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| 2. |
- Blach, S., et al.
(författare)
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Global change in hepatitis C virus prevalence and cascade of care between 2015 and 2020: a modelling study
- 2022
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Ingår i: Lancet Gastroenterology & Hepatology. - : Elsevier BV. - 2468-1253. ; 7:5, s. 396-415
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Tidskriftsartikel (refereegranskat)abstract
- Background Since the release of the first global hepatitis elimination targets in 2016, and until the COVID-19 pandemic started in early 2020, many countries and territories were making progress toward hepatitis C virus (HCV) elimination. This study aims to evaluate HCV burden in 2020, and forecast HCV burden by 2030 given current trends. Methods This analysis includes a literature review, Delphi process, and mathematical modelling to estimate HCV prevalence (viraemic infection, defined as HCV RNA-positive cases) and the cascade of care among people of all ages (age =0 years from birth) for the period between Jan 1, 2015, and Dec 31, 2030. Epidemiological data were collected from published sources and grey literature (including government reports and personal communications) and were validated among country and territory experts. A Markov model was used to forecast disease burden and cascade of care from 1950 to 2050 for countries and territories with data. Model outcomes were extracted from 2015 to 2030 to calculate population-weighted regional averages, which were used for countries or territories without data. Regional and global estimates of HCV prevalence, cascade of care, and disease burden were calculated based on 235 countries and territories. Findings Models were built for 110 countries or territories: 83 were approved by local experts and 27 were based on published data alone. Using data from these models, plus population-weighted regional averages for countries and territories without models (n=125), we estimated a global prevalence of viraemic HCV infection of 0.7% (95% UI 0.7-0.9), corresponding to 56.8 million (95% UI 55.2-67.8) infections, on Jan 1, 2020. This number represents a decrease of 6.8 million viraemic infections from a 2015 (beginning of year) prevalence estimate of 63.6 million (61.8-75.8) infections (0.9% [0.8-1.0] prevalence). By the end of 2020, an estimated 12.9 million (12.5-15.4) people were living with a diagnosed viraemic infection. In 2020, an estimated 641 000 (623 000-765 000) patients initiated treatment. Interpretation At the beginning of 2020, there were an estimated 56.8 million viraemic HCV infections globally. Although this number represents a decrease from 2015, our forecasts suggest we are not currently on track to achieve global elimination targets by 2030. As countries recover from COVID-19, these findings can help refocus efforts aimed at HCV elimination. Copyright (C) 2022 Elsevier Ltd. All rights reserved.
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| 3. |
- Ade, Peter, et al.
(författare)
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The Simons Observatory : science goals and forecasts
- 2019
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Ingår i: Journal of Cosmology and Astroparticle Physics. - : IOP Publishing. - 1475-7516. ; :2
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Tidskriftsartikel (refereegranskat)abstract
- The Simons Observatory (SO) is a new cosmic microwave background experiment being built on Cerro Toco in Chile, due to begin observations in the early 2020s. We describe the scientific goals of the experiment, motivate the design, and forecast its performance. SO will measure the temperature and polarization anisotropy of the cosmic microwave background in six frequency bands centered at: 27, 39, 93, 145, 225 and 280 GHz. The initial con figuration of SO will have three small-aperture 0.5-m telescopes and one large-aperture 6-m telescope, with a total of 60,000 cryogenic bolometers. Our key science goals are to characterize the primordial perturbations, measure the number of relativistic species and the mass of neutrinos, test for deviations from a cosmological constant, improve our understanding of galaxy evolution, and constrain the duration of reionization. The small aperture telescopes will target the largest angular scales observable from Chile, mapping approximate to 10% of the sky to a white noise level of 2 mu K-arcmin in combined 93 and 145 GHz bands, to measure the primordial tensor-to-scalar ratio, r, at a target level of sigma(r) = 0.003. The large aperture telescope will map approximate to 40% of the sky at arcminute angular resolution to an expected white noise level of 6 mu K-arcmin in combined 93 and 145 GHz bands, overlapping with the majority of the Large Synoptic Survey Telescope sky region and partially with the Dark Energy Spectroscopic Instrument. With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio. The survey will also provide a legacy catalog of 16,000 galaxy clusters and more than 20,000 extragalactic sources.
