Respiratory Infectious Diseases Research Articles

A miniaturized RPA-CRISPR/Cas12a-based nucleic acid diagnostic platform for rapid and simple self-testing of SARS-CoV-2.

Nucleic acid testing is the most effective detection method currently available for the diagnosis of respiratory infectious diseases. However, the conventional real-time fluorescent quantitative PCR technique, which is regarded as the gold standard method for nucleic acid detection, presents significant challenges for implementation in home self-testing and popularization in underdeveloped regions due to its rigorous experimental standards. It is therefore clear that an easy-to-use, miniaturized nucleic acid testing technology and products for nonprofessionals are of great necessity to define the pathogens and assist in controlling disease transmission. (87) RESULTS: In this study, we propose a strategy for self-testing of respiratory pathogen nucleic acid that is oriented towards the public and user-friendly. The proposed system integrates the processes of extraction-free nucleic acid release, RPA isothermal amplification, and CRISPR fluorescence detection into a compact configuration. A microfluidic testing chip actuated by air pouches and a battery/USB-powered reusable device has been developed to enable simultaneous detection of internal reference genes and viral targets in a fully enclosed condition. The system allows for sample-in, and result-out testing in less than 30min with a detection limit of 2 copies/μL. Additionally, a straightforward signal-light-based result display method has been developed to make it easy and intuitive for users to access the results. Furthermore, freeze-drying reagent is introduced to guarantee the storage and transportation of testing chips in ambient conditions. (135) SIGNIFICANCE: This work presents a miniaturized, portable, and highly sensitive nucleic acid detection system, where simple operating procedures have been designed for unskilled users. It is our belief that the testing system developed in this work is well suited for home-based self-testing and infection diagnosis in resource-limited areas, due to the above-mentioned advantages. (52).

Co-infections with Bordetella bronchiseptica in canine: A systematic review and meta-analysis.

Bordetella bronchiseptica is a primary pathogen in canine infectious respiratory disease (CIRD), or kennel cough, capable of independently causing respiratory illness and contributing significantly to co-infections with other viral and bacterial agents. Despite its critical role in disease transmission and persistence, the epidemiology of B. bronchiseptica in CIRD remains poorly understood. Limited data on co-infection prevalence and associated risk factors hinder effective management and control of this pathogen. This systematic review and meta-analysis aimed to clarify the prevalence and risk factors of co-infections with B. bronchiseptica in dogs. A comprehensive bibliographic search was conducted across four databases: PubMed, Scopus, Web of Science, and Embase. Data extraction included the number of co-infected cases among those with B. bronchiseptica, the identified co-infecting pathogens, study period, geographical location, shelter type, and age. From 3994 identified articles, 26 studies were included. The overall prevalence of B. bronchiseptica co-infection was 47 % (95 % CI: 37 %-57 %). Significant differences were observed only in the study period, with co-infection rates higher in the 1900s (77 %) compared to the 2000s (45 %). No significant differences were found for other factors. Frequently co-infecting pathogens included Mycoplasma and canine respiratory coronavirus (CRCoV). Co-infections with B. bronchiseptica are common in CIRD, indicating a need for the development of combined vaccines targeting co-infecting pathogens. Furthermore, the establishment of effective prevention and control strategies can be universally applied across different geographical locations, shelter types, and ages. This study provides valuable insights that can inform future research and enhance the overall management and treatment of CIRD in dogs.

Clinical value of miR-200b in diagnosis and prognosis of childhood pneumonia

BackgroundChildhood pneumonia (CP) is a common respiratory infectious disease in children with high morbidity and mortality. However, differential changes in miRNAs may interact with the regulation of CP. The aim of this paper was to discuss the miR-200b expression in CP and its diagnostic and prognostic value.MethodsRT-qPCR was used to measure the miR-200b expression in venous blood. ROC curve was adopted to assess the diagnostic ability of miR-200b in children with CP. Logistic analysis was adopted to study the risk factors are related to CP. Kaplan-Meier was employed to analyze the prognostic ability of miR-200b in CP. Transfection of MRC-5 cells in vitro was used to verify the impact of miR-200b on cell proliferation and apoptosis.ResultsThe expression of miR-200b in serum of CP children was upregulated. ROC analysis prompted that upregulation of miR-200b could be effective in distinguishing between CP and healthy children. Compared with the good prognosis group, miR-200b expression was elevated in the poor prognosis group. Kaplan-Meier reminded that high expression of miR-200b led to a poor prognosis in CP. Cells experiments demonstrated that transfection with miR-200b inhibitor encouraged cell proliferation and inhibited apoptosis, whereas transfection with miR-200b mimic had the contrary result.ConclusionsmiR-200b had high diagnostic and prognostic value for children with CP and may become a biomarker for clinical diagnosis and prognosis.Clinical trial numberNot applicable.

