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Big Data for the Advancement of Health Equity
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Big Data for the Advancement of Health Equity

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 11635

Special Issue Editor

Dr. Thu T. Nguyen

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA
Interests: social epidemiology; health inequities; racism; big data; social media research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research harnessing expansive and relatively untapped big data resources, including social media data, health insurance claims, medical records, Google street view images, police and court records, video surveillance, banking, and land use reports, explores an under-used source of data for the purposes of improving public health. Big data resources can be used to collect timely information to complement traditional data sources such as clinical trials or observational cohort studies, which are expensive, may include less diverse study participants, may be geographically restricted, and may involve a substantial delay between when the data are collected and when they become available for researchers. Leveraging big data sources may be one way to circumvent some of the limitations of traditional self-report measures and capture attitudes regarding sensitive topics such as racial prejudice and bias.

Big data can be used to answer a host of critical research questions related to health equity based on race, social class, and sexual orientation, across and within nations. This includes research to document existing health inequities, research to investigate the drivers of health inequities such as racism, as well as interventions to reduce these health inequities. We invite submissions covering any of these areas related to the use of big data to advance health equity.

Dr. Thu Nguyen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • social media
  • records
  • claims
  • images
  • health equity
  • racism
  • minority health
  • sexual orientation
  • social class

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Published Papers (2 papers)

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12 pages, 293 KiB  
Article
Advocacy, Hesitancy, and Equity: Exploring U.S. Race-Related Discussions of the COVID-19 Vaccine on Twitter
by Shaniece Criss, Thu T. Nguyen, Samantha Norton, Imaya Virani, Eli Titherington, Emma Lou Tillmanns, Courtney Kinnane, Gabrielle Maiolo, Anne B. Kirby and Gilbert C. Gee
Int. J. Environ. Res. Public Health 2021, 18(11), 5693; https://doi.org/10.3390/ijerph18115693 - 26 May 2021
Cited by 24 | Viewed by 6942
Abstract
Background: Our study aimed to describe themes of tweets related to COVID-19 vaccines, race, and ethnicity to explore the context of the intersection of these topics on Twitter. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect a random 1% sample [...] Read more.
Background: Our study aimed to describe themes of tweets related to COVID-19 vaccines, race, and ethnicity to explore the context of the intersection of these topics on Twitter. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect a random 1% sample of publicly available tweets from October 2020 to January 2021. The study team conducted a qualitative content analysis from the full data set of 1110 tweets. Results: The tweets revealed vaccine support through vaccine affirmation, advocacy through reproach, a need for a vaccine, COVID-19 and racism, vaccine development and efficacy, racist vaccine humor, and news updates. Vaccine opposition was demonstrated through direct opposition, vaccine hesitancy, and adverse reactions. Conspiracy and misinformation included scientific misinformation, political misinformation, beliefs about immunity and protective behaviors, and race extermination conspiracy. Equity and access focused on overcoming history of medical racism, pointing out health disparities, and facilitators to vaccine access. Representation touted pride in development and role models, and politics discussed the role of politics in vaccines and international politics. Conclusion: Our analysis demonstrates that Twitter can provide nuances about multiple viewpoints on the vaccine related to race and ethnicity and can be beneficial in contributing to insights for public health messaging. Full article
(This article belongs to the Special Issue Big Data for the Advancement of Health Equity)

Review

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18 pages, 3213 KiB  
Review
Adequacy of Existing Surveillance Systems to Monitor Racism, Social Stigma and COVID Inequities: A Detailed Assessment and Recommendations
by Chandra L. Ford, Bita Amani, Nina T. Harawa, Randall Akee, Gilbert C. Gee, Majid Sarrafzadeh, Consuela Abotsi-Kowu, Shayan Fazeli, Cindy Le, Ezinne Nwankwo, Davina Zamanzadeh, Anaelia Ovalle and Monica L. Ponder
Int. J. Environ. Res. Public Health 2021, 18(24), 13099; https://doi.org/10.3390/ijerph182413099 - 12 Dec 2021
Cited by 5 | Viewed by 3136
Abstract
The populations impacted most by COVID are also impacted by racism and related social stigma; however, traditional surveillance tools may not capture the intersectionality of these relationships. We conducted a detailed assessment of diverse surveillance systems and databases to identify characteristics, constraints and [...] Read more.
The populations impacted most by COVID are also impacted by racism and related social stigma; however, traditional surveillance tools may not capture the intersectionality of these relationships. We conducted a detailed assessment of diverse surveillance systems and databases to identify characteristics, constraints and best practices that might inform the development of a novel COVID surveillance system that achieves these aims. We used subject area expertise, an expert panel and CDC guidance to generate an initial list of N > 50 existing surveillance systems as of 29 October 2020, and systematically excluded those not advancing the project aims. This yielded a final reduced group (n = 10) of COVID surveillance systems (n = 3), other public health systems (4) and systems tracking racism and/or social stigma (n = 3, which we evaluated by using CDC evaluation criteria and Critical Race Theory. Overall, the most important contribution of COVID-19 surveillance systems is their real-time (e.g., daily) or near-real-time (e.g., weekly) reporting; however, they are severely constrained by the lack of complete data on race/ethnicity, making it difficult to monitor racial/ethnic inequities. Other public health systems have validated measures of psychosocial and behavioral factors and some racism or stigma-related factors but lack the timeliness needed in a pandemic. Systems that monitor racism report historical data on, for instance, hate crimes, but do not capture current patterns, and it is unclear how representativeness the findings are. Though existing surveillance systems offer important strengths for monitoring health conditions or racism and related stigma, new surveillance strategies are needed to monitor their intersecting relationships more rigorously. Full article
(This article belongs to the Special Issue Big Data for the Advancement of Health Equity)
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