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McMann TJ, Haupt MR, Le N, Meurice ME, Li J, Cuomo RE, Mackey TK. Abortion pill marketing and sourcing on twitter following Dobbs v. Jackson supreme court ruling. EUR J CONTRACEP REPR 2024; 29:139-144. [PMID: 38780176 DOI: 10.1080/13625187.2024.2354868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study examines abortion-related discourse on Twitter (X) pre-and post-Dobbs v. Jackson ruling, which eliminated the constitutional right to abortion. STUDY DESIGN We used a custom data collection tool to collect tweets directly from Twitter using abortion-related keywords. We used the BERTopic language model and examined the top 30 retweeted and top 30 textually similar tweets from relevant topic clusters using an inductive coding approach. We also conducted statistical testing to assess potential associations between abortion themes. RESULTS 166,799 unique tweets were collected from December 2020-December 2022. 464 unique tweets were coded for abortion-related themes with 154 identified as relevant. Of these, 66 tweets marketed abortion pills, 17 tweets were identified as offering consultations, and 91 tweets were relevant to self-managed abortion. All marketing and consultation tweets were posted post-Dobbs decision and 7 (7.69%) of self-managed tweets were posted pre-Dobbs versus 84 (92.30%) posted post-Dobbs. A positive association was found between tweets offering a medical consultation with tweets marketing abortion pills and discussing self-managed abortion. CONCLUSION This study detected online marketing of abortion pills, consultations and discussions about self-managed abortion following the Dobbs v. Jackson ruling. These results provide more context to the type of abortion-related information that is available online.
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Affiliation(s)
- Tiana J McMann
- Global Health Program, Department of Anthropology, University of California San Diego, La Jolla, CA, USA
- Global Health Policy and Data Institute, San Diego, CA, USA
- S-3 Research, San Diego, CA, USA
| | - Michael R Haupt
- Global Health Policy and Data Institute, San Diego, CA, USA
- Department of Cognitive Science, University of California San Diego, San Diego, CA, USA
| | - Nicolette Le
- Global Health Program, Department of Anthropology, University of California San Diego, La Jolla, CA, USA
- Global Health Policy and Data Institute, San Diego, CA, USA
| | - Marielle E Meurice
- Division of Complex Family Planning, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, San Diego, CA, USA
| | | | - Raphael E Cuomo
- Global Health Policy and Data Institute, San Diego, CA, USA
- S-3 Research, San Diego, CA, USA
- Department of Anesthesiology, University of California San Diego - School of Medicine, San Diego, CA, USA
| | - Tim K Mackey
- Global Health Program, Department of Anthropology, University of California San Diego, La Jolla, CA, USA
- Global Health Policy and Data Institute, San Diego, CA, USA
- S-3 Research, San Diego, CA, USA
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Zhu DT, Zhao L, Alzoubi T, Shenin N, Baskaran T, Tikhonov J, Wang C. Public health and clinical implications of Dobbs v. Jackson for patients and healthcare providers: A scoping review. PLoS One 2024; 19:e0288947. [PMID: 38551970 PMCID: PMC10980209 DOI: 10.1371/journal.pone.0288947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 01/05/2024] [Indexed: 04/01/2024] Open
Abstract
INTRODUCTION On June 24, 2022, the U.S. Supreme Court's decision in Dobbs v. Jackson reversed the precedent set forth by Roe v. Wade, empowering individual states to regulate abortion care. This aftermath of this ruling has given rise to widespread bans, limiting the accessibility of abortion services for patients and impeding providers' ability to deliver a comprehensive spectrum of reproductive health services. Of particular concern is the disproportionate impact on medically underserved groups, further heightening existing social and structural disparities in reproductive health. METHODS We conducted a scoping review to broadly evaluate the clinical and public health impact of Dobbs on patients' access to abortion care and related reproductive health services, in addition to the training and clinical practice of healthcare providers. We searched eight bibliographic databases (PubMed, Scopus, Embase, PsycINFO, Google Scholar, Science Direct, JSTOR, and Web of Science) and three preprint servers (medRxiv, bioRxiv, and Europe PMC) using various combinations of keywords related to 'abortion', 'Dobbs', and 'Roe' on March 22, 2023. Four reviewers independently screened the studies based on pre-specified eligibility criteria and one reviewer performed data extraction for pre-identified themes. The search was conducted based on PRISMA Extension for Scoping Reviews (PRSIMA-ScR) guidelines. RESULTS Eighteen studies, comprising 12 peer-reviewed articles and 6 study abstracts, met the inclusion criteria. The studies demonstrated that Dobbs increased demand for contraception, magnified existing travel- and cost-related barriers to access, further polarized views on abortion and complex family planning on social media (e.g., Twitter), and evoked substantial concerns among medical trainees regarding their scope of practice and potential legal repercussions for providing abortion care. CONCLUSION In the wake of Dobbs v. Jackson, further public health and clinical interventions are urgently needed to bridge disparities in abortion care and reproductive health, mitigating the deleterious consequences of this emerging public health crisis.
