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Benjamin Seligman
Researcher at Stanford University
Publications - 13
Citations - 2188
Benjamin Seligman is an academic researcher from Stanford University. The author has contributed to research in topics: Population & Life expectancy. The author has an hindex of 8, co-authored 12 publications receiving 1862 citations. Previous affiliations of Benjamin Seligman include University of California, Los Angeles & Harvard University.
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The Global Economic Burden of Noncommunicable Diseases
David E. Bloom,Elizabeth T. Cafiero,Eva Jané-Llopis,Shafika Abrahams-Gessel,Lakshmi Reddy Bloom,Sana F. Fathima,Andrea B. Feigl,Tom Gaziano,Ali Hamandi,Mona Mowafi,Danny O’Farrell,Emre Özaltin,Ankur Pandya,Klaus Prettner,Larry Rosenberg,Benjamin Seligman,Adam Z. Stein,Cara Weinstein,Jonathan Weiss +18 more
TL;DR: New estimates of the global economic burden of non-communicable diseases in 2010 are developed, and the size of the burden through 2030 is projected, to capture the thinking of the business community about the impact of NCDs on their enterprises.
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Global Perspective on Acute Coronary Syndrome: A Burden on the Young and Poor
TL;DR: Challenges remain in the development and implementation of cardiovascular health promotion activities across the entire life course, as well as in access to treatment for ACS and IHD, and addressing the hurdles and scaling successful health promotion, clinical and policy efforts in LMICs are necessary to adequately address the global burden.
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Multi-Country analysis of palm oil consumption and cardiovascular disease mortality for countries at different stages of economic development: 1980-1997
TL;DR: Increased palm oil consumption is related to higher IHD mortality rates in developing countries and represents a saturated fat source relevant for policies aimed at reducing cardiovascular disease burdens.
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Social determinants of mortality from COVID-19: A simulation study using NHANES.
TL;DR: In this article, the proportions of COVID-19 deaths by age, sex, race/ethnicity, income, education level, and veteran status were estimated from the 2017-2018 National Health and Nutrition Examination Survey (NHANES).
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Machine learning approaches to the social determinants of health in the health and retirement study.
TL;DR: The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.