Journal of Global Health最新文献

{"title":"Residential greenspace and multiple chronic health conditions in China: a cross-sectional study.","authors":"Siyuan Wang, Jingjie Sun, Zhiwei Xu, Gian Luca Di Tanna, Mingsheng Chen, Laura E Downey, Stephen Jan, Lei Si","doi":"10.7189/jogh.15.04218","DOIUrl":"10.7189/jogh.15.04218","url":null,"abstract":"<p><strong>Background: </strong>Multiple chronic conditions are imposing an increasing health and economic burden on the Chinese health system. While exposure to residential greenness has been shown to provide various health benefits, its relationship with multiple chronic conditions remains largely unexplored. This study aims to investigate this relationship using high-resolution satellite imagery and data from the 6th Health Services Survey (HSS) cohort in Shandong province.</p><p><strong>Methods: </strong>We linked health data from the HSS with 12-month average Normalised Difference Vegetation Index (NDVI) measurements based on reported residential geocodes. Multiple chronic condition status was defined as having two or more chronic conditions concurrently, according to the HSS's predefined disease classification. Generalised mixed regression models were utilised to assess both the likelihood and count of multiple chronic conditions in relation to greenspace exposure. Additionally, using the pre-defined disease classes, we also explored how greenspace influences multiple chronic conditions across various physiological systems and disease categories.</p><p><strong>Results: </strong>A total of 28 489 individuals were included in this cross-sectional analysis. After adjusting for potential confounding factors, we found that exposure to greenspace was significantly associated with a reduced prevalence and count of chronic conditions. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for were: Q2 (aOR = 0.74; 95% CI = 0.62, 0.88), Q3 (aOR = 0.69; 95% CI = 0.55, 0.86), and Q4 (aOR = 0.70; 95% CI = 0.56, 0.88), respectively, compared against the baseline Q1 quartile. Subgroup analyses revealed that higher residential greenspace exposure reduced risks of blood, endocrine, nutritional and metabolic chronic diseases. No clear associations were found for other chronic disease classes. Additionally, consistent results were observed across spatial and temporal sensitivity analyses.</p><p><strong>Conclusions: </strong>Our findings underscore the potential beneficial effects of residential greenness on multiple chronic conditions, with implications for urban planning, environmental policy, and community development.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04218"},"PeriodicalIF":4.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global geographic and socioeconomic disparities in COVID-associated acute kidney injury: a systematic review and meta-analysis.","authors":"Danyang Dai, Pedro Franca Gois, Digby Simpson, Souhayel Hedfi, Sally Shrapnel, Jason Donald Pole","doi":"10.7189/jogh.15.04166","DOIUrl":"10.7189/jogh.15.04166","url":null,"abstract":"<p><strong>Background: </strong>Acute kidney injury (AKI) is a common and severe complication of COVID-19, which significantly increases the risk of mortality. There has been a wide range of AKI prevalence reported throughout the pandemic, reflecting differences in geographic location, patient characteristics, and health care resources. We aimed to provide a global overview of the COVID-19 AKI prevalence reported in published studies to uncover geographic and socioeconomic disparities.</p><p><strong>Methods: </strong>We undertook a systematic review and meta-analysis, searching PubMed, Embase, Scopus, Web of Science, and Cochrane Library for full-text articles published in English reporting the prevalence of AKI from January 2020 to November 2023. All studies defined AKI according to the Kidney Disease Improving Global Outcomes criteria. Clinical characteristics were extracted and examined from 334 studies that met the inclusion criteria. With significant study heterogeneity, random-effect models were estimated. We reported pooled AKI prevalence by country, region, and income level. Meta-regression further examined the relationship between COVID-associated AKI and geographic location.</p><p><strong>Results: </strong>After removing studies that utilised the same data, 345 796 patients from 246 studies were included, covering 49 countries. Of 246 studies, 137 came from high-income countries, whereas only three were conducted in low-income countries. Among non-intensive care unit (ICU) patients, low-income countries had the lowest COVID-19 AKI prevalence (14.1%; 95% confidence interval (CI) = 11.4-17.2). Among ICU patients, lower-middle-income countries had the lowest COVID-19 AKI prevalence (27.9%;95% CI = 19.4-38.4).