International Health最新文献

Background: China's progress toward universal health coverage (UHC) faces persistent equity gaps in basic public health services. This study quantifies the equity of opportunity in mainland China's national essential public health services (NEPHS) from 2015 to 2020 and analyses its influencing factors through the lens of UHC dimensions to inform policy design aimed at advancing health equity and sustainable development.

Methods: Using panel data from 31 provinces, an evaluation index system was constructed. The entropy-weight method was applied to quantify the equity of opportunity in NEPHS across mainland China. A two-way fixedeffects model was employed to examine the impact mechanisms of factors across different UHC dimensions.

Results: The equity of opportunity in NEPHS showed an overall upward trend, with eastern and central regions demonstrating higher equity levels than western regions. Financial investment and central transfer payments significantly positively related to equity, whereas higher fiscal self-sufficiency attenuated the positive effect of transfers. An increase in the number of participants in basic medical insurance also positively related to equity. In contrast, a higher registration rate for antenatal care was negatively related to equity of opportunity.

Conclusions: Local governments should adjust the structure of public fiscal expenditures, appropriately increase investment in NEPHS and continue expanding basic medical insurance coverage to advance equalization of NEPHS.

Background: Antimicrobial resistance (AMR) is a top global public health and development threat. Antimicrobial stewardship programs (AMSPs) are one of the most cost-effective interventions to optimize the use of antimicrobials. This study reviews AMSPs that have been implemented in low- and lower middle-income countries.

Methods: A systematic search was conducted on electronic databases including MEDLINE, PubMed, Embase, OVID, Web of Science and Cochrane Library on 18 July 2024 for published papers from 2014 to 2024 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Relevant published literature was then selected based on the established inclusion/exclusion criteria. Each article was screened by two independent reviewers. Data were extracted and synthesized in the review.

Results: Of the 425 articles screened, only 13 were eligible for review and included in this study. Two studies were multinationals. Five studies were randomized controlled trials. Among the three key focuses of AMSPs, most of the interventions focused on optimizing antibiotic use (n=8), followed by improving diagnostics and monitoring (n=3) and education and training (n=2). The most commonly reported barriers to implementing AMSPs was a lack of resources (n=9). Facilitators reported included knowledge of AMS (n=8), availability of educational and training resources (n=8), adequate funding (n=6), accountable and transparent procedures (n=5) and positive communication within healthcare facilities (n=4).

Conclusions: All included studies show improvement in AMS through innovative programs. However, only a few have been adopted nationwide and influence policy formulation in the country. We recommend adoption of effective AMSPs into the national strategic planning and implementation across primary health settings.

The Democratic Republic of the Congo (DRC) conducted a national self-assessment of the 2005 International Health Regulations (IHR) core capacities using the revised 2021 electronic State Party Self-Assessment Annual Reporting tool. Held in March 2022, the hybrid workshop involved multisectoral stakeholders and evaluated 15 core capacities. The average national score was 36%, indicating limited capacity to implement IHR and the 2019 National Action Plan for Health Security. Four capacities, including points of entry and radiation emergencies, showed no capacity, while 10 were classified as limited. Only the IHR coordination reached the developed capacity level. Key weaknesses were identified in emergency operations, laboratory systems and legal preparedness. Strengths included One Health integration and risk communication efforts. These findings underscore the urgent need to improve multisectoral coordination, operational readiness and infrastructure investment to meet IHR requirements and better respond to public health threats. Strategic recommendations were proposed to guide national and international support.

Background: Bloodstream infections (BSIs) caused by Klebsiella pneumoniae pose a significant global health burden, complicated by rising antimicrobial resistance (AMR). This study aimed to characterize resistance patterns, identify predictors of carbapenem resistance, and develop a machine learning model to predict patient outcomes.

Methods: In a retrospective analysis of 109 279 K. pneumoniae BSIs from tertiary hospitals in China (2012-2023), 11 000 isolates underwent whole-genome sequencing (WGS) and antimicrobial susceptibility testing. Cox proportional hazards and logistic regression models identified predictors of 30-day mortality and carbapenem-resistant K. pneumoniae (CRKP), respectively. A random forest model predicted AMR trends and outcomes, evaluated by accuracy, precision, recall, and ROC-AUC using R Studio (R Studio, Inc., Boston, MA, USA).