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| 5. |
- Abazajian, Kevork, et al.
(författare)
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CMB-S4 : Forecasting Constraints on Primordial Gravitational Waves
- 2022
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 926:1
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Tidskriftsartikel (refereegranskat)abstract
- CMB-S4—the next-generation ground-based cosmic microwave background (CMB) experiment—is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the universe. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semianalytic projection tool, targeted explicitly toward optimizing constraints on the tensor-to-scalar ratio, r, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2–3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments, given a desired scientific goal. To form a closed-loop process, we couple this semianalytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for r > 0.003 at greater than 5σ, or in the absence of a detection, of reaching an upper limit of r < 0.001 at 95% CL.
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| 7. |
- Razavi-Shearer, Devin M., et al.
(författare)
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Adjusted estimate of the prevalence of hepatitis delta virus in 25 countries and territories
- 2024
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Ingår i: JOURNAL OF HEPATOLOGY. - 0168-8278 .- 1600-0641. ; 80:2, s. 232-242
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Tidskriftsartikel (refereegranskat)abstract
- Background & Aims: Hepatitis delta virus (HDV) is a satellite RNA virus that requires the hepatitis B virus (HBV) for assembly and propagation. Individuals infected with HDV progress to advanced liver disease faster than HBV-monoinfected individuals. Recent studies have estimated the global prevalence of anti-HDV antibodies among the HBV-infected population to be 5-15%. This study aimed to better understand HDV prevalence at the population level in 25 countries/territories. Methods: We conducted a literature review to determine the prevalence of anti-HDV and HDV RNA in hepatitis B surface antigen (HBsAg)-positive individuals in 25 countries/territories. Virtual meetings were held with experts from each setting to discuss the findings and collect unpublished data. Data were weighted for patient segments and regional heterogeneity to estimate the prevalence in the HBV-infected population. The findings were then combined with The Polaris Observatory HBV data to estimate the anti-HDV and HDV RNA prevalence in each country/territory at the population level. Results: After adjusting for geographical distribution, disease stage and special populations, the anti-HDV prevalence among the HBsAg+ population changed from the literature estimate in 19 countries. The highest anti-HDV prevalence was 60.1% in Mongolia. Once adjusted for the size of the HBsAg+ population and HDV RNA positivity rate, China had the highest absolute number of HDV RNA+ cases. Conclusions: We found substantially lower HDV prevalence than previously reported, as prior meta-analyses primarily focused on studies conducted in groups/regions that have a higher probability of HBV infection: tertiary care centers, specific risk groups or geographical regions. There is large uncertainty in HDV prevalence estimates. The implementation of reflex testing would improve estimates, while also allowing earlier linkage to care for HDV RNA+ individuals. The logistical and economic burden of reflex testing on the health system would be limited, as only HBsAg+ cases would be screened.
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| 9. |
- Caly, H., et al.
(författare)
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Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD
- 2021
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.
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| 10. |
- Gravesteijn, Benjamin Y., et al.
(författare)
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Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
- 2020
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Ingår i: Journal of Clinical Epidemiology. - : Elsevier. - 0895-4356 .- 1878-5921. ; 122, s. 95-107
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.STUDY DESIGN AND SETTING: We performed logistic (LR), lasso, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n=11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks, and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with GCS<13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (AUC) was quantified.RESULTS: In the IMPACT-II database, 3,332/11,022(30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale below 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies, and less so between the studied algorithms. The mean AUC was 0.82 for mortality and 0.77 for unfavorable outcome in CENTER-TBI.CONCLUSION: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe TBI. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations.
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