</p><p><strong>Conclusions: </strong>Our study shows significant geographic and socioeconomic disparities in the prevalence of COVID-associated AKI, with a higher prevalence in high-income countries and a lower prevalence in low- and lower-middle-income countries. This study is the most comprehensive systematic review and meta-analysis highlighting global disparities in COVID-associated AKI prevalence. Further studies are needed to explain the reasons behind these differences.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04166"},"PeriodicalIF":4.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting tuberculosis epidemics using an autoregressive fractionally integrated moving average model: a 17-year time series analysis.","authors":"Yongbin Wang, Yifang Liang, Bingjie Zhang, Shibei Yi, Peiping Zhou, Xianxiang Lan, Chenlu Xue, Yanyan Li, Xinxiao Li, Chunjie Xu","doi":"10.7189/jogh.15.04215","DOIUrl":"https://doi.org/10.7189/jogh.15.04215","url":null,"abstract":"<p><strong>Background: </strong>Tuberculosis (TB) remains a significant public health challenge in Henan, China, requiring accurate forecasting to guide prevention and control efforts. While traditional models like autoregressive integrated moving average (ARIMA) are commonly used, they may not fully capture long-term dependencies in the data. This study evaluates the autoregressive fractionally integrated moving average (ARFIMA) model, which incorporates fractional differencing, to improve TB forecasting by better modelling long-range dependencies and seasonal patterns.</p><p><strong>Methods: </strong>Monthly TB incidence data from January 2007 to May 2023 in Henan were collected. The data set was split into a training set (January 2007-May 2022) and a test set (June 2022-May 2023). Both ARIMA and ARFIMA models were developed using the training set, and their predictive accuracy was assessed on the test set using metrics such as mean absolute deviation, mean absolute percentage error, mean square error, and mean error rate. A sensitivity analysis was conducted to evaluate the robustness of the forecasts.</p><p><strong>Results: </strong>There were 1 074 081 TB incident cases in Henan during the study period. The TB incidence was reducing at an annual rate of 5.83%, with the seasonal factor >1 between March-July and seasonal factor <1 in other months. The ARIMA (2,0,1)(0,1,1)₁₂ and ARFIMA (2,0,1)(0,0.38,1)₁₂ models were identified as suitable for the data. The ARFIMA model consistently outperformed ARIMA model in the forecasting phase, with lower errors across all metrics (e.g. mean absolute deviation: 467 vs. 569.54; mean absolute percentage error: 0.19 vs. 0.21; mean square error: 620.48 vs. 690.11; mean error rate: 0.14 vs. 0.17). This indicated that the ARFIMA model better captures long-term dependencies and seasonal patterns, leading to more accurate forecasts.</p><p><strong>Conclusions: </strong>Tuberculosis incidence in Henan shows a clear downward trend with distinct seasonal variation. The ARFIMA model provides more accurate TB incidence forecasts than ARIMA, particularly in capturing long-term trends and seasonality. Effective management of TB at the population level requires proper monitoring and understanding of disease patterns. Forecasting serves as a critical tool for detecting deviations from expected trends, which may signal changes in disease dynamics. Continuous use of the ARFIMA model is essential for guiding public health interventions and ensuring timely responses to emerging challenges in TB control.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04215"},"PeriodicalIF":4.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for tuberculosis control: a scoping review of applications in public health.","authors":"Sonia Menon, Kobto Ghislain Koura","doi":"10.7189/jogh.15.04192","DOIUrl":"10.7189/jogh.15.04192","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has become an important tool in global health, improving disease diagnosis and management. Despite advancements, tuberculosis (TB) remains a public health challenge, particularly in low- and middle-income countries where diagnostic methods are limited. In this scoping review, we aim to examine the potential role of AI in TB control.</p><p><strong>Methods: </strong>We conducted a search on 25 August 2024 for the past five years, in the PubMed database using keywords related to AI and TB. We included laboratory-based and observational studies focussing on AI applications in TB, excluding non-original research.