Frontiers in Public Health最新文献

Background: Adolescent weight status is shaped by co-occurring behaviors, but variable-centered analyses may obscure heterogeneous patterns. Person-centered approaches can clarify how these patterns relate to BMI.

Objective: To identify adolescent health behavior patterns and assess their associations with BMI categories.

Methods: We conducted a cross-sectional survey of 1,212 students in grades 7-8 from Lin'an District, Hangzhou, China. Six behavior indicators (diet, sugary drinks, outdoor activity, anxiety control, weight-management awareness, and management needs) informed a latent class analysis. Logistic regression, adjusting for demographic and psychosocial factors, estimated associations between class membership and BMI status.

Results: Three health behavior patterns emerged: passive health maintenance (50.9%), self-disciplined health type (32.7%), and high-risk lifestyle (16.5%). Compared to the self-disciplined group, the passive group showed significantly increased risks of overweight (OR = 1.62, 95% CI: 1.02-2.57) and obesity (OR = 1.68, 95% CI: 1.13-2.50), while the high-risk group showed a trend toward increased obesity risk (OR = 1.57, 95% CI: 0.96-2.57, P = 0.072). Female students exhibited lower risks of overweight (OR = 0.56, 95% CI: 0.36-0.87) and obesity (OR = 0.41, 95% CI: 0.28-0.59) compared to males; eighth-grade students had a lower risk of obesity than seventh-grade students (OR = 0.59, 95% CI: 0.40-0.87). Additionally, good sleep quality reduced the likelihood of belonging to the high-risk group (OR = 0.30, 95% CI: 0.17-0.53), and emotional eating increased the risk of being in the passive group (OR = 1.74, 95% CI: 1.31-2.32).

Conclusions: Early adolescents show distinct health behavior patterns with differential weight outcomes. The large passive group, though not overtly high-risk, carries significant overweight risk, highlighting a "moderate-risk blind spot" in weight management. Identifying behavior clusters and tailoring interventions by behavioral profile and sociodemographic context may improve adolescent obesity prevention.

Introduction: Chronic kidney disease (CKD) is an important contributor to morbidity and mortality from non-communicable disease in children and young adults, but there is a lack of data on incidence, mortality, disability-adjusted life years (DALYs), risk factors, and trends in this population. This study aims to provide comprehensive estimates of the burden of CKD among children and young adults.

Methods: We conducted a trend analysis using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. Measures of burden at the global, sociodemographic index (SDI), and regional levels for children and young adults included incidence, mortality, and DALY rates per 100,000 population due to CKD, as well as attributable risks of death and DALYs, the annual percentage change (APC), and the average annual percentage change (AAPC). We also investigated the association between CKD burden and SDI and predicted the incidence from 2022 to 2050.

Results: Globally, from 1990 to 2021, children with CKD demonstrated declining trends in mortality and DALYs, with AAPCs of -1.925 and -1.820, respectively; however, the incidence rate did not change significantly, although it showed a negative trend overall (AAPCs: -0.095). Conversely, all three metrics showed upward trajectories in young adults, with AAPCs of 0.941, 0.256, and 0.187. The relationship between incidence and SDI exhibited an inverse U-shaped pattern, while also demonstrating significant negative associations with mortality and DALYs. Notably, region-specific disparities emerged in attributable risk factors for mortality and DALYs among young adults. Higher SDI regions displayed greater proportional contributions from high fasting plasma glucose, high body mass index, and diets high in processed meat and red meat. Projections for 2022-2050 suggest a continued reduction in children's CKD incidence, while projections for young adults' incidence suggest a continued increase.

Conclusion: The global burden of CKD shows divergent trends across age groups, with projected increases among young adults contrasting with projected declines among children. Lower SDI regions demonstrate a disproportionately higher burden of CKD.

Introduction: After the introduction of a new generation of disposable vapes (e.g., Elf Bar) in the UK in 2021, there was a rapid increase in their use by young people and concern about their availability and marketing. This study examined young people's vaping and smoking between 2020 and 2023, and perceptions of a disposable vape, a tank model and a traditional cigarette, to examine whether disposable vapes may have contributed to the rise in youth vaping.

Methods: Online cross-sectional surveys in 2020 (Youth Tobacco Policy Survey n = 2,121) and 2023 (Youth E-cigarettes Policy Survey n = 2,164) with 11-16-year-olds across the UK were conducted. Measures included demographics, vaping and vaping susceptibility, smoking and smoking susceptibility, type(s) of vapes used, and product ratings across 11 items/attributes covering product appeal, imagery and perceptions of harm.

