Antimicrobial resistance (AMR) is a multifactorial challenge driven by a complex interplay of proximal drivers, such as the overuse and misuse of antimicrobials and the high burden of infectious diseases, and distal factors, encompassing broader societal conditions such as poverty, inadequate sanitation, and healthcare system deficiencies. However, distinguishing between proximal and distal drivers remains a conceptual challenge. We conducted an umbrella review, aiming to systematically map current evidence about proximal and distal drivers of AMR and to investigate their relationships. Forty-seven reviews were analysed, and unique causal links were retained to construct a causality network of AMR. To distinguish between proximal and distal drivers, we calculated a 'driver distalness index (Di)', defined as an average relative position of a driver in its causal pathways to AMR. The primary emphasis of the literature remained on proximal drivers, with fragmented existing evidence about distal drivers. The network analysis showed that proximal drivers of AMR are associated with risks of resistance transmission (Di = 0.49, SD = 0.14) and antibiotic use (Di = 0.58, SD = 0.2), which are worsened by intermediate drivers linked with challenges of antibiotic discovery (Di = 0.62, SD = 0.07), infection prevention (Di = 0.67, SD = 0.14) and surveillance (Di = 0.69, SD = 0.16). Distal drivers, such as living conditions, access to sanitation infrastructure, population growth and urbanisation, and gaps in policy implementation were development and governance challenges, acting as deep leverage points in the system in addressing AMR. Comprehensive AMR strategies aiming to address multiple chronic AMR challenges must take advantage of opportunities for upstream interventions that specifically address distal drivers.

This study described the prevalence of suicidal behaviors and depression among adolescents in Hanoi, Vietnam, and examines the associated factors. In 2019, a school-based survey was conducted in three high schools in Hanoi and 661 high students were included into the study. We found that the prevalence of 12-month suicidal thoughts, suicide plans, and suicide attempts were 14.2%, 5.5%, and 3.0% respectively. Notably, the depression score mean was 16.1±4.9, among major predictors of suicidal ideation. Therefore, developing psychological care services in school is imperative to the early detection of mental disorders and the prevention of suicidal behaviors for the students.

Trichinellosis and cysticercosis remain challenges to human health and animal productivity worldwide, especially in developing countries. While information on the occurrence of both diseases is infrequent, they are endemic in parts of Vietnam and mainly related to indigenous pigs kept by ethnic minorities. This study aimed to determine the seroprevalence and risk factors of both diseases in indigenous pigs and explore the perception and awareness of both human and pig trichinellosis and cysticercosis of pig farmers. A total of 352 pig sera samples from 131 holdings were collected and analyzed using ELISA antibody tests in six communes in the Da Bac districts of Hoa Binh province, Vietnam. A survey was conducted with representatives from these households to understand the knowledge and perspective on food-borne parasitic diseases. Overall, the seroprevalence of trichinellosis and T. solium cysticercosis was 13.6% (95% CI 10.2–17.7) and 1.7% (95% CI 0.6–3.7), respectively. The seroprevalence of trichinellosis was significantly higher in female and older pigs. Risk perception and knowledge of interviewed people on both human and pig trichinellosis and cysticercosis of pig farmers was poor. Risky practices, including free roaming of pigs and eating undercooked or fermented pork, were observed. Educational and awareness campaigns aligned with further research on feasible practice changes are critical to addressing these issues.