</p><p><strong>Results: </strong>There were 34 eligible studies, identifying eight overarching aspects associated with TB control, including active case finding (ACF), triage, pleural effusion diagnosis, multidrug-resistant (MDR) TB and extensively drug-resistant (XDR) TB, differential diagnosis distinguishing active TB from TB infection and other pulmonary communicable diseases, TB and other pulmonary communicable and non-communicable diseases (NCDs), treatment outcome prediction, pleural effusion, and predictions of regional and national trends. AI may transform TB control through enhanced ACF methods and triage, improving detection rates in high-burden regions. With high accuracy, AI may diagnose pleural diagnosis, differentiate TB active and TB infection, TB and non-tuberculous mycobacterial lung disease, COVID-19, and pulmonary NCDs. AI applications may facilitate the prediction of treatment success and adverse effects. Furthermore, AI-driven hotspot mapping may identify undiagnosed TB cases at rates surpassing traditional notification methods. Lastly, predictive modelling and clinical decision support systems may improve the management of MDR-TB.</p><p><strong>Conclusions: </strong>This scoping review highlights the potential of AI-driven predictions in national TB programmes to enhance diagnostics, track trends, and strengthen public health surveillance. While promising for reducing transmission and supporting TB care in low-resource settings, these models require large-scale validation to ensure real-world applicability, especially for high-risk groups.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04192"},"PeriodicalIF":4.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial technologies for targeting priority areas on surveillance and response of visceral leishmaniasis in São Paulo state, Brazil: embracing a One Health integrative approach.","authors":"Rodrigo S Ferro, Elivelton S Fonseca, Felipe L Semensati, Edilson F Flores, Rogério Giufrida, Roberto M Hiramoto, Osias Rangel, Silvia Silva de Oliveira Altieri, Rosana Leal do Prado, Luiz E Prestes-Carneiro","doi":"10.7189/jogh.15.04200","DOIUrl":"10.7189/jogh.15.04200","url":null,"abstract":"<p><strong>Background: </strong>In 2023, Brazil accounted for 93.5% of the reported cases of visceral leishmaniasis (VL) in Latin America. This study, employing a One Health approach aims: i) to analyse the spatial distribution of VL using integrated geospatial methods, ii) the temporal trend of VL to assess the impact of the COVID-19 pandemic on the occurrence of cases, and iii) identify spatial clusters of municipalities with heightened environmental vulnerability to prioritise surveillance and control efforts for VL in São Paulo state, Brazil.</p><p><strong>Methods: </strong>Archival databases from 1999 to 2022 were analysed. High-risk clusters of human VL (HVL) were identified using the Local Moran Index. Incidence and mortality rates were modelled with the Generalized Additive Model. Data on the distribution of Lutzomyia longipalpis vectors were obtained from São Paulo's Supervision in Control of Endemics, while the spatial distribution of canine visceral leishmaniasis (CVL) was based on survey data from the Adolfo Lutz Institute. Environmental factors, including normalized difference vegetation index (NDVI), land surface temperature (LST), and geomorphology, were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data and Environmental Information Database (BDiA) platform.</p><p><strong>Results: </strong>Lutzomyia longipalpis was detected in 32.4% of municipalities, CVL in 29.0%, and HVL in 18.0%. The western region, characterised by plateau geomorphology, elevated deforestation, and higher temperatures, accounted for 30.6% of high-risk clusters, underscoring its priority status for control and surveillance. While VL cases remain stable during the COVID-19 pandemic, lethality rates increased.</p><p><strong>Conclusions: </strong>Addressing VL and reducing lethality rates will pose a significant challenge for public health authorities in São Paulo in the coming years.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04200"},"PeriodicalIF":4.