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

COVID-19 Twitter X maternal health opioid epidemic opioid use disorder perinatal care pregnancy thematic analysis women and child health

Journal

JMIR pediatrics and parenting
ISSN: 2561-6722
Titre abrégé: JMIR Pediatr Parent
Pays: Canada
ID NLM: 101727244

Informations de publication

Date de publication:
07 Oct 2024
Historique:
received: 13 09 2023
accepted: 03 05 2024
revised: 31 03 2024
medline: 7 10 2024
pubmed: 7 10 2024
entrez: 7 10 2024
Statut: epublish

Résumé

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. 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. 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. 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. 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.

Sections du résumé

BACKGROUND 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 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 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 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 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.

Identifiants

pubmed: 39374068
pii: v7i1e52735
doi: 10.2196/52735
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e52735

Informations de copyright

©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. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 07.10.2024.

Auteurs

Dezhi Wu (D)

Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.

Minnie Ng (M)

Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.

Saborny Sen Gupta (SS)

Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.

Phyllis Raynor (P)

Department of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, SC, United States.

Youyou Tao (Y)

Department of Information Systems and Business Analytics, Loyola Marymount University, Los Angeles, CA, United States.

Yang Ren (Y)

Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States.

Peiyin Hung (P)

Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.

Shan Qiao (S)

Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.

Jiajia Zhang (J)

Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.

Jennifer Fillo (J)

Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.

Xiaoming Li (X)

Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.

Constance Guille (C)

Department of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, United States.

Kacey Eichelberger (K)

Department of Obstetrics and Gynecology, University of South Carolina School of Medicine Greenville, Prisma Health, Greenville, SC, United States.

Bankole Olatosi (B)

Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.

Classifications MeSH