Evidence map›Paper›PMID 32532238›Full record

ArticleBMC public health2020

Declining trend in HIV new infections in Guangxi, China: insights from linking reported HIV/AIDS cases with CD4-at-diagnosis data.

Xiaodan Sun, Wenmin Yang, Sanyi Tang, Mingwang Shen, Tianyang Wang, Qiuying Zhu, Zhiyong Shen, Shuai Tang, Huanhuan Chen, Yuhua Ruan and 1 more

Abstract read
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Xiaodan SunSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.
Wenmin YangGuangxi Center for Disease Control and Prevention, Nanning, China.
Sanyi TangSchool of Mathematics and Information Science, Shaanxi Normal University, Xi'an, China.
Mingwang ShenDepartment of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
Tianyang WangSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.
Qiuying ZhuGuangxi Center for Disease Control and Prevention, Nanning, China.
Zhiyong ShenGuangxi Center for Disease Control and Prevention, Nanning, China.
Shuai TangGuangxi Center for Disease Control and Prevention, Nanning, China.
Huanhuan ChenGuangxi Center for Disease Control and Prevention, Nanning, China.
Yuhua RuanGuangxi Center for Disease Control and Prevention, Nanning, China. ruanyuhua92@163.com.
Yanni XiaoSchool of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China. yxiao@mail.xjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Guangxi Zhuang Autonomous Region bears a relatively high burden of HIV/AIDS infection. The number of accumulatively reported HIV/AIDS cases in Guangxi is the third highest among 31 provinces or Autonomous Region from 2004 to 2007, changed to the second highest between 2011 and 2013, then returned to the third highest again after 2014. We aim to estimate the new infections and evaluate the real-time HIV epidemic in Guangxi, China, in order to reveal the rule of HIV transmission.

methodsFirstly, the number of annually reported HIV and AIDS cases, as well as the number of cases linked with CD4 data are extracted from the HIV/AIDS information system in China. Secondly, two CD4-staged models are formulated by linking the with-host information on CD4 level to between-host transmission and surveillance data. Thirdly, new HIV infections, diagnosis rates and undiagnosed infections over time are estimated by using Bayesian method and Maximum Likelihood Estimation method.

resultsThe data reveal that the newly reported cases have been decreasing since 2011, while lots of cases are identified at late CD4 stage. The data fitted results indicate that both models can describe the trend of the epidemic well. The estimation results show that the new and undiagnosed infections began to decrease from the period2006 - 2008. However, the diagnosis probabilities/rates keep at a very low level, and there are still a large number of infections undiagnosed, most of which have a large probability to be identified at late CD4 stage.

conclusionsOur findings suggest that HIV/AIDS epidemic in Guangxi has been controlled to a certain extent, while the diagnosis rate still needs to be improved. More attentions should be paid to identify infections at their early CD4 stages. Meanwhile, comprehensive intervention measures should be continually strengthened in avoid of the rebound of new infections.

Indexed as

EpidemicsBayes TheoremCD4 Lymphocyte CountChinaHIV InfectionsHumansCD4-stage structured modelDiagnosis rateHIV/AIDSNew infectionsUndiagnosed infections

Identifiers

PMID32532238
PMCPMC7290136

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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.