The world’s governments have agreed on actions to address the challenge of antibiotic resistance. This raises the question of what level of national action is associated with improved outcomes, including both slower growth and lower levels of antibiotic resistance. Answering this question is challenged by variation in data availability and quality as well as disruptive events such as the COVID-19 pandemic. We investigate the association between level of national action and temporal trends in multiple indicators related to health system capacity, antibiotic use (ABU), absolute rates of resistance (ABR) and a Drug Resistance Index (DRI). Using the Global Database for Tracking Antimicrobial Resistance (TrACSS) to construct an index of national action, we apply cross-sectional regression across 73 countries to estimate the association between the level of action in 2016 and trends in national indicators (2000–2016). We find that national action is consistently associated with improved linear or categorical trends in all groups of indicators. Reductions are associated with a relatively high action index (range 0–4) for ABU (median 2.8, 25–75% quartile 2.6-3.3), ABR (3.0, 2.4-3.4), and DRI (3.5, 3.1-3.6). These associations are robust to the inclusion of other contextual factors related to socio-economic conditions, human population density, animal production and climate. Since 2016, a majority of both Low- and Middle-Income Countries (LMICs) and High-Income Countries (HICs) report increased action on repeated questions, while one third of countries report reduced action. The main limitations in interpretation are heterogeneity in data availability and in when actions have been implemented. Our findings highlight the importance of national action to address the domestic situation related to antibiotic resistance and indicate the value of both incremental changes in reducing adversity of outcomes and the need for high levels of action in delivering reduced levels of resistance.

Background: Sustainable development goals (SDGs) may play a pivotal role in mitigating antimicrobial resistance (AMR). This study examines how countries can integrate AMR mitigation with sustainable development strategies, providing evidence on the prioritisation of AMR-related efforts within both agendas. Methods: We conducted a comparative analysis of the international global action plan (GAP) and national action plan (NAP) on AMR and SDGs across 10 countries in Africa and Asia. We employed content analysis to map actions to AMR drivers, descriptive statistics to summarise the coverage and focus of the actions and inferential statistics to explore factors associated with the level of policy alignment. Results: Our findings highlight gaps in the current AMR policy landscape, where drivers are at risk of being redundantly addressed, narrowly focused or entirely overlooked. At the international level, over 50% of AMR drivers are addressed by both frameworks, but national-level overlap is lower (10.5%–47.4%), with Asian countries showing stronger alignment than African countries. Asian countries show a higher proportion of shared drivers than African countries. A considerable proportion of drivers are addressed solely by AMR-NAPs (23.7%–60.5%) or SDG-NAPs (13.2%–31.6%), raising concerns that actions may benefit either sustainable development or AMR at the expense of the other. Finally, 10.5%–26.3% of drivers, mostly distal, are not acknowledged by either framework, highlighting potential policy blind spots. Conclusions: The Agenda 2030 includes ambitious and cross-cutting goals with GAP-AMR, therefore it can facilitate intersectoral collaboration in addressing AMR. The effective implementation of both agendas will depend on national governments’ capacity to ensure that efforts in combating AMR also contribute to sustainable development.

Emerging pests and pathogens are a growing source of social-ecological disruption, with impacts reaching across sectors, regions, and scales. Understanding these impacts requires methods that move beyond individual cases to examine how impact dynamics unfold across a wider range of organisms. Web-based text data sources, including news articles and social media posts, have been used in event-based surveillance of problem species. However, these approaches often employ natural language processing to gather epidemiological signals, such as spatial and temporal occurrence, and are not designed to capture more complex relationships among entities. We present an integrated scalable Large Language Model (LLM) pipeline for impact analysis via structured event extraction from large news media corpora and demonstrate its feasibility in a pilot application across six case studies spanning human pathogens, crop pests, and livestock pests: dengue, Ebola, Zika, desert locust, fall armyworm, and screwworm. The pipeline combines Google News RSS search refinement, article collection, filtering, and event extraction from full article texts. LLMs automate and scale key processing steps, producing a corpus of 22,117 articles and 104,901 events. Unlike approaches that restrict information extraction to predefined categories, our pipeline allows novel impacts to be derived from the text. By storing event data in a standardized format, temporal and spatial trends in events can be used to monitor the progression of emerging pest and pathogen consequences. Our results demonstrate the potential of LLMs to support the scaling of news media text processing and structured event extraction for emerging pest and pathogen impact analysis.