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Record W3203166542 · doi:10.2196/29319

Association of Substance Use With Behavioral Adherence to Centers for Disease Control and Prevention Guidelines for COVID-19 Mitigation: Cross-sectional Web-Based Survey

2021· article· en· W3203166542 on OpenAlexvenueno aff
Mollie A. Monnig, Hayley Treloar Padovano, Alexander W. Sokolovsky, Grace DeCost, Elizabeth R. Aston, Carolina L. Haass‐Koffler, Claire Szapary, Patience Moyo, Jaqueline Avila, Jennifer W. Tidey, Peter M. Monti, Jasjit S. Ahluwalia

Bibliographic record

VenueJMIR Public Health and Surveillance · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institute on Drug AbuseNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsMedicineCross-sectional studyCannabisGuidelinePublic healthFamily medicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Substance use is a risk factor for COVID-19 infection and adverse outcomes. However, reasons for elevated risk for COVID-19 in substance users are not well understood. OBJECTIVE: The aim of this study was to evaluate whether alcohol or other drug use is associated with adherence to Centers for Disease Control and Prevention (CDC) guidelines for COVID-19 mitigation. Preregistered analyses tested the hypothesis that greater use of alcohol and other drugs would be associated with lower CDC guideline adherence. A secondary objective was to determine whether substance use was associated with the likelihood of COVID-19 testing or outcome. METHODS: A cross-sectional web-based survey was administered to a convenience sample recruited through Amazon's Mechanical Turk platform from June 18 to July 19, 2020. Individuals aged 18 years or older and residing in Connecticut, Massachusetts, New Jersey, New York, or Rhode Island were eligible to participate. The exposure of interest was past 7-day use of alcohol, cigarettes, electronic cigarettes, cannabis, stimulants, and nonmedical opioids. The primary outcome was CDC guideline adherence measured using a scale developed from behaviors advised to reduce the spread of COVID-19. Secondary outcomes were likelihood of COVID-19 testing and a positive COVID-19 test result. All analyses accounted for the sociodemographic characteristics. RESULTS: The sample consisted of 1084 individuals (mean age 40.9 [SD 13.4] years): 529 (48.8%) men, 543 (50.1%) women, 12 (1.1%) other gender identity, 742 (68.5%) White individuals, 267 (24.6%) Black individuals, and 276 (25.5%) Hispanic individuals. Daily opioid users reported lower CDC guideline adherence than nondaily users (B=-0.24, 95% CI -0.44 to -0.05) and nonusers (B=-0.57, 95% CI -0.76 to -0.38). Daily alcohol drinkers reported lower adherence than nondaily drinkers (B=-0.16, 95% CI -0.30 to -0.02). Nondaily alcohol drinkers reported higher adherence than nondrinkers (B=0.10, 95% CI 0.02-0.17). Daily opioid use was related to greater odds of COVID-19 testing, and daily stimulant use was related to greater odds of a positive COVID-19 test. CONCLUSIONS: In a regionally-specific, racially, and ethnically diverse convenience sample, adults who engaged in daily alcohol or opioid use reported lower CDC guideline adherence for COVID-19 mitigation. Any opioid use was associated with greater odds of COVID-19 testing, and daily stimulant use was associated with greater odds of COVID-19 infection. Cigarettes, electronic cigarettes, cannabis, or stimulant use were not statistically associated with CDC guideline adherence, after accounting for sociodemographic covariates and other substance use variables. Findings support further investigation into whether COVID-19 testing and vaccination should be expanded among individuals with substance-related risk factors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.423
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2021
Admission routes1
Has abstractyes

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