Evidence map›Paper›PMID 42147001›Full record

ArticleChina CDC weekly2026

Scaling Up Tuberculosis Preventive Treatment: Progress and Factors Influencing Optimization - China, 2022-2025.

Caihong Xu, Jinhao Li, Yaxin Wen, Dongmei Hu, Rongbin Yu, Yanlin Zhao

Abstract read
In one paragraph

Article in China CDC weekly, 2026. 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

6 authors.

Caihong XuDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Jinhao LiCenter for Tuberculosis Prevention and Control, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.
Yaxin WenCenter for Tuberculosis Prevention and Control, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.
Dongmei HuCenter for Tuberculosis Prevention and Control, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.
Rongbin YuDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing City, Jiangsu Province, China.
Yanlin ZhaoCenter for Tuberculosis Prevention and Control, Chinese Center for Disease Control and Prevention & Chinese Academy of Preventive Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

What is already known about this topic?: Tuberculosis preventive treatment (TPT) is critical in preventing latent tuberculosis infection (LTBI) progression to active tuberculosis. China has integrated TPT into national Tuberculosis (TB) control; however, systematic national data on TPT implementation in China are scarce. What is added by the report?: TPT recipient number in China increased 2.5-fold (2022-2025), with around 79% using short-course regimens. Among 2,676 individuals with LTBI, TPT acceptance and completion rates were 45.14% and 85.18%, respectively. Key acceptance factors included age, ethnicity, occupation, treatment institution, immunocompromised status, and purified protein derivative results. What are the implications for public health practice?: To improve TPT effectiveness, public health strategies should integrate TPT into routine clinics, promote short-course regimens, and target high-risk groups (e.g., immunocompromised individuals and the elderly) to increase acceptance rates and reduce disparities to achieve TB elimination.

Indexed as

Influencing FactorsLatent Tuberculosis InfectionShort-Course RegimenTuberculosis Preventive Treatment

Identifiers

PMID42147001
PMCPMC13171612

What OpenQuestion holds

Textmetadata
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.