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Record W4315698004 · doi:10.2196/43635

Peer-Led Community-Based Support Services and HIV Treatment Outcomes Among People Living With HIV in Wuxi, China: Propensity Score–Matched Analysis of Surveillance Data From 2006 to 2021

2023· article· en· W4315698004 on OpenAlexvenueno aff
Xiaojun Meng, Hanlu Yin, Wenjuan Ma, Jing Gu, Zhen Lu, Thomas Fitzpatrick, Huachun Zou

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesYoung Scientists FundNational Institute of Allergy and Infectious DiseasesSanming Project of Medicine in ShenzhenScience, Technology and Innovation Commission of Shenzhen MunicipalityNational Natural Science Foundation of China
KeywordsMedicinePropensity score matchingCohortCohort studyProportional hazards modelHazard ratioGerontologyEnvironmental healthDemographyInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Community-based organizations deliver peer-led support services to people living with HIV. Systematic reviews have found that peer-led community-based support services can improve HIV treatment outcomes; however, few studies have been implemented to evaluate its impact on mortality using long-term follow-up data. OBJECTIVE: We aimed to evaluate the associations between the receipt of peer-led community-based support services and HIV treatment outcomes and survival among people living with HIV in Wuxi, China. METHODS: We performed a propensity score-matched retrospective cohort study using data collected from the Chinese National HIV/AIDS Comprehensive Information Management System for people living with HIV in Wuxi, China, between 2006 and 2021. People living with HIV who received adjunctive peer-led community-based support for at least 6 months from a local community-based organization (exposure group) were matched to people living with HIV who only received routine clinic-based HIV care (control group). We compared the differences in HIV treatment outcomes and survival between these 2 groups using Kaplan-Meier curves. We used competing risk and Cox proportional hazards models to assess correlates of AIDS-related mortality (ARM) and all-cause mortality. We reported adjusted subdistribution hazard ratio and adjusted hazard ratio with 95% CIs. RESULTS: A total of 860 people living with HIV were included (430 in the exposure group and 430 in the control group). The exposure group was more likely to adhere to antiretroviral therapy (ART; 396/430, 92.1% vs 360/430, 83.7%; P<.001), remain retained in care 12 months after ART initiation (402/430, 93.5% vs 327/430, 76.1%; P<.001), and achieve viral suppression 9 to 24 months after ART initiation (357/381, 93.7% vs 217/243, 89.3%; P=.048) than the control group. The exposure group had significantly lower ARM (1.8 vs 7.0 per 1000 person-years; P=.01) and all-cause mortality (2.3 vs 9.3 per 1000 person-years; P=.002) and significantly higher cumulative survival rates (P=.003). The exposure group had a 72% reduction in ARM (adjusted subdistribution hazard ratio 0.28, 95% CI 0.09-0.95) and a 70% reduction in all-cause mortality (adjusted hazard ratio 0.30, 95% CI 0.11-0.82). The nonrandomized retrospective nature of our analysis prevents us from determining whether peer-led community-based support caused the observed differences in HIV treatment outcomes and survival between the exposure and control groups. CONCLUSIONS: The receipt of peer-led community-based support services correlated with significantly improved HIV treatment outcomes and survival among people living with HIV in a middle-income country in Asia. The 15-year follow-up period in this study allowed us to identify associations with survival not previously reported in the literature. Future interventional trials are needed to confirm these findings.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.357
Teacher spread0.291 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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