Evidence map›Paper›PMID 42638703›Full record

ArticleFrontiers in tuberculosis2025

Trends and impact of government prevention policies on pulmonary tuberculosis notification and mortality in Ningbo, China (2004-2021).

Tianfeng He, Lin Lin, Xujun Qian, Weitao Yao, Guoxing Li, Jing Huang

Abstract read
In one paragraph

Article in Frontiers in tuberculosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Tianfeng He *Department of Infectious Disease Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, Ningbo, China.
Lin Lin *Department of Economics and Management, School of Public Management, East China Normal University, Shanghai, China.
Xujun QianDepartment of Primary Health Management, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Weitao YaoDepartment of Infectious Disease Prevention and Control, Ningbo Municipal Center for Disease Control and Prevention, Ningbo, China.
Guoxing LiDepartment of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
Jing HuangDepartment of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tuberculosis (TB) is a major global health threat, with Ningbo reporting over 3,000 annual cases of pulmonary tuberculosis (PTB). This study analyzes the notification and mortality rates of PTB in Ningbo from 2004 to 2021. Methods: We calculated age-standardized annual notification and mortality rates for PTB per 100,000 population and employed interrupted time series regression to evaluate the impact of government policies on these rates. Results: From 2004 to 2021, Ningbo recorded 68,392 PTB cases and 236 deaths. The unstandardized notification and mortality rates were 51.31 and 0.18 per 100,000 population, respectively. Joinpoint trend analysis showed a significant decline in PTB notification from 2007 to 2016 (APC = -7.3%, Conclusion: Between 2004 and 2021, Ningbo saw a notable decrease in both the notification and mortality rates of PTB due to effective control measures. To achieve the End TB Strategy targets for 2035, it is crucial to enhance efforts in diagnosing and treating PTB-especially among males, adolescents, and older adults.

Indexed as

interrupted time seriesjoinpoint regressionmortalitynotificationpulmonary tuberculosis (PTB)

Identifiers

PMID42638703
PMCPMC13491424

What OpenQuestion holds

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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.