Evidence map›Paper›PMID 41981571›Full record

ArticleBMC public health2026

Associations of exposure to ambient PM

Xinqiang Wang, Leonardo Martinez, Lauren Linde, Qi Li, Xiaolu Wang, Jiayi Tang, XiaoHong Kan, Changhong Yang, Qu Cheng

Abstract read
In one paragraph

Article in BMC public 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.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

9 authors.

Xinqiang WangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Leonardo MartinezDepartment of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.
Lauren LindeDepartment of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.
Qi LiDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Xiaolu WangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Jiayi TangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
XiaoHong KanAnhui Provincial Center for Disease Control and Prevention, Hefei, Anhui, People's Republic of China.
Changhong YangSichuan Public Health General Clinical Center, Chengdu, Sichuan, People's Republic of China.
Qu ChengDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China. chengqu@hust.edu.cn.ORCID 0000-0003-3074-9218

Funding

National Natural Science Foundation of China 82204110
6 · The paper itself

Abstract

backgroundChina, as well as many other populous countries in Asia, continues to face a dual burden of high pulmonary tuberculosis (PTB) incidence and severe air pollution, especially fine particulate matter with a diameter ≤ 2.5 μm (PM2.5). However, few studies have explored the effect of ambient PM2.5, especially its specific components, on the risk of developing PTB.

methodsWe obtained active PTB cases from 37 prefectures in China over a 7-year period. Prefecture-level monthly PTB counts were analyzed in relation to population-weighted mean concentrations of PM2.5 and its major components. Exposure data were derived from the Tracking Air Pollution in China (TAP) dataset and spatiotemporally linked to each case by prefecture of residence and month of diagnosis. To investigate the lagged exposure-response relationship, we applied an integrated spatiotemporal Bayesian hierarchical model within a distributed lag non-linear modeling framework. We further evaluated potential effect modification by sex, age, local PTB transmission intensity, greenness level, and temperature to assess effect heterogeneity and provide preliminary insights into potential underlying pathways. Mixed-effects meta-regression models were used to evaluate potential effect modification, incorporating interaction terms between the pollutant and each potential modifier.

results636,391 PTB cases were reported in our study area during the study period. We found positive associations between all components and PTB incidence and the effects are stronger and last longer at higher concentrations. At the 95th percentile of the concentrations the strongest effect occurring at a one-month lag and lasted for up to three months, the maximum incidence rate ratio were 1.069 (95% CI: 1.046–1.092 ) for PM2.5, 1.048 (95% CI: 1.029–1.068) for sulfate, 1.066 (95% CI: 1.040–1.092) for nitrate, 1.063 (95% CI: 1.039–1.087) for ammonium, 1.060 (95% CI: 1.038–1.083) for OM, and 1.065 (95% CI: 1.045–1.086) for BC. The peak incidence rate ratios (IRRs) per interquartile range (IQR) increase were comparable across components, although the cumulative effects of ammonium were slightly stronger than those of the other pollutants. Meta-regression analyses indicated that the effect sizes varied by sex, age group, PTB transmission intensity, greenness, and annual mean temperature. Stronger associations were observed among males, younger age groups, regions with lower transmission intensity and greenness, and those with higher annual mean temperature, although the magnitude and statistical significance of effect modification differed across components.

conclusionsExposure to ambient PM2.5 and its components may be associated with an increased risk of PTB. The observed short peak lag times and consistently larger incidence rate ratios in settings with lower local transmission intensity suggest that the increased risk may be more strongly related to disease reactivation or progression from recently acquired infections than to enhanced transmission.

Indexed as

Air PollutantsAir PollutionEnvironmental ExposureParticulate MatterTuberculosis, PulmonaryBayes TheoremChinaFemaleHumansIncidenceMaleSpatio-Temporal AnalysisAir PollutantsParticulate MatterDistributed lag non-linear modelPM2.5 componentsPulmonary tuberculosisRisk assessmentSpatiotemporal Bayesian hierarchical model

Identifiers

PMID41981571
PMCPMC13195948

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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.