Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleSangkaew S, Daniels BC, Ming DK, et al., 2026,
Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.
, PLOS Digit Health, Vol: 5Dengue severity prediction models are usually developed using hospitalized patient data, but triage and hospital admission are mainly evaluated in outpatient settings. This study developed models using clinical and laboratory data from patients in outpatient settings during the febrile phase. Data from two cohort studies in Vietnam and Thailand were used to develop and validate six models: logistic regression with warning signs, Lasso-selected logistic regression, random forest, extreme gradient boosted classification, support vector machine, and artificial neural network. Models predicted dengue shock syndrome (DSS) as the primary endpoint and moderate plasma leakage and/or DSS as the secondary endpoint. We assessed model performance, discrimination, and calibration, using sensitivity, specificity, accuracy, Brier score, AUROC, CITL, calibration slope, calibration plots, and decision curve analysis. The optimal model was the Lasso-selected logistic regression for predicting DSS and the combined endpoint of moderate plasma leakage and/or DSS (Brier score: 0.044 [95% CI 0.043, 0.044] and 0.104 [95% CI 0.104, 0.105]; AUROC: 0.789 [95% CI 0.787, 0.791] and 0.741 [95% CI 0.740, 0.742]). We identified hematocrit, platelet count, lymphocyte count, and aspartate aminotransferase as predictors for DSS, and abdominal pain or tenderness, vomiting, mucosal bleeding, white blood cell count, lymphocyte count, platelet count, aspartate aminotransferase, and serum albumin as predictors for the secondary endpoint. Logistic regression and machine learning models using clinical and laboratory data during the febrile phase can support early prediction of severe disease in outpatient settings. Integrating risk prediction models into a decision support system could improve triage and optimize healthcare and resource allocation in endemic and resource-limited areas.
-
Journal articleBerden J, Hanley-Cook GT, Chimera B, et al., 2026,
Synergies between food biodiversity, processing levels, and the EAT-Lancet diet for nutrient adequacy and environmental sustainability: a multiobjective optimization using the European Prospective Investigation into Cancer and Nutrition cohort.
, Am J Clin Nutr, Vol: 123BACKGROUND: Diets have become increasingly monotonous and high in ultraprocessed foods (UPFs), contributing to poor health outcomes and environmental degradation. Although sustainable diets, food biodiversity, and food processing levels have each been linked to nutritional and environmental outcomes, their combined impact has not been assessed. OBJECTIVES: This study aims to examine whether food biodiversity, intakes of UPFs, and adherence to the EAT-Lancet diet can simultaneously optimize nutrient adequacy while reducing environmental impacts. METHODS: Using data from 368,733 adults in the European Prospective Investigation into Cancer and Nutrition, we assessed associations and interactions between dietary species richness (DSR) (disaggregated into DSRPlant and DSRAnimal), food processing levels (Nova categories; % g/d), and adherence to EAT-Lancet recommendations [healthy reference diet (HRD) score; 0-140 points] with the Probability of Adequate Nutrient Intake Diet (PANDiet) score, dietary greenhouse gas emissions (GHGe; kg CO2-eq/d), and land use (m2/d). Regression models subsequently informed multiobjective optimization to identify optimal dietary patterns balancing nutritional and environmental outcomes. RESULTS: Compared with observed diets, optimal diets showed a mean HRD score increase of 13.91 (95% confidence interval: 13.89, 13.93) points; DSRPlant increased by mean of 1.36 (1.35, 1.37) species, and a mean substitution of 12.44 (12.40, 12.49) percentage points of UPFs with unprocessed or minimally processed foods. Correspondingly, the mean PANDiet score increased by 4.12 (4.10, 4.14) percentage points, whereas GHGe and land use reduced by 1.07 (1.05, 1.09) kg CO2-eq/d and 1.43 (1.41, 1.45) m2/d, respectively. CONCLUSIONS: Diets that adhere to the EAT-Lancet diet, are more biodiverse, and prioritize unprocessed and minimally processed foods over UPFs, have the potential to synergistically enhance nutrient adequacy while minimizing environmental impacts. T
-
Journal articleXu H, Wang H, Prentice IC, et al., 2026,
Global variation in the ratio of sapwood to leaf area explained by optimality principles
, New Phytologist, ISSN: 0028-646X• The sapwood area supporting a given leaf area (Huber value, vH) reflects the coupling between carbon uptake and water transport and loss at a whole-plant level. Geographic variation in vH presumably reflect plant strategic adaptations but the lack of a general explanation for such variation hinders its representation in vegetation models and assessment of how its impact on the global carbon and water cycles. • Here we develop a simple hydraulic trait model to predict optimal vH by matching stem water supply and leaf water loss, and test its performance against two extensive plant hydraulic datasets. • We show that our eco-evolutionary optimality-based model explains nearly 60% of global vH variation in response to light, vapour pressure deficit, temperature and sapwood conductivity. Enhanced hydraulic efficiency with warmer temperatures reduces the sapwood area required to support a given leaf area, whereas high irradiance (supporting increased photosynthetic capacity) and drier air increase it. • This study thus provides a route to modelling variation in functional traits through the coordination of carbon uptake and water transport processes.
-
Journal articleSmith JR, Grobler C, Hodgson PJ, et al., 2026,
The climate opportunities and risks of contrail avoidance
, Nature Communications, ISSN: 2041-1723Navigational contrail avoidance presents an opportunity for rapid reduction in aviation-attributable warming. Here, we use the Aviation Climate and Air Quality Impacts model to evaluate the global temperature changes associated with contrail avoidance towards 2050. If no avoidance is adopted, aviation is projected to contribute 0.040 K of CO2 warming and 0.054 K of contrail warming by 2050. The combined warming from aviation CO2 and contrails is 19% of the difference between current temperatures and the +2 °C limit above pre-Industrial levels, i.e. 19% of our remaining temperature budget. An avoidance strategy phased in over 2035-2045 may recover 9% of this budget, but a 10-year delay may reduce this to 2%. The warming due to additional CO2 emitted during avoidance is two orders of magnitude lower than the expected contrail warming reduction. For every year of delay, the world will be on average 0.003 K hotter in 2050. The most significant climate risk associated with contrail avoidance is therefore inaction.
-
Journal articleSymons TL, Moran A, Balzarolo A, et al., 2026,
Projected impacts of climate change on malaria in Africa.
, NatureThe implications of climate change for malaria eradication this century remain poorly resolved1,2. Many studies focus on parasite and vector ecology in isolation, neglecting the interactions between climate, malaria control and the socioeconomic environment, including disruption from extreme weather3,4. Here we integrate 25 years of African data on climate, malaria burden and control, socioeconomic factors, and extreme weather. Using a geotemporal model linked to an ensemble of climate projections under the Shared Socioeconomic Pathway 2-4.5 (SSP 2-4.5) scenario5, we estimate the future impact of climate change on malaria burden in Africa, including both ecological and disruptive effects. Our findings indicate that climate change could lead to 123 million (projection range 49.5 million to 203 million) additional malaria cases and 532,000 (195,000-912,000) additional deaths in Africa between 2024 and 2050 under current control levels. Contrary to the prevailing focus on ecological mechanisms, extreme weather events emerge as the primary driver of increased risk, accounting for 79% (50-94%) of additional cases and 93% (70-100%) of additional deaths. Most increases stem from intensification in existing endemic areas rather than range expansion, with significant regional variation in impact. These results highlight the urgent need for climate-resilient malaria control strategies and robust emergency response systems to safeguard progress towards malaria eradication.
