ArticleJMIR public health and surveillance2023
The Environmental and Socioeconomic Effects and Prediction of Patients With Tuberculosis in Different Age Groups in Southwest China: A Population-Based Study.
Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 4 citations in OpenAlex.
- HIV-tuberculosis co-infection in Liangshan, a resource-limited region of Southwest China: trends and implications in the context of integrated prevention, 2019-2024.Infectious diseases of poverty · 2026Article
- A novel time-series modeling framework for predicting tuberculosis incidence in Sichuan, China.BMC infectious diseases · 2026Article
- Tuberculosis incidence trends and risk factors among people living with human immunodeficiency virus (HIV) in Sichuan, China: a retrospective cohort study (2010-2024).BMC infectious diseases · 2026Article
- Routine Molecular Surveillance of Drug-Resistant Tuberculosis: Translating Mutation Proxies into Clinical Governance Intelligence in Rural Eastern Cape.Healthcare (Basel, Switzerland) · 2026Article
- Spatiotemporal patterns, associated factors, and forecasting of pulmonary tuberculosis: a 19-year township-level case study in Zigong City, China.BMC public health · 2026Article
- The impact of population aging on tuberculosis prevention and control in Shanghai: a prediction based on age-period-cohort models.Aging clinical and experimental research · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhile the End Tuberculosis (TB) Strategy has been implemented worldwide, the cause of the TB epidemic is multifactorial and not fully understood.
objectiveThis study aims to investigate the risk factors of TB and incorporate these factors to forecast the incidence of TB infection across different age groups in Sichuan, China.
methodsCorrelation and linear regression analyses were conducted to assess the relationships between TB cases and ecological factors, including environmental, economic, and social factors, in Sichuan Province from 2006 to 2017. The transfer function-noise model was used to forecast trends, considering both time and multifactor effects.
resultsFrom 2006 to 2017, Sichuan Province had a reported cumulative incidence rate of 1321.08 cases per 100,000 individuals in male patients and 583.04 cases per 100,000 individuals in female patients. There were significant sex differences in the distribution of cases among age groups (trend χ
conclusionsThe End TB Strategy in Sichuan should consider environmental, educational, medical, social, personal, and other conditions, and further substantial efforts are needed especially for male patients aged 0-24 years, female patients aged 0-14 years, and female patients older than 64 years.
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.