Join the National Academies for a workshop exploring how an Earth systems science approach could be used to address climate change impacts and their influence on human migration, building on the 2021 report Next Generation Earth Systems Science at the National Science Foundation. To learn more about this workshop, visit the event webpage. The event will take place on March 18th from 10:00am-4pm (ET) and March 19th from 10am-1pm (ET). Register here!
Limited Submission Opportunity – NSF Call for General Social Survey Competition (LOI due 3/20/24)
Please submit:
- A one‐page letter of intent with a description of proposed aims and approach.
- If the final application requires a diversity statement or statement of broader impacts, please summarize your plans to address the specific requirements on an additional page.
- CV (not biosketch) of the PI including past grant funding.
Above materials should be submitted to limitedsubs@uw.edu by 5:00 PM Wednesday, March 20, 2024. If given the go‐ahead by the Limited Submissions review committee, a required LOI with AOR signature is due 6/3/2024, then the full application is due 8/15/2024. Other open limited submissions opportunities, as well as the limited submissions review committee review and selection process, are here:http://depts.washington.edu/research/funding/limited-submissions. Please feel free to email us at limitedsubs@uw.edu with questions or information on any limited submission opportunities that should be but are not already listed on that page.
Chen and Colleagues Introduce Remote Sensing Method to Identify Landslides
CSDE Affiliate Tzu-Hsin Karen Chen (Urban Design and Planning, Environmental & Occupational Health Sciences) released new research with colleagues in Science of the Total Environment, titled “Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya“. This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. Authors used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The work reveals that most landslides in the Himalayas are not new, demonstrating how “landslides follow landslides.”
*New* UW Data Science Postdoctoral Fellow
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New Study by Prusynski, Mroz, and Co-authors Compares Home Health Services Under Traditional Medicare and Medicare Advantage
CSDE Affiliates Rachel Prusynski (Rehabilitation Medicine) and Tracy Mroz (Rehabilitation Medicine) published an article with co-authors in JAMA Health Forum, titled “Differences in Home Health Services and Outcomes Between Traditional Medicare and Medicare Advantage“. Private Medicare Advantage (MA) plans recently surpassed traditional Medicare (TM) in enrollment. However, MA plans are facing scrutiny for burdensome prior authorization and potential rationing of care, including home health. MA beneficiaries are less likely to receive home health, but recent evidence on differences in service intensity and outcomes among home health patients is lacking. This study sought to examine differences in home health service intensity and patient outcomes between MA and TM.
*New* Several Statistician (Demography) Positions
Catalyst Award Competition from the National Academy of Sciences
Hess and Co-authors Model the Role of Weather and Pilgrimage on Dengue Fever in Saudi Arabia
CSDE Affiliate Jeremy Hess (Emergency Medicine, Environmental & Occupational Health Sciences, Global Health) co-authored new research in Pathogens, titled “Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia“. The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF’s epidemiology in Saudi Arabia. In this study, authors assess the impact of these variables on the DF burden of disease in Saudi Arabia and attempt to create robust DF predictive models. Findings can inform DF early warning systems and preparedness in Saudi Arabia.