Journal of Global Health最新文献

Background: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, demands prompt and precise identification and phenotyping for effective management. This study aims to develop a multimodal multi-task learning framework that concurrently performs automated detection and classification of COPD.

Methods: Retrospective multi-task model fusing computed tomography (CT) and clinical data (n = 2320) at a tertiary hospital. Predictive performance for lung-function metrics was assessed using the concordance correlation coefficient (CCC) and mean absolute error (MAE). Classification efficacy was evaluated via the area under the receiver operating characteristic curve (AUC), accuracy (ACC), precision, recall, and F1-score. Generalisability was further verified by replicating the experiments on three distinct backbone networks.

Results: This study included 1624 patients for model training, 348 patients for the validation set, and an additional 348 patients for the independent test set. The optimal model achieved a maximum CCC of 0.75 for forced vital capacity (FVC), corresponding to an MAE of 0.37, and a maximum CCC of 0.77 for forced expiratory volume in one second (FEV1), corresponding to an MAE of 0.33. For the binary classification task (COPD/Non-COPD), the highest AUC achieved through multi-task learning was 0.88, with a maximum ACC of 0.83. In the ternary classification task (COPD/preserved ratio impaired spirometry (PRISm)/Normal), the highest AUC reached 0.87, with a maximum ACC of 0.79.

Conclusions: Multitask-learning models that integrate chest CT images with basic clinical variables outperform their single-task counterparts in both the identification and classification of COPD. This approach supports evidence-based clinical decision-making and advances the delivery of precision medicine.

Background: The multifactorial mechanisms driving childhood obesity, a global public health challenge, are yet to be fully elucidated. We aimed to develop and externally validate three widely applied machine learning models alongside logistic regression in 2-18-year-old children and adolescents in Beijing and Tangshan to predict obesity risk. As a further step, we wanted to interpret the optimised model and translate it into a web-based tool to inform clinical decision-making.

Methods: We analysed data of 19 024 (training/testing) and 2410 (external validation) children and adolescents from Beijing and Tangshan, respectively. Using a set of factors including demographic, familial, socioeconomic, lifestyle, and perinatal variables, we developed four models (light gradient boosting machine, random forest, eXtreme gradient boosting (XGBoost), and logistic regression) and compared their predictive performance. After validation, we selected an optimised model and interpreted it using SHapley Additive exPlanations (SHAP) analysis. Then, we developed an online calculator with interpretable visualisations to enable real-time risk assessment.

Results: The XGBoost model exhibited superior performance, with an area under the receiver operating characteristic curve (AUROC) of 0.875 on the external validation set, significantly outperforming the logistic regression model (AUROC = 0.718). To identify the minimal feature subset that maintained model efficacy, we incrementally incorporated predictors in the descending order of SHAP importance values while assessing key performance metrics (accuracy, AUROC, and F-beta score). This SHAP-based analysis identified nine key predictors of childhood obesity: birth length, paternal body mass index (BMI), maternal BMI, sleep duration, physical activity, birth weight, maternal age at delivery, delivery mode, and gestational age. The deployed online tool provides individualised risk probabilities and SHAP-derived explanations.

Conclusions: The XGBoost model in our study was the superior ensemble learning method for predicting childhood obesity. The digital tool integrates this model and can help clinical practitioners determine individuals' risk of childhood obesity.

Background: Bangladesh faces significant challenges in accurately documenting causes of death (COD), largely due to incomplete vital registration systems, which lack COD reporting. A substantial number of deaths occur outside health facilities, often without medical certification, leading to further gaps in mortality data. Verbal autopsy (VA) has emerged as a viable method in low-resource settings to bridge this gap. We aimed to explore the feasibility and appropriateness of using VA by tracking burial records' contact information to enhance mortality documentation and inform health policies in the graveyards of urban Bangladesh.

