1. |
|
|
2. |
- Ikuta, K. S., et al.
(author)
-
Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019
- 2022
-
In: Lancet. - : Elsevier BV. - 0140-6736. ; 400:10369, s. 2221-2248
-
Journal article (peer-reviewed)abstract
- Background Reducing the burden of death due to infection is an urgent global public health priority. Previous studies have estimated the number of deaths associated with drug-resistant infections and sepsis and found that infections remain a leading cause of death globally. Understanding the global burden of common bacterial pathogens (both susceptible and resistant to antimicrobials) is essential to identify the greatest threats to public health. To our knowledge, this is the first study to present global comprehensive estimates of deaths associated with 33 bacterial pathogens across 11 major infectious syndromes. Methods We estimated deaths associated with 33 bacterial genera or species across 11 infectious syndromes in 2019 using methods from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, in addition to a subset of the input data described in the Global Burden of Antimicrobial Resistance 2019 study. This study included 343 million individual records or isolates covering 11 361 study-location-years. We used three modelling steps to estimate the number of deaths associated with each pathogen: deaths in which infection had a role, the fraction of deaths due to infection that are attributable to a given infectious syndrome, and the fraction of deaths due to an infectious syndrome that are attributable to a given pathogen. Estimates were produced for all ages and for males and females across 204 countries and territories in 2019. 95% uncertainty intervals (UIs) were calculated for final estimates of deaths and infections associated with the 33 bacterial pathogens following standard GBD methods by taking the 2.5th and 97.5th percentiles across 1000 posterior draws for each quantity of interest. Findings From an estimated 13.7 million (95% UI 10.9-17.1) infection-related deaths in 2019, there were 7.7 million deaths (5.7-10.2) associated with the 33 bacterial pathogens (both resistant and susceptible to antimicrobials) across the 11 infectious syndromes estimated in this study. We estimated deaths associated with the 33 bacterial pathogens to comprise 13.6% (10.2-18.1) of all global deaths and 56.2% (52.1-60.1) of all sepsis-related deaths in 2019. Five leading pathogens-Staphylococcus aureus, Escherichia coli, Streptococcus pneumoniae, Klebsiella pneumoniae, and Pseudomonas aeruginosa-were responsible for 54.9% (52.9-56.9) of deaths among the investigated bacteria. The deadliest infectious syndromes and pathogens varied by location and age. The age-standardised mortality rate associated with these bacterial pathogens was highest in the sub-Saharan Africa super-region, with 230 deaths (185-285) per 100 000 population, and lowest in the high-income super-region, with 52.2 deaths (37.4-71.5) per 100 000 population. S aureus was the leading bacterial cause of death in 135 countries and was also associated with the most deaths in individuals older than 15 years, globally. Among children younger than 5 years, S pneumoniae was the pathogen associated with the most deaths. In 2019, more than 6 million deaths occurred as a result of three bacterial infectious syndromes, with lower respiratory infections and bloodstream infections each causing more than 2 million deaths and peritoneal and intra-abdominal infections causing more than 1 million deaths. Interpretation The 33 bacterial pathogens that we investigated in this study are a substantial source of health loss globally, with considerable variation in their distribution across infectious syndromes and locations. Compared with GBD Level 3 underlying causes of death, deaths associated with these bacteria would rank as the second leading cause of death globally in 2019; hence, they should be considered an urgent priority for intervention within the global health community. Strategies to address the burden of bacterial infections include infection prevention, optimised use of antibiotics, improved capacity for microbiological analysis, vaccine development, and improved and more pervasive use of available vaccines. These estimates can be used to help set priorities for vaccine need, demand, and development. Copyright (c) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
|
|
3. |
|
|
4. |
|
|
5. |
|
|
6. |
|
|
7. |
|
|
8. |
- Kranenbarg, Jelle, et al.
(author)
-
Sensitivity analysis of a swirling flow to the GEKO model
- 2022
-
In: 31st IAHR Symposium on Hydraulic Machinery and Systems 26/06/2022 - 01/07/2022 Trondheim, Norway. - : Institute of Physics Publishing (IOPP).
-
Conference paper (peer-reviewed)abstract
- Accurate numerical models for hydraulic turbine applications are highly coveted. They must be able to correctly capture the swirling flow found at off-design operating conditions in the turbine draft tube. The GEKO model is a relatively fast and flexible eddy viscosity turbulence model with adjustable coefficients to tune the model to different flow scenarios. In this study, the GEKO model is tested on a swirling flow inside a diffusor similar to the flow conditions found at part-load operation of a propeller turbine. The diffusor investigated corresponds to the Porjus U9 draft tube cone section, including the runner cone. Results showed that the near-wall coefficient, with a value of 2, increased the wall shear stress and moved the separation point from the runner cone further downstream. Moreover, with a value of 0.7, the separation coefficient increased the eddy viscosity, which also moved the separation point from the runner cone further downstream. Both coefficients gave velocity profiles closer to experimental values and increased the swirl number at the outlet of the diffusor by up to 36.9 % compared to the GEKO default model. Overall, the near-wall coefficient with a value of 2 gave the best results. The GEKO model provides an opportunity to tweak numerical models to swirling flow.
|
|
9. |
- Kranenbarg, Jelle, et al.
(author)
-
Uncertainty in the numerical prediction of the tangential velocity in axial turbines at part load operations: A parametric study
- 2023
-
In: Energy Reports. - : Elsevier. - 2352-4847. ; 10, s. 2597-2611
-
Journal article (peer-reviewed)abstract
- Numerical simulations of axial hydraulic turbines away from the best efficiency point are challenging. Previous studies especially show difficulties predicting the tangential velocity at Part Load (PL) operating conditions, where the swirl is high, in comparison to experiments. This is a reoccurring problem, and it is essential to understand, as the high tangential velocity is a fundamental characteristic of the flow in hydraulic turbines and is directly related to the swirling flow stability and the turbine's power output. The objective of this study is to numerically investigate and understand the origin of the tangential velocity deviation from experimental results by performing simulations with the finite volume method of an axial turbine operated at PL. A parametric study is performed to address the abovementioned. Specifically, the effects of the blade clearance, blade angle, flow rate, and different turbulence models are studied on this issue. Results are analyzed by comparing the predicted axial and tangential velocity profiles and torque to experimentally obtained values. Primarily the runner inter-blades flow is studied as there is a knowledge gap. In addition, the physical phenomena responsible for head losses are studied in detail. Results show that the model can predict the flow relatively well at optimal flow conditions with low swirl but has problems at part load; the tangential velocity between the runner blades is underestimated by ∼20%. The undervalued head losses are the root cause. They result in an overestimated torque and an underestimated tangential velocity as the runner extracts too much energy from the fluid. A small modeling error of 0.5° in the blade angle and a change of 3% in the flow rate significantly affect the tangential velocity and torque prediction. The studied parameters must be considered carefully when building a numerical model.
|
|
10. |
- Wilkinson, John L., et al.
(author)
-
Pharmaceutical pollution of the world's rivers
- 2022
-
In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 119:8
-
Journal article (peer-reviewed)abstract
- Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.
|
|