.
Antimicrobial resistance (AMR) poses a major global health threat, yet the effectiveness of national action plans (NAPs) remains uncertain. Here we developed a multidimensional One Health governance index through a structured Delphi expert consultation to evaluate AMR governance across 193 countries (2017-2022), integrating 269 policy documents, expert-weighted indicators and multinational survey and surveillance datasets. Difference-in-differences and joinpoint regression analyses were used to link governance to antimicrobial use, AMR prevalence and AMR-related mortality. Global governance scores improved from 30.7 to 44.5/100, although implementation and monitoring lagged behind policy design, particularly in animal and environmental sectors. A significant increase in AMR prevalence scores was observed only 5 years after NAP adoption (two-stage difference-in-differences, β = 2.43, 95% confidence interval (CI) 1.02-3.85, P < 0.05), underscoring the delayed impact of policy. Multisector engagement (early-adopting countries, β = 0.05, 95% CI 0.02-0.08, P < 0.01) and antimicrobial use surveillance system (early-adopting countries, β = 0.05, 95% CI 0.03-0.07, P < 0.01) showed the strongest associations with improvement in AMR outcomes. As the 2026 Global Action Plan update approaches, sustained financing and integrated One Health surveillance, with stronger environmental and agricultural engagement, are essential for translating NAPs into sustained reductions in resistance.
BACKGROUND:Ultrafine particles (≤100 nm diameter) may have a higher toxicity than larger particles but are still not regulated nor part of routine air pollution monitoring. So far, health effects of long-term exposure to ambient ultrafine particles are not well understood, owing to a lack of exposure data and epidemiological studies. METHODS:We conducted a systematic review and meta-analysis on the health effects of long-term exposure to ultrafine particles, including studies published until December 2024. A meta-analysis was conducted for outcomes with at least four available effect estimates. Confidence in the body of evidence was evaluated using the Office of Health Assessment and Translation method. RESULTS:We identified 85 studies investigating various mortality, morbidity and subclinical outcomes. In meta-analyses of single-pollutant models, we found positive associations with natural mortality (hazard ratio 1.06, 95% CI 1.04-1.08) and C-reactive protein (10.14% increase (95% CI -0.51-21.99%) per 10 000 pt·cm-3 increase in long-term exposure to ultrafine particles, with low and inadequate levels of evidence, respectively. The remaining studies revealed overall limited evidence for adverse effects on a wide range of outcomes. Less than half of the studies adjusted for co-pollutants. CONCLUSION:The evidence base on long-term health effects of ultrafine particles has increased substantially in the past decade, while the overall evidence for independent effects of long-term ultrafine particle exposure remains inadequate to low. More studies are needed to draw firm conclusions about the independent adverse effects of long-term ultrafine particles on various health end-points, with a special focus on the influence of co-pollutant adjustment.
Urban greenery, as a critical urban landscape component, plays an important role in improving the living environments' and residents' well-being. Previous studies have predominantly adopted satellite image-based vegetation measurements. This study aims to quantify pedestrian-perspective greenery visibility using Google Street View (GSV) images and to understand how greenery types and built environment characteristics influence the correlation between pedestrian and aerial greenery assessments. We collected GSV images located on 34,601 sampling points and applied the DeepLab v3+ deep learning model to quantify green view index (GVI) from the pedestrian perspective. We distinguished green vegetation view index (GVVI) and green terrain view index (GTVI) to differentiate vertical and horizontal greenery types. Normalized difference vegetation index (NDVI) was extracted from Sentinel-2 images using circular buffers of varying radii (10-200 m) centered on GSV sampling points. Sampling points were filtered based on the buffer distance to avoid overlapping NDVI pixels in neighboring sampling points. Spearman correlation analysis was conducted across different typologies (urban, intermediate, rural) to examine GVI-NDVI relationships. Street-level greenery exhibited substantial spatial heterogeneity across the whole of the study area (Basel, Switzerland). GVI vs. NDVI in buffers with different radii had strong positive correlations, with a maximum Spearman coefficient of 0.77 for the 15 m NDVI buffer. Correlation coefficients decreased progressively from urban (0.77) to intermediate (0.72) and rural (0.66) areas. Correlation coefficients strongly decreased with increasing buffer sizes. Analysis of GSV images with high NDVI but low GVI values indicates that greenery types and building distributions significantly affect the street-level visible greenery. This study links street-level greenery with features in the built environment by using different methods for assessing green exposure. The findings provide methodological insights for greenery exposure studies and inform evidence-based urban planning strategies for optimizing green visibility.
The growing and pervasive use of social network sites (SNS) has raised concerns about their impact on adolescent mental health during this sensitive developmental phase. Existing longitudinal studies are constrained by methodological limitations and limited exploration of underlying mechanisms. We investigated the longitudinal associations between SNS use and depressive and anxiety symptoms in adolescents and whether sleep mediated these associations. We analysed longitudinal data from 2350 adolescents from 31 schools in London, participating in the Study of Cognition, Adolescents, and Mobile Phones (SCAMP). The exposure was self-reported duration of SNS use at baseline (aged 11–12 years). Outcomes were depressive and anxiety symptoms at follow-up, analysed as symptom severity and clinically significant symptoms (aged 13–15 years). The associations between SNS use and depressive and anxiety symptoms were assessed via multi-level ordinal logistic regression (symptom severity) and logistic regression (clinically significant symptoms). The mediation effects of insufficient sleep, sleep onset latency, and sleep disturbance were assessed by mediation analysis. Compared to 0–30 min per day, more than 3 h per day of SNS use at baseline was associated with higher severity levels of depressive and anxiety symptoms (adjusted odds ratio (OR) = 1.47, 95
Digital technologies have the potential to transform early childhood development (ECD) interventions by delivering personalized support at scale. We conducted a cluster-randomized controlled trial in rural Peru to evaluate the effectiveness and cost-effectiveness of an artificial intelligence-supported digital parenting chatbot as well as traditional home visits as interventions to improve child development. Among 2461 caregiver-child dyads, both the digital and home-visiting interventions improved child development outcomes at 2.5 years of age, with standardized effect sizes of 0.11 and 0.17, respectively. At 1/15 of the cost of in-person support, the digital intervention yielded superior cost-effectiveness. These findings suggest that digital platforms can be a viable, scalable alternative to support children's development in resource-constrained settings.
