Using AI and Social Media to Understand Health Disparities for Transgender Cancer Care
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ABSTRACT:
SUBMITTER: Annan A
PROVIDER: S-EPMC11344235 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature
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Project description:Background: Low health literacy is associated with decreased patient compliance and worse outcomes - with clinicians increasingly relying on printed materials to lower such risks. Yet, many of these documents exceed recommended comprehension levels. Furthermore, patients look increasingly to social media (SoMe) to answer healthcare questions. The character limits built into Twitter encourage users to publish small quantities of text, which are more accessible to patients with low health literacy. The present authors hypothesize that SoMe posts are written at lower grade levels than traditional medical sources, improving patient health literacy. Methods: The data sample consisted of the first 100 original tweets from three trending medical hashtags, leading to a total of 300 tweets. The Flesch-Kincaid Readability Formula (FKRF) was used to derive grade level of the tweets. Data was analyzed via descriptive and inferential statistics. Results: The readability scores for the data sample had a mean grade level of 9.45. A notable 47.6% of tweets were above ninth grade reading level. An independent-sample t-test comparing FKRF mean scores of different hashtags found differences between the means of the following: #hearthealth versus #diabetes (t = 3.15, p = 0.002); #hearthealth versus #migraine (t = 0.09, p = 0.9); and #diabetes versus #migraine (t = 3.4, p = 0.001). Conclusions: Tweets from this data sample were written at a mean grade level of 9.45, signifying a level between the ninth and tenth grades. This is higher than desired, yet still better than traditional sources, which have been previously analyzed. Ultimately, those responsible for health care SoMe posts must continue to improve efforts to reach the recommended reading level (between the sixth and eighth grade), so as to ensure optimal comprehension of patients.
| S-EPMC5381615 | biostudies-literature
Project description:ObjectiveTransgender people face substantial mental health disparities, and this population's emotional well-being can be particularly volatile during gender transition. Understanding gender transition sentiment patterns can positively impact transgender people by enabling them to anticipate, and put support in place for, particularly difficult time periods. Yet, tracking sentiment over time throughout gender transition is challenging using traditional research methods. This study's objective was to use social media data to understand average gender transition sentiment patterns.Materials and methodsComputational sentiment analysis and statistics were used to analyze 41 066 posts from 240 Tumblr transition blogs (online spaces where transgender people document gender transitions) to understand sentiment patterns over time and quantify relationships between transgender identity disclosures, sentiment, and social support.ResultsFindings suggest that sentiment increases over time on average throughout gender transition, particularly when people receive supportive responses to transgender identity disclosures. However, after disclosures to family members, people experienced temporary increased negative sentiment, followed by increased positive sentiment in the long term. After transgender identity disclosures on Facebook, an important means of mass disclosure, those with supportive networks experienced increased positive sentiment.ConclusionsWith foreknowledge of sentiment patterns likely to occur during gender transition, transgender people and their mental healthcare professionals can prepare with proper support in place throughout the gender transition process. Social media are a novel data source for understanding transgender people's sentiment patterns, which can help reduce mental health disparities for this marginalized population during a particularly difficult time.
| S-EPMC6696505 | biostudies-literature
Project description:IntroductionDespite the potential of social media to influence public health and generate insights, the process of monitoring and analyzing the dissemination of health care messages on social media has been described as difficult and in need of improvement.ObjectivesThe objective of this study was to describe and demonstrate a reproducible methodology for cataloging and analyzing health care-related social media comments and provide insight into how clinicians and members of the general public respond to health care messaging on social media.MethodsWe collected social media comments related to the American Dental Association's 2016 "Evidence-Based Clinical Practice Guideline for the Use of Pit-and-Fissure Sealants" between April 10, 2017, and October 31, 2017, from Facebook, Twitter, LinkedIn, Reddit, and online message boards for the New York Times, FiveThirtyEight, and Dentaltown. Using data provided in the comments, we conducted engagement analysis as well as content, network, and sentiment analysis across 8 categories.ResultsWe collected 671 comments. Among our findings, Facebook (472 of 671) was the most popular platform among commentators; almost half of all comments (335 of 671) aligned with the recommendations of the 2016 American Dental Association sealants guideline; clinicians were more likely than the general public to like a comment that suggested an improvement to the guideline; and >75% of comments (521 of 671) were supported by anecdotal evidence.ConclusionAs the prevalence of anecdotes on social media suggests, the likelihood of falsehoods spreading on social media is high. Insights gleaned from the methodology described in this research could help combat the spread of such misinformation by providing disseminators of health care messaging with insight into their target audiences. Armed with this knowledge, disseminators can craft health care messages that more effectively engage clinicians and the general public.Knowledge transfer statementThe methodology used in this research provides a reproducible strategy for tracking social media engagement with health care messages. Engagement results can assist future delivery of health care messages to key stakeholders and ensure better implementation and adoption of these communications.
