ArticleBMC infectious diseases2022
Effect of different interventions for latent tuberculosis infections in China: a model-based study.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Transmission dynamics and control of tuberculosis in high-altitude regions: a modelling study in Xizang, China.BMJ open · 2026Article
- The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study.Emerging microbes & infections · 2025Article
- Cost-effectiveness of preventive treatment regimens for latent tuberculosis infection among key community-level populations.BMC medicine · 2025Article
- Cost-effectiveness of latent tuberculosis infection testing and treatment with 6-week regimen among key population in rural communities in China: a decision analysis study.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2024Article
- Population-level health and economic impacts of introducing Vaccae vaccination in China: a modelling study.BMJ global health · 2023Article
- Innovative Therapeutic Approaches Based on Nanotechnology for the Treatment and Management of Tuberculosis.International journal of nanomedicine · 2023Review
- The burden and predictors of latent tuberculosis infection among elder adults in high epidemic rural area of tuberculosis in Zhejiang, China.Frontiers in cellular and infection microbiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTuberculosis (TB) has a serious impact on people's health. China is one of 30 countries that has a high TB burden. As the currently decreasing speed of the incidence of TB, the WHO's goal of "End TB Strategy" is hard to achieve by 2035. As a result, a SEIR model that determines the impact of different tuberculosis preventive treatments (TPTs) in different age groups, and the effect of different interventions on latent TB infections (LTBIs) in China is developed.
methodsA Susceptible-Exposed-Infectious-Recovered (SEIR) model was established. Goodness-of-fit tests were used to assess model performance. Predictive analysis was used to assess the effect of different interventions on LTBIs and achieving the goals of the "End TB Strategy".
resultsThe Chi-square test indicated the model provided a good statistical fit to previous data on the incidence of TB (χ
conclusionThe incidence of TB in China declined gradually from 2005 to 2019. Implementation of ACF for LTBIs and TPT nation-wide or in areas with high incidence, in the elderly, or administration of a new and effective vaccine could greatly reduce the number of TB cases and achieve the 2035 target of the "End TB Strategy" in China.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.