Methods: We employed an exploratory design using both quantitative and qualitative methods. We conducted VAs using the contact details from six graveyards' burial records of Dhaka North City Corporation in Bangladesh, identifying participants through random sampling. In-depth interviews with data collectors, graveyard managers, and study participants provided insights into the feasibility and challenges of this process. We collected the data using the World Health Organization VA tool and assigned CODs using the InSilicoVA algorithm, applying thematic analysis to qualitative findings. We compared mortality trends with national data sets.

Results: We conducted 531 VAs using the contact information from burial site records in Dhaka North City Corporation graveyards, with sub-optimal consent rates varying by location. The leading CODs were acute respiratory infections (21%) and cardiac disease (19%), demonstrating the practicality of obtaining COD from the VA, and the feasibility of collecting burial records and contact details, if consent rates could be improved. Qualitative findings indicated that using burial records for such data collection faces obstacles, including low response rates, socioeconomic disparities in participation, difficulty finding contacts, and sampling inconsistencies.

Conclusions: We are the first to explore VA using contact information from burial records in urban Bangladesh. While the approach shows promise, the current feasibility results are of limited value without substantially improving consent coverage, representativeness, and standardisation. Only with these improvements can this method meaningfully strengthen COD documentation and provide reliable insights into population-level mortality trends.

Female genital schistosomiasis (FGS) is one of the most neglected tropical diseases in the world, affecting over 56 million women and girls in Africa alone. It is a sequala of Schistosoma haematobium infection and is characterised by lesions on the cervix and other reproductive structures. Schistosomiasis and FGS are prevalent in women living in, migrating from, or traveling to Schistosoma haematobium-endemic countries. FGS is associated with significant morbidity, including adverse pregnancy outcomes. Unfortunately, imported schistosomiasis and FGS often remain undiagnosed or are diagnosed only at late stages of disease progression, months to years after arrival in non-endemic settings. This is due to limited diagnostic and screening test availability for schistosomiasis and an absence of awareness and guidelines to diagnose imported FGS, especially among sexual and reproductive health providers. Fragmented care pathways between infectious disease, travel/tropical medicine, and reproductive health services further contribute to missed diagnoses, while structural and social inequities due to migration status and stigma lead to barriers in FGS diagnosis and management.

Background: In 2012, World Health Organization (WHO) recommended outpatient oral amoxicillin for children aged 2-59 months with chest indrawing pneumonia without general danger signs, based on randomised trials. We assessed mortality and case management for such children routinely managed at primary healthcare centres (PHCs) in Lagos, Nigeria.

Methods: This prospective observational cohort study (September 2021-September 2023) was conducted in Ikorodu Local Government Area (LGA), nested within the Integrated Sustainable childhood Pneumonia and Infectious disease Reduction in Nigeria (INSPIRING) Lagos study across 16 PHCs. PHC healthcare workers (HCWs) trained in Integrated Management of Childhood Illness (IMCI) provided routine care, while INSPIRING staff independently assessed eligibility using IMCI criteria. The primary outcome was the 14-day case fatality rate (CFR) among children with chest indrawing pneumonia without general danger signs; secondary outcomes included antibiotic use, treatment adherence, and referral practices.

Results: PHC HCW identified 24 chest indrawing cases, while INSPIRING staff diagnosed 247 cases, including 19 of the 24 identified by PHC HCWs. Among those followed up (n = 16), the CFR was 6.3% (n/N = 1/16; 95% confidence interval (CI) = 0.2-30.2) for PHC HCW identified cases; with the same death identified by INSPIRING staff (n/N = 1/197; CFR = 0.5%). The single event in each cohort, and high loss to follow-up, imply that these CFR estimates are statistically fragile and should be interpreted as indicative only. Only 4% (n/N = 1/24) of children received routine care aligned with IMCI protocols. Of those prescribed antibiotics, 50% (n/N = 4/8) completed the full course, and just 1 of the 6 of referred children was admitted to hospital.

Conclusions: PHC HCWs rarely diagnosed chest indrawing pneumonia, and one-third of the patients were lost to follow up leading to a smaller than expected sample and therefore an imprecise CFR. Improving HCW capacity to identify and manage pneumonia, alongside strengthening IMCI implementation, is critical to reducing preventable child deaths in this setting.