| S-EPMC6918023 | biostudies-literature
Project description:We investigated health, economic, and social disparities among transgender adults (transgender women, men, and nonbinary) aged 18 years and older. Using population-based data from the Washington State Behavioral Risk Factor Surveillance System (WA-BRFSS), we pooled 2016 through 2019 data (n = 47,894). We estimated weighted distributions and prevalence by gender identity for background characteristics, economic, social and health indicators. We performed regressions of these indicators on gender identity, including transgender versus cisgender adults and transgender nonbinary adults compared to cisgender adults, followed by subgroup analyses: transgender women and men compared to each cisgender group and to one another, adjusting for covariates. Compared to cisgender adults, transgender adults overall were significantly younger and lower income with less education; more likely single with fewer children; and had several elevated health risks, including poor physical and mental health, and higher rates of chronic conditions and disability. Alternatively, transgender men and women had higher rates of flu vaccination than cisgender men. Between transgender subgroups, transgender men and transgender nonbinary adults were younger than transgender women; transgender men were significantly less likely married or partnered than transgender women; and, transgender women were more likely to live alone than nonbinary respondents. This is one of the first population-based studies to examine both between and within subgroup disparities among cisgender, transgender binary, and transgender nonbinary adults, revealing patterns of inequities across subgroups. More research understanding the mechanisms of these disparities and the development of targeted interventions is needed to address the unique needs of subgroups of transgender people.
| S-EPMC8954758 | biostudies-literature
Project description:ObjectiveTo understand the perspectives of primary care clinicians and health system leaders on the use of artificial intelligence (AI) to derive information about patients' social determinants of health.DesignQualitative study.SettingOntario, Canada.MethodsSemistructured, 30-minute virtual interviews were conducted with eligible participants across Ontario wherein they were asked about their perceptions of using AI to derive social data for patients. A descriptive content analysis was used to elicit themes from the data.Main findingsA total of 12 interviews were conducted with 7 family physicians, 3 clinical team members of various health professions, and 2 health system leaders. Five main themes described the current state of social determinants of health information, perceived benefits of and concerns with using AI to derive social data, how participants would want to see and use AI-derived social data, and suggestions for ethical principles that should underpin the development of this AI tool.ConclusionMost participants were enthusiastic about the possibility of using AI to derive social data for patients in primary care but noted concerns that should be addressed first. These findings can guide the development of AI-based tools for use in primary care settings.
| S-EPMC11328713 | biostudies-literature
Project description:BackgroundHazardous drinking among college students persists, despite ongoing university alcohol education and alcohol intervention programs. College students often post comments or pictures of drinking episodes on social media platforms.ObjectiveThis study aimed to understand one university's student attitudes toward alcohol use by examining student posts about drinking on social media platforms and to identify opportunities to reduce alcohol-related harm and inform novel alcohol interventions.MethodsWe analyzed social media posts from 7 social media platforms using qualitative inductive coding based on grounded theory to identify the contexts of student drinking and the attitudes and behaviors of students and peers during drinking episodes. We reviewed publicly available social media posts that referenced alcohol, collaborating with undergraduate students to select their most used platforms and develop locally relevant search terms; all posts in our data set were generated by students associated with a specific university. From the codes, we derived themes about student culture regarding alcohol use.ResultsIn total, 1151 social media posts were included in this study. These included 809 Twitter tweets, 113 Instagram posts, 100 Greekrank posts, 64 Reddit posts, 34 College Confidential posts, 23 Facebook posts, and 8 YouTube posts. Posts included both implicit and explicit portrayals of alcohol use. Across all types of posts reviewed, positive drinking attitudes were most common, followed by negative and then neutral attitudes, but valence varied by platform. Posts that portrayed drinking positively received positive peer feedback and indicate that drinking is viewed by students as an essential and positive part of university student culture.ConclusionsSocial media provide a real-time picture of students' behavior during their own and others' heavy drinking. Posts portray heavy drinking as a normal part of student culture, reinforced by peers' positive feedback on posts. Interventions for college drinking should help students manage alcohol intake in real time, provide safety information during alcohol use episodes, and raise student awareness of web-based privacy concerns and reputation management. Additional interventions for students, alumni, and parents are needed to address positive attitudes about and traditions of drinking.