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Affiliation(s)
- David T. Zhu
- Medical Scientist Training Program, School of Medicine, Virginia Commonwealth University, Richmond, VA, United States of America
| | - Lucy Zhao
- Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Tala Alzoubi
- Faculty of Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Novera Shenin
- Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | | | - Julia Tikhonov
- School of Interdisciplinary Sciences, Faculty of Science, McMaster University, Hamilton, ON, Canada
| | - Catherine Wang
- Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
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Guo Y, Zehrung R, Genuario K, Lu X, Mei Q, Chen Y, Zheng K. Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:951-960. [PMID: 38222378 PMCID: PMC10785943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care is once again in the national spotlight. A key issue central to this debate is patient privacy, as in the post-HITECH Act era it has become easier for medical records to be electronically accessed and shared. This study analyzed a large Twitter dataset from May to December 2022 to examine the public's reactions to Roe v. Wade's overruling and its implications for privacy. Using a mixed-methods approach consisting of computational and qualitative content analysis, we found a wide range of concerns voiced from the confidentiality of patient-physician information exchange to medical records being shared without patient consent. These findings may inform policy making and healthcare industry practices concerning medical privacy related to reproductive rights and women's health.
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Affiliation(s)
- Yawen Guo
- University of California, Irvine, Irvine, CA, USA
| | | | | | - Xuan Lu
- University of Michigan, Ann Arbor, MI, USA
| | | | - Yunan Chen
- University of California, Irvine, Irvine, CA, USA
| | - Kai Zheng
- University of California, Irvine, Irvine, CA, USA
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Swanson K, Ravi A, Saleh S, Weia B, Pleasants E, Arvisais-Anhalt S. Effect of Recent Abortion Legislation on Twitter User Engagement, Sentiment, and Expressions of Trust in Clinicians and Privacy of Health Information: Content Analysis. J Med Internet Res 2023; 25:e46655. [PMID: 37171873 DOI: 10.2196/46655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 05/13/2023] Open
Abstract
BACKGROUND The Supreme Court ruling in Dobbs v Jackson Women's Health Organization (Dobbs) overrules precedents established by Roe v Wade and Planned Parenthood v Casey and allows states to individually regulate access to abortion care services. While many states have passed laws to protect access to abortion services since the ruling, the ruling has also triggered the enforcement of existing laws and the creation of new ones that ban or restrict abortion. In addition to denying patients the full spectrum of reproductive health care, one major concern in the medical community is how the ruling will undermine trust in the patient-clinician relationship by influencing perceptions of the privacy of patient health information. OBJECTIVE This study aimed to study the effect of recent abortion legislation on Twitter user engagement, sentiment, expressions of trust in clinicians, and privacy of health information. METHODS We scraped tweets containing keywords of interest between January 1, 2020, and October 17, 2022, to capture tweets posted before and after the leak of the Supreme Court decision. We then trained a Latent Dirichlet Allocation model to select tweets pertinent to the topic of interest and performed a sentiment analysis using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-training Approach model and a causal impact time series analysis to examine engagement and sentiment. In addition, we used a Word2Vec model to study the terms of interest against a latent trust dimension to capture how expressions of trust for our terms of interest changed over time and used term frequency, inverse-document frequency to measure the volume of tweets before and after the decision with respect to the negative and positive sentiments that map to our terms of interest. RESULTS Our study revealed (1) a transient increase in the number of daily users by 576.86% (95% CI 545.34%-607.92%; P<.001), tweeting about abortion, health care, and privacy of health information postdecision leak; (2) a sustained and statistically significant decrease in the average daily sentiment on these topics by 19.81% (95% CI -22.98% to -16.59%; P=.001) postdecision leak; (3) a decrease in the association of the latent dimension of trust across most clinician-related and health information-related terms of interest; (4) an increased frequency of tweets with these clinician-related and health information-related terms and concomitant negative sentiment in the postdecision leak period. CONCLUSIONS The study suggests that the Dobbs ruling has consequences for health systems and reproductive health care that extend beyond denying patients access to the full spectrum of reproductive health services. The finding of a decrease in the expression of trust in clinicians and health information-related terms provides evidence to support advocacy and initiatives that proactively address concerns of trust in health systems and services.
