Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health. Wesson, P., Hswen, Y., Valdes, G., Stojanovski, K., & Handley, M. A. Annual Review of Public Health, 43(1):59–78, April, 2022. ![Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.
@article{wesson_risks_2022,
title = {Risks and {Opportunities} to {Ensure} {Equity} in the {Application} of {Big} {Data} {Research} in {Public} {Health}},
volume = {43},
issn = {0163-7525, 1545-2093},
url = {https://www.annualreviews.org/doi/10.1146/annurev-publhealth-051920-110928},
doi = {10.1146/annurev-publhealth-051920-110928},
abstract = {The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.},
language = {en},
number = {1},
urldate = {2023-08-21},
journal = {Annual Review of Public Health},
author = {Wesson, Paul and Hswen, Yulin and Valdes, Gilmer and Stojanovski, Kristefer and Handley, Margaret A.},
month = apr,
year = {2022},
pages = {59--78},
}
Downloads: 0
{"_id":"MxKTvnb5ykSsbcQME","bibbaseid":"wesson-hswen-valdes-stojanovski-handley-risksandopportunitiestoensureequityintheapplicationofbigdataresearchinpublichealth-2022","author_short":["Wesson, P.","Hswen, Y.","Valdes, G.","Stojanovski, K.","Handley, M. A."],"bibdata":{"bibtype":"article","type":"article","title":"Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health","volume":"43","issn":"0163-7525, 1545-2093","url":"https://www.annualreviews.org/doi/10.1146/annurev-publhealth-051920-110928","doi":"10.1146/annurev-publhealth-051920-110928","abstract":"The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.","language":"en","number":"1","urldate":"2023-08-21","journal":"Annual Review of Public Health","author":[{"propositions":[],"lastnames":["Wesson"],"firstnames":["Paul"],"suffixes":[]},{"propositions":[],"lastnames":["Hswen"],"firstnames":["Yulin"],"suffixes":[]},{"propositions":[],"lastnames":["Valdes"],"firstnames":["Gilmer"],"suffixes":[]},{"propositions":[],"lastnames":["Stojanovski"],"firstnames":["Kristefer"],"suffixes":[]},{"propositions":[],"lastnames":["Handley"],"firstnames":["Margaret","A."],"suffixes":[]}],"month":"April","year":"2022","pages":"59–78","bibtex":"@article{wesson_risks_2022,\n\ttitle = {Risks and {Opportunities} to {Ensure} {Equity} in the {Application} of {Big} {Data} {Research} in {Public} {Health}},\n\tvolume = {43},\n\tissn = {0163-7525, 1545-2093},\n\turl = {https://www.annualreviews.org/doi/10.1146/annurev-publhealth-051920-110928},\n\tdoi = {10.1146/annurev-publhealth-051920-110928},\n\tabstract = {The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.},\n\tlanguage = {en},\n\tnumber = {1},\n\turldate = {2023-08-21},\n\tjournal = {Annual Review of Public Health},\n\tauthor = {Wesson, Paul and Hswen, Yulin and Valdes, Gilmer and Stojanovski, Kristefer and Handley, Margaret A.},\n\tmonth = apr,\n\tyear = {2022},\n\tpages = {59--78},\n}\n\n\n\n\n\n\n\n","author_short":["Wesson, P.","Hswen, Y.","Valdes, G.","Stojanovski, K.","Handley, M. A."],"key":"wesson_risks_2022","id":"wesson_risks_2022","bibbaseid":"wesson-hswen-valdes-stojanovski-handley-risksandopportunitiestoensureequityintheapplicationofbigdataresearchinpublichealth-2022","role":"author","urls":{"Paper":"https://www.annualreviews.org/doi/10.1146/annurev-publhealth-051920-110928"},"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/drdanovak","dataSources":["xZ5hJJktDAHAFpiAW"],"keywords":[],"search_terms":["risks","opportunities","ensure","equity","application","big","data","research","public","health","wesson","hswen","valdes","stojanovski","handley"],"title":"Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health","year":2022}