Zachary Butzin-Dozier
Stanford University, CA·Public Health & Epidemiology·Early-career
Many people who recover from a viral infection do not fully return to health: lingering symptoms and raised risk of death after infection are a growing burden. Which existing medicines, and which vaccination timing, actually reduce those long-term consequences is largely unknown, and running a trial for every candidate is impractical.
- Fields
Public Health & Epidemiology
Medicine & Clinical Research
Mathematics & Statistics
- Topics
infectious disease epidemiology
long-term sequelae
electronic health records
- Grants
- 1 known grant, through Aug 2029
What they work on
- Estimate whether the diabetes drugs metformin and GLP-1 receptor agonists, which mimic a blood-sugar-regulating gut hormone, reduce death and long-term symptoms among patients with both diabetes and viral infection, using national electronic health record data.
- Assess whether the interleukin-6-blocking drugs tocilizumab and sarilumab change mortality and long-term consequences of viral infection in patients with moderate rheumatoid arthritis.
- Develop, evaluate and disseminate causal inference, machine learning and biostatistical methods suited to electronic health record data from the National Clinical Cohort Collaborative.
- Evaluate how the timing of vaccination relative to acute infection relates to long-term sequelae, in order to describe an optimized vaccination schedule.
More about the research
This project instead mines the medical records of large patient populations and applies statistical methods built to estimate cause and effect from data that were never collected as an experiment.
Why it matters
Long-term illness and death following viral infection affect large numbers of patients with few proven treatments. Testing whether already-approved drugs such as metformin, GLP-1 receptor agonists, tocilizumab and sarilumab reduce these outcomes could point to repurposed therapies quickly, and clearer evidence on vaccination timing could inform schedules. The methods developed may also make health-record data more trustworthy for answering treatment questions generally.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jonathan Chen
Stanford University, CA·Medicine & Clinical Research
Harmonize and share electronic health record data on suspected urinary tract infections across Stanford, UT Southwestern and Harvard to support multi-site model building.
Peiyin Hung
University of South Carolina-Columbia, SC·Public Health & Epidemiology
Measure how the COVID-19 pandemic changed maternal care access, quality and maternal and birth outcomes across maternal characteristics, using N3C electronic health records and South Carolina and Florida statewide data.
Nosheen Reza
University of Pennsylvania, PA·Medicine & Clinical Research·Early-career
Build and validate a machine learning model that detects hypertrophic cardiomyopathy from electrocardiograms in Penn Medicine electronic health record data, and test whether adding other EHR-derived traits improves detection.
The school
Stanford University: 87 labs on PI Pages, $1.6B a year on research, 739 research doctorates a year.
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