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Vincent C. Iannucci

Researcher at Institute for Health Metrics and Evaluation

Publications -  10
Citations -  9469

Vincent C. Iannucci is an academic researcher from Institute for Health Metrics and Evaluation. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 4, co-authored 4 publications receiving 1579 citations. Previous affiliations of Vincent C. Iannucci include Guy's and St Thomas' NHS Foundation Trust & University of Washington.

Papers
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Journal ArticleDOI

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Theo Vos, +2419 more
- 17 Oct 2020 - 
TL;DR: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates, and there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries.
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Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Christopher J L Murray, +2272 more
- 17 Oct 2020 - 
TL;DR: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure.
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Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Marissa B Reitsma, +484 more
- 19 Jun 2021 - 
TL;DR: In this paper, the authors estimated the prevalence of smoking tobacco use and attributable disease burden for 204 countries and territories, by age and sex, from 1990 to 2019 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study.
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Health effects associated with consumption of unprocessed red meat: a Burden of Proof study

TL;DR: In this paper , the authors conducted a systematic review and implemented a meta-regression-relaxing conventional log-linearity assumptions and incorporating between-study heterogeneity-to evaluate the relationships between unprocessed red meat consumption and six potential health outcomes.