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Affiliation(s)
- Karl Swanson
- Department of Medicine, University of California San Francisco, San Franicsco, CA, United States
| | - Akshay Ravi
- Department of Medicine, University of California San Francisco, San Franicsco, CA, United States
| | - Sameh Saleh
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Biomedical and Health Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Benjamin Weia
- Department of Medicine, University of California San Francisco, San Franicsco, CA, United States
| | - Elizabeth Pleasants
- School of Public Health, University of California, Berkeley, CA, United States
| | - Simone Arvisais-Anhalt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, United States
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Ujah OI, Olaore P, Nnorom OC, Ogbu CE, Kirby RS. Examining ethno-racial attitudes of the public in Twitter discourses related to the United States Supreme Court Dobbs vs. Jackson Women's Health Organization ruling: A machine learning approach. Front Glob Womens Health 2023; 4:1149441. [PMID: 37214560 PMCID: PMC10193152 DOI: 10.3389/fgwh.2023.1149441] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/18/2023] [Indexed: 05/24/2023] Open
Abstract
Background The decision of the US Supreme Court to repeal Roe vs. Wade sparked significant media attention. Although primarily related to abortion, opinions are divided about how this decision would impact disparities, especially for Black, Indigenous, and people of color. We used advanced natural language processing (NLP) techniques to examine ethno-racial contents in Twitter discourses related to the overturn of Roe vs. Wade. Methods We screened approximately 3 million tweets posted to Roe vs. Wade discussions and identified unique tweets in English-language that had mentions related to race, ethnicity, and racism posted between June 24 and July 10, 2022. We performed lexicon-based sentiment analysis to identify sentiment polarity and the emotions expressed in the Twitter discourse and conducted structural topic modeling to identify and examine latent themes. Results Of the tweets retrieved, 0.7% (n = 23,044) had mentions related to race, ethnicity, and racism. The overall sentiment polarity was negative (mean = -0.41, SD = 1.48). Approximately 60.0% (n = 12,092) expressed negative sentiments, while 39.0% (n = 81,45) expressed positive sentiments, and 3.0% (n = 619) expressed neutral sentiments. There were 20 latent themes which emerged from the topic model. The predominant topics in the discourses were related to "racial resentment" (topic 2, 11.3%), "human rights" (topic 2, 7.9%), and "socioeconomic disadvantage" (topic 16, 7.4%). Conclusions Our study demonstrates wide ranging ethno-racial concerns following the reversal of Roe and supports the need for active surveillance of racial and ethnic disparities in abortion access in the post-Roe era.
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Affiliation(s)
- Otobo I. Ujah
- College of Public Health, University of South Florida, Tampa, FL, United States
| | - Pelumi Olaore
- College of Public Health, University of South Florida, Tampa, FL, United States
| | - Onome C. Nnorom
- Department of Community Medicine, Jos University Teaching Hospital, Jos, Nigeria
| | - Chukwuemeka E. Ogbu
- College of Public Health, University of South Florida, Tampa, FL, United States
| | - Russell S. Kirby
- College of Public Health, University of South Florida, Tampa, FL, United States
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Ahmad PN, Shah AM, Lee K. A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain. Healthcare (Basel) 2023; 11:1268. [PMID: 37174810 PMCID: PMC10178605 DOI: 10.3390/healthcare11091268] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.
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Affiliation(s)
- Pir Noman Ahmad
- School of Computer Science, Harbin Institute of Technology, Harbin 150001, China
| | - Adnan Muhammad Shah
- Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
| | - KangYoon Lee
- Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
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