BACKGROUND:Although numerous studies have investigated the link between tropical cyclones and dengue transmission, this body of research is predominantly ecological. How individual-level factors modify vulnerability to this risk remains understudied. We investigated how socioeconomic status, knowledge, attitudes, practices (KAP), and risk perception modulate post-cyclone vulnerability to inform targeted interventions. METHODS:We conducted a 1:1 matched case-control study in Guangzhou, Zhongshan, and Foshan cities between September 14 and October 1, 2024, following the Super Typhoon Yagi on September 7, 2024. Cases were confirmed using the local surveillance system and matched to controls recruited via community sampling by age and sex. Structural equation modeling (SEM) was used to identify the mediating pathways between SES and vulnerability, while multivariable conditional logistic regression was used to identify independent direct predictors of dengue infection. RESULTS:Structural equation modeling identified a significant pathway in which a higher socioeconomic status predicted greater knowledge (β = 0.34, p < 0.01), which in turn was associated with stronger preventive practices. In the final multivariable logistic regression, higher scores for practice (aOR = 0.62, 95 % CI: 0.43, 0.88), environment risk perception (aOR = 0.73, 95 % CI: 0.59, 0.91), and knowledge (aOR = 0.57, 95 % CI: 0.42, 0.77) were significant protective factors against dengue. Conversely, frequent mosquito exposure emerged as the strongest risk factor. Compared to individuals bitten weekly or less often, those experiencing daily bites had more than double the odds of infection (aOR = 2.38, 95 % CI: 1.43, 3.97). CONCLUSION:Individual-level determinants, particularly adaptive practices and environmental risk perception, are critical mediators of post-cyclone dengue risk. Public health interventions should be tailored to bolster these protective behaviors, mitigating outbreak threats in vulnerable populations.
Wastewater-based epidemiology (WBE) has emerged as a scalable and cost-effective strategy for monitoring SARS-CoV-2 throughout the COVID-19 pandemic. In the post-COVID era, however, reliance on conventional molecular techniques, such as reverse transcription quantitative PCR (RT-qPCR) and amplicon-based targeted next-generation sequencing (tNGS), limits comprehensive pathogen surveillance due to restricted genomic coverage and throughput, hindering early detection of emerging public health threats. In this study, we evaluated a hybrid capture-based sequencing (HybCapSeq) workflow using five probe panels, including three commercial and two custom panels, to compare SARS-CoV-2 detection performance and per-sample reagent cost with those of RT-qPCR and tNGS. We then applied the comprehensive viral capture panel for longitudinal wastewater surveillance (N = 343) across seven types of sampling sites in two Chinese cities. Temporal trends in SARS-CoV-2 genomic coverage derived from capture sequencing preceded reported clinical case trends by 1-2 weeks, demonstrating its potential as an early-warning indicator for wastewater monitoring. Compared with RT-qPCR, tNGS, and metatranscriptomic sequencing (MetaT), the capture-based approach showed comparable or superior performance in SARS-CoV-2 detection and variant tracking, while also enabling simultaneous detection of diverse human-pathogenic viruses, identification of putative novel viral taxa, and characterization of environmental virome composition. In addition, bacterial reads accounted for 95.05% of total reads in hybrid capture libraries (95% CI: 94.42-95.67%), highlighting the potential of this approach for profiling of bacterial and antimicrobial resistance genes in wastewater. These findings endorse HybCapSeq, combined with strategic probe design and site selection, as a robust WBE platform for pathogen surveillance, epidemic preparedness, and public health response in the post-COVID era.
Sustainable urban development requires integrating geographic approaches and data-driven technologies to address health disparities arising from rapid urbanisation, where identifying spatially heterogeneous potential environmental drivers of chronic diseases is critical for effective governance. Taking the Pearl River Delta (PRD) as a case study, we integrated 2014–2022 lung cancer registry data with multi-source geospatial data using an integrated SHAP-interpretable machine learning and exploratory LiNGAM causal discovery framework, which addresses key limitations of traditional correlation-based models. Results show that PRD lung cancer incidence exhibited a fluctuating upward trend, evolving from localized core aggregation to multi-regional expansion with notable gender disparities. The interpretable machine learning model identifies employment characteristics, PM10 concentration, education level, industrial land use, and medical resources as the top five potential drivers, explaining 63.88% of the total variance, with significant nonlinear threshold effects and spatial heterogeneity. Marked gender heterogeneity was observed: male incidence correlates strongly with industrialization and occupational exposure, while female incidence is more sensitive to residential density and atmospheric particulates. Exploratory LiNGAM analysis suggests a potential pathway of “economic activity–resource polarization–population aggregation”, and PM2.5 emerges as the only factor with consistent potential direct associations across genders. This study offers a preliminary interpretable machine learning-based tool for spatial environmental health risk assessment, to support targeted pollution control and evidence-based sustainable urban development.
Vaccination is the cornerstone of global public health by curbing emerging infectious diseases, preventing some cancers, and establishing herd immunity, thereby protecting population health and mitigating disease burden. In recent years, however, challenges, such as stagnated vaccination coverage, increased vaccination hesitancy, and implementation strategies hindered further progress. From a global perspective, this paper summarizes the key factors constraining the sustained development of immunization program in China, analyses these challenges from two dimensions: population behavioral determinants and governmental decision-making mechanisms, discuss the strategic priorities and transformative directions for future immunization program and analyze the key behavioral problems affecting the immunization program in China based on the cross-level theory of individual behavioral cognition and social influence to provide theorical reference and strategic evidence for improvements of the immunization program system, government decision-making, and public confidence, and the development of global immunization barriers.
BackgroundOral health significantly impacts overall health. Periodontal disease and chronic obstructive pulmonary disease (COPD) are prevalent chronic inflammatory conditions among older adults, but their causal link is not well-established.MethodsMultivariable logistic regression, including subgroup, sex-stratified, and interaction analyses, was used to investigate the link between self-reported periodontal disease and COPD. Two-sample Mendelian randomization (MR) was utilized to explore causality, primarily through inverse-variance weighting (IVW), and supported by four additional MR approaches. Sensitivity analyses were performed to evaluate pleiotropy and heterogeneity. Normal human bronchial epithelial cells were stimulated in vitro with the periodontal pathogens F. nucleatum and P. gingivalis. The expression levels of interleukin-6 (IL-6) and interleukin-8 (IL-8) were then quantified using qPCR and chemiluminescence immunoassay. In vivo, a combined rat model of periodontitis and COPD was established to compare pulmonary inflammatory severity with a COPD-only model.ResultsMultivariable logistic regression revealed significant associations between self-reported periodontal disease, oral hygiene practices, and COPD. The self-reported periodontal disease-COPD association was moderated by hypertension and flossing behavior. Mendelian randomization studies provided suggestive, exploratory evidence that periodontal disease may be associated with a higher risk of COPD in European populations, although the association did not survive multiple-testing correction and was not observed in East Asian populations. In vitro, both pathogens significantly upregulated IL-6 and IL-8 at both mRNA and protein levels. In vivo, rats with comorbid periodontitis and COPD exhibited significantly exacerbated pulmonary inflammation compared with those having COPD alone.ConclusionPeriodontal disease may promote COPD pathogenesis, with in vitro data supporting a direct pro-inflammatory role of P. gingivalis and F. nucleatum in airway epithelium. In vivo, periodontitis as a complex disease state exacerbates pulmonary inflammation in COPD. Maintaining good oral hygiene may represent a potential supportive strategy that warrants further investigation in interventional studies, with potential public health implications, especially among high-risk populations.
