Evidence map›Paper›PMID 39374068›Full record

ArticleJMIR pediatrics and parenting2024

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

Dezhi Wu, Minnie Ng, Saborny Sen Gupta, Phyllis Raynor, Youyou Tao, Yang Ren, Peiyin Hung, Shan Qiao, Jiajia Zhang, Jennifer Fillo and 4 more

Abstract read
In one paragraph

Article in JMIR pediatrics and parenting, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Dezhi WuDepartment of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0002-3554-1136
Minnie NgDepartment of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0009-0004-1581-3730
Saborny Sen GuptaDepartment of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0009-0008-0937-5366
Phyllis RaynorDepartment of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0002-7311-0978
Youyou TaoDepartment of Information Systems and Business Analytics, Loyola Marymount University, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-8572-4830
Yang RenDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0002-6128-5826
Peiyin HungDepartment of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0002-1529-0819
Shan QiaoDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0003-1834-1834
Jiajia ZhangDepartment of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0003-4566-0822
Jennifer FilloDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0002-6062-1146
Xiaoming LiDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0002-5555-9034
Constance GuilleDepartment of Psychiatry and Behavioral Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, United States.ORCID https://orcid.org/0000-0001-6004-3027
Kacey EichelbergerDepartment of Obstetrics and Gynecology, University of South Carolina School of Medicine Greenville, Prisma Health, Greenville, SC, United States.ORCID https://orcid.org/0000-0003-4519-3929
Bankole OlatosiDepartment of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID https://orcid.org/0000-0002-8295-8735

Funding

P.A.R.E.N.T.S.S Project - Parents Adopting Recovery-management through Enhanced New Technology for Self-care and Support (for Mothers)K23DA051626 · NIDA · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI RAYNOR, PHYLLIS ANN · 2021 to 2025
$881k
NIDA NIH HHS K23 DA051626
6 · The paper itself

Abstract

backgroundIn 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.

objectiveThe 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.

methodsWe 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.

resultsSeven 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.

conclusionsSocial 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.

Indexed as

COVID-19maternal healthopioid epidemicopioid use disorderperinatal carepregnancythematic analysisTwitterwomen and child healthX

Identifiers

PMID39374068
PMCPMC11494255

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.