Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health
Abstract Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new r...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2023-01-01
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| Series: | Journal of Clinical and Translational Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866123006702/type/journal_article |
| _version_ | 1797397111230169088 |
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| author | Sandra Tilmon Sharmilee Nyenhuis Anthony Solomonides Bruno Barbarioli Ankur Bhargava Suzi Birz Kathryn Bouzein Celine Cardenas Bradley Carlson Ellen Cohen Emily Dillon Brian Furner Zhong Huang Julie Johnson Nivedha Krishnan Kevin Lazenby Kaitlyn Li Sonya Makhni Doriane Miler Jonathan Ozik Carlos Santos Marc Sleiman Julian Solway Sanjay Krishnan Samuel Volchenboum |
| author_facet | Sandra Tilmon Sharmilee Nyenhuis Anthony Solomonides Bruno Barbarioli Ankur Bhargava Suzi Birz Kathryn Bouzein Celine Cardenas Bradley Carlson Ellen Cohen Emily Dillon Brian Furner Zhong Huang Julie Johnson Nivedha Krishnan Kevin Lazenby Kaitlyn Li Sonya Makhni Doriane Miler Jonathan Ozik Carlos Santos Marc Sleiman Julian Solway Sanjay Krishnan Samuel Volchenboum |
| author_sort | Sandra Tilmon |
| collection | DOAJ |
| description |
Abstract
Background/Objective:
Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors.
Methods:
This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets.
Results:
The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index.
Conclusion:
The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.
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| first_indexed | 2024-03-09T01:05:17Z |
| format | Article |
| id | doaj.art-ff0f7c19fa95487d87eea91bd8033b00 |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-03-09T01:05:17Z |
| publishDate | 2023-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-ff0f7c19fa95487d87eea91bd8033b002023-12-11T10:18:35ZengCambridge University PressJournal of Clinical and Translational Science2059-86612023-01-01710.1017/cts.2023.670Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of healthSandra Tilmon0https://orcid.org/0000-0002-1990-1197Sharmilee Nyenhuis1Anthony Solomonides2https://orcid.org/0000-0003-2117-2461Bruno Barbarioli3Ankur Bhargava4Suzi Birz5Kathryn Bouzein6Celine Cardenas7Bradley Carlson8Ellen Cohen9Emily Dillon10Brian Furner11Zhong Huang12Julie Johnson13Nivedha Krishnan14Kevin Lazenby15https://orcid.org/0000-0001-5520-2855Kaitlyn Li16Sonya Makhni17Doriane Miler18Jonathan Ozik19Carlos Santos20Marc Sleiman21Julian Solway22Sanjay Krishnan23Samuel Volchenboum24Pediatrics, University of Chicago, Chicago, IL, USAPediatrics, University of Chicago, Chicago, IL, USA Medicine, University of Chicago, Chicago, IL, USANorthShore University Health System, Research Institute, Evanston, IL, USAComputer Science, University of Chicago, Chicago, IL, USAChicago Medicine, Chicago, IL, USAPediatrics, University of Chicago, Chicago, IL, USAPediatrics, University of Chicago, Chicago, IL, USAWake Forest University, Winston-Salem, NC, USAPritzker School of Medicine, University of Chicago, Chicago, IL, USAPediatrics, University of Chicago, Chicago, IL, USAPsychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USAPediatrics, University of Chicago, Chicago, IL, USAPritzker School of Medicine, University of Chicago, Chicago, IL, USAClinical Research Informatics, University of Chicago, Chicago, IL, USAUniversity of Illinois at Chicago, Chicago, IL, USAPritzker School of Medicine, University of Chicago, Chicago, IL, USAUniversity of Chicago, Chicago, IL, USAChicago Medicine, Chicago, IL, USAMedicine, University of Chicago, Chicago, IL, USADecision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USAInternal Medicine, Rush University Medical Center, Chicago, IL, USAPritzker School of Medicine, University of Chicago, Chicago, IL, USAMedicine, University of Chicago, Chicago, IL, USAComputer Science, University of Chicago, Chicago, IL, USAPediatrics, University of Chicago, Chicago, IL, USA Abstract Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals. https://www.cambridge.org/core/product/identifier/S2059866123006702/type/journal_articleAsthmahealth disparitiesChicagodata commonsSDOH |
| spellingShingle | Sandra Tilmon Sharmilee Nyenhuis Anthony Solomonides Bruno Barbarioli Ankur Bhargava Suzi Birz Kathryn Bouzein Celine Cardenas Bradley Carlson Ellen Cohen Emily Dillon Brian Furner Zhong Huang Julie Johnson Nivedha Krishnan Kevin Lazenby Kaitlyn Li Sonya Makhni Doriane Miler Jonathan Ozik Carlos Santos Marc Sleiman Julian Solway Sanjay Krishnan Samuel Volchenboum Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health Journal of Clinical and Translational Science Asthma health disparities Chicago data commons SDOH |
| title | Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health |
| title_full | Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health |
| title_fullStr | Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health |
| title_full_unstemmed | Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health |
| title_short | Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health |
| title_sort | sociome data commons a scalable and sustainable platform for investigating the full social context and determinants of health |
| topic | Asthma health disparities Chicago data commons SDOH |
| url | https://www.cambridge.org/core/product/identifier/S2059866123006702/type/journal_article |
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