Observational studyJournal of global health2026
Weather elements and the risk of tuberculosis incidence in China from 2005 to 2019: a county-level large observational study.
Observational study in Journal of global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Tuberculosis (TB) remains a major public health challenge in China. Although meteorological factors are known to influence its transmission, their nonlinear and lagged impacts across regions and seasons remain unclear. We quantified these effects using the most detailed national data set available and explored how climate information can enhance TB prediction and control. Methods: We conducted a nationwide ecological time-series study by integrating weekly TB surveillance data (2005-19) with high-resolution meteorological and air pollution models. We assessed associations between TB incidence and meteorological factors using negative binomial regression and distributed lag nonlinear models to account for nonlinear and delayed effects. Results: From 2005 to 2019, TB cases in China decreased from 1.23 million to 0.75 million (estimated annual percent change <0 across all regions), with the burden remaining highest in western and southern China. Higher weekly mean temperature (incidence rate ratio (IRR) = 1.33) and precipitation (IRR = 1.03) increased TB risk, while greater temperature differences (IRR = 0.96) and relative humidity (IRR = 0.92) had protective effects. Temperature effects peaked in summer (IRR = 1.80; P < 0.05). Lagged analyses showed that extreme high temperatures and high wind speeds initially suppressed, but subsequently elevated TB risk, while higher precipitation and humidity showed delayed risk effects. Conclusions: By integrating fine-scale epidemiological and meteorological data, our study adds to our knowledge on TB epidemiology by more accurately characterising climate-disease interactions and enhancing the predictive capability of risk models. The findings provide empirical evidence to support the development of risk stratification tools and guide the implementation of proactive, phased intervention strategies aimed at mitigating the persistent TB burden in high-risk regions.
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.