Addressing data scarcity in humanitarian health and environmental justice
Abstract/Contents
- Abstract
- Modern computational approaches promise to address issues of health equity through identifying potential disparities or interventions. However, such approaches are subject to data scarcity: structurally marginalized populations tend to have poorer-quality data, and computational tools reliant on high-quality data may exacerbate existing inequities. Through examples in humanitarian, occupational, and environmental health, we examine data science approaches to circumvent data scarcity. We investigate the use of machine learning to model forced migration in humanitarian settings, microsimulation models for high-risk occupational health contexts, and systems-level public health risk estimation for an impending environmental catastrophe. Taken together, this body of work demonstrates how unconventional data sources, novel approaches, and rigorous study design can be employed to advance health equity and environmental justice in the absence of high-quality data
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Huynh, Benjamin Quoc |
|---|---|
| Degree supervisor | Rehkopf, David |
| Thesis advisor | Rehkopf, David |
| Thesis advisor | Chen, Jonathan H |
| Thesis advisor | Geldsetzer, Pascal |
| Degree committee member | Chen, Jonathan H |
| Degree committee member | Geldsetzer, Pascal |
| Associated with | Stanford University, School of Medicine, Department of Biomedical Data Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Benjamin Q. Huynh |
|---|---|
| Note | Submitted to the Department of Biomedical Data Science |
| Thesis | Thesis Ph.D. Stanford University 2022 |
| Location | https://purl.stanford.edu/ry824gt4408 |
Access conditions
- Copyright
- © 2022 by Benjamin Quoc Huynh
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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