Evidence map›Paper›PMID 28880917›Full record

ArticlePloS one2017

Analysis of HIV prevalence among pregnant women in Liangshan Prefecture, China, from 2009 to 2015.

Shujuan Yang, Chao Yang, Qiang Liao, Wenwen Zhai, Gang Yu, Lin Xiao, Qixing Wang, Yuhan Gong, Suhua Zhang, Yongna Yao and 4 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 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

14 authors at 2 institutions in 1 country.

Shujuan YangDepartment of Health Related Social and Behavioral Science, West China School of Public Health, Sichuan University, Chengdu, China.ORCID http://orcid.org/0000-0002-6929-4823
Chao YangDepartment of Epidemiology and Statistics, School of Public Health, Southwest Medical University, Luzhou, China.
Qiang LiaoLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Wenwen ZhaiDepartment of Health Related Social and Behavioral Science, West China School of Public Health, Sichuan University, Chengdu, China.
Gang YuLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Lin XiaoLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Qixing WangLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Yuhan GongLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Suhua ZhangDepartment of Health Related Social and Behavioral Science, West China School of Public Health, Sichuan University, Chengdu, China.
Yongna YaoDepartment of Health Related Social and Behavioral Science, West China School of Public Health, Sichuan University, Chengdu, China.
Ke WangLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Ju WangLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Shaochao BianLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Qian LiuLiangshan Prefecture Center for Disease Control and Prevention, Xichang, China.
Sichuan University · CNSouthwest Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundYi people make up about 50% of the population in Liangshan Prefecture, Sichuan Province, China, but accounted for 88.07% of new HIV cases in the prefecture from 2011 to 2013. This study evaluated HIV prevalence in pregnant women of Liangshan Prefecture using HIV sentinel surveillance (HSS) data over the period of 2009 to 2015.

methodsXichang, Zhaojue County, and Butuo County were selected as HSS sites. We investigated the temporal trends in HIV prevalence in these areas, and the association between demographic and behavioral characteristics and risk of HIV infection.

resultsData on a total of 2797 pregnant women in Xichang and 3983 pregnant women in Zhaojue and Butuo was collected for the period 2009 to 2015. There was a fluctuating HIV prevalence among pregnant women of Xichang, with a rate of 0.75% in 2015 (χ2trend = 2.27, P = 0.13). HIV prevalence among pregnant women of Zhaojue and Butuo was consistently high, varying between 3.4% (9/267, 2011) and 10.3% (82/796, 2012) over the period of 2010 to 2015 (χ2trend = 0.12, P = 0.73). In Xichang, we found that Yi ethnicity (OR = 11.37, 95% CI = 2.92-44.25) and a husband who used drugs (OR = 32.13, 95% CI = 5.33-193.67) were significantly associated with HIV risk in pregnant women. For Zhaojue and Butuo, we observed that pregnant women had a higher risk of HIV infection when they were over 30 years old (OR = 1.72, 95% CI = 1.17-2.52), when they had a higher number of previous births, when their husbands had a history of migrating for work (OR = 1.56, 95% CI = 1.16-2.08), and when they had a history of other sexually transmitted infections (OR = 2.19, 95% CI = 1.16-2.08). Compared to those with a primary school education or below, pregnant women with a secondary or high school education level had a lower risk of HIV infection (OR = 0.28, 95% CI = 0.09-0.89).

conclusionOur results indicate that there is a serious HIV epidemic among pregnant Yi women, especially for those with less education, more past births, or a husband with a history of out-migrating for work or STD infection.

Indexed as

AdultChinaFemaleHIV InfectionsHumansLogistic ModelsMultivariate AnalysisPregnancyPrevalenceSurveys and Questionnaires

Identifiers

PMID28880917
PMCPMC5589085
OpenAlexW2751930174

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.