Optimize surveillance, prevention and control, vaccine strategies to respond to seasonal epidemics of respiratory infectious diseases

The prevalence and evolution of respiratory pathogens are changing, and issues related to drug-resistance are becoming increasingly prominent in the post-COVID era. The urgency of controlling respiratory pathogens has intensified. This issue organizes expert discussions to address the resurgence of various respiratory pathogens, summarizing current status of pathogen surveillance, drug-resistance research and vaccine development, while suggesting countermeasures. It emphasizes the need to integrate of medical and preventive measures, conduct multi-pathogen surveillance and management, enhance the response capacity of respiratory infectious diseases, optimize vaccine policy and immunization services, improve the level of diagnosis and treatment, and strengthen protection for vulnerable populations, so as to reduce the burden of respiratory infectious diseases.

Simultaneous Detection of Five Infectious Diseases in a Single Strip: Oligo dT-Utilized Lateral Flow Immunoassay.

The need for accurate and simultaneous diagnosis of multiple respiratory infectious diseases has become increasingly critical due to ongoing viral mutations and the similarity of symptoms among various viruses. Here, we have advanced our detection capabilities by developing a multiplex lateral flow immunoassay (LFA) platform that integrates oligonucleotides and antibodies, enabling the simultaneous detection of five respiratory viruses: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Influenza A (FluA), Influenza B (FluB), Respiratory syncytial virus (RSV), and Adenovirus (ADV), on a single membrane. By applying the oligonucleotide and antibody-conjugated AuNPs, the platform enables highly sensitive and specific detection. In addition, signal amplification using RPA70A-conjugated gold nanoparticles that developed in the previous study can further be applied optionally for low-concentration biomarkers. Our interferences and cross-reactivity tests confirmed that these complexes do not produce false positives, substantiating the assay's utility in clinical settings. This platform, therefore, provides a robust solution for the precise and rapid diagnosis of complex viral infections, positioning it as suitable for application in pandemic response scenarios.

The strategy to control the outbreak of an emerging respiratory infectious disease in a simulated Chinese megacity.

The emergence of respiratory infectious diseases (ERID) poses a significant threat to global public health. However, effectively managing ERID outbreaks in large cities remains a challenge. An age-structured Susceptible-Exposed-Infectious-Removed (SEIR) model was developed to predict the effectiveness of non-pharmaceutical interventions (NPIs) in controlling ERID outbreaks. Four ERID outbreak scenarios were created based on varying levels of infectivity and pathogenicity. Based on the World Health Organization's (WHO) categorization for responding to the influenza pandemic, the combinations of NPIs were classified into five levels: base, any, moderate, high, and extraordinary levels (from mild to severe). The simulated progression of ERID outbreaks in a megacity were compared across different levels of NPI. Our findings indicate that the response strategies should be formulated based on the epidemiological characteristics of ERID. In the low transmission scenarios, the mandatory NPIs were unnecessary to control ERID outbreaks regardless of their pathogenicity. However, even with low pathogenicity, severe NPIs are required to control the spread of ERID and minimize harm to the public in high transmission scenarios. The NPIs for the EIRD outbreak in a city should be tailored to the epidemiological characteristics to control its impact and protect public health.

Discovering the climate dependent disease transmission mechanism through learning-explaining framework.

There are evidence showing that meteorological factors, such as temperature and humidity, have critical effects on transmission of some infectious diseases, while quantifying the influence is challenging. In this study we develop a learning-explaining framework to discover the particular dependence of transmission mechanisms on meteorological factors based on multiple source data. The incidence rate based on the epidemic data and epidemic model is theoretically identified, and meanwhile the practical discovery of particular formula is feasible through deep neural networks (DNN), symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy). In particular, we initially learn the incidence rate in an SIRS model based on epidemic data, then use mechanism discovery methods to explore the possible explicit forms of the incidence rate, and consequently explore the possible relationship between transmission rate and meteorological factors. We finally use information criteria and a definition of evaluation score to make model selection, and hence suggest the optimal explicit formula. We illustrate the idea by derive the incidence rate and transmission rate of respiratory infectious diseases based on the case data on influenza-like illness (ILI) in Xi'an, Shaanxi Province of China and meteorological data from 1st January 2010 to 10th November 2016. The finding reveals that the influence of meteorological factors on transmission exhibits very strong nonlinearity, and modeling the effect should be of great care.

Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks

ABSTRACT Forecasting infectious respiratory diseases is crucial for effective prevention and intervention strategies. However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data costs and constraints. Using the COVID-19 pandemic as a case study, this research explores whether integrating social media-based place and social connectivity networks can improve predictions of disease transmission at the county level across various regions. Place connectivity networks, derived from Twitter users and tweets, and social connectivity networks, based on Facebook interactions, were used to map spatial and social linkages between locations. These networks were integrated into weekly COVID-19 incidence data across 2,927 U.S. counties using Long Short-Term Memory (LSTM) models. The combined connectivity-weighted model significantly enhanced prediction accuracy, reducing Mean Absolute Percentage Error (MAPE) by 49.38% across 96.62% of the counties, with the greatest improvements observed in urban and Northeastern counties. The results demonstrate that combining connectivity networks enhances prediction accuracy, offering a scalable and sustainable solution for localized disease forecasting on a large scale across diverse geographic areas using publicly accessible social media data.

Sociodemographic Profile of Severely Malnourished Children Admitted to a Nutritional Rehabilitation Centre in One of the Districts of Maharashtra: A Cross-sectional Study

Introduction: Globally malnutrition remains the one of the leading causes of mortality contributing to 60% of deaths in under five children. Severely acute malnutrition children are admitted to Nutritional rehabilitation centre for promoting their clinical and psychological growth. Objective: To describe the sociodemographic profile of severely malnourished children admitted in NRC. Method: The study was observational cross sectional study conducted in a Nutritional Rehabilitation Centre (NRC) of a District Hospital of Thane in Maharashtra among the under five children of age group 6 to 60 months who were admitted in NRC and were presently staying in Thane block were included in the study. Complete enumeration method was used . A Total Sample size of 96(based on the inclusion criteria) was obtained for the purpose of record based data, while for interview sample size was 73(20 cases were non traceable and 3 death were reported) with the parents/guardians of the children. Data was collected using a pre-validated semi structured interview guide via face to face interview. The study was conducted during the 18 month period from March 2017 to August 2018. Data was analysed by using SPSS software version 22.0. Results: Majority of the children admitted to the NRC were male (55.2%). Majority of the mothers were educated till middle school (34.3%) as were the majority fathers (37%). Majority of children had birth order 3 (76.7%). Acute respiratory infection (34.4%), acute gastroenteritis (26%) and acute febrile illness (19.8%) were the most common comorbidities observed among the children. Pre-lacteal feed was given in 5.1% children and colostrum was given to 56.1%, exclusive breast feeding was not done in majority of the children (65.8%).Conclusion: Severe acute malnutrition appears to be more prevalent among households belonging to middle and lower middle class. Educational status of parents was found to be low among majority of the study participants. Exclusive breast feeding was also lacking in majority of the children.

Digital auscultation in clear and present threat of novel respiratory infectious disease: a narrative review.

The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment. This review examines the feasibility and limitations of smartphone-based digital stethoscopes powered by AI, focusing on their ability to perform real-time analyses of audible and inaudible sound frequencies. We also explore the regulatory barriers, data storage challenges, and diagnostic accuracy issues that must be addressed to facilitate broader adoption. The implementation of these devices in veterinary medicine is discussed as a practical step toward refining their applications. With targeted improvements and careful consideration of existing limitations, smartphone-based AI stethoscopes could enhance diagnostic capabilities in human and animal healthcare settings.

Assessing Prevention Protocols by a Brief Questionnaire in the Infectious Respiratory Diseases Pandemics: An Application in Pedestrian Population in COVID-19 Pandemic

To perform effective prevention programs, we need to know how well people follow prevention protocols, like mask use and social distancing, measuring using standard tools. This study aims to develop and validate a brief questionnaire to assess the compliance with prevention protocols against infectious respiratory diseases pandemics including COVID-19 in pedestrians. This cross-sectional study was conducted using convenience sampling method in May and June 2021. Construct validity of the questionnaire was assessed using Exploratory Factor Analysis (EFA). Content validity was evaluated by quantitative method, thus, Content Validity Ratio (CVR) and Content Validity Index (CVI) were calculated. For reliability of the questionnaire, the internal consistency using Cronbach’s alpha, and test-retest reliability using Spearman-Brown correlation coefficient were assessed. A total of 324 persons from three provinces of Iran participated in this study. The mean age (SD) of participants was 41.5 (16.7). The CVR values resulted in the elimination of two questions. The Scale level CVI/Average (S-CVI/Ave) was equal to 0.992; and Scale level CVI/Universal Agreement (S-CVI/UA) was 0.889. One factor with five items emerged from principal component factor analysis accounting for 51.99% of the variance. Cronbach’s alpha coefficient equal to 0.76 indicated an acceptable internal consistency and the Spearman-Brown correlation coefficient of 0.939 depicted stability of the questionnaire. This questionnaire is a brief tool with acceptable validity and reliability to evaluate the compliance with preventive protocols during infectious respiratory diseases outbreaks in order for policy-makers to make effective interventions to slow down the spread of disease.

Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm

Influenza is an acute respiratory infectious disease marked by its high contagiousness and rapid spread, caused by influenza viruses. Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. Firstly, the most suitable indicators were selected using Spearman correlation coefficient. Secondly, a GWO-LightGBM model was established to obtain the residuals between the predicted and actual values. The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. The incorporation of the Baidu Index was shown to enhance the precision of the proposed model’s predictions. The proposed model outperforms comparison models in terms of evaluation metrics such as RMSE and MAPE. Additionally, our study found that the revised Baidu Index indicators show a notable association with ILI trends.

Predicting Tuberculosis Risk in Cattle, Buffaloes, Sheep, and Goats in China Based on Air Pollutants and Meteorological Factors.

Tuberculosis is a zoonotic chronic respiratory infectious disease caused by the Mycobacterium tuberculosis complex. The outbreak and epidemic of tuberculosis can seriously threaten human and veterinary health. To investigate the effects of environmental factors on tuberculosis in domestic ruminants, we collected data regarding the prevalence of tuberculosis in cattle, buffaloes, sheep, and goats in China (1956-2024) from publicly published literature and available databases. We identified the key risk factors among six major air pollutants and 19 bioclimatic variables; simulated the risk distribution of tuberculosis in cattle, buffaloes, sheep, and goats in China using the maximum entropy ecological niche model; and evaluated the effects of environmental factors. The area under the curve of the model was 0.873 (95% confidence interval, 0.851-0.895). The risk factors that most significantly influenced the prevalence of tuberculosis were the nitrogen dioxide (NO2) level, mean temperature of the coldest quarter, cattle distribution density, sheep distribution density, ozone (O3) level, and precipitation of the driest month. The predicted map of tuberculosis risk in cattle, buffaloes, sheep, and goats indicated that the high-risk regions were mainly distributed in South, North, East, and Northwest China. Improved surveillance is needed in these high-risk areas, and early preventive measures must be implemented based on the risk factors identified to reduce the future prevalence of tuberculosis in cattle, buffaloes, sheep, and goats.

Temporal, spatial and demographic distributions characteristics of COVID-19 symptom clusters from chinese medicine perspective: a systematic cross-sectional study in China from 2019 to 2023

BackgroundThe subtypes diagnosis of disease symptom clusters, grounded in the theory of “Treatment in Accordance with Three Categories of Etiologic Factors” and International Classification of Diseases 11th Revision (ICD-11), is a vital strategy for Chinese Medicine (CM) in treating unknown respiratory infectious diseases. However, the classification of disease symptom clusters continues to depend on empirical observations and lacks robust scientific evidence. Consequently, this study seeks to explore the temporal, spatial and demographic distributions characteristics of Corona Virus Disease 2019 (COVID-19) symptom clusters in China.MethodsPubMed, Web of Science, Science direct, WHO, Litcovid, CNKI databases were searched from inception until December 31, 2023. Optical character recognition technology and image recognition technology were employed to identify tables within the papers. Four researchers independently screened and extracted data, resolving conflicts through discussion. Heat mapping and hierarchical clustering techniques were utilized to analyze COVID-19 symptom clusters. Data analysis and visualization were conducted using R software (4.2.0), while the association analysis of symptom clusters was performed using Cytoscape (3.10.2).ResultsA total of 366 COVID-19 clinical trials with 86,972 cases including 66 clinical symptoms of 7 disease systems and other clinical manifestations in China were included. In temporal distribution, 63 symptoms centered around fatigue and 44 symptoms focused on chest tightness are characteristic of symptom clusters in spring and winter, respectively. With the addition of spatial distribution, the symptom clusters in middle and low latitudes during spring are characterized by 53 symptoms centered around fatigue and cough, and 51 symptoms focused on fatigue, respectively. During winter, the symptom clusters in middle and low latitudes are characterized by 38 symptoms centered around chest tightness and 37 symptoms focused on fever, respectively. When considering demographic distribution, the symptom clusters for < 50 years are characterized by fatigue as the core symptom in middle (44 symptoms)/low (28 symptoms) latitudes during spring and middle latitude (25 symptoms) during winter. For ≥ 50 years, the symptom clusters in middle latitude (49 symptoms) during spring and low latitudes (35 symptoms) during winter are centered around cough, while in low latitude (27 symptoms) focuses on diarrhea during spring, and middle latitude (35 symptoms) emphasizes both diarrhea and chest tightness during winter.ConclusionIn summary, variations in symptom clusters and core symptoms of COVID-19 in temporal, spatial and demographic distributions in China offer a scientific rationale for the “Treatment in Accordance with Three Categories of Etiologic Factors” theory. These interesting findings prompt further investigation into CM patterns in the ICD-11, and suggest potential strategies for personalized precision treatment of COVID-19. High-quality clinical studies focusing on individual symptoms are warranted to enhance understanding of respiratory infectious diseases.