Background: The COVID-19 pandemic has disrupted early childhood environments globally, raising concerns about its potential impacts on neurodevelopment. Although early childhood is a critical developmental period, large-scale evidence from South Korea - where strict social distancing and unique caregiving structures were in place - remains limited. We aim to evaluate age- and domain-specific neurodevelopmental outcomes among children aged 0-5 years before and during the pandemic, focusing on differences by age and sex.

Methods: We analysed children aged 0-5 years using data from a national health screening programme and a pre-post comparison design with repeated cross-sectional data. We compared the pre-pandemic (July 2018-March 2020) and pandemic (April 2020-December 2021) periods. We categorised children into infants (9-12 months), toddlers (18-36 months), and preschoolers (42-71 months). We measured developmental outcomes using the Korean Developmental Screening Test across six domains: gross motor, fine motor, cognition, language, social skills, and self-help. We conducted multivariable logistic regression and difference-in-differences analyses.

Results: We analysed 6 253 076 assessments from 2 797 459 children. Peer-level developmental status declined significantly during the pandemic across all age groups, with the most pronounced decrease among toddlers (adjusted odds ratio (aOR) = 0.92; 95% confidence interval (CI) = 0.91-0.92), followed by infants and preschoolers. The language domain experienced the greatest decline (aOR = 0.87; 95% CI = 0.86-0.88), whereas the gross motor domain showed significant improvement (aOR = 1.13; 95% CI = 1.11-1.15). Boys were more adversely affected than girls, particularly in gross motor and social skill domains.

Conclusions: The COVID-19 pandemic led to significant developmental declines among young children, particularly in language and social domains and among toddlers. Boys were more adversely affected than girls, especially in language and socioemotional skills, highlighting sex-related vulnerabilities. Prioritising early screening and interventions targeting these key domains, alongside sex-sensitive strategies and caregiver support, will be essential to mitigate developmental disruptions during future pandemics.

Background: Tuberculosis (TB) remains a major public health challenge in China. Although meteorological factors are known to influence its transmission, their nonlinear and lagged impacts across regions and seasons remain unclear. We quantified these effects using the most detailed national data set available and explored how climate information can enhance TB prediction and control.

Methods: We conducted a nationwide ecological time-series study by integrating weekly TB surveillance data (2005-19) with high-resolution meteorological and air pollution models. We assessed associations between TB incidence and meteorological factors using negative binomial regression and distributed lag nonlinear models to account for nonlinear and delayed effects.

Results: From 2005 to 2019, TB cases in China decreased from 1.23 million to 0.75 million (estimated annual percent change <0 across all regions), with the burden remaining highest in western and southern China. Higher weekly mean temperature (incidence rate ratio (IRR) = 1.33) and precipitation (IRR = 1.03) increased TB risk, while greater temperature differences (IRR = 0.96) and relative humidity (IRR = 0.92) had protective effects. Temperature effects peaked in summer (IRR = 1.80; P < 0.05). Lagged analyses showed that extreme high temperatures and high wind speeds initially suppressed, but subsequently elevated TB risk, while higher precipitation and humidity showed delayed risk effects.

Conclusions: By integrating fine-scale epidemiological and meteorological data, our study adds to our knowledge on TB epidemiology by more accurately characterising climate-disease interactions and enhancing the predictive capability of risk models. The findings provide empirical evidence to support the development of risk stratification tools and guide the implementation of proactive, phased intervention strategies aimed at mitigating the persistent TB burden in high-risk regions.

Earthquakes are the deadliest natural hazards. While stronger building codes are essential for reducing their health impacts, achieving full compliance is a costly, long-term effort, particularly for low- and middle-income countries. We highlight low-cost, high-impact interventions across three areas that can complement building code implementation and significantly reduce risks. First, strengthening health emergency governance to ensure that lines of command and accountability are clear when a disaster strikes. Governments need response plans that outline actions, roles, and responsibilities, and enable coordinated action through emergency operations centres and incident command systems. Second, enhancing risk communication and community engagement is critical. Communities are often the first responders to seismic events. Training them in first aid and search and rescue, involving them in preparedness planning, and co-developing communication approaches ensure more tailored and actionable responses. Finally, improving international coordination can enhance the predictability and effectiveness of external support by establishing aid agreements, harmonising border-entry procedures, and ensuring staff are trained to work with and coordinate international Emergency Medical Teams. Governments and the international community should increase investments to limit the health impact of earthquakes, including through the outlined low-cost interventions.