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic plasma exchange in critically ill patients in low-income and lower-middle-income countries: medical need and feasibility.","authors":"Rosita Bihariesingh-Sanchit, Rakesh Bansie, Angélique Bastienne van 't Wout, Rocade Ma, Dimitri Adriaan Diavatopoulos, Marien Isaäk de Jonge, Arno Pieter Nierich","doi":"10.7189/jogh.15.04214","DOIUrl":"10.7189/jogh.15.04214","url":null,"abstract":"<p><strong>Background: </strong>Therapeutic plasma exchange (TPE) is a blood purification technique designed for the removal of large molecules such as pathogenic antibodies and lipoproteins. The procedure involves removing plasma from the patient in exchange for replacement fluid, and it can be performed either by membrane separation or centrifugation. These conventional techniques are expensive and require the training of skilled personnel. This severely limits their use in low-income countries (LICs) and lower-middle-income countries (LMICs), leading to morbidity and mortality for patients in LICs and LMICs suffering from the diseases where TPE is indicated.</p><p><strong>Methods: </strong>A novel gravity-driven blood separation method might provide the needed access to TPE for LICs and LMICs. We reviewed the medical need, the practical aspects, as well as the possible complications of applying this novel technology in LICs and LMICs. Furthermore, we describe a feasibility study of implementing TPE in Suriname for various diseases and conditions.</p><p><strong>Results: </strong>Where data was available (n /N = 10/11), supportive care combined with TPE using the new device resulted in improved values for the disease-specific markers evaluated in these patients. In addition, eight patients showed complete clinical recovery, and one patient showed partial clinical recovery upon TPE within 0.5-6 months of follow-up. Importantly, none of the patients experienced any serious side effects.</p><p><strong>Conclusions: </strong>This experience in the resource-limited setting in Suriname illustrates that its application is feasible in LICs and LMICs settings, at least for these five diseases with first-line indications for TPE and a significant number of patients.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04214"},"PeriodicalIF":4.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence, combination patterns, and quality of life factors of multimorbidity among older adults in southern China based on the health ecological model.","authors":"Chunxiao Long, Jiaqi Huang, Di Liu, Can Liu, Mengting Wu, Haiyang Wu, Jun Deng, Yinjuan Zhang, Lei Shi, Yanze Cui","doi":"10.7189/jogh.15.04215","DOIUrl":"10.7189/jogh.15.04215","url":null,"abstract":"<p><strong>Background: </strong>Multimorbidity is increasingly prevalent among older adults and poses significant challenges to health and well-being. This study applied a health ecological model to investigate the prevalence, determinants, and common disease patterns of multimorbidity, as well as the factors associated with quality of life (QoL) among older adults in southern China.</p><p><strong>Methods: </strong>A cross-sectional survey was conducted among 2404 individuals aged 60 years and older using a multi-stage random sampling method. Quality of life was assessed using the EQ-5D-5L scale. Multimorbidity was defined as the presence of two or more chronic conditions. The Apriori algorithm identified common multimorbidity combinations. Factors influencing multimorbidity were analysed using univariate and multivariate logistic regression based on a health ecological model. Tobit regression was used to assess associated factors of QoL among patients with multimorbidity.</p><p><strong>Results: </strong>The prevalence of multimorbidity was 44.3%. Hypertension featured prominently in disease clusters, with 'hypertension + hyperlipidemia' as the top two-disease combination. Risk factors for multimorbidity included QoL, age, body mass index (BMI), exercise, sleep quality, social participation, education level, per capita monthly household income, and region. The number of chronic diseases was negatively associated with QoL. Factors significantly influencing QoL included age(≥80, β = -0.087, P < 0.001), number of chronic diseases(>3 diseases, β = -0.029, P = 0.012), fresh fruit intake (occasionally: β = 0.052; often: β = 0.064, all P < 0.005), dietary balance (always: β = 0.078, P = 0.007), exercise frequency (1-3 days: β = -0.039; >3 days: β = 0.024, all P < 0.005), sleep quality (better: β = -0.034; worse: β = -0.070; very bad: β = -0.161; all P < 0.005), social participation (β = 0.034; P = 0.006), education level (primary school: β = 0.028, P = 0.028; college/higher vocational school: β = 0.083, P = 0.010), and region (western: β = 0.083; northern: β = 0.064; eastern: β = 0.132; all P < 0.001).