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Background <p>Epidemiological studies of <i>Talaromyces marneffei</i> (<i>T. marneffei</i>) infection are largely confined to HIV-positive populations and specific geographic regions, whereas its population-scale characteristics and environmental factors associated with infection in China are lacking. This study investigates spatiotemporal patterns, host susceptibility, and environmental correlates of <i>T. marneffei</i> detection in Chinese mainland.</p> Methods <p>Targeted next-generation sequencing (tNGS) data from patients hospitalized for acute respiratory tract infections (ARTIs) from 2022 to 2024 were used for epidemiological, spatiotemporal, and co-detection analyses. Geographical detector models with the <i>q</i>-statistic were employed to quantify the associations of meteorological, host distribution, and social factors on detection risk, where the <i>q</i>-statistic measures the proportion of spatial variance explained by each factor.</p> Results <p>Among 2,316 reported cases, we identified significant spatial clustering, with bimodal seasonal peaks in detection rate. Males and individuals aged 41–50 showed the highest susceptibility. <i>Pneumocystis jirovecii</i> was the predominant co-detected pathogen; 5 pathogens were positively and 16 were negatively correlated with <i>T. marneffei</i>. Univariate analysis using the <i>q</i>-statistic revealed that dew point temperature had the strongest explanatory power for <i>T. marneffei</i> detection at 48.55%. Bivariate interaction analysis demonstrated that paired factor combinations exhibited enhanced explanatory power for disease prevalence compared with single factors. Average air pressure, which alone explained only 1.4% of the spatial variance, showed markedly higher explanatory power when paired with other variables.</p> Conclusions <p>This study revealed spatiotemporal heterogeneity, population susceptibility, and environmental factors associated with <i>T. marneffei</i> detection in Chinese mainland, which can guide disease monitoring and control through enhanced surveillance and tailored interventions in epidemic hotspots and among high-risk groups.</p>

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Epidemiology and factors associated with Talaromyces marneffei detection in the mainland of China, 2022–2024: a cross-sectional study of hospitalized patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Xinchang Lun,
  • Jiale Yuan,
  • Wenyin Qiao,
  • Min Wang,
  • Pei Li,
  • Xi Wang,
  • Ronghua Jin,
  • Jianguo Xu,
  • Cao Chen,
  • Rui Song,
  • Yamin Sun

摘要

Background

Epidemiological studies of Talaromyces marneffei (T. marneffei) infection are largely confined to HIV-positive populations and specific geographic regions, whereas its population-scale characteristics and environmental factors associated with infection in China are lacking. This study investigates spatiotemporal patterns, host susceptibility, and environmental correlates of T. marneffei detection in Chinese mainland.

Methods

Targeted next-generation sequencing (tNGS) data from patients hospitalized for acute respiratory tract infections (ARTIs) from 2022 to 2024 were used for epidemiological, spatiotemporal, and co-detection analyses. Geographical detector models with the q-statistic were employed to quantify the associations of meteorological, host distribution, and social factors on detection risk, where the q-statistic measures the proportion of spatial variance explained by each factor.

Results

Among 2,316 reported cases, we identified significant spatial clustering, with bimodal seasonal peaks in detection rate. Males and individuals aged 41–50 showed the highest susceptibility. Pneumocystis jirovecii was the predominant co-detected pathogen; 5 pathogens were positively and 16 were negatively correlated with T. marneffei. Univariate analysis using the q-statistic revealed that dew point temperature had the strongest explanatory power for T. marneffei detection at 48.55%. Bivariate interaction analysis demonstrated that paired factor combinations exhibited enhanced explanatory power for disease prevalence compared with single factors. Average air pressure, which alone explained only 1.4% of the spatial variance, showed markedly higher explanatory power when paired with other variables.

Conclusions

This study revealed spatiotemporal heterogeneity, population susceptibility, and environmental factors associated with T. marneffei detection in Chinese mainland, which can guide disease monitoring and control through enhanced surveillance and tailored interventions in epidemic hotspots and among high-risk groups.