Background: This study aimed to assess the burden of early-onset cancers in China and the trends in their associated risk factors based on the latest cancer statistics.

Methods: We integrated and analysed data on 34 cancers in China from the Global Burden of Disease 2021 study and the Global Cancer Observatory 2022 project. The primary outcomes included age-standardised incidence (ASIR), mortality (ASMR), and disability-adjusted life years rates (ASDR), and the average annual percent change.

Results: In 2022, an estimated 743 688 new cases and 159 167 cancer-related deaths caused by early-onset cancers in China, with an ASIR of 98.37 per 100 000, and an ASMR of 21.40 per 100 000. Thyroid cancer, breast cancer, and cervical cancer were the most common cancers among female, while thyroid cancer, liver and intrahepatic bile ducts cancer, and trachea, bronchus, and lung (TBL) cancer were the most common cancers among male. Breast cancer, cervical cancer, and TBL cancer had the highest mortality rates in female, while liver and intrahepatic bile ducts cancer, TBL cancer, and colorectal cancer had the highest rates in male. From 1990-2021, the largest increases in ASIR, ASMR, and ASDR were observed for early-onset neuroblastoma and other peripheral nervous cell tumours, multiple myeloma, and kidney cancer. Smoking and high body mass index remained the primary risk factors contributing to disability-adjusted life years for most early-onset cancers.

Conclusions: Developing targeted health prevention strategies for specific cancer types and promoting healthy lifestyles could help reduce the burden of early-onset cancers in China.

Background: Suboptimal sleep and diabetes are major contributors to mortality. However, whether sleep patterns differentially affect mortality across glycaemic statuses remains unclear. This study examined associations of sleep patterns (sleep duration and sleep disorders) with all-cause mortality among individuals with normoglycaemia, prediabetes, and diabetes.

Methods: Data were obtained from the Taiwan MJ cohort, including 534 238 participants enrolled between 1996 and 2022. Sleep duration ('less than 6 hours', '6-8 hours', 'more than 8 hours') and sleep disorders (yes/no) were assessed via standardised questionnaires. Glycaemic status was classified as normoglycaemia, prediabetes, or diabetes. Mortality data were obtained from the Taiwan Death Registry. Cox proportional hazards regression models were employed to evaluate the association between sleep patterns and the risk of all-cause mortality.

Results: The study included 363 863 participants with normoglycaemia, 144 602 with prediabetes, and 25 773 with diabetes. Over a median follow-up period of 19 years, 52 208 deaths were recorded. Compared with those who slept 6-8 hours, normoglycaemia individuals who slept less than 6 hours had a higher risk of all-cause mortality (hazard ratio (HR) = 1.05; 95% confidence interval (CI) = 1.02-1.08) and those who slept more than eight hours had a higher risk of all-cause mortality across all glycaemic groups: normoglycaemia (HR = 1.19; 95% CI = 1.15-1.24), prediabetes (HR = 1.24; 95% CI = 1.19-1.30), and diabetes (HR = 1.29; 95% CI = 1.22-1.36). Sleep disorders were also associated with increased mortality among individuals with prediabetes (HR = 1.04; 95% CI = 1.01-1.07) and diabetes (HR = 1.07; 95% CI = 1.02-1.11).

Conclusions: Long sleep durations and sleep disorders were associated with increased mortality, especially among individuals with impaired glucose regulation while short sleep duration was discovered to associate with increased risk of mortality in people with normoglycaemia. These findings highlight the potential role of sleep assessment in risk stratification, although the observational nature of the study limits causal inference.