Evaluating the capacity of tertiary general hospitals in Beijing to prevent and treat respiratory infectious diseases: a delphi study

BackgroundThe capacity of tertiary general hospitals to prevent and treat respiratory infectious diseases plays a key role in effectively containing the spread of major epidemics within hospitals and ensuring early treatment for patients. This, in turn, helps control the transmission of diseases at the societal level and safeguard public health. However, there is a lack of detailed indicators for assessing hospitals’ capacity in this area, which makes it difficult to evaluate and manage practices. Therefore, this study aimed to develop a comprehensive evaluation index system to assess the capabilities of tertiary general hospitals in preventing and treating respiratory infectious diseases.MethodsThis study utilizes a three-dimensional evaluation model, “Structure-Process-Outcome” as the theoretical basis and first-level index of the indicator system for evaluating the abilities of tertiary general hospitals in preventing and treating respiratory infectious disease. A combination of literature review, semi-structured interviews, and Delphi expert consultation was employed to ensure comprehensive and reliable indicators. The hierarchical analysis method (AHP) was used to determine the weights of the indicators.ResultsThe effective response rates for the two rounds of the Delphi survey were 95.2% and 100%, respectively. The expert authority coefficients were 0.905 and 0.923, respectively. Kendall’s W for the importance of all the indicators increased from 0.274 to 0.327 (P < 0.001), and for feasibility, it increased from 0.198 to 0.240 (P < 0.001). After two rounds of expert consultation, a final evaluation indicator system was established, including 3 first-level indicators, 12 s-level indicators, and 41 third-level indicators, with assigned weights.ConclusionThis study establishes a comprehensive evaluation index system, providing a scientific basis for evaluating the capacity of tertiary general hospitals to prevent and treat respiratory infectious diseases. The system provides valuable reference for improving hospital preparedness and response strategies, and for evaluating the infectious disease prevention and treatment system in Beijing, China.

Optimization of Bacterial Image Processing for Early Detection of Acute Respiratory Infection (ARI) Disease

Optimization of Bacterial Image Processing for Early Detection of Acute Respiratory Infection (ARI) Disease

Circulatory microRNAs as potential biomarkers for different aspects of COVID-19.

The coronavirus disease of 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can alter the expression levels of host microRNAs (miRNAs). Increasing evidence suggests that circulating miRNAs can potentially play an important role in the diagnosis and prognosis of respiratory infectious diseases, especially COVID-19, and might serve as sensitive indicators of disease before the emergence of clinical symptoms. Here, we review the potential of circulatory microRNAs as novel biomarkers for different aspects of COVID-19. Recent studies have suggested that they can be useful not only for COVID-19 prognosis but also for prediction of disease severity and mortality among intensive care unit (ICU) and ward patients. Moreover, extracellular vesicle (EV) miRNAs can be associated with antibody titer after COVID-19 vaccination. This review provides an overview of miRNA-based biomarkers.

Characterizing US contact patterns relevant to respiratory transmission from a pandemic to baseline: Analysis of a large cross-sectional survey.

