Evidence map›Paper›PMID 41316121›Full record

ArticleBMC public health2025

Assessing three-decade HIV testing transformation: TOPSIS-radar evaluation of tiered health system performance in rural-urban Southwestern China.

Sisi Li, Xianyan Tang, Pinghua Zhu, Mu Li, Bingyu Liang, Man Cheng, Qian Lin, Hongyang Tang, Yi Feng, Yiming Shao

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Sisi Li *Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
Xianyan Tang *Department of Epidemiology and Bio-Statistics, School of Public Health, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
Pinghua Zhu *School of Humanities and Social Science, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
Mu LiGuangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
Bingyu LiangGuangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
Man ChengDepartment of Epidemiology and Bio-Statistics, School of Public Health, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
Qian LinDepartment of Epidemiology and Bio-Statistics, School of Public Health, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
Hongyang TangNanning Center for Disease Control and Prevention, No. 55Th, Xiangzhu Road, Nanning, Guangxi Zhuang Autonomous Region, 530023, People's Republic of China.
Yi FengState of Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, No. 155Th, Changbai Road, Beijing, 102206, China.
Yiming ShaoGuangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, No. 22Nd, Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China. yshao@bjmu.edu.cn.

Funding

the State Key Laboratory of Infections Disease Prevention and Control 2019SKLID602
6 · The paper itself

Abstract

backgroundChina's expanded HIV testing program (since 2015) necessitates longitudinal evaluation of healthcare institution (HCI) performance, which provides critical evidence to enhance the precision and effectiveness of focused testing interventions, to optimize service delivery.

methodsWe analyzed HIV surveillance data from 202 HCIs in Nanning, China (1989-2020), characterizing: 1) HCI infrastructure distribution, 2) newly reported HIV/AIDS cases and testing volumes, and 3) test positivity rates. Spatiotemporal trends were geo-visualized (ArcGIS 10.7). TOPSIS synthesized case reports and test positivity rates (2010-2020) into composite indices, with radar mapping identifying regional disparities.

resultsReporting shifted from CDC-dominated systems (99.9%, 1989-2004) to hospital-led models (75.6%, 2015-2020). While annual testing volume and case reports increased significantly, test positivity rates declined. Distinct hospital-level stratification emerged: County/township HCIs served primarily local residents (96.2%) and older patients (≥ 50 years; 65.7%). Municipal/provincial HCIs reported higher non-local residents (48.6%) and younger patients (15-49 years; 53.4%). Geospatial diffusion showed progression from urban to rural areas. Regions with integrated AIDS treatment centers and robust primary care networks demonstrated enhanced case-finding performance.

conclusionsTiered hospitals and primary care centers may provide complementary case-finding functions. Geographically stratified interventions, which leveraged primary networks for localized epidemics and referral hospitals for mobile populations, represent evidence-based strategies for regions with suboptimal performance.

Indexed as

HIV InfectionsHIV TestingRural PopulationAdultChinaFemaleHumansMaleMiddle AgedUrban PopulationAIDSComprehensive assessmentDetective capabilityTOPSIS

Identifiers

PMID41316121
PMCPMC12751608

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Registered trials

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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.