that occurs in immunocompromised individuals. The early detection of accurate risk stratification is essential in identifying patients who are at high risk of mortality and may benefit from more aggressive treatment. In this study, we developed and validated a risk stratification model for HSV bronchopneumonia using an elastic net penalized Cox proportional hazard algorithm. We analyzed data from a cohort of 104 critically ill patients with HSV bronchopneumonia identified in Chang Gung Memorial Hospital, Linkou, Taiwan: one of the largest tertiary medical centers in the world. A total of 109 predictors, both clinical and laboratory, were identified in this process to develop a risk stratification model that could accurately predict mortality in patients with HSV bronchopneumonia. This model was able to differentiate the risk of death and predict mortality in patients with HSV bronchopneumonia compared to the APACHE II score in the early stage of ICU admissions. Both hazard ratio coefficient and selection frequency were used as the metrics to enhance the explainability of the informative predictors. Our findings suggest that the elastic net penalized Cox proportional hazard algorithm is a promising tool for risk stratification in patients with HSV bronchopneumonia and could be useful in identifying those at high risk of mortality.

Describe, Spot and Explain: Interpretable Representation Learning for Discriminative Visual Reasoning.

Despite the recent success achieved by deep neural networks (DNNs), it remains challenging to disclose/explain the decision-making process from the numerous parameters and complex non-linear functions. To address the problem, explainable AI (XAI) aims to provide explanations corresponding to the learning and prediction processes for deep learning models. In this paper, we propose a novel representation learning framework of Describe, Spot and eXplain (DSX). Based on the architecture of Transformer, our proposed DSX framework is composed of two learning stages, descriptive prototype learning and discriminative prototype discovery. Given an input image, the former stage is designed to derive a set of descriptive representations, while the latter stage further identifies a discriminative subset, offering semantic interpretability for the corresponding classification tasks. While our DSX does not require any ground truth attribute supervision during training, the derived visual representations can be practically associated with physical attributes provided by domain experts. Extensive experiments on fine-grained classification and person re-identification tasks qualitatively and quantitatively verify the use our DSX model for offering semantically practical interpretability with satisfactory recognition performances.

Visit to Visit Hemoglobin A1c Variation and Long-term Risk of Major Adverse Limb Events in Patients With Type 2 Diabetes.

CONTEXT: Glycemic variation had been demonstrated to be associated with several complications of diabetes. OBJECTIVE: Investigation of the association between visit to visit hemoglobin A1c (HbA1c) variation and the long-term risk of major adverse limb events (MALEs). METHODS: Retrospective database study. Average real variability was used to represent glycemic variations with all the HbA1c measurements during the 4 following years after the initial diagnosis of type 2 diabetes. Participants were followed from the beginning of the fifth year until death or the end of the follow-up. The association between HbA1c variations and MALEs was evaluated after adjusting for mean HbA1c and baseline characteristics. Included were 56 872 patients at the referral center with a first diagnosis of type 2 diabetes, no lower extremity arterial disease, and at least 1 HbA1c measurement in each of the 4 following years were identified from a multicenter database. The main outcome measure was incidence of a MALE, which was defined as the composite of revascularization, foot ulcers, and lower limb amputations. RESULTS: The average number of HbA1c measurements was 12.6. The mean follow-up time was 6.1 years. The cumulative incidence of MALEs was 9.25 per 1000 person-years. Visit to visit HbA1c variations were significantly associated with MALEs and lower limb amputation after multivariate adjustment. People in the highest quartile of variations had increased risks for MALEs (HR 1.25, 95% CI 1.10-1.41) and lower limb amputation (HR 3.05, 95% CI 1.97-4.74). CONCLUSION: HbA1c variation was independently associated with a long-term risk of MALEs and lower limb amputations in patients with type 2 diabetes.

Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective.

Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".

Outcomes of catheter ablation of atrial tachyarrhythmia guided exclusively by activation mapping.

