Evidence map›Paper›PMID 40269753›Full record

ArticleBMC infectious diseases2025

Coinfection of hepatitis B, tuberculosis, and HIV/AIDS in Beijing from 2016 to 2023: a surveillance data analysis.

Chao Wang, Yunping Shi, Yang Liu, Ying Zhou, Jing Du, Xiao Hu, Wei Li, Jiaze Li, Yanlin Gao, Gang Li

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Emergence ofMicroorganisms · 2025
    Article
  4. 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.

Chao WangInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Yunping ShiInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Yang LiuInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Ying ZhouInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Jing DuInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Xiao HuInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Wei LiInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Jiaze LiInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China.
Yanlin GaoInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China. gaoyl@bjcdc.org.
Gang LiInstitute of Statistics and Information, Beijing Center for Disease Prevention and Control, Beijing, 100013, China. ligang@bjcdc.org.

Funding

National Key Research and Development Program of China 2023YFC2308703the High-level Public Health Talent Development Program of Beijing Discipline Backbone-02-12the High-level Public Health Talent Development Program of Beijing Discipline Leader-01-09the Special Fund for Health Development Research of Beijing 2024-2G-30121
6 · The paper itself

Abstract

backgroundHepatitis B (HB), tuberculosis (TB), HIV infection and AIDS (HIV/AIDS) are the major public health threats in China. The existing domestic research shows significant regional differences in the coinfection of HB, TB, and HIV/AIDS and mainly focused on two diseases. This study aims to analyse the coinfection of HB, TB, and HIV/AIDS patients in Beijing from 2016 to 2023.

methodsWe obtained data on cases diagnosed with HB, TB, or HIV/AIDS between 1 January 2016 and 31 December 2023 in Beijing from the National Notifiable Disease Reporting System (NNDRS). After removing duplicate cards with the same disease, we compared the demographic, temporal, and spatial characteristics between coinfections and mono-infections and among coinfections with different diagnostic sequences using chi-squared test. We also explored the risk factors for coinfection by multivariate logistic models.

resultsOverall, 104,141 cards from 103,595 cases in Beijing were included in this study. The number of cases infected with HIV/AIDS, HB, or TB alone was 20,884 (20.12%), 23,853 (23.03%), and 58,357 (56.33%), respectively. Furthermore, 47 cases (0.05%) were diagnosed with HIV/AIDS and HB, 153 (0.15%) with HB and TB, and 336 (0.32%) with HIV/AIDS and TB. And only five cases were diagnosed with all three diseases. 0.22% HB and 1.58% TB were coinfected among HIV/AIDS patients; 0.64% TB and 0.20% HIV/AIDS were coinfected in HB patients, and 0.26% HB and 0.57% HIV/AIDS in TB patients. Differences in demographic characteristics, residential areas, and diagnosis years were found between coinfected patients and those with a single disease. In contrast, almost no significant difference in characteristics and diagnostic time intervals was found among comorbidity patients with the same diseases but different diagnostic sequences. The multivariate logistic model shown that males were more susceptible to be coinfected (ORs ranged from 1.50 to 27.81). Compared with the cases aged 60 and above, younger TB patients were more likely to be coinfected with HIV/AIDS (OR = 2.76[95%CI:1.59-4.77], 5.36[3.16-9.07]and 2.75[1.62-4.65] for those aged 15-29, 30-44 and 45-59), while younger HB or HIV/AIDS patients were less likely to be coinfected with TB (OR = 0.37[0.18-0.73], 0.34[0.20-0.59], 0.63[0.40-0.98] for younger HB and OR = 0.27[0.15-0.48],0.46[0.26-0.81],0.59[0.33-1.03] for younger HIV/AIDS). Compared with TB patients in urban areas, suburban patients are less likely to be infected with HIV/AIDS (OR = 0.63[0.50-0.79] for inner suburbs and 0.33[0.16-0.72] for outer suburbs). Patients infected with TB or HIV/AIDS living in outer suburbs were more likely to be coinfected with HB (OR = 2.65[1.62-4.34] for TB and OR = 4.04[1.18-13.85] for HIV/AIDS). Patients coinfected with TB and HB had longer diagnostic intervals than those infected with HIV.

conclusionsThe incidence of comorbidity of HB, TB, and HIV/AIDS was low and showed a declining trend in Beijing. Among various types of coinfections, HIV/AIDS coinfected with TB has the highest incidence. Males should be considered as the primary target population for preventing coinfections. In addition to promoting the development of information technology and integrating data from multiple sources, it is essential to conduct thematic investigations, enhance education among key populations, and intensify the tracking of close contacts of existing patients to prevent and control the spread of these diseases.

Indexed as

Acquired Immunodeficiency SyndromeCoinfectionHepatitis BHIV InfectionsTuberculosisAdolescentAdultAgedBeijingChildChild, PreschoolFemaleHumansMaleMiddle AgedRisk FactorsAIDS or HIVComorbidityHepatitis BTuberculosis

Identifiers

PMID40269753
PMCPMC12016106

What Socratic holds

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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 Socratic graph.