Evidence map›Paper›PMID 36517902›Full record

ArticleHarm reduction journal2022

Exploring substance use disorder discussions in Native American communities: a retrospective Twitter infodemiology study.

Alec J Calac, Tiana McMann, Mingxiang Cai, Jiawei Li, Raphael Cuomo, Tim K Mackey

Abstract read
In one paragraph

Article in Harm reduction journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
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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

6 authors.

Alec J CalacUC San Diego School of Medicine, La Jolla, CA, USA.
Tiana McMannGlobal Health Policy and Data Institute, San Diego, CA, USA.
Mingxiang CaiGlobal Health Policy and Data Institute, San Diego, CA, USA.
Jiawei LiGlobal Health Policy and Data Institute, San Diego, CA, USA.
Raphael CuomoUC San Diego School of Medicine, La Jolla, CA, USA.
Tim K MackeyGlobal Health Policy and Data Institute, San Diego, CA, USA. tkmackey@ucsd.edu.

Funding

Using Machine Learning to Detect and Characterize Synthetic Psychoactive Drug Digital Marketing and DistributionR21DA050689 · NIDA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MACKEY, TIMOTHY KEN · 2020 to 2021
$434k
NIDA NIH HHS R21 DA050689
6 · The paper itself

Abstract

backgroundThe opioid epidemic has had a devastating impact on youth from American Indian and Alaska Native (AI/AN) Tribes and Villages, which also experience disparate suicide rates. The use of publicly available social media data originating from AI/AN communities may enhance public health response time to substance use disorder (SUD)-related overdose and augment Tribal public health surveillance systems, but these concepts have yet to be adequately explored. The goal of this exploratory analysis was to identify primary and secondary accounts of overdose and characterize relevant contextual factors in the AI/AN population on social media.

methodsThe Twitter application programming interface was queried for all Tweets containing geocoded data between March 2014 and June 2020 and filtered for the keyword ['overdose']. This sample of Tweets (n = 146,236) was then restricted to those geolocated from US Tribal lands (n = 619). Tweets were manually annotated for primary or secondary accounts of overdose as well as suicidal ideation, substance(s) used, stigma of drug use, and community-wide incidents.

resultsWe collected a total of 146,235 tweets that were geocoded and contained the word 'overdose,' of which 9.5% were posted on Tribal lands (n = 619). 9.4% of these tweets (n = 58) met our study inclusion criteria and were mainly posted from Oklahoma (n = 26, 45%) and North Carolina (n = 13, 22.4%). Most Tweets (n = 41, 71%) described a primary account of an overdose and were mostly posted from 2014 to 2015. Less than half of the Tweets (n = 27, 46.5%) referenced a specific substance. Those substances mentioned included alcohol, marijuana, methamphetamine, heroin, laundry softener, cocaine, K2-Spice (synthetic cannabinoid), codeine, morphine, Nyquil, and Xanax. DISCUSSION: Though exploratory, our study identified SUD-related content self-reported by AI/AN communities on Twitter, especially in Oklahoma and North Carolina. These results may assist in the future design and detection of infodemiology trends and early warning signs that can better facilitate intervention specific to the ongoing Tribal opioid epidemic. While all data were collected from the public domain, additional care should be given to individual and community privacy.

Indexed as

Drug OverdoseSocial MediaSubstance-Related DisordersAdolescentAmerican Indian or Alaska NativeAnalgesics, OpioidHumansInfodemiologyRetrospective StudiesAnalgesics, Opioid

Identifiers

PMID36517902
PMCPMC9753320

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.