INTRODUCTION: Complex atrial tachyarrhythmias (CATs) are commonly observed in patients with prior catheter ablation or cardiac surgery. These arrhythmias are challenging to map and ablate. Historically, entrainment mapping was utilized to characterize CAT. With the advent of high-definition mapping (HDM), full visualization of the CAT circuit is possible which may obviate the need for entrainment mapping. METHODS: We sought to investigate the outcomes of catheter ablation of CAT guided only by HDM. Consecutive patients who underwent CAT ablation from 2017 to 2021 were included in our study (excluding right atrial tachyarrhythmias). Patients were sorted by the type of mapping performed. Group I consisted of patients where HDM alone was utilized with no attempt of entrainment. Group II consisted of patients where both entrainment and HDM were utilized. RESULTS: A total of 67 patients were included in our study, with 40 patients in HDM group (I) and 27 patients in entrainment group (II). No statistically significant difference regarding 1-year freedom from atrial arrhythmias was found between the two groups (80% vs 77.8%, p = 0.819). Four CATs were terminated by entrainment during procedure versus none in the HDM-only group (p = 0.011). CONCLUSIONS: CAT ablation with HDM alone yielded similar 1-year freedom from atrial arrhythmias compared to ablation with HDM and entrainment. Entrainment combined with HDM was associated with higher undesired CAT interruption rate. Further validation is needed with randomized control trials.

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study.

Atrial fibrillation (AF) is a common arrhythmia affecting 8-10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

Day-to-day variation in sleep duration is associated with increased all-cause mortality.

STUDY OBJECTIVES: There is a paucity of data on the association between day-to-day variation in sleep pattern and all-cause mortality. We aimed to investigate whether day-to-day variation in sleep duration and onset of sleep are associated with cardiovascular and all-cause mortality. METHODS: We used data belonging to 388 unique patients from the Midlife in the United States 2 Biomarker study (2004-2009). Information on sleep onset, duration, and sleep-wake cycles was collected for 7 consecutive days using the Actiwatch device. Sleep irregularity was assessed using mean and standard deviations in sleep duration and time of onset of sleep over 7 days. Cox proportional regression analysis and the Fine and Gray subdistribution method were used with all-cause and cardiovascular mortality, respectively. RESULTS: Over a median of 8.6 years of follow-up, 37 patients died, including 10 deaths resulting from cardiovascular causes. There was no statistically significant increase in cardiovascular mortality with variation in sleep duration in the highest vs the lowest tertile (hazard ratio, 4.00; 0.45-35.48; P = .21). However, increased all-cause mortality was seen in the highest vs the lowest tertile (hazard ratio, 3.99; 1.33-11.94; P = .01). Multivariable model adjusting for confounders had higher all-cause mortality with increased sleep duration variation in the highest vs the lowest tertile: hazard ratio, 4.85; 1.52-15.49; P < .01). CONCLUSIONS: Day-to-day variation in sleep duration is associated with increased all-cause mortality but not cardiovascular mortality after adjusting for mean sleep duration, inflammation, diabetes, age, body mass index, renal function, and blood pressure. Irregularity in the onset of sleep is not associated with all-cause mortality or cardiovascular mortality. CITATION: Katamreddy A, Uppal D, Ramani G, et al. Day-to-day variation in sleep duration is associated with increased all-cause mortality. J Clin Sleep Med. 2022;18(3):921-926.

Learning of 3D Graph Convolution Networks for Point Cloud Analysis.

Point clouds are among the popular geometry representations in 3D vision. However, unlike 2D images with pixel-wise layouts, such representations containing unordered data points which make the processing and understanding the associated semantic information quite challenging. Although a number of previous works attempt to analyze point clouds and achieve promising performances, their performances would degrade significantly when data variations like shift and scale changes are presented. In this paper, we propose 3D graph convolution networks (3D-GCN), which uniquely learns 3D kernels with graph max-pooling mechanisms for extracting geometric features from point cloud data across different scales. We show that, with the proposed 3D-GCN, satisfactory shift and scale invariance can be jointly achieved. We show that 3D-GCN can be applied to point cloud classification and segmentation tasks, with ablation studies and visualizations verifying the design of 3D-GCN.

Amiodarone is associated with increased short-term mortality in elderly atrial fibrillation patients with preserved ejection fraction.