Contact plays a critical role in infectious disease transmission. Characterizing heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict age differences in vulnerability, and inform social distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and potentially unrepresentative of behavior in the US. We seek to understand the variation in contact patterns across time, spatial scales, and demographic and social classifications, and what social behavior looks like at baseline in the absence of an ongoing pandemic. We analyze spatiotemporal non-household contact patterns across 10.7 million survey responses from June 2020 - April 2021 post-stratified on age and gender to correct for sample representation. To characterize spatiotemporal heterogeneity in respiratory contact patterns at the county-week scale, we use generalized additive models. In the absence of non-pandemic US contact data, we employ a regression approach to estimate baseline contact and address this gap. Although contact patterns varied over time during the pandemic, contact is relatively stable after controlling for disease. We find that the mean number of non-household contacts is spatially heterogeneous regardless of disease. There is additional heterogeneity across age, gender, race/ethnicity, and contact setting, with mean contact decreasing with age and lower in women. The contacts of White individuals and contacts at work or social events change the most under increased national incidence. We develop the first county-level estimates of non-pandemic contact rates for the US that can fill critical gaps in parameterizing future disease models. Our results identify that spatiotemporal, demographic, and social heterogeneity in contact patterns is highly structured, informing the risk landscape of respiratory infectious disease transmission in the US. Research reported in this publication was supported by the National Institutes of Health under award number R01GM123007 and R35GM153478 (SB).

Evaluation and analysis of respiratory infectious disease prevention behaviors in older adults.

Respiratory infectious diseases (RIDs) are a global public health problem, characterized by strong infectivity, high transmissibility, and a high incidence in the population. This study aimed to explore RID prevention behaviors among older adults and analyze their influencing factors. A cross-sectional study was conducted to examine RID prevention behaviors among 2219 Chinese older adults. Analysis of variance and the Kruskal-Wallis test were used to compare behaviors among subjects with different characteristics. Pearson's correlation analysis was used to assess the relationships between knowledge, skills, and behavior, and a generalized linear model was used for multi-factor analysis. The participants in this questionnaire survey were predominantly older adults individuals aged 65-70 years (45.65%), with no more than a primary school educational level (86.70%). Univariate analysis revealed statistically significant associations between age, education, place of residence, living condition, marital status, annual income in the past year, type of medical insurance, health status, smoking status, drinking status, weekly exercise durations, and sleep status in older adults and their RID prevention behavior. Pearson's correlation analysis indicated a moderate correlation between knowledge, skills, and behavioral scores. Multivariate analysis identified place of residence, annual income, smoking habits, alcohol consumption, exercise frequency, knowledge level, and skill level as influential factors for behaviors related to the prevention and control of RID in older adults. Our results confirm the importance of a healthy lifestyle in RID prevention among older adults, especially in terms of smoking, alcohol consumption, and regular exercise, and provide empirical evidence for the development of health promotion programs for older adults people, particularly in rural areas.

Causes of COVID-19 Outbreaks During Sports and Exercise: A Systematic Review.

Physical activity is beneficial for preventing non-communicable and infectious diseases, such as pneumonia. Physical activity is also a potential protective factor for reducing coronavirus disease 2019 (COVID-19) severity. Conversely, outbreaks of respiratory viral infections are more likely to occur owing to group activities, opportunities for contact with individuals and vocalisations. Since the onset of the COVID-19 pandemic, several cases of COVID-19 outbreaks during various sports and exercise have been reported. However, the common causes underlying these outbreaks remain unclear. The objective of this study is to identify the causes of COVID-19 outbreaks during sports and exercise using systematic review approach. Our eligibility criteria were published articles reporting case investigation on COVID-19 outbreaks and the cause during sports and exercise. Studies such as reviews and observational studies without case investigations were excluded. PubMed, CINAHL, WHO COVID-19 Research Database and Ichushi Web were searched on 28 August 2023. The quality of included studies was rated using a quality criteria checklist adapted from a previous systematic review of influenza outbreaks. Vote counting of outbreak causes was performed by type of sports (team or individual). Twenty-one articles reporting 22 outbreaks were identified (quality: high, 9; medium, 9; and low, 3). The outbreaks were most frequently reported in fitness classes, followed by soccer. Most studies listed multiple causes of the outbreaks. The most common suspected cause of outbreaks in individual exercise, mostly from fitness classes, was poor ventilation and not wearing masks, followed by not maintaining physical distance and participation of individuals with some symptoms. In team sports, the most common cause was interaction outside the game, such as social events. This systematic review found a limited number of case investigations suggesting that COVID-19 outbreaks during sports and exercise may be associated with the inhalation of aerosols in indoor settings, interactions outside of team sports games and participation of individuals with some symptoms. Prevention strategies that focus on mitigating these issues may be effective at preventing sports and exercise-associated respiratory infectious diseases outbreaks. CRD42023443158.