Frontiers in Public Health最新文献

Background and aims: Endoscopic visualization for the diagnosis of Helicobacter pylori (HP) infection status is highly important for helping endoscopists quickly understand the status of gastric background mucosa and assisting in subsequent diagnosis and treatment. In this study, a deep learning algorithm was designed to construct a three-class classification model of HP infection status, providing a new approach to address the subjectivity in the interpretation of endoscopic features.

Methods: The clinical data of patients who completed gastroscopy were collected, and 16 endoscopic features were evaluated and recorded. On the basis of the status of HP infection, the patients were classified into three groups: the current HP infection group (CI), the previous HP infection group (PI), and the negative HP infection group (NI), with 1,000 patients screened in each group. In this study, an HP infection classification model based on the transformer network was constructed, which uses a self-attention mechanism to capture feature associations for the task of HP infection classification and recognition. Model interpretability was achieved by screening key features through SHapley Additive exPlanations (SHAP) value analysis.

Results: A total of 3,000 subjects were included in the study, and comparative analysis revealed that the 1D-transformer model demonstrated superior performance in the HP recognition task. The accuracy, specificity, sensitivity, and F1_scores produced by the model were 98.9 ± 0.28, 98.8 ± 0.14, 99.4 ± 0.27, and 98.9 ± 0.28, respectively. In addition, it has better performance than other algorithms and models. In terms of model interpretability, this study highlights the importance rankings of different features in model decision-making and the directions of their influence. The results show that map redness (SHAP value of 0.220), xanxoma (SHAP value of 0.101), atrophy (SHAP value of 0.065), and intestinal metaplasia (SHAP value of 0.008) are key features for identifying the PI. Diffuse redness (SHAP 0.186), thickened folds (SHAP 0.126), mucus coverage (SHAP 0.094), and nodular changes (SHAP 0.043) are key features for identifying CI. The presence of RAC (SHAP 0.262) and ridge redness (SHAP 0.026) are key features for identifying NI.

Conclusion: This study applies a 1D-transformer model to the task of classifying HP infection status, and compared with other models, it can precisely screen out populations with three different HP infection statuses, with higher performance and reliability.

Background: Children living in low socioeconomic status (SES) environments are more likely to develop behavior problems. Breastfeeding is one behavior that has been positively linked to mental health throughout childhood. We investigated whether breastfeeding modifies the association between low SES and behavior problems.

Methods: We studied a subset of the Canadian CHILD cohort (N = 2,342). Lower SES (n = 592) was defined as one or more of: (1) low income based on family size, (2) single parenthood, or (3) maternal education below a post-secondary degree. Breastfeeding was reported by caregivers from birth to 2 years. The Child Behavior Checklist (mean 50, SD (10), comprising internalizing, externalizing, and total behavior scores) was administered at 5 years. We tested main effects and interactions between SES and breastfeeding on child behavior, adjusting for several maternal and child characteristics.

Results: Lower SES was related to higher (worse) behavior scores (B = 2.06 [95%CI: 1.06, 3.07] for total behavior scores), while longer and more exclusive breastfeeding was related to lower (better) behavior scores (B = -2.43 [95% CI: -3.74, -1.11] for exclusive breastfeeding at 6 months, compared to no breastfeeding, for total behavior scores). We observed significant interactions between longer and more exclusive breastfeeding and family SES on internalizing and total behavior scores, indicating that the "socioeconomic gap" in behavior scores becomes smaller with more exclusive and longer breastfeeding.

Conclusion: This study provides new evidence that breastfeeding may be one factor that can help reduce socioeconomic inequities in child behavior scores.

Background: Physical exercise (PE) is important for health. Girls are reported to participate less compared to boys. Multiple factors influence participation and non-participation, including neighborhood, socioeconomic status, social support, and disability. Factors may combine and form paths to participation or non-participation, yet these paths are unknown. To our knowledge, this is the first study to combine cluster analysis with a configurational comparative method to explore paths to participation and non-participation in PE among adolescents.

Methods: Data from 178 Swedish 15-18-year-olds revealed two exercise-related clusters: Not exercising and Sporting & Exercising. Girls and boys in these clusters were analyzed separately by coincidence analysis to identify paths leading to membership in each cluster. The initial analysis included 41 questions aggregated into 24 variables.

Results: Not having quick access to pocket money is a path by itself to Not exercising among girls and part of the path to Sporting & Exercising. The paths to Not exercising are more complex for boys. Participation in adult-led activities differs between boys in the Not exercising and Sporting & Exercising clusters. Having a disability is only a difference-making factor among boys when combined with "not avoiding anyone in the neighborhood" and "sometimes meeting friends in person."

Conclusion: Multiple distinct paths lead to Sporting & Exercising and Not exercising, and these differ between girls and boys. Paths to exercising and non-exercising can be multi-factorial in nature, where several factors must be jointly present to explain membership in a particular exercise-related cluster. Future research may wish to adopt a similar configurational approach to explore other high-priority activities in which adolescents do or do not participate to improve interventions and policies aimed at increasing adolescent participation in PE.

Background: Sanitation workers are exposed to heavy metals due to improper disposal of waste. This study analyzes heavy metal concentrations in the serum, urine, and blood of sanitation workers exposed to improperly disposed e-waste and industrial residues, and examines how protective measures affect these levels.

Methods: This study examines heavy metal levels in 102 sanitation workers (22 operators, 37 drivers, and 43 sanitary workers) versus a control group of 50 unrelated workers. Samples were pre-treated with 0.5% nitric acid and Triton X-100 for serum and blood, and 0.5% nitric acid with 200 μg/L gold for urine. ICP-MS in KED mode was used to measure concentrations of 10 heavy metals (Cr, Mn, Co, Ni, As, Cd, Sn, Sb, Hg, and Pb) in the samples.

Results: Long working hours, working at disposal and hand injury were associated with higher concentrations of Cd, Sb, and Hg in human blood. Elevated serum levels of Mn (2.46 μg/L), Cd (0.14 μg/L), Sn (1.19 μg/L), and Sb (9.99 μg/L) were observed in sanitation workers. Among drivers, increased serum concentrations were noted for Mn (2.67 μg/L), Cd (0.15 μg/L), Sn (1.18 μg/L), and Hg (0.57 μg/L). Serum Sb (10.10 μg/L) was particularly elevated in the sanitary workers. Sanitation workers exhibited heightened urine levels of Cr (1.91 μg/L), Co (0.35 μg/L), Ni (2.79 μg/L), Cd (0.92 μg/L), Sn (6.43 μg/L), and Sb (0.14 μg/L). Operators demonstrated an increased concentration of Cr (2.37 μg/L). Drivers showed elevated levels of As (95.29 μg/L), Cd (1.34 μg/L), Sb (0.14 μg/L), and Hg (0.31 μg/L). Sanitary workers exhibited higher concentrations of Co (0.41 μg/L), Ni (3.54 μg/L), and Sn (6.79 μg/L). Blood concentration levels of As (8.70 μg/L), Cd (3.62 μg/L), Hg (3.82 μg/L), and Pb (16.71 μg/L) were highest in drivers' group followed by operators, while lowest in sanitary workers. The median concentrations were all below the BEI (ACGIH, GBZ, WS/T).

Conclusion: Sanitation workers face an elevated risk of exposure to heavy metals, may posing significant threats to their occupational health. There is a critical need for comprehensive prevention and intervention strategies to mitigate heavy metal exposure among sanitation workers.

Introduction: In the digital workplace, "always on" is progressively increasing the psychological burden on employees and having a negative impact on their mental health. As a proactive self-regulation strategy, digital disconnection aims to alleviate the psychological stress caused by continuous connection by intentionally limiting their use of electronic devices and digital applications. Existing research has preliminarily defined the concept of digital disconnection, but discussions on this topic remain insufficient, particularly given the lack of effective measurement tools applicable to the Chinese organizational context. This has led to inadequate exploration of the mechanisms through which digital disconnection operates among employees in China's collectivist culture.

Methods: Therefore, this study investigates the structural dimensions and measurement of employee digital disconnection in the Chinese organizational context through the questionnaire survey method.

Results: By analyzing 795 questionnaires in two stages, it was found that the Chinese Version of the Employee Digital Disconnection Scale (Chinese-EDDS) includes four dimensions: digital disconnection from private information and communication technologies (ICTs) outside work (DD POW), digital disconnection from work-related ICTs outside work (DD WOW), digital disconnection from private ICTs during work (DD PDW), and digital disconnection from work-related ICTs during work (DD WDW), demonstrating good reliability and validity.

Discussion: This study provides a reliable quantitative tool for researching employee digital disconnection in the Chinese organizational context, thereby offering a basis for organizations to develop supportive tools and training programs to improve the mental health of employees.

Introduction: Mpox has re-emerged as a public health issue in West Africa, underscoring the need for robust surveillance systems that can detect outbreaks and facilitate effective responses. This study evaluates Senegal's mpox surveillance system, focusing on performance, data quality, governance, and potential for Artificial Intelligence-powered, predictive epidemic intelligence. It reviews trends and system operations while exploring AI and modeling to improve early warnings.

Methodology: A descriptive, exploratory approach combined quantitative and qualitative data from various sources. A retrospective review of mpox cases from January 2024 to October 2025 utilized DHIS2 Tracker to analyze geographical, temporal, and demographic patterns, as well as reporting delays and biases. Data-quality checks and stakeholder interviews provided insights into system performance, intersectoral coordination, and preparedness for advanced analytics.