PURPOSE: Amiodarone is commonly used in atrial fibrillation (AF). Long-term use of amiodarone is associated with significant toxicities especially in elderly patients. However, in the short term after hospitalization of AF, it remains uncertain whether the use of amiodarone will increase mortality. We aim to investigate whether Amiodarone affects short-term mortality in elderly patients after hospitalization for atrial fibrillation. METHODS: We conducted a single-center retrospective cohort study including patients (Age > = 60 years old) who were hospitalized between 07/01/2004 and 06/30/2019 with primary diagnosis of AF and left ventricular ejection fraction (LVEF) > = 50%. Patients who were prescribed amiodarone during hospitalization but not before hospitalization are classified into Amiodarone group (341 patients). Patients who were not prescribed amiodarone are classified into non-amiodarone group (2171 patients). Propensity score matching was performed with 1:1 nearest-neighbor matching of Amiodarone group and Non-amiodarone group based on baseline variables. Univariate and Multivariate logistic regression were used to calculate the odds ratio of amiodarone use on in-hospital mortality, and multivariate cox regression was adopted to calculate the hazard ratio of amiodarone use on 100-day mortality. RESULTS: Patients' baseline demographic and clinical characteristics were well matched in both groups. Both univariate and multivariate logistic regression showed amiodarone group had higher in-hospital mortality (OR 10.27, p = 0.0268; 16.50, p = 0.0171) than non-amiodarone group and multivariate Cox regression suggested increased 100-day all-cause mortality (HR 2.34, p = 0.022). CONCLUSION: Amiodarone use in elderly patients with preserved ejection fraction is associated with increased in-hospital and 100-day all-cause mortality after hospitalization for AF.

Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms.

Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072-0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI-TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment.

Joint Feature Disentanglement and Hallucination for Few-Shot Image Classification.

Few-shot learning (FSL) refers to the learning task that generalizes from base to novel concepts with only few examples observed during training. One intuitive FSL approach is to hallucinate additional training samples for novel categories. While this is typically done by learning from a disjoint set of base categories with sufficient amount of training data, most existing works did not fully exploit the intra-class information from base categories, and thus there is no guarantee that the hallucinated data would represent the class of interest accordingly. In this paper, we propose Feature Disentanglement and Hallucination Network (FDH-Net), which jointly performs feature disentanglement and hallucination for FSL purposes. More specifically, our FDH-Net is able to disentangle input visual data into class-specific and appearance-specific features. With both data recovery and classification constraints, hallucination of image features for novel categories using appearance information extracted from base categories can be achieved. We perform extensive experiments on two fine-grained datasets (CUB and FLO) and two coarse-grained ones (mini-ImageNet and CIFAR-100). The results confirm that our framework performs favorably against state-of-the-art metric-learning and hallucination-based FSL models.

Nuclear Imaging for the Diagnosis of Cardiac Amyloidosis in 2021.

Cardiac amyloidosis is caused by the deposition of misfolded protein fibrils into the extracellular space of the heart. The diagnosis of cardiac amyloidosis remains challenging because of the heterogeneous manifestations of the disease. There are many different types of amyloidosis with light-chain (AL) amyloidosis and transthyretin (ATTR) amyloidosis being the most common types of cardiac amyloidosis. Endomyocardial biopsy is considered the gold standard for diagnosing cardiac amyloidosis and differentiating amyloid subtypes, but its use is limited because of the invasive nature of the procedure, with risks for complications and the need for specialized training and centers to perform the procedure. Radionuclide cardiac imaging has recently become the most commonly performed test for the diagnosis of ATTR amyloidosis but is of limited value for the diagnosis of AL amyloidosis. Positron emission tomography has been increasingly used for the diagnosis of cardiac amyloidosis and its applications are expected to expand in the future. Imaging protocols are under refinement to achieve better quantification of the disease burden and prediction of prognosis.

Dabigatran, rivaroxaban, and apixaban are superior to warfarin in Asian patients with non-valvular atrial fibrillation: An updated meta-analysis.

