Evidence map›Paper›PMID 39302713›Full record

ArticleJournal of medical Internet research2024

Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study.

Dezhi Wu, Hannah Shead, Yang Ren, Phyllis Raynor, Youyou Tao, Harvey Villanueva, Peiyin Hung, Xiaoming Li, Robert G Brookshire, Kacey Eichelberger and 3 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 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

13 authors.

Dezhi WuDepartment of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-3554-1136
Hannah SheadDepartment of Mathematics, Augusta University, Augusta, GA, United States.ORCID 0000-0003-4643-3189
Yang RenDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-6128-5826
Phyllis RaynorCollege of Nursing, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-7311-0978
Youyou TaoDepartment of Information Systems and Business Analytics, Loyola Marymount University, Los Angeles, CA, United States.ORCID 0000-0001-8572-4830
Harvey VillanuevaDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States.ORCID 0009-0000-5510-6177
Peiyin HungArnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-1529-0819
Xiaoming LiArnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-5555-9034
Robert G BrookshireDepartment of Integrated Information Technology, University of South Carolina, Columbia, SC, United States.ORCID 0000-0001-5953-8137
Kacey EichelbergerSchool of Medicine Greenville, University of South Carolina, Greenville, SC, United States.ORCID 0000-0003-4519-3929
Constance GuilleCollege of Medicine, Medical University of South Carolina, Charleston, SC, United States.ORCID 0000-0001-6004-3027
Alain H LitwinSchool of Medicine Greenville, University of South Carolina, Greenville, SC, United States.ORCID 0000-0002-6717-0288
Bankole OlatosiArnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 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
Big Data Health Science Scholar Program for Infectious DiseasesT35AI165252 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI HIKMET, NESET, OLATOSI, BANKOLE · 2021 to 2025
$721k
NIAID NIH HHS T35 AI165252NIDA NIH HHS K23 DA051626
6 · The paper itself

Abstract

backgroundAccording to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications.

objectiveBased on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies?

methodsWe used X's application programming interface to extract >6 million raw tweets worldwide containing ≥2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non-English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying κ score of 0.7748 for intercoder reliability.

resultsOur quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36%) than in the non-PPU sample (1323/4848, 27.29%). Paired polysubstance use (4134/4848, 85.27%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others' drug use, legal implications, and public health.

conclusionsThis study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36%), positive (1884/4848, 38.86%), and neutral (474/4848, 9.78%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU.

Indexed as

Social MediaSubstance-Related DisordersDisclosureFemaleHumansPerinatal CarePregnancyperinatal carepolysubstance usepregnant careprenatal caresentiment analysissocial mediaTwitter

Identifiers

PMID39302713
PMCPMC11452753

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.