Evidence map›Paper›PMID 34986877›Full record

Observational studyInfectious diseases of poverty2022

Treatment outcomes of HIV patients with hepatitis B and C virus co-infections in Southwest China: an observational cohort study.

Jingya Jia, Qiuying Zhu, Luojia Deng, Guanghua Lan, Andrew Johnson, Huanhuan Chen, Zhiyong Shen, Jianjun Li, Hui Xing, Yuhua Ruan and 5 more

Open access · goldAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Infectious diseases of poverty, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 5% of its field
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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

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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

15 authors at 4 institutions in 2 countries.

Jingya Jia *SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Qiuying Zhu *Guangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China.
Luojia Deng *Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Guanghua LanGuangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China.
Andrew JohnsonGroton School, Groton, MA, USA.
Huanhuan ChenGuangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China.
Zhiyong ShenGuangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China.
Jianjun LiGuangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China.
Hui XingGuangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China.
Yuhua RuanGuangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China.
Jing LiSJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Hui LuSJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
Sten H VermundSchool of Public Health, Yale University, New Haven, CT, USA.
Jinhui ZhuGuangxi Key Laboratory of Major Infectious Disease Prevention Control and Biosafety Emergency Response, Guangxi Center for Disease Control and Prevention, Nanning, China. gxzhujinhui@qq.com.
Han-Zhu QianSJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China. han-zhu.qian@yale.edu.ORCID http://orcid.org/0000-0003-2785-2628
Guangxi Center for Disease Prevention and Control · CNShanghai Jiao Tong University · CNChinese Center For Disease Control and Prevention · CNYale University · US

Funding

Guangxi Key Laboratory of AIDS Prevention Control and Translation ZZH2020010national natural science foundation of china 31871329National Natural Science Foundation of China 11971479National Natural Science Foundation of China 82160636Natural Science Foundation of Guangxi Province 2020GXNSFAA159020
6 · The paper itself

Abstract

backgroundAntiretroviral therapy (ART) has reduced mortality among people living with HIV (PLWH) in China, but co-infections of hepatitis B virus (HBV) and hepatitis C virus (HCV) may individually or jointly reduce the effect of ART. This study aimed to evaluate the impacts of HBV/HCV coinfections on treatment drop-out and mortality among PLWH on ART.

methodsA retrospective cohort study analysis of 58 239 people living with HIV (PLWH) who initiated antiretroviral therapy (ART) during 2010-2018 was conducted in Guangxi Province, China. Data were from the observational database of the National Free Antiretroviral Treatment Program. Cox proportional hazard models were fitted to evaluate the effects of baseline infection of HBV or HCV or both on death and treatment attrition among PLWH.

resultsOur study showed high prevalence of HBV (11.5%), HCV (6.6%) and HBV-HCV (1.5%) co-infections. The overall mortality rate and treatment attrition rate was 2.95 [95% confidence interval (CI) 2.88-3.02] and 5.92 (95% CI 5.82-6.01) per 100 person-years, respectively. Compared with HIV-only patients, HBV-co-infected patients had 42% higher mortality [adjusted hazard ratio (aHR) = 1.42; 95% CI 1.32-1.54], HCV-co-infected patients had 65% higher mortality (aHR = 1.65; 95% CI 1.47-1.86), and patients with both HCV and HBV co-infections had 123% higher mortality (aHR = 2.23; 95% CI 1.87-2.66).

conclusionsHBV and HCV coinfection may have an additive effect on increasing the risk of all-cause death among PLWH who are on ART. It is suggested that there is need for primary prevention and access to effective hepatitis treatment for PLWH.

Indexed as

CoinfectionHepatitis BHepatitis CHIV InfectionsChinaCohort StudiesHepatitis B virusHumansPrevalenceRetrospective StudiesTreatment OutcomeAntiretroviral therapyHepatitis B virusHepatitis C virusHIVMortality, Retrospective cohort

Identifiers

PMID34986877
PMCPMC8734096
OpenAlexW4205851646

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.