# Weather elements and the risk of tuberculosis incidence in China from 2005 to 2019: a county-level large observational study

## TL;DR

This study explores how weather factors like temperature and humidity affect tuberculosis risk in China, revealing seasonal and regional patterns that could help improve disease control.

## Contribution

The study introduces a detailed national analysis of nonlinear and lagged climate effects on TB incidence using county-level data and advanced statistical models.

## Key findings

- Higher temperature and precipitation increased TB risk, while greater temperature differences and humidity had protective effects.
- Temperature effects peaked in summer, and extreme high temperatures and wind speeds showed delayed elevation of TB risk.
- The study supports the use of climate data to improve TB risk prediction and inform targeted interventions in high-risk regions.

## Abstract

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.

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.

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.

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.