Effects of meteorological factors on tuberculosis and potential modifiers in Zhejiang Province, China

Although some studies have explored the role of meteorological factors in the development of tuberculosis (TB), the majority have been confined to single regions, leading to inconsistent findings. Consequently, we conducted a multi-city study not only to determine whether meteorological factors significantly influence the risk of developing TB but also to assess the magnitude of these effects and explore potential modifying factors. Data on daily reported TB cases and meteorological factors were collected from January 1, 2013, to December 31, 2022, across 11 cities in Zhejiang Province. A distributed lag non-linear model using a quasi-Poisson distribution was employed. Multivariate meta-regression was used to obtain overall pooled estimates and assess heterogeneity. From 2013 to 2022, 267,932 TB cases were reported in Zhejiang Province. Notably, a nonlinear relationship was observed between temperature and TB, with the relative risk (RR) peaking at 1.0 °C (RR = 1.882, 95% CI 1.173-3.020). The effect of low temperature was immediate and significant for a 13-day lag period, with the maximum effect at lag0 (RR = 1.014, 95% CI 1.008-1.021). The exposure-response curve between relative humidity (RH) and TB exhibited an M-shape, with the RR peaking at 47.7% (RR = 1.642, 95% CI 1.044-2.582). The lag effect of low RH was significant at lag 25-59, with the highest RR observed at lag 32 (RR = 1.011, 95% CI 1.001-1.022). Gross domestic product (GDP) per person, population density, and latitude demonstrated significant modification effects. Our study showed that low temperature and RH were associated with an increased risk of TB. Additionally, GDP per person, population density, and latitude may play important roles in explaining the association between RH and TB. These findings provide scientific evidence for the development of geographically specific public health policies.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2024-10-25
DOI
https://doi.org/10.1038/s41598-024-76785-0
Citations
3
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
0.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effects of meteorological factors on tuberculosis and potential modifiers in Zhejiang Province, China

Songhua Chen, Kui Liu, Wei Wang, Yu Zhang et al.
3 citations
Scientific Reports
Climate Change and Health Impacts
0.94
article

Effects of meteorological factors on tuberculosis and potential modifiers in Zhejiang Province, China

Songhua Chen, Kui Liu, Wei Wang, Yu Zhang, Qian Wu, Bin Chen
article en
3 citations

Abstract

Although some studies have explored the role of meteorological factors in the development of tuberculosis (TB), the majority have been confined to single regions, leading to inconsistent findings. Consequently, we conducted a multi-city study not only to determine whether meteorological factors significantly influence the risk of developing TB but also to assess the magnitude of these effects and explore potential modifying factors. Data on daily reported TB cases and meteorological factors were collected from January 1, 2013, to December 31, 2022, across 11 cities in Zhejiang Province. A distributed lag non-linear model using a quasi-Poisson distribution was employed. Multivariate meta-regression was used to obtain overall pooled estimates and assess heterogeneity. From 2013 to 2022, 267,932 TB cases were reported in Zhejiang Province. Notably, a nonlinear relationship was observed between temperature and TB, with the relative risk (RR) peaking at 1.0 °C (RR = 1.882, 95% CI 1.173-3.020). The effect of low temperature was immediate and significant for a 13-day lag period, with the maximum effect at lag0 (RR = 1.014, 95% CI 1.008-1.021). The exposure-response curve between relative humidity (RH) and TB exhibited an M-shape, with the RR peaking at 47.7% (RR = 1.642, 95% CI 1.044-2.582). The lag effect of low RH was significant at lag 25-59, with the highest RR observed at lag 32 (RR = 1.011, 95% CI 1.001-1.022). Gross domestic product (GDP) per person, population density, and latitude demonstrated significant modification effects. Our study showed that low temperature and RH were associated with an increased risk of TB. Additionally, GDP per person, population density, and latitude may play important roles in explaining the association between RH and TB. These findings provide scientific evidence for the development of geographically specific public health policies.

Scientific ReportsVol. 14(1)
Zhejiang Center for Disease Control and Prevention (CN)
Openalex Percentile: Top 24%
Climate Change and Health Impacts
0.94
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.