-
Journal articleDi Natale G, Brindley H, Murray J, et al., 2026,
Achieving consistency between in-situ and remotely sensed optical and microphysical properties of Arctic cirrus: the impact of far-infrared radiances
, Atmospheric Chemistry and Physics (ACP), Vol: 26, Pages: 1373-1394, ISSN: 1680-7316This paper explores whether it is possible to achieve consistency between ground-based infrared radiance measurements made in the presence of cirrus, co-located in-situ aircraft measurements of the cirrus microphysics, and ancillary ground-based remote sensing. Specifically we use spectrally resolved radiances covering the range 400–1500 cm−1, in-situ measurements of cirrus particle sizes and habits, backscatter ceilometer observations of cloud vertical structure and microwave inferred temperature and humidity profiles to investigate whether we can obtain consistency between the derived cloud properties and atmospheric state from these independent sources of data. The primary focus of this study is on the sensitivity of the retrieved cloud particle size to the assumed crystal habit. Excellent consistency between the retrieved cloud parameters is achieved both with the ceilometer derived optical depth and the size distribution measured by the aircraft by assuming the crystal habit to be comprised of bullet rosettes. The averaged values of the effective diameter and optical depth obtained from radiometric measurements are (26.5 ± 1.8) µm and (0.12 ± 0.01) in comparison with the values derived from in-situ and ceilometer measurements equal to (31.5 ± 5.0) µm and (0.13 ± 0.01), respectively. Furthermore, we show that the radiance information contained within the far-infrared (wavenumbers < 650 cm−1) spectrum is critical to achieving this level of agreement with the in-situ aircraft observations. The results emphasize why it is vital to expand the current limited database of measurements encompassing the far-infrared spectrum, particularly in the presence of cirrus, to explore whether this finding holds over a wider range of conditions.
-
Journal articleHassan A, Prentice IC, Liang X, 2026,
Insights into evapotranspiration partitioning based on hydrological observations using the generalized proportionality hypothesis
, Hydrology and Earth System Sciences (HESS), Vol: 30, Pages: 317-341, ISSN: 1027-5606Evapotranspiration comprises transpiration, soil evaporation, and interception. The partitioning of evapotranspiration is challenging due to the lack of direct measurements and uncertainty of existing evapotranspiration partitioning methods. We propose a novel method to estimate long-term mean transpiration to evapotranspiration (Et/E) ratios based on the generalized proportionality hypothesis using long-term mean hydrological observations at the watershed scale. We tested the method using 648 watersheds in the United States classified into six vegetation types. We mitigated impacts of the variability associated with different Ep data products by rescaling their original Ep values using the product E/Ep ratios in combination with the observed E calculated from watershed water balance. With Ep thus rescaled, our method produced consistent Et/E across six widely used Ep products. Shrubs (0.33) and grasslands (0.32) showed lower mean Et/E than croplands (0.48) and forests (respectively 0.69, 0.60, and 0.70 for evergreen needleleaf, deciduous broadleaf, and mixed forests). Et/E showed significant dependence on aridity, leaf area index, and other hydrological and environmental conditions. Using Et/E estimates, we calculated transpiration to precipitation ratios (Et/P) ratios and revealed a bell-shaped curve at the watershed scale, which conformed to the bell-shaped relationship with the aridity index (AI) observed at the field and remote-sensing scales (Good et al., 2017). This relationship peaked at an Et/P between 0.5 and 0.6, corresponding to an AI between 2 and 3 depending on the Ep dataset used. These results strengthen our understanding of the interactions between plants and water and provide a new perspective on a long-standing challenge for hydrology and ecosystem science.
-
Journal articleXu H, Rehkamper M, Jia Z, et al., 2026,
Anthropogenic emissions of volatile Cd detected in western tropical North Atlantic surface seawater
, Communications Earth & Environment, ISSN: 2662-4435The Cd concentrations and isotope compositions of open ocean surface waters are generally thought to be governed by internal cycling, and particularly by the balance between upwelling of more Cd-rich deeper water masses and Cd uptake by phytoplankton. Here we present a new dataset of coupled Cd isotope compositions and concentrations for seawater depth profiles sampled in the western tropical Atlantic Ocean during Leg 2 of the GEOTRACES GA02 section. A box model for the Cd source and sink fluxes of the oligotrophic surface waters of the study area shows that the observed light Cd isotope compositions and low Cd concentrations are a consequence of biological Cd uptake and atmospheric deposition of isotopically light anthropogenic Cd. Aerosols enriched in anthropogenic Cd thereby contributed at least 19%, and possibly more than 45%, to the dissolved surface water Cd inventory during the sampling period. This reveals that anthropogenic emissions of volatile Cd can have a key impact on the distribution of Cd in open ocean surface waters.
