Simon I. Hay (ORCID: 0000-0002-0611-7272) Google Scholar: h-index, Total Citations, and Citation Map
Simon I. Hay (ORCID: 0000-0002-0611-7272)'s h-index is 278 (706 i10-index, 556,662+ total citations across 775+ publications) according to Google Scholar as of October 2026. Simon I. Hay (ORCID: 0000-0002-0611-7272) is affiliated with Institute for Health Metrics and Evaluation, University of Washington (grid.34477.33), UNITED STATES.
Simon I. Hay (ORCID: 0000-0002-0611-7272) is a researcher affiliated with Institute for Health Metrics and Evaluation, University of Washington (grid.34477.33), UNITED STATES, specializing in IHME, Health Metrics, Global Health. Their work has been cited 556,662 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Simon I. Hay (ORCID: 0000-0002-0611-7272)'s Citation Metrics
Bibliometric impact based on 775 indexed publications.
- H-Index
- 278
- i10-Index
- 706
- Total Citations
- 556,662
- Citing Countries
- 132
As of October 2026.
Simon I. Hay (ORCID: 0000-0002-0611-7272) has an h-index of 278 and 556,662 total citations across 775 publications, with research cited by institutions in 132 countries.
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Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013
201417,169
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
5 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher produced a seminal systematic analysis quantifying global, regional, and national overweight and obesity prevalence from 1980 to 2013, establishing a critical benchmark for public health monitoring.
The researcher established a foundational global assessment of dengue distribution and burden, providing a critical baseline for epidemiological understanding and public health planning.
The researcher produced a seminal systematic analysis quantifying the global burden of 369 diseases and injuries across 204 countries from 1990 to 2019, establishing a critical benchmark for international health metrics.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Simon I. Hay (ORCID: 0000-0002-0611-7272)'s research
Simon I. Hay (ORCID: 0000-0002-0611-7272) is a researcher in IHME, Health Metrics and Global Health at Institute for Health Metrics and Evaluation, University of Washington (grid.34477.33), UNITED STATES. Their work has been cited 556,662 times across 775 publications (h-index 278), according to Google Scholar.
Their most-cited work, “Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013” (2014), has accumulated 17,169 citations. Other influential works include “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” (2020) with 17,039 citations and “Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis” (2022) with 14,863 citations.
Citations of Simon I. Hay (ORCID: 0000-0002-0611-7272)'s research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