Results: Carbapenem resistance occurred in 32.3% of isolates, with rates of 41.9% for third-generation cephalosporins and 41.2% for fluoroquinolones. Among sequenced isolates, ST11 with blaKPC was the dominant CRKP genotype (12.0%). blaKPC (OR 3.97, 95% CI 3.10-5.11) and blaNDM (OR 2.80, 95% CI 2.07-3.71) strongly predicted carbapenem resistance; ICU admission predicted 30-day mortality (HR 2.10, 95% CI 1.80-2.46, p<0.001). Mortality was higher in CRKP (40.2%) vs. susceptible cases (21.5%). The random forest model achieved 89.2% accuracy and 0.92 ROC-AUC, with drug share, age, and CRKP status as top predictors.

Conclusions: CRKP, especially ST11-blaKPC, drives excess mortality. Key predictors highlight the urgency for enhanced AMR surveillance and targeted therapy.

Background: In Cambodia, limited conceptual understanding of antimicrobials and the wide availability of over-the-counter medications increases antimicrobial resistance (AMR) threats. Community-based campaigns are critical to foster fundamental concepts of antimicrobials, their appropriate use and potential consequences of AMR.

Methods: A circus-based drama on AMR was co-designed with a non-profit arts school and local youth groups, in coordination with two provincial health authorities.

Results: Events held across three venues were attended by >1200 people and the accompanying social media campaign received >0.5 million views.

Conclusions: Following the success of the campaign, the circus drama's key messages are being developed into educational materials for school children nationwide.

Background: The World Health Organization (WHO) has approved the use of a verbal autopsy (VA), a survey-based approach to generate out-of-hospital causes of death (CoDs). Through this study, an adaptive Bayesian networks machine learning model was developed and tested. The model is scalable and adaptable for predicting new causes as the dataset expands.

Methods: The 2016 WHO questionnaire was used to collect data from Iringa, Tanzania, and data augmentation was performed using the Synthetic Minority Oversampling Technique for nominal features to increase the dataset size and reduce bias in the CoD classification. The model development was guided by a CoD decision flow that integrates essential factors and steps for accurate CoD prediction. To our knowledge, no previous study has provided this operational guide for VA cause of death prediction.

Results: The model was evaluated using accuracy, sensitivity, specificity and F1 score metrics and compared with Support Vector Machine and Naïve Bayesian models. Results indicated an average accuracy of 97%, specificity of 97%, sensitivity of 94% and F1 score of 94%, which are superior compared with Naïve Bayesian and Support Vector Machine models.

Conclusions: The reported performance of the developed model demonstrates the potential for this model to enhance VA-based CoD data by integrating a machine learning approach with physician expertise. The results highlight the effectiveness of combining Bayesian networks with physician Symptom Cause Information as a valuable tool in advancing the performance of CoD predictions.

Neonatal death in sub-Saharan African countries accounts for 80% of the global burden. An estimated 30 deaths per 1000 live births occurred in Ethiopia in 2019. Neonatal necrotizing enterocolitis (NEC) is the most common contributing factor to these deaths. Nevertheless, there is no strong evidence of neonatal NEC in Ethiopia. Therefore, this study aimed to determine the pooled prevalence of neonatal NEC and its predictors among neonates admitted to neonatal intensive care units in Ethiopia. Databases (Scopus, CINAHL, CAB Abstract, Embase, PubMed, Web of Science, Google and Google Scholar) and lists of references were used to search literature in Ethiopia. Stata version 17 (StataCorp, College Station, TX, USA) was used for analysis and the odds ratios (ORs) of the outcome variable were determined using the random-effects model. Heterogeneity among the studies was assessed by computing values for I2 and p-values. Also, sensitivity analysis and funnel plots were used to assess the stability of pooled values to outliers and publication bias. A total of 12 studies with a sample size of 7675 were included in this study. The overall prevalence of neonatal NEC was 16% (95% confidence interval 11 to 22). Meta-analyses and sensitivity analyses showed the stability of the pooled ORs and the funnel plots did not show publication bias. Preterm birth, very low birth weight and duration of stay in a neonatal intensive care unit (NICU) were identified as predictors of neonatal NEC. In Ethiopia, one in six (16%) neonates admitted to NICUs had NEC, with preterm birth, very low birth weight and duration of stay in the NICU identified as key predictors. Moreover, these findings imply the need for targeted interventions to reduce the risk of this condition in vulnerable neonates using large-scale prevention strategies through high-quality healthcare for mothers and children.

