CAREER: Learning from When, Where and by Whom Data is Generated for Advancing Public Health Studies

Award Number and Duration
1845487
June 1, 2019 – May 31, 2024

PI
Rumi Chunara, PhD

Overview
Improving disease prevention through robust and high-granularity measures of lifestyle, environmental and social factors from daily life will improve healthcare by enabling precise and focused proactive interventions. This will dramatically change the healthcare paradigm in this country and significantly reduce costs and illnesses, more so than a solely reactive focus on disease diagnosis and treatment. Public health is the study of these daily life factors and prevention efforts. New person-generated data (PGD) from Internet and mobile data sources, such as mHealth, social media, wearables, and data from smartphone apps, offer unprecedented opportunity to provide sub-daily, as well as local, neighborhood-level measures of lifestyle, environmental and social factors from daily life. However, the impact of this data has yet to be fully realized for public health efforts. In part, this is because existing research efforts on PGD often focus on processing the content of data in isolation, and do not consider human data sharing patterns, that is, who contributes the data, when it is contributed and from where it is contributed. By accounting for these attributes, this project aims to improve the validity and reliability of measures extracted from PGD and enable improved understanding of high-granularity health risks and outcomes. The project will also provide a highly-integrated research and educational program for public health practitioners, students, and community members in the context of PGD and public health by: (1) preparing students to use computer science in today’s job landscape via a problem-based learning class; (2) increasing high-school students’ exposure to computer science in the real-world with a focus on applications of computer science; and (3) disseminating scientific understanding of computer science in the public health and general community. In conjunction, this work will improve both computer science and public health practice and research through method development and exposure of diverse community members and community-oriented professionals to the utility of data mining and machine learning.

Products

N. Mirin, H. Mattie, L. Jackson, Z. Samad, R. Chunara. 2022. Data Science in Public Health: Building Next Generation Capacity. Harvard Data Science Review, 4(4). [link]

Z. Hoodbhoy, R. Chunara, A. Waljee, A. AbuBakr, & Z. Samad. (2023). Is there a need for graduate-level programmes in health data science? A perspective from Pakistan. The Lancet Global Health, 11(1), e23-e25. [link]

D.T. Duncan, S.H. Cook, E.P. Wood, S.D. Regan, B. Chaix, Y. Tian, Y. and R. Chunara, 2023. Structural racism and homophobia evaluated through social media sentiment combined with activity spaces and associations with mental health among young sexual minority men. Social Science & Medicine, 320, p.115755.

M. Zhao, H. Singh, L. Chok, R. Chunara
Segmenting across places: The need for fair transfer learning with satellite imagery
2022 IEEE CVPR Workshop on Fair, Data Efficient and Trusted Computer Vision. [pre-print]

H. Singh, V. Mhasawade, R. Chunara
Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database [link]
2022. PLOS Digital Health 1.4 (2022): e0000023.

V. Mhasawade, Y. Zhao, R. Chunara.
Machine learning and algorithmic fairness in public and population health. [link]
2021. Nature Machine Intelligence 3, no. 8 pp. 659-666.

Y. Zhao, E.P. Wood, N. Mirin, S.H. Cook, and R. Chunara
Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review. [link]
2021. American journal of preventive medicine.

V. Mhasawade, R. Chunara
Causal Multi-level Fairness. [pre-print]
2021. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES).