Disclosure Patterns of Opioid Use Disorders in Perinatal Care During the Opioid Epidemic on X From 2019 to 2021: Thematic Analysis.

IF 2.1 Q2 PEDIATRICS
Dezhi Wu, Minnie Ng, Saborny Sen Gupta, Phyllis Raynor, Youyou Tao, Yang Ren, Peiyin Hung, Shan Qiao, Jiajia Zhang, Jennifer Fillo, Xiaoming Li, Constance Guille, Kacey Eichelberger, Bankole Olatosi
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引用次数: 0

Abstract

Background: In 2021, the United States experienced a 14% rise in fatal drug overdoses totaling 106,699 deaths, driven by harmful opioid use, particularly among individuals in the perinatal period who face increased risks associated with opioid use disorders (OUDs). Increased concerns about the impacts of escalating harmful opioid use among pregnant and postpartum persons are rising. Most of the current limited perinatal OUD studies were conducted using traditional methods, such as interviews and randomized controlled trials to understand OUD treatment, risk factors, and associated adverse effects. However, little is known about how social media data, such as X, formerly known as Twitter, can be leveraged to explore and identify broad perinatal OUD trends, disclosure and communication patterns, and public health surveillance about OUD in the perinatal period.

Objective: The objective is 3-fold: first, we aim to identify key themes and trends in perinatal OUD discussions on platform X. Second, we explore user engagement patterns, including replying and retweeting behaviors. Third, we investigate computational methods that could potentially streamline and scale the labor-intensive manual annotation effort.

Methods: We extracted 6 million raw perinatal-themed tweets posted by global X users during the opioid epidemic from May 2019 to October 2021. After data cleaning and sampling, we used 500 tweets related to OUD in the perinatal period by US X users for a thematic analysis using NVivo (Lumivero) software.

Results: Seven major themes emerged from our thematic analysis: (1) political views related to harmful opioid and other substance use, (2) perceptions of others' substance use, (3) lived experiences of opioid and other substance use, (4) news reports or papers related to opioid and other substance use, (5) health care initiatives, (6) adverse effects on children's health due to parental substance use, and (7) topics related to nonopioid substance use. Among these 7 themes, our user engagement analysis revealed that themes 4 and 5 received the highest average retweet counts, and theme 3 received the highest average tweet reply count. We further found that different computational methods excel in analyzing different themes.

Conclusions: Social media platforms such as X can serve as a valuable tool for analyzing real-time discourse and exploring public perceptions, opinions, and behaviors related to maternal substance use, particularly, harmful opioid use in the perinatal period. More health promotion strategies can be carried out on social media platforms to provide educational support for the OUD perinatal population.

2019年至2021年阿片类药物流行期间X围产期护理中阿片类药物使用障碍的披露模式:专题分析。
背景:2021 年,美国因过量使用阿片类药物而导致的死亡人数上升了 14%,共计 106,699 人,尤其是围产期人群,他们面临的阿片类药物使用失调(OUDs)风险更大。人们越来越关注孕妇和产后人群中阿片类药物的有害使用不断升级所造成的影响。目前有限的围产期 OUD 研究大多采用传统方法,如访谈和随机对照试验,以了解 OUD 治疗、风险因素和相关不良影响。然而,人们对如何利用社交媒体数据(如 X,以前称为 Twitter)来探索和确定围产期 OUD 的广泛趋势、披露和交流模式以及围产期 OUD 的公共卫生监测却知之甚少:目标有三个方面:首先,我们旨在确定 X 平台上围产期 OUD 讨论的关键主题和趋势;其次,我们探索用户参与模式,包括回复和转发行为。第三,我们研究有可能简化和扩展劳动密集型人工注释工作的计算方法:我们提取了全球 X 用户在 2019 年 5 月至 2021 年 10 月阿片类药物流行期间发布的 600 万条围产期主题原始推文。经过数据清理和取样,我们使用 NVivo (Lumivero) 软件对美国 X 用户发布的 500 条与围产期 OUD 相关的推文进行了专题分析:我们的专题分析产生了七大主题:(1)与有害使用阿片类药物和其他药物相关的政治观点;(2)对他人使用药物的看法;(3)使用阿片类药物和其他药物的生活经历;(4)与使用阿片类药物和其他药物相关的新闻报道或论文;(5)医疗保健措施;(6)父母使用药物对儿童健康的不利影响;(7)与使用非阿片类药物相关的主题。在这 7 个主题中,我们的用户参与分析表明,主题 4 和 5 获得的平均转发数最高,主题 3 获得的平均推文回复数最高。我们进一步发现,不同的计算方法在分析不同的主题时表现出色:X 等社交媒体平台可作为分析实时讨论和探索公众对孕产妇使用药物,尤其是围产期使用有害阿片类药物的看法、观点和行为的重要工具。可以在社交媒体平台上实施更多的健康促进策略,为围产期使用阿片类药物的人群提供教育支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Pediatrics and Parenting
JMIR Pediatrics and Parenting Medicine-Pediatrics, Perinatology and Child Health
CiteScore
5.00
自引率
5.40%
发文量
62
审稿时长
12 weeks
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