Evidence map›Paper›PMID 36875364›Full record

ArticleFrontiers in public health2023

Global, regional, and national HIV/AIDS disease burden levels and trends in 1990-2019: A systematic analysis for the global burden of disease 2019 study.

Xuebin Tian, Jingjing Chen, Xi Wang, Yiwen Xie, Xiaodi Zhang, Dating Han, Haijing Fu, Wanpeng Yin, Nanping Wu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 5 pooled it
21.0field-weighted citation impact, top 1% 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

61 citing papers in PubMed, 5 syntheses or guidelines pooled it, 108 citations in OpenAlex.

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1 more citing papers are in PubMed but not listed here.

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 at 4 institutions in 1 country.

Xuebin TianState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Jingjing ChenShandong Second Provincial General Hospital, Jinan, Shandong, China.
Xi WangCenter for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Yiwen XieState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xiaodi ZhangState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Dating HanState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Haijing FuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Wanpeng YinState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Nanping WuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
National Clinical Research Center for Digestive Diseases · CNFirst Affiliated Hospital Zhejiang University · CNGuangxi Medical University · CNState Key Laboratory of Diagnosis and Treatment of Infectious Diseases

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Since the first HIV/AIDS case appeared in 1980s, HIV/AIDS has been the focus of international attention. As a major public health problem, there are epidemiological uncertainties about the future of HIV/AIDS. It is important to monitor the global statistics of HIV/AIDS prevalence, deaths, disability adjusted life years (DALYs), and risk factors for adequate prevention and control. Methods: The Global Burden of Disease Study 2019 database was used to analyze the burden of HIV/AIDS in 1990-2019. By extracting global, regional, and national data on HIV/AIDS prevalence, deaths, and DALYs, we described the distribution by age and sex, explored the risk factors, and analyzed the trends in HIV/AIDS. Results: In 2019, there were 36.85 million HIV/AIDS cases (95% UI: 35.15-38.86 million), 863.84 thousand deaths (95% UI: 78.61-99.60 thousand), and 47.63 million (95% UI: 42.63-55.65 million) DALYs. The global age-standardized HIV/AIDS prevalence, death, and DALY rates were 454.32 (95% UI: 433.76-478.59), 10.72 (95% UI: 9.70-12.39), and 601.49 (95% UI: 536.16-703.92) per 100,000 cases, respectively. In 2019, the global age-standardized HIV/AIDS prevalence, death, and DALY rates increased by 307.26 (95% UI: 304.45-312.63), 4.34 (95% UI: 3.78-4.90), and 221.91 (95% UI: 204.36-239.47) per 100,000 cases, respectively, compared to 1990. Age-standardized prevalence, death, and DALY rates decreased in high sociodemographic index (SDI) areas. High age-standardized rates were observed in low sociodemographic index areas, while low age-standardized rates were observed in high sociodemographic index areas. In 2019, the high age-standardized prevalence, death, and DALY rates were predominant in Southern Sub-Saharan Africa, and global DALYs peaked in 2004 and subsequently decreased. The highest global HIV/AIDS DALYs were in the 40-44 age group. The main risk factors affecting HIV/AIDS DALY rates included behavioral risks, drug use, partner violence, and unsafe sex. Conclusions: HIV/AIDS disease burden and risk factors vary by region, sex, and age. As access to health care increases across countries and treatment for HIV/AIDS infection improves, the HIV/AIDS disease burden is concentrated in areas with low SDIs, particularly in South Africa. Regional differences should be fully considered to target optimal prevention strategies and treatment options based on risk factors.

Indexed as

Cost of IllnessGlobal Burden of DiseasePublic HealthRisk FactorsSouth Africaburden of diseasedeathsdisability-adjusted life yearsHIV/AIDSprevalencerisk factorstrend

Identifiers

PMID36875364
PMCPMC9975742
OpenAlexW4320921653

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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.