Journal of Global Health最新文献

{"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":"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":"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":"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}

{"title":"Artificial intelligence platform to predict children's hospital care for respiratory disease using clinical, pollution, and climatic factors.","authors":"William Cabral-Miranda, Cauê Beloni, Felipe Lora, Rogério Afonso, Thales Araújo, Fátima Fernandes","doi":"10.7189/jogh.15.04207","DOIUrl":"10.7189/jogh.15.04207","url":null,"abstract":"<p><strong>Background: </strong>Hospitals and health care systems may benefit from artificial intelligence (AI) and big data to analyse clinical information combined with external sources. Machine learning, a subset of AI, uses algorithms trained on data to generate predictive models. Air pollution is a known risk factor for various health outcomes, with children being a particularly vulnerable group.</p><p><strong>Methods: </strong>This study developed and validated an AI-based platform to forecast paediatric emergency visits and hospital admissions for respiratory diseases, using clinical and environmental data in the Metropolitan Area of São Paulo, Brazil. We applied XGBoost, a tree-based machine learning algorithm, to predict hospital use at Sabará Children's Hospital, incorporating clinical, pollution, and climatic variables.</p><p><strong>Results: </strong>We analysed 24 366 emergency department visits and 2973 hospital admissions for respiratory diseases International Classification of Diseases, 10th Revision, Chapter J (ICD-10 J), excluding COVID-19, from January to December 2022. Only geocoded cases within the spatial accuracy thresholds of the study were included. Logistic regression revealed that outpatient visits were associated with higher particulate matter with a diameter of 10 µm or less (PM₁₀) concentrations near children's residences on the day of hospital arrival. In contrast, admissions were linked to lower relative humidity, particularly on drier days. Additional associations were found between admissions and the spring season, as well as male sex.</p><p><strong>Conclusions: </strong>We developed a platform that integrates clinical and environmental databases within a big data framework to process and analyse information using AI techniques. This tool predicts daily emergency department and hospital admission flows related to paediatric respiratory diseases. The algorithms can distinguish whether a child arriving at the emergency department is likely to be treated and discharged or will require hospital admission. This predictive capability may support hospital planning and resource allocation, particularly in contexts of environmental vulnerability.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04207"},"PeriodicalIF":4.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676240","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":"Exploring healthcare facilities' readiness for standard precautions in infection prevention and control: a cross-country comparative analysis of six low- and middle-income countries using national cross-sectional surveys.","authors":"Md Abdullah Al Jubayer Biswas, Scott J Adams, Li Xing, Prosanta Mondal, Michael Szafron","doi":"10.7189/jogh.15.04205","DOIUrl":"10.7189/jogh.15.04205","url":null,"abstract":"<p><strong>Background: </strong>Despite the significant morbidity and mortality caused by healthcare-associated infections worldwide, especially in low- and middle-income countries (LMICs), there is a lack of understanding of the readiness to apply standard precautions for infection prevention and control (IPC) in healthcare facilities across different LMICs.</p><p><strong>Methods: </strong>We analysed nationally representative health system data from the Service Provision Assessment surveys for six selected LMICs - Afghanistan, the Democratic Republic of Congo, Haiti, Nepal, Senegal, and Bangladesh. We recorded seven tracer items of standard precautions into binary elements. We calculated a readiness index based on the World Health Organization's Service Availability and Readiness Assessment manual. We utilised survey-weighted multivariable generalised estimating equations to identify factors associated with the readiness index.</p><p><strong>Results: </strong>Among 6054 healthcare facilities, 55% (95% confidence interval (CI) = 53.1, 56.5) of necessary standard precautions were available, ranging from 48.1% in the Democratic Republic of the Congo to 65% in Nepal. Readiness varied by service area, with the tuberculosis service area being the least prepared at 38% and the general outpatient service area being the most prepared at 66%. Facilities in Nepal and the urban regions showed higher readiness, with mean (x̄) differences of 16% (95% CI = 13.6, 17.9) and 3% (95% CI = 1.8, 4.9) compared to the Democratic Republic of the Congo and rural areas, respectively.</p><p><strong>Conclusions: </strong>We revealed significant deficiencies in standard precautions within healthcare facilities across six LMICs, notably in rural areas. The findings underscore an urgent need for targeted interventions to improve IPC strategies, particularly in domains like tuberculosis care.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04205"},"PeriodicalIF":4.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676244","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":"Determining the cause of death through mortality surveillance using verbal autopsy in Karachi, Pakistan.","authors":"Raheel Allana, Inci Yildirim, Shabina Ariff, Sameer M Belgaumi, Nazia Ahsan, Obianuju Aguolu, Sabeen Umair, Sehrish Amir Ali, Tehreem Maqsood, Mohammad Iqbal, Fauzia Aman Malik, Saad B Omer, Abdul Momin Kazi","doi":"10.7189/jogh.15.04199","DOIUrl":"10.7189/jogh.15.04199","url":null,"abstract":"<p><strong>Background: </strong>In Pakistan, cultural and religious beliefs restrict autopsies, limiting their prevalence. Additionally, many deaths occur at home, outside of hospital systems, making cause-of-death (CoD) determination challenging. This study aims to overcome these challenges by using a community-based verbal autopsy approach in Karachi to identify CoD.</p><p><strong>Methods: </strong>The research was conducted in two peri-urban communities within the Health Demographic Site Surveillance catchment area. A total of 1500 deaths were investigated using the World Health Organization 2016 Verbal Autopsy Questionnaire. Interviewers received extensive training to ensure culturally sensitive data collection, and physicians analysed the data to determine CoD. The 10th edition of the International Classification of Diseases (ICD-10) was integrated with verbal autopsy data for a detailed analysis of mortality causes.</p><p><strong>Results: </strong>The study identified that 52.8% of deaths were male, and 47.1% female, with 51.2% occurring in hospitals and 48.7% at home. Among home deaths, 31.5% were children under five years and 55.4% were above 18 years. Analysis revealed that major CoD included non-communicable diseases: acute cardiac disease (12.6%), liver cirrhosis (7%), and stroke (4.3%), alongside communicable diseases like diarrheal disease (6.4%), pneumonia (4.1%), and sepsis (3.4%). In adults over 18, acute cardiac disease (25.0%) and liver cirrhosis (13.1%) were prevalent, whereas neonatal sepsis (12.8%) and perinatal asphyxia (11.7%) were the most common causes in children under five years. External causes included road traffic crashes (1.6%) and accidental drowning (0.7%).</p><p><strong>Conclusions: </strong>The study underscores the need for targeted health care strategies to address the diverse CoD and varying health-seeking behaviours observed. Improving access to health care, particularly for home-based deaths and vulnerable age groups, is essential for better health outcomes. Tailored interventions are crucial to address both communicable and non-communicable diseases effectively in resource-constrained settings.</p>","PeriodicalId":48734,"journal":{"name":"Journal of Global Health","volume":"15 ","pages":"04199"},"PeriodicalIF":4.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12278687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676242","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}