| 1. |
- Cousin, E., et al.
(author)
-
Diabetes mortality and trends before 25 years of age: an analysis of the Global Burden of Disease Study 2019
- 2022
-
In: Lancet Diabetes & Endocrinology. - : Elsevier BV. - 2213-8587. ; 10:3, s. 177-192
-
Journal article (peer-reviewed)abstract
- Background Diabetes, particularly type 1 diabetes, at younger ages can be a largely preventable cause of death with the correct health care and services. We aimed to evaluate diabetes mortality and trends at ages younger than 25 years globally using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. Methods We used estimates of GBD 2019 to calculate international diabetes mortality at ages younger than 25 years in 1990 and 2019. Data sources for causes of death were obtained from vital registration systems, verbal autopsies, and other surveillance systems for 1990-2019. We estimated death rates for each location using the GBD Cause of Death Ensemble model. We analysed the association of age-standardised death rates per 100 000 population with the Socio-demographic Index (SDI) and a measure of universal health coverage (UHC) and described the variability within SDI quintiles. We present estimates with their 95% uncertainty intervals. Findings In 2019, 16 300 (95% uncertainty interval 14 200 to 18 900) global deaths due to diabetes (type 1 and 2 combined) occurred in people younger than 25 years and 73.7% (68.3 to 77.4) were classified as due to type 1 diabetes. The age-standardised death rate was 0.50 (0.44 to 0.58) per 100 000 population, and 15 900 (97.5%) of these deaths occurred in low to high-middle SDI countries. The rate was 0.13 (0.12 to 0.14) per 100 000 population in the high SDI quintile, 0.60 (0.51 to 0.70) per 100 000 population in the low-middle SDI quintile, and 0.71 (0.60 to 0.86) per 100 000 population in the low SDI quintile. Within SDI quintiles, we observed large variability in rates across countries, in part explained by the extent of UHC (r(2)=0.62). From 1990 to 2019, age-standardised death rates decreased globally by 17.0% (-28.4 to -2.9) for all diabetes, and by 21.0% (-33.0 to -5.9) when considering only type 1 diabetes. However, the low SDI quintile had the lowest decline for both all diabetes (-13.6% [-28.4 to 3.4]) and for type 1 diabetes (-13.6% [-29.3 to 8.9]). Interpretation Decreasing diabetes mortality at ages younger than 25 years remains an important challenge, especially in low and low-middle SDI countries. Inadequate diagnosis and treatment of diabetes is likely to be major contributor to these early deaths, highlighting the urgent need to provide better access to insulin and basic diabetes education and care. This mortality metric, derived from readily available and frequently updated GBD data, can help to monitor preventable diabetes-related deaths over time globally, aligned with the UN's Sustainable Development Targets, and serve as an indicator of the adequacy of basic diabetes care for type 1 and type 2 diabetes across nations. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Mehta, Raghav, et al.
(author)
-
QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
- 2022
-
In: Journal of Machine Learning for Biomedical Imaging. - 2766-905X. ; , s. 1-54
-
Journal article (peer-reviewed)abstract
- Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS
|
|
| 6. |
- Pontes, Gabriel M., et al.
(author)
-
Mid-Pliocene El Niño/Southern Oscillation suppressed by Pacific intertropical convergence zone shift
- 2022
-
In: Nature Geoscience. - : Springer Science and Business Media LLC. - 1752-0894 .- 1752-0908. ; 15:9, s. 726-734
-
Journal article (peer-reviewed)abstract
- The El Niño/Southern Oscillation (ENSO), the dominant driver of year-to-year climate variability in the equatorial Pacific Ocean, impacts climate pattern across the globe. However, the response of the ENSO system to past and potential future temperature increases is not fully understood. Here we investigate ENSO variability in the warmer climate of the mid-Pliocene (~3.0–3.3 Ma), when surface temperatures were ~2–3 °C above modern values, in a large ensemble of climate models—the Pliocene Model Intercomparison Project. We show that the ensemble consistently suggests a weakening of ENSO variability, with a mean reduction of 25% (±16%). We further show that shifts in the equatorial Pacific mean state cannot fully explain these changes. Instead, ENSO was suppressed by a series of off-equatorial processes triggered by a northward displacement of the Pacific intertropical convergence zone: weakened convective feedback and intensified Southern Hemisphere circulation, which inhibit various processes that initiate ENSO. The connection between the climatological intertropical convergence zone position and ENSO we find in the past is expected to operate in our warming world with important ramifications for ENSO variability.
|
|
