Evidence map›Paper›PMID 41034927›Full record

ArticleBMC public health2025

Analyzing factors affecting tuberculosis incidence in various mainland Chinese economic regions and predicting trends: a comprehensive regression study.

Hengliang Lv, Hui Chen, Xueli Zhang, Xuan Li, Lisha Liu, Caixia Dang, Xihao Liu, Chunyu Zhao, Xin Zhang, Junzhu Bai and 3 more

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

13 authors.

Hengliang Lv *Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Hui Chen *Chinese PLA Center for Disease Control and Prevention, Beijing, China.
Xueli Zhang *Changchun University of Chinese Medicine, Changchun, China.
Xuan LiDepartment of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.
Lisha LiuDepartment of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.
Caixia DangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Xihao LiuDepartment of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, China.
Chunyu ZhaoChinese PLA Center for Disease Control and Prevention, Beijing, China.
Xin ZhangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Junzhu BaiChinese PLA Center for Disease Control and Prevention, Beijing, China.
Shumeng YouChinese PLA Center for Disease Control and Prevention, Beijing, China.
Wenyi ZhangChinese PLA Center for Disease Control and Prevention, Beijing, China. zwy0419@126.com.
Yuanyong XuChinese PLA Center for Disease Control and Prevention, Beijing, China. xyy_827@sina.com.

Funding

National Natural Science Foundation of China 12031010
6 · The paper itself

Abstract

backgroundThe tuberculosis (TB) burden differs significantly across various regions of China, and these differences influence the effort focused on eradicating TB nationwide. The main factors influencing variations in TB incidence rates between different regions remain unclear. Therefore, the aim of this study was to analyze the factors influencing TB rates in different economic regions of China as well as determine the actual TB incidence rates during the COVID-19 pandemic and to project 2025 rates.

methodsThis study was based on the surveillance data of TB incidence from the Chinese Center for Disease Control and Prevention. Joinpoint regression analysis was employed to analyze the temporal trends of the TB incidence rate, and a generalized additive model was used to analyze the influencing factors and their differences in distribution in China and different economic zones. The machine learning models were used to determine the actual incidence of TB in China during the COVID-19 pandemic and forecast the incidence rate up to 2025.

resultsFrom 2004 to 2020, the incidence rate of TB increased in all areas, except for Xizang. Other provinces in China showed a downward trend, and the inflection point of the decline appeared near 2008. Western China had a notably higher incidence rate than other regions. The number of medical and health institutions, the number of health personnel, and gross domestic product per capita were negatively correlated with the incidence rate, especially in the western region. The seasonal autoregressive integrated moving average model achieved the optimal fit. Through this model, the following predictions were made: the incidence of TB in central, western, northeastern, and eastern China will be 52.460/100,000, 81.438/100,000, 59.152/100,000, and 52.401/100,000, respectively, with all incidence rates higher than the TB incidence rates reported during COVID-19 pandemic in 2020.

conclusionExcept in the eastern region, China is unlikely to achieve its 2025 goals. Regional economic disparities coupled with strained medical resources during the COVID-19 crisis have hindered TB control efforts. To address this issue, it is recommended that the central and western regions prioritize optimizing health resource allocation and strengthening the management of patients with TB.

Indexed as

COVID-19TuberculosisChinaForecastingHumansIncidenceRegression AnalysisChinaFactorsIncidencePredictionTuberculosis

Identifiers

PMID41034927
PMCPMC12487523

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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.