</p><p><strong>Conclusions: </strong>Multimorbidity among older adults in southern China is associated with demographic, behavioral, interpersonal, socioeconomic, and regional factors. Therefore, it is recommended to implement differentiated insurance reimbursement, reinforce county-level resource allocation, integrate community services via the World Health Organization's (WHO) Integrated Care for Older People (ICOPE) framework, and promote individual lifestyle measures. Given the reliance on self-reported cross-sectional data, the findings are constrained by limited causal inference and possible recall bias. Longitudinal studies are needed to validate and refine the conclusions.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04215"},"PeriodicalIF":4.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor's view: What makes science successful?","authors":"Igor Rudan","doi":"10.7189/jogh.15.01005","DOIUrl":"10.7189/jogh.15.01005","url":null,"abstract":"&lt;p&gt;&lt;p&gt;This editorial examines the factors contributing to the success of science, tracing its evolution from fundamental human curiosity to contemporary advancements propelled by technology, data, and artificial intelligence (AI). Beginning with the hypothesis-testing process, it highlights how imaginative individuals throughout history have offered explanations for the natural world, designed experiments, and amassed evidence to confirm or reject their ideas and theories, thus generating new knowledge and understanding of nature. Early humans formulated simple myths and legends as the first scientific hypotheses, partly to lessen their fear of the unknown. A more scientific turn appeared when rare explorer-scientists ventured beyond their ancestral homes, gathered empirical information using their limited senses, made choices based on observations, and sometimes relocated entire communities. Their efforts reflected the timeless elements of the scientific method: from generating a hypothesis to its experimental proof, broad validation and application of new knowledge. The paper then examines the characteristics of successful scientific disciplines. They attract many researchers who generate novel ideas and hypotheses, building an accelerating momentum of discovery. Further hallmarks of such fields are swift and fair peer validation and robust mechanisms for applying new knowledge to improve human well-being. By contrast, less successful fields will struggle with attracting talent, leading to slower progress, which could also be coupled with resistance to new ideas and obstacles to real-world translation of new knowledge. A central theme of the paper is the contribution of measurement and tools to science's success. Modern instruments, from microscopes and telescopes to satellites and statistical tools, have extended our perception of nature, revealing realms far smaller and far larger than human senses can access. The paper also addresses the revolution of 'hypothesis-free science', driven by computers and big data. Rather than framing a single hypothesis, modern researchers gather enormous datasets and use algorithms to test large numbers of possible hypotheses simultaneously and systematically, free of human bias introduced through existing knowledge. Finally, the paper explores how AI could advance science to unprecedented successes: not just by improving human senses like a microscope does, providing additional ones like the Large Hadron Collider does, or extending human memory and computational capacity like computers do, but also by expanding human reasoning itself. Unlike previous tools, AI can synthesise human knowledge and generate hypotheses, design studies, explore patterns and write papers, thus becoming both a 'philosopher 2.0' and a 'scientist 2.0'. Therefore, AI may transform science from a human-centred endeavour into collaborative effort that relies on hybrid intelligence. This unprecedented new frontier will require attention to","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"01005"},"PeriodicalIF":4.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of malnutrition and associated factors in Chinese children and adolescents aged 3-14 years using machine learning algorithms.","authors":"Fangjieyi Zheng, Kening Chen, Xiaoqian Zhang, Qiong Wang, Zhixin Zhang, Wenquan Niu","doi":"10.7189/jogh.15.04204","DOIUrl":"10.7189/jogh.15.04204","url":null,"abstract":"<p><strong>Background: </strong>Child malnutrition represents a critical global public health issue and it is characterised by high prevalence and severe long-term consequences for growth and development. A better understanding of its contributory factors is essential to inform the design of targeted prevention strategies and evidence-based interventions. We aimed to estimate the prevalence of malnutrition in children and adolescents aged 3-14 years, and further to identify promising factors associated with child malnutrition using machine learning algorithms.