| S-EPMC9153908 | biostudies-literature
Project description:Targeting of location-specific aid for the U.S. opioid epidemic is difficult due to our inability to accurately predict changes in opioid mortality across heterogeneous communities. AI-based language analyses, having recently shown promise in cross-sectional (between-community) well-being assessments, may offer a way to more accurately longitudinally predict community-level overdose mortality. Here, we develop and evaluate, TROP (Transformer for Opiod Prediction), a model for community-specific trend projection that uses community-specific social media language along with past opioid-related mortality data to predict future changes in opioid-related deaths. TOP builds on recent advances in sequence modeling, namely transformer networks, to use changes in yearly language on Twitter and past mortality to project the following year's mortality rates by county. Trained over five years and evaluated over the next two years TROP demonstrated state-of-the-art accuracy in predicting future county-specific opioid trends. A model built using linear auto-regression and traditional socioeconomic data gave 7% error (MAPE) or within 2.93 deaths per 100,000 people on average; our proposed architecture was able to forecast yearly death rates with less than half that error: 3% MAPE and within 1.15 per 100,000 people.
| S-EPMC9992514 | biostudies-literature
Project description:Transgender and gender-diverse (TGD) adolescents experience limited access to gender-affirming care (GAC) and information and use social media platforms for informational and social support. We conducted conventional content analysis of posts on the platform, Tumblr and applied univariate statistics to characterize health and wellness themes represented by this content. Content was predominantly transmasculine-relevant. Posts addressing the trans health care paradigm often co-occurred with content referencing affirmation logs, the physical and emotional impact of affirmation, surgery, and unspecified medical interventions. Gender-affirming hormone therapy was the most prevalent intervention referenced in posts related to non-professional/non-licensed medical care and advice requests. Transgender and gender-diverse youth-serving individuals can use this information to mitigate harm, enhance patient education, and improve the overall well-being of TGD youth. Further research is needed to characterize the effect of content encountered on social media on pediatric patient experiences and on outcomes of GAC.
| S-EPMC11778161 | biostudies-literature
Project description:Urban parks and green spaces are among the few places where city dwellers can have regular contact with nature and engage in outdoor recreation. Social media data provide opportunities to understand such human-environment interactions. While studies have demonstrated that geo-located photographs are useful indicators of recreation across different spaces, recreation behaviour also varies between different groups of people. Our study used social media to assess behavioural patterns across different groups of park users in tropical Singapore. 4,674 users were grouped based on the location and content of their photographs on the Flickr platform. We analysed how these groups varied spatially in the parks they visited, as well as in their photography behaviour. Over 250,000 photographs were analysed, including those uploaded and favourited by users, and all photographs taken at city parks. There were significant differences in the number and types of park photographs between tourists and locals, and between user-group axes formed from users' photograph content. Spatial mapping of different user groups showed distinct patterns in the parks they were attracted to. Future work should consider such variability both within and between data sources, to provide a more context-dependent understanding of human-environment interactions and preferences for outdoor recreation.
| S-EPMC6972848 | biostudies-literature
Project description:BackgroundWhile scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians.ObjectiveIn this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool.MethodsWe used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries.ResultsUmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada.ConclusionsThe outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs.International registered report identifier (irrid)RR2-10.1101/2022.12.14.22283419.
| S-EPMC10510753 | biostudies-literature