Evidence map›Paper›PMID 39483445›Full record

ArticleGlobal health & medicine2024

HIV disease progression among heterosexually-infected individuals before the introduction of universal ART in China: A linear mixed-effects model.

Lin Tang, Fangfang Chen, Qian Ling, Peilong Li, Lin Ge, Chang Cai, Houlin Tang, Fan Lv, Dongmin Li

Abstract read
In one paragraph

Article in Global health & medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

9 authors.

Lin TangDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Fangfang ChenDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Qian LingDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Peilong LiDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Lin GeDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Chang CaiDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Houlin TangDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Fan LvDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Dongmin LiDepartment of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In 2016, China introduced universal antiretroviral therapy (ART) for all HIV-infected individuals regardless of CD4 cell count. However, the natural history and rate of CD4 count decline among heterosexually-infected individuals remain uncharacterized. Analyzing national surveillance data can address this gap and shed light on the pathogenesis of HIV in this population. We used a linear mixed-effects model to assess CD4 trajectory over time before ART initiation and estimated the median time from HIV seroconversion to reaching CD4 thresholds of < 500, < 350, and < 200 cell/mm

Indexed as

CD4+ T cell countdisease progressionHIV/AIDSlinear mixed-effects model

Identifiers

PMID39483445
PMCPMC11514634

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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.