Results: Between 2020 and 2023, prevalence of ever smoking reduced from 12.2 to 9.3%, whilst ever vaping increased from 10.1 to 17.5%. Vaping experimentation was not confined to young people who had already tried smoking: in 2023, never smokers accounted for the majority of ever vapers (56.6%). Of those who had tried vaping, disposable vapes were the most commonly used device type, with 72.8% having used a disposable vape the first time they tried vaping. There was an association between having tried vaping and susceptibility to smoke. Disposable vapes were rated more favourably compared with a tank device on 9 of 11 items across appeal, imagery and harm (range adjusted odds ratio (AOR): 1.40-4.16; p < 0.001). Traditional cigarettes were rated less favourably than a tank device on all items (range AOR: 0.08-0.61; p < 0.001).

Conclusion: This study highlights the appeal of disposable vapes to young people. Causality cannot be inferred due to the cross-sectional design of the study, however, positive perceptions of disposable vapes across dimensions of appeal, imagery and harm, may have contributed to trends in youth vaping. Further monitoring of the nicotine market, product marketing, and young people's response is critical, particularly as the market adapts in response to the disposable vapes ban.

Background: Older adults with ischemic stroke (IS) are prone to develop comorbidities, thus worsening clinical outcome and intensifying cost burden. Limited studies have revealed evidence linking types of combined diseases with economic burden in IS patients. In this study, prevalent combined diseases and clusters of comorbidity among IS patients aged≥ 60 years were identified. Meanwhile, we explored the combined diseases significantly correlated with incremental hospital costs, aiming to promote the individualized and comprehensive management of IS patients.

Methods: The study was a multicenter, cross-sectional study based on clinical data of IS patients (aged ≥60 years) obtained from three tertiary centers of PLA General Hospital between 2018 and 2023. Patients were stratified into three age groups: 60-69 years, 70-79 years, and ≥80 years. Descriptive analyses were performed to show patient number, the composition of combined diseases, and medical costs. Apriori association rules mapped the clusters of comorbidity. Spearman correlation analysis combined with age-stratified quantile regression identified cost-intensive health conditions.

Results: Apriori correlation analysis revealed a dominant cardio-metabolic-cluster and the intermediary role of diabetes. Hypoproteinemia aggregated with pulmonary infection and anemia, forming a clinically significant malnutrition-infection-anemia triad. The healthcare costs were highest in advanced older adult group despite an overall expenditure declining from 2018 to 2023. Spearman correlation and quantile regression analyses showed correlation between incremental costs and malnutrition-infection-anemia triad, especially at higher cost quantiles. Pulmonary infection was associated with relatively higher cost burdens in patients aged ≥80 years, with significant estimated increases of about ¥5,953, ¥8,538, ¥13,810, and ¥18,945 at the 10th, 25th, 50th, and 75th percentiles. Hypoproteinemia tended to correspond to the significant rise in costs at the 50th percentile for patients aged 60-69 years (β = ¥20,957) and for those aged ≥80 years (β = ¥12,962).

Conclusion: This study identified three prevalent comorbidity clusters in the study population: cardio-metabolic, diabetes-mediated multi-organ injury, and malnutrition-infection-anemia clusters. From the perspective of healthcare cost, pulmonary infection, hypoproteinemia, and anemia might represent the principal cost-intensive clusters of comorbidity, especially in those aged ≥80 years. The persistent cost-age gradient necessitates risk-stratified resource allocation. Implementation of geriatric-specific comorbidity control protocols, particularly targeting the identified clusters with high costs, may optimize both clinical outcomes and healthcare economics in aging populations.

Introduction: Global healthcare systems face escalating challenges due to an aging population, an increasing illness burden, and significant shortages in the healthcare workforce. In this context, rapid advances in digitalisation and metaverse technologies have positioned virtual hospitals as a potentially transformative solution. However, research has largely focused on technological and clinical implementation, leaving open the question of how avatar-based design features and users' eHealth literacy jointly shape psychological experiences and adoption behaviors in virtual hospital environments. This study addressed this gap by examining whether avatar customisation, avatar identification, social presence, and eHealth literacy influence users' intention to use virtual hospitals.

Methods: An online experiment was conducted with 415 participants recruited from an online panel. Participants were exposed to a virtual hospital scenario featuring varying levels of avatar customisation and then completed validated measures of avatar identification, social presence, eHealth literacy, and intention to use the virtual hospital.

Results: Avatar customisation significantly increased users' intention to use the virtual hospital by enhancing their sense of presence and engagement. Avatar identification and social presence operated as sequential mediators: higher avatar customisation strengthened avatar identification, which in turn increased social presence, ultimately leading to greater usage intention. In addition, eHealth literacy moderated the effect of social presence on usage intention, such that this relationship was stronger among individuals with higher levels of eHealth literacy.

