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Record W4408245628 · doi:10.1111/hiv.70008

Impact of mental health service use on the HIV care cascade among women

2025· article· en· W4408245628 on OpenAlexafffundabout
Seerat Chawla, Marie‐Josée Brouillette, Bluma Kleiner, Danièle Dubuc, Lashanda Skerritt, Ann N. Burchell, Danielle Rouleau, Mona Loutfy, Alexandra de Pokomandy

Bibliographic record

VenueHIV Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsWomen's College HospitalUniversité de MontréalPublic Health OntarioUniversity of TorontoSt. Michael's HospitalMcGill University Health CentreSimon Fraser UniversityMcGill University
FundersFonds de Recherche du Québec - SantéCanada Research ChairsOxford Academic Health Science CentreCanadian HIV Trials Network, Canadian Institutes of Health ResearchCanadian Institutes of Health ResearchOntario HIV Treatment Network
KeywordsMedicineMental healthCohortDepression (economics)Health careViral loadCohort studyPsychiatryMultinomial logistic regressionLogistic regressionBaseline (sea)Human immunodeficiency virus (HIV)Family medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: While the negative effects of mental health issues on HIV clinical outcomes have been well-documented, the impact of mental health treatment on the HIV care cascade is largely uncharacterized. The objective of this study was to describe the engagement of women with mental health conditions and symptoms, who reported using mental health services, across the HIV care cascade and to assess the relationship between mental health service use and HIV care steps. METHODS: Longitudinal data were analysed from participants enrolled in the Canadian HIV Women's Sexual and Reproductive Health Cohort Study (CHIWOS) (2013-2018) who had clinically significant depressive symptoms or reported a mental health diagnosis at baseline. Among this subset, four states of HIV care were defined at baseline, 18 months and 36 months: (1) unengaged in care (did not visit an HIV provider in the past year), (2) not on antiretroviral therapy (ART) (visited an HIV provider in the past year but did not report current ART use), (3) detectable (reported current ART use but a detectable viral load) and (4) optimal (reported current ART use and an undetectable viral load). Sankey diagrams were used to illustrate the engagement of women across the HIV care cascade over 3 years based on their self-reported use of mental health services at baseline. The association between mental health service use and care state at baseline was analysed using multinomial logistic regression models. RESULTS: Of the 898 women in the cohort with significant depressive symptoms or mental health conditions at baseline, 3.8% (n = 34) were unengaged in care, 10.9% (n = 98) were not on ART, 12.4% (n = 111) were detectable and 72.9% (n = 655) were optimal. Over the 36 months, 51.0% of women transitioned between states at least once. When stratified by service use, women who reported use of mental health services at baseline had better engagement across the care cascade and had fewer transitions between states over the 3 years, 37.2% of which were to better states of care. The use of mental health services at baseline was also significantly associated with greater odds of engagement in the optimal state compared with not on ART (adjusted odds ratio [aOR]: 1.72, 95% confidence interval [CI]: 1.07-2.77). A similar but statistically insignificant association was found with the detectable care state (aOR: 1.67, 95% CI: 0.92-3.03). CONCLUSIONS: Our findings demonstrate that linkage to mental health care has a positive impact on HIV care outcomes among women. Accessible mental health services may serve to improve both mental well-being and progression of these patients along the HIV care cascade, thereby achieving individual and public health goals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.383
Teacher spread0.354 · 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.

Study designNot applicable
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
Published2025
Admission routes3
Has abstractyes

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