BACKGROUND: Most of the randomized clinical trials that led to the wide use of non-vitamin K antagonist oral anticoagulants for stroke prevention in patients with atrial fibrillation (AF) originated from western countries. AIM: To systematically review and quantitatively synthesize the real-world data regarding the efficacy and safety of dabigatran, rivaroxaban, and apixaban compared to warfarin for stroke prevention in Asian patients with non-valvular AF. METHODS: Medline, Cochrane, and ClinicalTrial.gov databases were reviewed. A random-effect model meta-analysis was used and I-square was utilized to assess the heterogeneity. The primary outcome was ischemic stroke. The secondary outcomes were all-cause mortality, major bleeding, intracranial hemorrhage, and gastrointestinal bleeding. RESULTS: Twelve studies from East Asia or Southeast Asia and 441450 patients were included. Dabigatran, rivaroxaban, and apixaban were associated with a significant reduction in the incidence of ischemic stroke [hazard ratio (HR) = 0.78, 95% confidence interval (CI): 0.65-0.94; HR = 0.79, 95%CI: 0.74-0.85, HR = 0.70, 95%CI: 0.62-0.78; respectively], all-cause mortality (HR = 0.68, 95%CI: 0.56-0.83; HR = 0.66, 95%CI: 0.52-0.84; HR = 0.66, 95%CI: 0.49-0.90; respectively), and major bleeding (HR = 0.61, 95%CI: 0.54-0.69; HR = 0.70, 95%CI: 0.54-0.90; HR = 0.58, 95%CI: 0.43-0.78; respectively) compared to warfarin. CONCLUSION: Dabigatran, rivaroxaban, and apixaban appear to be superior to warfarin in both efficacy and safety in Asians with non-valvular AF.

Use of Opioids and Outcomes of Pneumonia: Results From the US Nationwide Inpatient Sample.

BACKGROUND: Opioids have been shown to increase risk of pneumonia among susceptible population. However, the effect of opioid abuse on the outcome of pneumonia has not been evaluated at the population level. We aimed to compare the outcomes of pneumonia among patients with opioid use disorder and patients without substance use disorder using a large population database. METHODS: We assembled a pneumonia cohort composed of 11,186,564 adult patients from the National Inpatient Sample (NIS; 2005-2014). Patients with opioid disorder were identified using the International Classification of Diseases, 9th Revision, Clinical Modification codes. We compared health-related and economic outcomes between patients with and without opioid disorders using propensity score matching (PSM) analysis to balance baseline differences. The survival differences between two groups of patients were assessed using a Cox proportional hazard model. We further explored the possibility of effect modification by interaction analyses in different populations. RESULTS: After PSM, patients with opioid use disorder were at increased risk of ventilator use (odds ratio [OR]: 1.22, 95% confidence interval [CI]: 1.08 to 1.38, p = 0.0014) and associated with increased length of hospital stay by 0.59 days (95% CI: 0.35 to 0.83, p < 0.001), compared with those without substance use disorder. Patients with opioid use also had higher daily (228.00 USD, 95% CI: 180.51 to 275.49, p < 0.001) and total (1,875.72 USD, 95% CI: 1,259.63 to 2,491.80, p < 0.001) medical costs. Subgroup analyses showed similar results. CONCLUSIONS: Compared with patients without any drug dependence, patients with opioid use disorders had increased risk of complications and resource utilization. This study adds evidence for increased risk for pneumonia complications in the growing patients with opioid use disorders.

Serum trough level as a postmarketing quality measure of generic vancomycin products.

BACKGROUND: The vancomycin trough level (VTL) is the most widely used pharmacokinetic parameter for monitoring its clinical efficacy. Whether the VTL is affected in patients receiving different vancomycin products has not previously been determined. METHODS: From 2005 to 2015, five vancomycin products, including the innovator (designated as VAN-Lilly) and four generic versions (designated as VAN-A, VAN-B, VAN-C and VAN-D), were sequentially used in a teaching hospital. The initial VTLs were compared between patients who received different vancomycin products after propensity score (PS) weighting and matching for clinical covariates. RESULTS: Among 8735 patients with initial VTL levels available for analysis, a significant association was identified between the VTL and different vancomycin products in children aged 1 month to 12 years (P < 0.0001). The PS weighting analysis in the paediatric group disclosed children on VAN-C had higher VTL compared to those on other four products (P = 0.0008). PS matching analysis revealed that children who received VAN-C had significantly higher VTLs than those who received VAN-Lily (P = 0.0001), VAN-A (P = 0.0008), VAN-B (P = 0.0002) or VAN-D (P = 0.0015). Furthermore, the coefficient of variation of the VTL was much greater in patients who received VAN-C than in those who received the other four versions, suggesting an unstable quality of this product. CONCLUSION: A generic version of vancomycin generated significantly higher concentrations and greater variation of VTLs than the innovator and other generic vancomycin products in children. The VTL can serve as an indicator to monitor the quality of vancomycin products after marketing.

