Evidence map›Paper›PMID 35578642›Full record

ArticleJMIR infodemiology

US Black Maternal Health Advocacy Topics and Trends on Twitter: Temporal Infoveillance Study.

Diana Grigsby-Toussaint, Ashley Champagne, Justin Uhr, Elizabeth Silva, Madeline Noh, Adam Bradley, Patrick Rashleigh

Abstract read
In one paragraph

Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Diana Grigsby-ToussaintDepartment of Epidemiology School of Public Health Brown University Providence, RI United States.ORCID https://orcid.org/0000-0003-0796-7284
Ashley ChampagneBrown University Library Providence, RI United States.ORCID https://orcid.org/0000-0002-4739-7178
Justin UhrBrown University Library Providence, RI United States.ORCID https://orcid.org/0000-0001-8895-3786
Elizabeth SilvaDepartment of Behavioral and Social Sciences School of Public Health Brown University Providence, RI United States.ORCID https://orcid.org/0000-0002-3323-4706
Madeline NohDepartment of Anthropology School of Public Health Brown University Providence, RI United States.ORCID https://orcid.org/0000-0001-5134-8021
Adam BradleyBrown University Library Providence, RI United States.ORCID https://orcid.org/0000-0003-1443-6596
Patrick RashleighBrown University Library Providence, RI United States.ORCID https://orcid.org/0000-0003-2398-8384

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Black women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts. Objective: This study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic. Methods: Tweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets. Results: A variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73%, and discussions about creating space for Black women decreased by just under 30%. Conclusions: The results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts.

Indexed as

advocacyBlack maternal healthCOVID-19digital humanitiesdisparityfeasibilityinfoveillancematernal healthminoritymodelsocial mediatopic modelingtrendTwitterwomen

Identifiers

PMID35578642
PMCPMC9092478

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.