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Individual and joint associations of PM2.5 components with the mumps incidence: based on quantile g-computation and

Rui Li1, Xiaoshu Zhang2, Donghua Li3

  • 1School of Public Health, Lanzhou University, Lanzhou, Gansu, China.

International Journal of Environmental Health Research
|January 5, 2026
PubMed

Insights

Particulate matter (PM2.5) components like sulfate, black carbon, and organic matter are linked to mumps incidence. Controlling these specific PM2.5 sources can help reduce mumps cases.

Area of Science:

  • Environmental Health
  • Epidemiology
  • Air Pollution Research

Background:

  • Total PM2.5 mass is associated with mumps.
  • Individual and joint effects of PM2.5 components on mumps remain unclear.

Purpose of the Study:

  • To investigate the individual and joint associations of PM2.5 components with mumps incidence in Northwest China.
  • To identify the most influential PM2.5 components affecting mumps.
  • To provide evidence for targeted air pollution control strategies.

Main Methods:

  • Ecological study design using monthly mumps incidence data (2013-2019).
  • Employed geographical probes, quantile g-computation (qgcomp), and Bayesian kernel-machine regression (BKMR) models.
  • Analyzed associations between PM2.5 components (sulfate, black carbon, organic matter) and mumps.

Main Results:

  • Sulfate (SO4^2-) and black carbon (BC) showed associations with mumps.
  • Quantile g-computation indicated positive correlations for sulfate (weight: 0.143) and organic matter (OM) (weight: 0.857).
  • BKMR identified BC and OM as dominant factors in increasing monthly mumps incidence; joint effects were positive at high concentrations.

Conclusions:

  • Targeted control of anthropogenic sources of PM2.5 components, particularly black carbon and organic matter, is crucial for mitigating mumps incidence.
  • This study is the first ecological investigation into the specific PM2.5 components associated with mumps.
  • Findings support evidence-based public health interventions for air quality management and disease prevention.
Keywords:
Bayesian kernel machine regressionPM2.5-related componentsmumpsquantile g-computation

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