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Record W4385552854 · doi:10.1111/add.16283

Incarceration history is associated with HIV infection among community‐recruited people who inject drugs in Europe: A propensity‐score matched analysis of cross‐sectional studies

2023· article· en· W4385552854 on OpenAlexaff
Anneli Uusküla, Jürgen Rannap, Lisa Weijler, Adrian Abagiu, Vic Arendt, Gregorio Barrio, Henrique Barros, Henrikki Brummer‐Korvenkontio, Jordi Casabona, Esther A. Croes, Don C. Des Jarlais, Carole Seguin‐Devaux, Mária Dudás, Ksenia Eritsyan, Cinta Folch, Angelos Hatzakis, Robert Heimer, Ellen Heinsbroek, Vivian Hope, Raluca Jipa, Anda Ķīvīte‐Urtāne, Olga Levina, Alexandra Lyubimova, Artur Malczewski, Amy Matser, Andrew McAuley, Paula Meireles, Viktor Mravčík, Eline Op de Coul, Sven E. Ojavee, Oleguer Parés‐Badell, Maria Prins, José Pulido, Elena Romanyak, Magdalena Rosińska, Thomas Seyler, Jack Stone, Vana Sypsa, Ave Talu, Anna Tarján, Avril Taylor, Peter Vickerman, Sigrid Vorobjov, Kate Dolan, Lucas Wiessing

Bibliographic record

VenueAddiction · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Mental HealthRijksinstituut voor Volksgezondheid en MilieuFundação para a Ciência e a TecnologiaUniverzita Karlova v PrazeUniversity of BristolUniversidade do PortoNational Institute for Health and Care ResearchNational Institute for Health Research Health Protection Research UnitWellcome Trust
KeywordsMedicinePropensity score matchingDemographyOdds ratioHarm reductionCross-sectional studyConfidence intervalPopulationHeroinHuman immunodeficiency virus (HIV)Environmental healthInternal medicinePsychiatryDrugImmunology

Abstract

fetched live from OpenAlex

AIMS: We measured the association between a history of incarceration and HIV positivity among people who inject drugs (PWID) across Europe. DESIGN, SETTING AND PARTICIPANTS: This was a cross-sectional, multi-site, multi-year propensity-score matched analysis conducted in Europe. Participants comprised community-recruited PWID who reported a recent injection (within the last 12 months). MEASUREMENTS: Data on incarceration history, demographics, substance use, sexual behavior and harm reduction service use originated from cross-sectional studies among PWID in Europe. Our primary outcome was HIV status. Generalized linear mixed models and propensity-score matching were used to compare HIV status between ever- and never-incarcerated PWID. FINDINGS: Among 43 807 PWID from 82 studies surveyed (in 22 sites and 13 countries), 58.7% reported having ever been in prison and 7.16% (n = 3099) tested HIV-positive. Incarceration was associated with 30% higher odds of HIV infection [adjusted odds ratio (aOR) = 1.32, 95% confidence interval (CI) = 1.09-1.59]; the association between a history of incarceration and HIV infection was strongest among PWID, with the lowest estimated propensity-score for having a history of incarceration (aOR = 1.78, 95% CI = 1.47-2.16). Additionally, mainly injecting cocaine and/or opioids (aOR = 2.16, 95% CI = 1.33-3.53), increased duration of injecting drugs (per 8 years aOR = 1.31, 95% CI = 1.16-1.48), ever sharing needles/syringes (aOR = 1.91, 95% CI = 1.59-2.28) and increased income inequality among the general population (measured by the Gini index, aOR = 1.34, 95% CI = 1.18-1.51) were associated with a higher odds of HIV infection. Older age (per 8 years aOR = 0.84, 95% CI = 0.76-0.94), male sex (aOR = 0.77, 95% CI = 0.65-0.91) and reporting pharmacies as the main source of clean syringes (aOR = 0.72, 95% CI = 0.59-0.88) were associated with lower odds of HIV positivity. CONCLUSIONS: A history of incarceration appears to be independently associated with HIV infection among people who inject drugs (PWID) in Europe, with a stronger effect among PWID with lower probability of incarceration.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.057
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.098
GPT teacher head0.342
Teacher spread0.244 · 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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

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