Translational Health Disparities Research in a Data-Rich World
Background: Despite decades of research and interventions, significant health disparities persist. Seventeen years is the estimated time to translate scientific discoveries into public health action. This Narrative Review argues that the translation process could be accelerated if representative dat...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Mary Ann Liebert
2019-11-01
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| Series: | Health Equity |
| Subjects: | |
| Online Access: | https://www.liebertpub.com/doi/full/10.1089/HEQ.2019.0042 |
| _version_ | 1797361386022502400 |
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| author | Nancy Breen David Berrigan James S. Jackson David W.S. Wong Frederick B. Wood Joshua C. Denny Xinzhi Zhang Philip E. Bourne |
| author_facet | Nancy Breen David Berrigan James S. Jackson David W.S. Wong Frederick B. Wood Joshua C. Denny Xinzhi Zhang Philip E. Bourne |
| author_sort | Nancy Breen |
| collection | DOAJ |
| description | Background: Despite decades of research and interventions, significant health disparities persist. Seventeen years is the estimated time to translate scientific discoveries into public health action. This Narrative Review argues that the translation process could be accelerated if representative data were gathered and used in more innovative and efficient ways.
Methods: The National Institute on Minority Health and Health Disparities led a multiyear visioning process to identify research opportunities designed to frame the next decade of research and actions to improve minority health and reduce health disparities. ?Big data? was identified as a research opportunity and experts collaborated on a systematic vision of how to use big data both to improve the granularity of information for place-based study and to efficiently translate health disparities research into improved population health. This Narrative Review is the result of that collaboration.
Results: Big data could enhance the process of translating scientific findings into reduced health disparities by contributing information at fine spatial and temporal scales suited to interventions. In addition, big data could fill pressing needs for health care system, genomic, and social determinant data to understand mechanisms. Finally, big data could lead to appropriately personalized health care for demographic groups. Rich new resources, including social media, electronic health records, sensor information from digital devices, and crowd-sourced and citizen-collected data, have the potential to complement more traditional data from health surveys, administrative data, and investigator-initiated registries or cohorts. This Narrative Review argues for a renewed focus on translational research cycles to accomplish this continual assessment.
Conclusion: The promise of big data extends from etiology research to the evaluation of large-scale interventions and offers the opportunity to accelerate translation of health disparities studies. This data-rich world for health disparities research, however, will require continual assessment for efficacy, ethical rigor, and potential algorithmic or system bias. |
| first_indexed | 2024-03-08T15:52:57Z |
| format | Article |
| id | doaj.art-b3a80cddc4804d1e942e226f9fa5e3f1 |
| institution | Directory Open Access Journal |
| issn | 2473-1242 |
| language | English |
| last_indexed | 2024-03-08T15:52:57Z |
| publishDate | 2019-11-01 |
| publisher | Mary Ann Liebert |
| record_format | Article |
| series | Health Equity |
| spelling | doaj.art-b3a80cddc4804d1e942e226f9fa5e3f12024-01-09T04:11:09ZengMary Ann LiebertHealth Equity2473-12422019-11-013158860010.1089/HEQ.2019.0042Translational Health Disparities Research in a Data-Rich WorldNancy BreenDavid BerriganJames S. JacksonDavid W.S. WongFrederick B. WoodJoshua C. DennyXinzhi ZhangPhilip E. BourneBackground: Despite decades of research and interventions, significant health disparities persist. Seventeen years is the estimated time to translate scientific discoveries into public health action. This Narrative Review argues that the translation process could be accelerated if representative data were gathered and used in more innovative and efficient ways. Methods: The National Institute on Minority Health and Health Disparities led a multiyear visioning process to identify research opportunities designed to frame the next decade of research and actions to improve minority health and reduce health disparities. ?Big data? was identified as a research opportunity and experts collaborated on a systematic vision of how to use big data both to improve the granularity of information for place-based study and to efficiently translate health disparities research into improved population health. This Narrative Review is the result of that collaboration. Results: Big data could enhance the process of translating scientific findings into reduced health disparities by contributing information at fine spatial and temporal scales suited to interventions. In addition, big data could fill pressing needs for health care system, genomic, and social determinant data to understand mechanisms. Finally, big data could lead to appropriately personalized health care for demographic groups. Rich new resources, including social media, electronic health records, sensor information from digital devices, and crowd-sourced and citizen-collected data, have the potential to complement more traditional data from health surveys, administrative data, and investigator-initiated registries or cohorts. This Narrative Review argues for a renewed focus on translational research cycles to accomplish this continual assessment. Conclusion: The promise of big data extends from etiology research to the evaluation of large-scale interventions and offers the opportunity to accelerate translation of health disparities studies. This data-rich world for health disparities research, however, will require continual assessment for efficacy, ethical rigor, and potential algorithmic or system bias.https://www.liebertpub.com/doi/full/10.1089/HEQ.2019.0042big datatranslationinterventionsNIMHD Methods PillarAIalgorithmic bias |
| spellingShingle | Nancy Breen David Berrigan James S. Jackson David W.S. Wong Frederick B. Wood Joshua C. Denny Xinzhi Zhang Philip E. Bourne Translational Health Disparities Research in a Data-Rich World Health Equity big data translation interventions NIMHD Methods Pillar AI algorithmic bias |
| title | Translational Health Disparities Research in a Data-Rich World |
| title_full | Translational Health Disparities Research in a Data-Rich World |
| title_fullStr | Translational Health Disparities Research in a Data-Rich World |
| title_full_unstemmed | Translational Health Disparities Research in a Data-Rich World |
| title_short | Translational Health Disparities Research in a Data-Rich World |
| title_sort | translational health disparities research in a data rich world |
| topic | big data translation interventions NIMHD Methods Pillar AI algorithmic bias |
| url | https://www.liebertpub.com/doi/full/10.1089/HEQ.2019.0042 |
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