Results: By late October 2025, Senegal had reported seven mpox cases, all in Dakar, primarily affecting young, mobile populations, with a higher incidence among children and working-age adults. Transmission followed population movement along the Dakar-Thiès-Diourbel corridor, showing how urban density and mobility influence spread. The surveillance system improved reporting, geolocation, and follow-up, supported by One Health coordination and digital health infrastructure. Challenges include underreporting in rural areas, uneven coverage, limited real-time analytics, and gaps in data interoperability and responsible AI regulation. The AI4MPOX-SN initiative offers an opportunity to enhance epidemic intelligence by integrating human-animal-environment data, using AI for anomaly detection and predictive modeling to inform interventions.

Conclusion: To develop predictive epidemic intelligence in Senegal, it's vital to involve local stakeholders, promote transparency, build workforce capacity, and establish safeguards for the ethical use of data. Combining technology, participatory governance, and institutional strengthening will enable Senegal to transition from reactive detection to proactive surveillance, positioning it as a regional leader in health security in West Africa.

Disease epidemiology during the COVID-19 pandemic differed greatly across the globe. In contrast to early pandemic predictions, Africa recorded the fewest SARS-CoV-2 related hospitalizations and deaths. Hypotheses proposed to explain this paradox include underreporting, age demographics, climate, national mitigation strategies, lifestyle factors, pre-existing cross-reactive protection, and host genetic determinants. This traditional, narrative review evaluates these hypotheses investigated in the published literature, and highlights knowledge gaps which limit our understanding and obscure validation of potential explanations. It also discusses how responses to vaccines, the primary intervention sought to control infectious disease outbreaks, may vary both within the African population and across other continents. Potential explanations in the literature include pre-existing immunity, poor nutrition, immune modulating co-infections, comorbidities, microbiome composition, genetic polymorphisms, and demographic factors. Previous studies have shown that pre-existing (infection-derived) immunity or cross-reactive immune responses can augment vaccine-elicited positive responses and can protect against reinfection in a way similar to immunization. Conversely, there are also studies showing that prior immunity interferes with the efficacy of new vaccines through mechanisms like original antigenic sin and immune imprinting. Thus, there is need for more immunology studies to understand the relative contribution of pre-existing cross-reactive immune responses to the epidemiology of new pathogens. These studies are particularly essential to understand the differences between pandemic preparedness and population vulnerability, as well as to inform vaccine development and vaccine effectiveness monitoring studies. SARS-CoV-2 serves as an important case study to understand heterogeneity between and within populations in immune responses to both the pathogen and to vaccination. This understanding is crucial in informing vaccine research and development aimed at supporting the 100-day mission for when the next pandemic threat emerges.

Global increases in longevity have led to a rapidly growing population of older adults entering advanced old age, facing increased risks of chronic illness, mobility limitations, sensory impairments, cognitive decline, poor emotional health, and social losses. These vulnerabilities make it more difficult for them to maintain independent living arrangements and to age optimally in their current homes, neighborhoods, and communities. Routine tasks like climbing stairs, reaching high shelves, or avoiding slippery floors become unsafe. Older people struggle to reach inhospitable community destinations to meet their obligatory and discretionary needs. As their "activity spaces" shrink to the home's confines, they experience new health stressors that further complicate age-related declines. Research and intervention strategies have emphasized making homes age-ready and reducing barriers to community access. However, rapid advances in digital technologies necessitate a reevaluation of how older adults interact with their surroundings. Homes can function as digital environments, equipped with internet-connected devices and AI-powered software. Interior sensors can detect dangerous temperatures, and wearables can monitor vital signs and medications. Motion detectors can summon emergency responses in the event of falls or other medical emergencies. Older adults can access virtual or "digital destinations" in real time-to buy goods, order food, use patient portals, access financial or government services, and engage in social or recreational activities. We are witnessing a paradigm shift in how older adults can maintain their independence and age in place. Their homes have transformed into environmental monitoring hubs and digital control centers, increasing their safety and health and reducing their dependence on neighborhood and community resources. Although older people are increasingly adopting these solutions, many remain reluctant. This provides the rationale for the ELDERDES model, which identifies the factors that influence older adults' adoption of digital environment technologies and how four categories of digital environment solutions (DES) contribute to their positive residential experiences, improved health outcomes, independent living, and optimal aging. There are many reasons to argue that increasing older people's access to their digital environments will become a more salient arena for research and intervention than primarily adapting the features of their traditional physical or social environments.

[This corrects the article DOI: 10.3389/fpubh.2025.1592213.].