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<p>This study meticulously examines the intricate relationship of environmental factors, including Air Quality Index (AQI) and meteorological conditions, on the growth rate of pulmonary tuberculosis (PTB) in Dongguan, in the context of climate change and urbanization. Data analysis from 2014 to 2023 (from 52,283 confirmed PTB cases) identified a significant 3-month lag between these factors and the growth rate of PTB. Seasonal analysis indicated that the growth rate of PTB rose three months after rapid increases in temperature, sharp drops in relative humidity, and increases in AQI. A quadratic regression framework centered on AQI was formulated, with parameters optimized via genetic algorithms, yielding a methodological advance. This approach enhances the understanding of climate impacts on health risks related to air quality, particularly during extreme weather events. Consequently, the model not only predicts PTB growth trends under different environmental conditions but helps define the corresponding health risk levels, providing valuable insights for public health policy and environmental management.</p> Graphical abstract <p></p>

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Deciphering the dynamic interplay of air pollution and climate change on pulmonary tuberculosis: a data-driven analysis

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Xiang Yi,
  • Shixiao Liu

摘要

This study meticulously examines the intricate relationship of environmental factors, including Air Quality Index (AQI) and meteorological conditions, on the growth rate of pulmonary tuberculosis (PTB) in Dongguan, in the context of climate change and urbanization. Data analysis from 2014 to 2023 (from 52,283 confirmed PTB cases) identified a significant 3-month lag between these factors and the growth rate of PTB. Seasonal analysis indicated that the growth rate of PTB rose three months after rapid increases in temperature, sharp drops in relative humidity, and increases in AQI. A quadratic regression framework centered on AQI was formulated, with parameters optimized via genetic algorithms, yielding a methodological advance. This approach enhances the understanding of climate impacts on health risks related to air quality, particularly during extreme weather events. Consequently, the model not only predicts PTB growth trends under different environmental conditions but helps define the corresponding health risk levels, providing valuable insights for public health policy and environmental management.

Graphical abstract