A Multi-domain and Multi-modal Representation Disentangler for Cross-Domain Image Manipulation and Classification.

Learning interpretable data representation has been an active research topic in deep learning and computer vision. While representation disentanglement is an effective technique for addressing this task, existing works cannot easily handle the problems in which manipulating and recognizing data across multiple domains are desirable. In this paper, we present a unified network architecture of Multi-domain and Multi-modal Representation Disentangler (M2RD), with the goal of learning domain-invariant content representation with the associated domain-specific representation observed. By advancing adversarial learning and disentanglement techniques, the proposed model is able to perform continuous image manipulation across data domains with multiple modalities. More importantly, the resulting domain-invariant feature representation can be applied for unsupervised domain adaptation. Finally, our quantitative and qualitative results would confirm the effectiveness and robustness of the proposed model over state-of-the-art methods on the above tasks.

Transfer Neural Trees: Semi-Supervised Heterogeneous Domain Adaptation and Beyond.

Heterogeneous domain adaptation (HDA) addresses the task of associating data not only across dissimilar domains but also described by different types of features. Inspired by the recent advances of neural networks and deep learning, we propose a deep leaning model of Transfer Neural Trees (TNT), which jointly solves cross-domain feature mapping, adaptation, and classification in a unified architecture. As the prediction layer in TNT, we introduce Transfer Neural Decision Forest (Transfer- NDF), which is able to learn the neurons in TNT for adaptation by stochastic pruning. In order to handle semi-supervised HDA, a unique embedding loss term is introduced to TNT for preserving prediction and structural consistency between labeled and unlabeled target-domain data. We further show that our TNT can be extended to zero shot learning for associating image and attribute data with promising performance. Finally, experiments on different classification tasks across features, datasets, and modalities would verify the effectiveness of our TNT.

Humoral immunity to mumps in a highly vaccinated population in Taiwan.

BACKGROUND: A resurgence of mumps was noted recently and outbreaks were increasingly reported in populations with high vaccine coverage. We aimed to evaluate the seroprevalence to mumps in Taiwan, where a two-dose childhood mumps-containing vaccine program, with a high coverage rate, had been implemented for >20 years. METHODS: The anti-mumps IgG was determined in 3552 participants of all ages in Taiwan. The age-specific seropositivity rates were calculated and the sociodemographic variables associated with the seronegative sera were analyzed with a logistic regression method. RESULTS: The overall seroprevalence to mumps was 71%, with a higher rate in adults ≥19 years old than in the pediatric population <19 years old (80.4% versus 62.0%, P < 0.0001). In participants aged 2-20 years, who had been given at least one mumps-containing vaccine, the seropositivity fluctuated across different age subgroups and the lowest rate (36.8%) occurred in the 17-18 years age group. The multivariate analysis identified age within 17-18 years (adjusted odds ratio [aOR] 8.598, 95% confidence interval [CI] 2.990-24.722, P < 0.0001), within 19-20 years (aOR 5.076, 95% CI 1.702-15.133, P = 0.0080), and being a resident of the suburban area of northern Taiwan (aOR 1.089, 95% CI 0.823-1.414, P = 0.0008) as independent factors associated with an increased risk of seronegative sera. CONCLUSION: The seropositivity to mumps was unexpectedly low in highly vaccinated generations, and with a significant geographical discrepancy in Taiwan, which may have been responsible for the sustained reports of mumps cases in Taiwan.