</p><p><strong>Methods: </strong>Thirty kindergartens and 26 schools were randomly selected from Beijing and Tangshan. Child malnutrition was defined according to WHO standards. Factors for child malnutrition were selected by Logistic regression and three ensemble learning algorithms. An open-access web platform was developed to facilitate calculating probabilities of child malnutrition.</p><p><strong>Results: </strong>Total 18 503 children and adolescents were surveyed, and 10.93% (n = 2022) of them were found to be malnourished. Random forest emerged as the best model, as it carried the highest area under the receiver operating characteristic curve (AUROC) at 0.929. Under the implementation of random forest, top eight factors that formed the optimal set for child malnutrition prediction were identified, including age, frequency of fast food intake, frequency of late-night snacking, family history of diabetes, duration of breastfeeding, sedentary time, and parental body mass index. Further Logistic regression analyses confirmed the predictive significance of these individual factors.</p><p><strong>Conclusions: </strong>We have identified eight contributory factors for malnutrition in 3-14-year-old children and adolescents in Beijing and Tangshan, with their prediction performance optimal under random forest. More studies among independent populations are warranted to validate our findings.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04204"},"PeriodicalIF":4.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction workers' depression, anxiety, stress, and risk factors in China: a cross-sectional study.","authors":"Zhicheng Ling, Yuying Xu, Minmin Tao, Binbin Zhang, Meng Zhang, Zhiding Zhang, Xiaoya DA, Xinmin Liu, Long Huang","doi":"10.7189/jogh.15.04167","DOIUrl":"10.7189/jogh.15.04167","url":null,"abstract":"<p><strong>Background: </strong>Frontline construction workers are generally faced with risk factors such as alcoholism, smoking, and being far away from home, which pose a great threat to their mental health. However, this issue has not yet attracted significant attention form the global community. For this reason, we examined depression, anxiety, and stress levels among construction workers in China and identified their key risk factors, such as education, occupational tenure, geographical mobility, physical well-being, COVID-19 status, insomnia, and alcohol dependency.</p><p><strong>Methods: </strong>We conducted an online survey using validated scales, including the Depression, Anxiety, and Stress Scale, Insomnia Severity Index Scale, Alcohol Dependence Scale, Family-work Conflict Scale, Leadership Support Scale, Workplace Exclusion Scale, and Proactive Personality Scale.</p><p><strong>Results: </strong>We analysed 912 valid responses (790 males, 122 females; mean age = 36.35 years (standard deviation = 10.11). Depression, anxiety, and stress levels were significantly influenced by age, education, work-related injuries, COVID-19 status, insomnia, alcohol dependence, workplace exclusion, and work-family conflict among construction workers (all P-values < 0.05). The regression analysis showed that work-family conflict, workplace exclusion, alcohol dependence, and insomnia were positively associated with depression (P < 0.001), while proactive personality and leadership support were negatively associated with depression (all P-values <0.05). Similarly, physical health, workplace exclusion, alcohol dependence, and insomnia were positively associated with anxiety (all P-values <0.001). Additionally, having a proactive personality negatively influenced depression (P < 0.001). Anxiety positively predicted physical health issues (P < 0.001), workplace exclusion (P < 0.001), alcohol dependence (P < 0.001), and insomnia (P < 0.001), whereas leadership support reduced anxiety levels (P = 0.01).</p><p><strong>Conclusions: </strong>Mental health risks among construction workers are linked to work and personal factors, including insomnia, alcohol dependence, workplace exclusion, and work-family conflict. Employers should implement targeted interventions to improve the work environment, leadership support, and social support systems to enhance the workers' mental well-being.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04167"},"PeriodicalIF":4.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}