Statistical and algorithmic approaches for health policy and fairness
Abstract/Contents
- Abstract
- Advances in statistics, econometrics, and computer science have the potential to facilitate data-driven decision making in improving the health of populations. However, adapting modern data science methods to eliminate health disparities remains challenging because interventions based singularly on health data do not fully address health issues borne from structural, upstream inequities. A multi-level approach that integrates social and health data to characterize how specific social systems perpetuate health inequities provides opportunities to create more tailored health and social policies. I will discuss examples of addressing health inequity through data science in three contexts: (1) mass incarceration in relationship to public health policies, (2) equity for structurally vulnerable populations in public health and social policies, and (3) methods for "small data" in precision health. An underlying theme is the importance of statistical methodology and study design informed by a holistic understanding of the interplay between social and health systems
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 | Chin, Elizabeth T |
|---|---|
| Degree supervisor | Hastie, Trevor |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Andrews, Jason Randolph |
| Degree committee member | Andrews, Jason Randolph |
| Associated with | Stanford University, School of Medicine, Department of Biomedical Data Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Elizabeth T. Chin |
|---|---|
| Note | Submitted to the Department of Biomedical Data Science |
| Thesis | Thesis Ph.D. Stanford University 2022 |
| Location | https://purl.stanford.edu/xs182tb0285 |
Access conditions
- Copyright
- © 2022 by Elizabeth Chin
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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