Observational studyJMIR public health and surveillance2022
Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020.
Observational study in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 13 citations in OpenAlex.
- Temporal trends in tuberculosis incidence among the aging population in Southwest China: a retrospective study.BMC geriatrics · 2026Article
- Impact of policy shifts and festival holidays on tuberculosis surveillance: a time-series analysis in Southwest China (2014-2024).Frontiers in 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
- Trends and delays in pulmonary tuberculosis diagnosis among elderly patients (≥ 60 Years) in Southern China: a 13-year surveillance data analysis (2010-2022).BMC public health · 2025Article
- Effect of population aging on pulmonary tuberculosis burden in Zhejiang Province, China: a population-based study.Frontiers in public health · 2025Article
- Analyzing spatial delays of tuberculosis from surveillance and awareness surveys in Eastern China.Scientific reports · 2024Article
- Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China.BMC public health · 2024Article
- Trends and Projection of the Incidence of Active Pulmonary Tuberculosis in Southwestern China: Age-Period-Cohort Analysis.JMIR public health and surveillance · 2023Article
- Mass Tuberculosis Screening Among the Elderly: A Population-Based Study in a Well-Confined, Rural County in Eastern China.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Ending Tuberculosis in Older People: New Strategies for an Age-old Disease.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- The effect of diabetes mellitus on tuberculosis in eastern China: A decision-tree analysis based on a real-world study.Journal of diabetes · 2023Article
- Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.International journal of environmental research and public health · 2023Article
- Spatial-temporal analysis of pulmonary tuberculosis among students in the Zhejiang Province of China from 2007-2020.Frontiers in public health · 2023Article
- Assessment of Effective Anti-TB Regimens and Adverse Outcomes Related Risk Factors in the Elderly and Senile-Aged TB Patients.Infection and drug resistance · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith a progressive increase in the aging process, the challenges posed by pulmonary tuberculosis (PTB) are also increasing for the elderly population.
objectiveThis study aimed to identify the epidemiological distribution of PTB among the elderly, forecast the achievement of the World Health Organization's 2025 goal in this specific group, and predict further advancement of PTB in the eastern area of China.
methodsAll notified active PTB cases aged ≥65 years from Zhejiang Province were screened and analyzed. The general epidemiological characteristics were depicted and presented using the ArcGIS software. Further prediction of PTB was performed using R and SPSS software programs.
resultsAltogether 41,431 cases aged ≥65 years were identified by the surveillance system from 2015 to 2020. After excluding extrapulmonary TB cases, we identified 39,832 PTB cases, including laboratory-confirmed (23,664, 59.41%) and clinically diagnosed (16,168, 40.59%) PTB. The notified PTB incidence indicated an evident downward trend with a reduction of 30%; however, the incidence of bacteriologically positive cases was steady at approximately 60/100,000. Based on the geographical distribution, Quzhou and Jinhua Cities had a higher PTB incidence among the elderly. The delay in PTB diagnosis was identified, and a significantly prolonged treatment course was observed in the elderly. Moreover, a 50% reduction of PTB incidence by the middle of 2024 was predicted using a linear regression model. It was found that using the exponential smoothing model would be better to predict the PTB trend in the elderly than a seasonal autoregressive integrated moving average model.
conclusionsMore comprehensive and effective interventions such as active PTB screening combined with physical checkup and succinct health education should be implemented and strengthened in the elderly. A more systematic assessment of the PTB epidemic trend in the elderly population should be considered to incorporate more predictive factors.
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.