-
Journal articleBucyibaruta G, Pirani M, Mitsakou C, et al., 2026,
Two-stage Bayesian factor analysis for air pollution source apportionment and health risk assessment
, Journal of Applied Statistics: Environmental Statistics and Data Science, ISSN: 2998-4696Air pollution, especially particulate matter (PM), presents significant public health challenges and is associated with several Sustainable Development Goals (SDGs), notably SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). Effective policy development requires robust statistical approaches to identify pollution sources and quantify their health impacts with appropriate uncertainty. This study develops a novel two-stage Bayesian framework for air pollution source apportionment and health risk assessment, with the aim of quantifying the contribution of distinct particle sources to respiratory health outcomes in children, using particle number size distribution (PNSD) data from London. In the first stage, we construct a Bayesian dynamic factor model with autoregressive components to infer latent pollution sources, incorporating non-negativity constraints and accounting for temporal dependence. In the second stage, we assess the relationship between source-specific exposures and respiratory hospital admissions in children via a Poisson regression model, explicitly propagating uncertainty from the source apportionment stage to the health model. The model identifies four main sources: nucleation, traffic, urban activities, and secondary aerosols. Among these, traffic and secondary sources exhibit the strongest and most consistent associations with increased respiratory hospital admissions. Importantly, models that do not accountfor uncertainty propagation tend to overestimate health risk associations, underscoring the value of the proposed Bayesian framework. This work illustrates the advantages of integrating Bayesian methods for source apportionment and health effect estimation, with formal uncertainty propagation across model stages. The proposed framework enhances interpretability and supports evidence-based public health and environmental policy. It is readily extensible to other pollutants and settings, contributing to improved air
-
Journal articleLeelavanich D, Dorigatti I, Turner H, 2026,
The economic burden of dengue: a systematic literature review of unit costs for non-fatal episodes treated in the formal healthcare system
, BMC Infectious Diseases, ISSN: 1471-2334Background: Dengue, a vector-borne disease caused by the dengue virus, has emerged as a global public health concern, given the tenfold rise in reported cases over the last two decades. In light of the upcoming dengue interventions, country-specific cost-of-illness estimates are required to evaluate the cost-effectiveness of new interventions against dengue. This study aims to conduct an updated systematic review of dengue cost-of-illness studies, extracting the relevant data, and conducting regression analysis to explore potential factors contributing to the cost variations among countries. Methods: We used the MEDLINE, EMBASE, PubMed, and Web of Science databases to systematically search for published dengue cost-of-illness studies reporting primary data on costs per dengue episode. A descriptive analysis was conducted across all extracted studies. Linear regression analysis was performed to investigate the association between the GDP per capita and cost per episode. The quality of the included studies was also assessed. Results: Fifty-six studies were included, of which 22 used the societal perspective. The reported total cost per episode ranged from $15.0 for outpatients in Burkina Faso to $9,386.1 for intensive care unit patients in Mexico. Linear regression analysis revealed that the cost of dengue illness varies significantly across countries and regions, and was positively related to the setting’s GDP per capita. The quality assessment demonstrated that improvements are needed in future studies, particularly in the reporting of the methodology. Conclusions: Cost of dengue illness varies widely across countries and regions. Future research should focus on understanding other drivers of cost variations beyond GDP per capita to improve the cost estimates for economic evaluation studies. The results presented in this study can serve as crucial input parameters for future economic evaluations, supporting decision makers in allocating resources for dengue in
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.
Join the network
Contact Hsuan-Yi to join the network.