Discussion: These findings suggest that avatar-based design and users' eHealth literacy jointly shape key psychological processes underlying the adoption of virtual hospitals. The results are consistent with and extend prior work on avatar identification and social presence by demonstrating their combined mediating role in a virtual healthcare context. The study offers practical implications for designing virtual hospital systems that foster identification and social presence, and it highlights the importance of supporting users' eHealth literacy. Future research could further explore these mechanisms in different cultural settings and with longitudinal designs.

Background: Hand eczema is a common, debilitating condition affecting quality of life. This study aimed to assess the knowledge, attitudes, and practices (KAP) of patients with hand eczema in Guangdong, China, toward their condition.

Methods: A cross-sectional study was conducted at our hospital from September 21, 2023, to July 31, 2024, using a self-designed KAP questionnaire.

Results: A total of 612 valid questionnaires were collected. The mean (SD) scores for knowledge, attitude, and practice were 9.25 (2.55) (possible range: 0-23), 35.68 (6.02) (possible range: 10-50), and 24.03 (5.18) (possible range: 8-40), respectively. Multivariate logistic regression indicated that a higher attitude score (OR = 1.209, p < 0.001) was independently associated with more proactive practices. The path analysis revealed that knowledge directly influenced attitude (β = 0.191, p < 0.001), and attitude had a direct effect on practice (β = 0.591, p < 0.001). Additionally, knowledge indirectly affected practice through attitude (β = 0.113, p < 0.001).

Conclusion: Our findings highlight a significant gap in patient knowledge, suggesting that targeted educational interventions may be a valuable strategy to improve self-management. Future research should test the efficacy of such interventions.

Importance: As fewer jurisdictions report national COVID-19 hospitalization rates and testing of mild and asymptomatic cases is reduced, there is a need to better understand the relationship between COVID-19 hospitalizations and alternative measures of COVID-19 circulation, such as wastewater surveillance.

Objective: We described the association between levels of SARS-CoV-2 in wastewater and COVID-19 hospitalization rates at the national level during and after the pandemic and explored whether wastewater virus level can predict COVID-19 hospitalization rates.

Design setting and participants: Retrospective analysis of public health reports of national wastewater surveillance and COVID-19 hospitalizations from Denmark, the Netherlands, and the United States from 2022 to 2024.

Main outcomes and measures: For each country, we calculated Pearson correlation coefficients and hospitalization multipliers (defined as the hospitalization rate for a given scaled wastewater virus level), for the overall study period and by Omicron subvariant predominance. Additionally, we developed linear regression models using scaled wastewater virus levels to predict concurrent and projected (1-4 weeks) COVID-19 hospitalization rates.

Results: There was a strong correlation between national SARS-CoV-2 wastewater virus levels and weekly new COVID-19 hospitalization rates per million (0.86 [95% CI, 0.82 to 0.90], 0.80 [95% CI, 0.72 to 0.85], and 0.89 [95% CI, 0.85 to 0.92] in Denmark, the Netherlands, and the United States, respectively). Correlations were consistently strong across all subvariant predominance periods in all three countries (range, 0.72 to 1.0). Results from linear regression models showed that hospitalization rate lagged wastewater SARS-CoV-2 levels by approximately 1 week. Linear regression models best predicted hospitalizations 1 week into the future (range of mean absolute percentage error, MAPE, 11.2 to 22.6%) with decreasing prediction accuracy within the range of 2-4 weeks (range of MAPE, 32.5 to 62.3% at 4 weeks). The median hospitalization multiplier (defined as ratio of weekly hospitalization rate to scaled wastewater SARS-CoV-2 level) were 859.3 (IQR, 621.7 to 1210.4), 178.3 (IQR, 133.7 to 243.7), and 245.9 (IQR, 184.0 to 293.7) for Denmark, the Netherlands, and the United States, respectively, during the study period. Regression models identified significant reduction in hospitalizations for a given wastewater virus level over time in all three countries.

Conclusions and relevance: SARS-CoV-2 wastewater virus levels were strongly correlated with COVID-19 hospitalization rates in the upcoming week. Wastewater to hospitalization ratios can be leveraged to enhance public health decision-making and resource allocation.

Introduction: At the end of 2022, an outbreak of the Omicron BF.7 and BA.5.2 subvariants of SARS-CoV-2 occurred in China. In this prospective cohort study, we investigated the pattern of development of major symptom burden and influencing factors in infected Chinese patients.