Background: Poor iron status is a common nutritional condition affecting preschool-age children (PSC) in low-income countries like Malawi. This study aimed to examine the determinants of iron deficiency (ID), functional iron deficiency (FID) and iron deficiency anaemia (IDA) among Malawian PSC.

Methods: The study analysed data from the 2015-2016 Malawi Micronutrients Survey, which collected information from a nationally representative sample of PSC across Malawi. Iron status was evaluated through biochemical markers, including serum ferritin, soluble transferrin receptors and haemoglobin levels. Logistic regression analyses were performed to identify independent factors associated with iron status.

Results: The prevalence of ID was 19.6%, FID was 50.3% and IDA was 4.7%. Males had higher odds of both ID and FID compared with females. Children <36 months of age were at significantly greater risk for ID, FID and IDA compared with children ages 48-59 months. Additionally, children referred for clinical malaria or those with inflammation were more likely to have ID and FID compared with those children who did not. Underweight children had a higher risk of both ID and IDA compared with children who were well nourished. Living in rural areas or the southern region of Malawi further increased the likelihood of developing ID compared with children from urban areas and the northern region.

Conclusions: The study emphasizes the need for comprehensive healthcare initiatives addressing nutritional deficiencies and malaria prevention in vulnerable groups, particularly young children in rural areas.

Background: Despite significant progress, trachoma persistence and recrudescence in Ethiopia pose a challenge to global elimination of trachoma as a public health problem. Developing evidence-based, expert-informed strategies is crucial for achieving global elimination targets by 2030. Our aim was to develop strategies to address persistent and recrudescent trachoma using Delphi techniques.

Methods: The study reviewed literature and studies from districts in southern Ethiopia where trachoma prevalence remains above the elimination threshold. A modified Delphi technique was employed to achieve consensus on strategies. Initially, 78 interventions were identified and reviewed by eight experts, resulting in 46 interventions and 6 strategies advancing to Delphi round 1. In round 1, 16 experts evaluated the strategies and interventions for relevance. In round 2, 32 experts from various institutions assessed them for relevance, feasibility and scalability. In round 3, 20 experts scored the final strategies and interventions for adoption or rejection. Data analysis was conducted using Microsoft Excel, with frequencies, percentages and summaries presented. Stability and descent analysis ensured the quality and consistency of the results.

Results: Six strategies and 28 interventions were recommended, including enhancing service delivery, addressing workforce gaps, overcoming technological and supply chain barriers and ensuring robust data systems. Additionally, healthcare financing and enhanced leadership, management and stakeholder engagement were recommended.

Conclusions: The study identified strategies and interventions to address trachoma persistence and recrudescence in Ethiopia, reinforcing efforts to strengthen the surgery, antibiotics, facial cleanliness and environmental improvement strategy.

Background: One in two people with diabetes in sub-Saharan Africa remains undiagnosed, which has contributed to the region's high rates of diabetes-related morbidity and mortality. While the COVID-19 pandemic likely exacerbated diabetes prevalence, limited data from the region, including Uganda, hampers our understanding of this impact. To address this gap, we analysed the diabetes prevalence and correlates among adults from three rural districts of Uganda using data from a community-based screening programme conducted by community health workers (CHWs) during the pandemic.

Methods: We used anonymised data of 2587 records to analyse the prevalence and correlates of diagnosed and undiagnosed diabetes. Prevalence was presented as proportions with 95% CIs. Correlates of diabetes and undiagnosed diabetes prevalence were analysed using logistic regression and presented as ORs.

Results: The overall prevalence of diabetes was 11.3% (95% CI 10.2 to 12.5%), with a 7.2% (95% CI 6.2 to 8.1%) prevalence for diagnosed diabetes. A high proportion (36.4%) of diabetes cases were undiagnosed. Older age, high body mass index and pre-existing hypertension were associated with high diabetes prevalence.

Conclusions: There was a high proportion of undiagnosed diabetes among rural adults during the pandemic. Engaging CHWs in routine diabetes screening and awareness programmes can reduce the burden of undiagnosed diabetes.