Evidence map›Paper›PMID 39839385›Full record

ArticleFrontiers in public health2024

Tuberculosis disease burden in China: a spatio-temporal clustering and prediction study.

Jingzhe Guo, Ce Liu, Fang Liu, Erkai Zhou, Runxue Ma, Ling Zhang, Bin Luo

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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.

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3 · Its place in the literature

Who cites it

14 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

7 authors.

Jingzhe Guo *Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China.
Ce Liu *Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China.
Fang Liu *Gansu Provincial Center for Disease Prevention and Control, Lanzhou, China.
Erkai ZhouInstitute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China.
Runxue MaInstitute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China.
Ling ZhangInstitute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China.
Bin LuoInstitute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The primary aim of this study is to investigate and predict the prevalence and determinants of tuberculosis disease burden in China. Leveraging high-quality data sources and employing a methodologically rigorous approach, the study endeavors to enhance our understanding of tuberculosis control efforts across different regions of China. First, through nationwide spatio-temporal cluster analysis, we summarized the status of tuberculosis burden in various regions of China and explore the differences, thereby providing a basis for formulating more targeted tuberculosis prevention and control policies in different regions; Subsequently, using a time series-based forecasting model, we conducted the first-ever national tuberculosis burden trend forecast to offer scientific guidance for timely adjustments in planning and resource allocation. This research seeks to contribute significantly to China's existing tuberculosis prevention and control system. Materials and methods: This research draws upon publicly available pulmonary tuberculosis (PTB) incidence and mortality statistics from 31 provinces and municipalities of mainland China between 2004 and 2018. We organized and classified these data according to province, month, year, and patient age group. Overall, the sample included 14,816,329 new instances of PTB and 42,465 PTB-related fatalities. We used spatiotemporal cluster analysis to record the epidemiological characteristics and incidence patterns of PTB during this period. Additionally, a time series model was constructed to forecast and analyze the incidence and mortality trends of PTB in China. Results: This study reveals significant regional variations in PTB incidence and mortality in China. Tibet (124.24%) and Xinjiang (114.72%) in western China exhibited the largest percentage change in tuberculosis (TB) incidence, while Zhejiang Province (-50.45%) and Jiangsu Province (-51.33%) in eastern China showed the largest decreases. Regions with significant percentage increases in PTB mortality rates (>100%) included four western regions, six central regions, and five eastern regions. The regions with relatively large percentage decreases in the mortality rate of PTB include Tianjin (-52.25%) and Shanghai (-68.30%). These differences are attributed to two main factors: (1) economic imbalances leading to poor TB control in underdeveloped areas, and (2) differences in TB-related policies among provinces causing uneven distribution of disease risks. Consequently, China may still face challenges in achieving the World Health Organization's 2030 tuberculosis control goals. Nationwide, the mortality rate of PTB in China increased between 2004 and 2018 (percentage change: 105.35%, AAPC: 4.1), while the incidence of PTB showed a downward trend (percentage change: -20.59%, AAPC: -2.1). Among different age groups, the 0-19 age group has the smallest disease burden. While incidence and mortality from TB were primarily found in adults 60 years of age or older, the age group of 0-19 years has the smallest burden of TB, highlighting obvious differences in age characteristics. It is predicted that the mortality rate of TB in China will continue to increase. In summary, the TB epidemic in China has been largely controlled due to the implementation of many public health programs and policies targeting specific groups and geographical areas. Finding and supporting effective health programs will make it possible to achieve the World Health Organization's goal of controlling tuberculosis in China.

Indexed as

Cost of IllnessTuberculosisTuberculosis, PulmonaryAdolescentAdultAgedChildChinaCluster AnalysisFemaleForecastingHumansIncidenceMaleMiddle AgedPrevalenceChinadisease burdenprediction studypulmonary tuberculosisspatial–temporal cluster analysis

Identifiers

PMID39839385
PMCPMC11747482

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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.