背景:根据美国疾病控制和预防中心(CDC)的数据,从2022年5月至2024年3月,在美国爆发了32,063例病例和58例死亡,在全球范围内导致95,912例病例。像其他疾病爆发一样(例如,艾滋病毒)与感知的社区协会,天痘会产生耻辱的风险,加剧同性恋恐惧症,并可能阻碍医疗保健的获得和社会公平。然而,关于天花的现有文献对性少数男性和性别多样化(SMMGD)个体的观点表示有限。
目标:为了填补这一空白,这项研究旨在综合SMMGD个人之间的讨论主题,并听取SMMGD的声音,以识别当前围绕水痘的公共卫生沟通中的问题,以提高包容性,股本,和正义。
方法:我们分析了在2020年10月至2022年9月期间由2326名用户发布的与mpox相关的帖子(N=8688),这些用户在Twitter/X上自我标识为SMMGD,并位于美国。我们在推文中应用了BERTopic(一种主题建模技术),通过人工标签和注释验证了机器生成的主题,并对每个主题的推文进行了内容分析。地理分析是针对与加利福尼亚大学相关的美国各州最突出的主题的大小进行的,洛杉矶(UCLA)女同性恋,同性恋,和双性恋(LGB)社会气候指数。
结果:BERTopic确定了11个主题,哪些注释者被标记为水痘健康行动主义(n=2590,29.81%),水痘疫苗接种(n=2242,25.81%),和不良事件(n=85,0.98%);讽刺,笑话,和情绪表达(n=1220,14.04%);COVID-19和水痘(n=636,7.32%);政府或公共卫生反应(n=532,6.12%);水痘症状(n=238,2.74%);病例报告(n=192,2.21%);关于病毒命名的双关语(即,水痘;n=75,0.86%);媒体宣传(n=59,0.68%);儿童水痘(n=58,0.67%)。Spearman等级相关表明,在美国州一级,健康行动主义的主题大小与UCLALGB社会气候指数之间存在显着负相关(ρ=-0.322,P=.03)。
结论:SMMGD个体对天花的讨论包括两种功利主义(例如,疫苗接入,病例报告,和天花症状)和情绪激动(即,提高认识,倡导反对同性恋恐惧症,错误信息/虚假信息,和健康耻辱)主题。在LGB社会接受度较低的美国各州,水痘健康活动更为普遍,这表明SMMGD个体在面对公共卫生压迫时具有弹性的沟通模式。我们的社会倾听方法可以促进未来的公共卫生工作,提供一种具有成本效益的方式来捕捉受影响人群的观点。这项研究阐明了SMMGD与水痘话语的参与,强调需要更具包容性的公共卫生规划。研究结果还强调了水痘的社会影响:健康耻辱。我们的发现可以为干预措施提供信息和有形卫生资源的优化交付,利用计算混合方法分析(例如,BERTopic)和大数据。
BACKGROUND: The mpox outbreak resulted in 32,063 cases and 58 deaths in the United States and 95,912 cases worldwide from May 2022 to March 2024 according to the US Centers for Disease Control and Prevention (CDC). Like other disease outbreaks (eg, HIV) with perceived community associations, mpox can create the risk of stigma, exacerbate homophobia, and potentially hinder health care access and social equity. However, the existing literature on mpox has limited representation of the perspective of sexual minority men and gender-diverse (SMMGD) individuals.
OBJECTIVE: To fill this gap, this
study aimed to synthesize themes of discussions among SMMGD individuals and listen to SMMGD voices for identifying problems in current public health communication surrounding mpox to improve inclusivity, equity, and justice.
METHODS: We analyzed mpox-related posts (N=8688) posted between October 2020 and September 2022 by 2326 users who self-identified on Twitter/X as SMMGD and were geolocated in the United States. We applied BERTopic (a topic-modeling technique) on the tweets, validated the machine-generated topics through human labeling and annotations, and conducted content analysis of the tweets in each topic. Geographic analysis was performed on the size of the most prominent topic across US states in relation to the University of California, Los Angeles (UCLA) lesbian, gay, and bisexual (LGB) social climate index.
RESULTS: BERTopic identified 11 topics, which annotators labeled as mpox health activism (n=2590, 29.81%), mpox vaccination (n=2242, 25.81%), and adverse events (n=85, 0.98%); sarcasm, jokes, and emotional expressions (n=1220, 14.04%); COVID-19 and mpox (n=636, 7.32%); government or public health response (n=532, 6.12%); mpox symptoms (n=238, 2.74%); case reports (n=192, 2.21%); puns on the naming of the virus (ie, mpox; n=75, 0.86%); media publicity (n=59, 0.68%); and mpox in children (n=58, 0.67%). Spearman rank correlation indicated significant negative correlation (ρ=-0.322, P=.03) between the topic size of health activism and the UCLA LGB social climate index at the US state level.
CONCLUSIONS: Discussions among SMMGD individuals on mpox encompass both utilitarian (eg, vaccine access, case reports, and mpox symptoms) and emotionally charged (ie, promoting awareness, advocating against homophobia, misinformation/disinformation, and health stigma) themes. Mpox health activism is more prevalent in US states with lower LGB social acceptance, suggesting a resilient communicative pattern among SMMGD individuals in the face of public health oppression. Our method for social listening could facilitate future public health efforts, providing a cost-effective way to capture the perspective of impacted populations. This
study illuminates SMMGD engagement with the mpox discourse, underscoring the need for more inclusive public health programming. Findings also highlight the social impact of mpox: health stigma. Our findings could inform interventions to optimize the delivery of informational and tangible health resources leveraging computational mixed-method analyses (eg, BERTopic) and big data.