Methods: First-time infected outpatients were enrolled from December 7, 2022, to January 11, 2023 (N = 355). The prevalence of symptoms was monitored by a repeated patient-reported quantitative symptom survey over nine months.

Results: At the onset of the infection, the most prevalent symptoms (score ≥1 on a 0-10 numeric rating scale) were fatigue (91.8%), cough (91.8%), and sore throat (91.5%) among 33 symptoms monitored. Patients with higher scores for symptom Cluster II (lack of appetite, disturbed sleep, shivering, drowsiness, sweating, nausea, depression, and anxiety) and symptom cluster V (fatigue, sore throat, dry mouth, and dizziness) reported poorer quality of life than other patients during the first month after enrolment. The most severe symptoms (score≥7) lasted during 3-9 months were depression (5.2%), fatigue (4.8%), anxiety (4.8%), runny nose (4.3%), muscle or joint pain (3.3%), nasal congestion (3.0%), disturbed sleep(2.6%). Younger age, female sex, and body mass index of at least 24 kg/m² predicted more severe baseline symptoms and slower resolution (all p < 0.01).

Conclusion: This cohort study identified patterns and characteristics of symptom evolution in outpatients at 9 months post-COVID-19 diagnosis and provides targets for long-term care.

Introduction: Due to the unbalanced spatial distribution of healthcare resources, the suburbanization of the older adult population may contribute to new health inequities. This study investigates public hospital accessibility of different regions and groups in Shanghai, exploring the equitability of resource distribution within the broader context of the city's evolving spatial organization and political economy.

Methods: Drawing on data from the 2000 and 2010 population censuses and the geographical distribution of hospitals, this study employs two complementary measures: the healthcare supply and demand matching index, and the average distance to the closest hospital, evaluating both the diversity of healthcare options and geographical convenience.

Results: The findings reveal significant healthcare inequities in public hospital accessibility: (1) Continuous increase in the number of healthcare hospitals has brought about some improvements in supply levels, but in suburban Shanghai, the accessibility of public hospital is rather poor, and the average distance for all older adults to the closest hospitals has shown almost no improvement, increasing from 2.3 km in 2000 to 2.4 km in 2010; (2) older adults in suburban areas face greater distances to the closest hospital compared to their counterparts not in suburban areas. In 2010, the average distance was 4.4 km for older adults outside the outer-ring, compared to only 0.9 km within the inner-ring; and (3) there is a widening gap in health inequities among individuals from different socioeconomic strata between 2000 and 2010. The distance to the closest tertiary hospital increased by approximately 6 km for older adults in blue-collar communities, but only 1 km for those in white-collar and advanced white-collar communities.

Conclusion: The study concludes that the inequitable accessibility to public hospitals for suburban older adults is a profound structural issue in Shanghai. Policy interventions must extend beyond simply increasing hospital numbers to address the root causes in healthcare governance and urban spatial planning to achieve health equity.

Introduction: Infectious diseases present significant challenges to global healthcare systems due to their rapid spread and associated profound health implications. Early detection of unusual increases in case numbers is crucial for achieving efficient resource allocation and effective response planning.

Method: Therefore, this research proposes and develops a time series predictive framework based on long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) neural network models to forecast the number of COVID-19 cases in Saudi Arabia and detect any unusual increase in cases. Google Trends and time series data for search terms, including "fever," "COVID," and "cough," serve as input, enabling models to detect the temporal patterns associated with a surge in cases. The framework is specifically designed to model temporal dependencies in sequential data, allowing the identification of early signs of anomalies in COVID-19 case trends. Therefore, we propose training the models on preprocessed time series data while adjusting for time lags to improve predictive accuracy. Evaluations of performance are conducted using mean square error (MSE) and F1-score metrics.

Results and discussion: The experimental results demonstrate that BiLSTM returns the highest F1-score of 0.83 for the term "COVID", while LSTM and GRU reach 0.73 and 0.77, respectively. Moreover, BiLSTM outperforms LSTM and GRU at all early time lags for the search terms "fever" and "cough". The results reveal the F1-scores for the term "fever" to be 0.77, 0.62, and 0.5 for BiLSTM, GRU, and LSTM, respectively. Whereas, the F1-scores for the search term "cough" are 0.62, 0.62, and 0.5 for BiLSTM, GRU, and LSTM, respectively. Although BiLSTM incurs higher computational costs, LSTM and GRU offer efficient alternatives to deliver rapid execution. These results highlight the effectiveness of deep learning models in instances of early anomaly detection, supporting timely healthcare interventions and advancing the development of real-time monitoring systems.