Air pollution and hospital outpatient visits for conjunctivitis: a time-series analysis in Tai’an, China

Renchao Chen, Jun Yang, Di Chen, Wen-jing Liu, Chunlin Zhang, Hao Wang, Bixia Li, Peng Xiong, Boguang Wang, Yi Wang, Shanshan Li, Yuming Guo

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19 Citations (Scopus)

Abstract

Conjunctivitis is one of the most common eye-related health problems and significantly influences patients’ quality of life. Whether air pollution increased the risks of conjunctivitis is still unclear. Daily counts of outpatient visits for conjunctivitis, air pollution, and meteorological data during January 1, 2015-December 31, 2019 were collected from Tai’an, China. Generalized additive model with Poisson distribution was used to estimate the relationship between air pollution and visits for conjunctivitis, after controlling for the long-term and seasonal trends, weather variables, and day of the week. The effect of air pollution on visits for conjunctivitis was generally acute and significant at the current day and disappeared after 2 days. The relative risk of conjunctivitis visits associated with per 10 μg/m3 increases in PM2.5, PM10, SO2, and NO2 at lag 0-2 days was 1.006 (95% CI: 1.001-1.011), 1.003 (95% CI: 1.000-1.0107), 1.023 (95% CI: 1.009-1.037), and 1.025 (95% CI: 1.010-1.040), respectively. The impact of air pollution on visits for conjunctivitis varied greatly by individual characteristics. The impact of NO2 was higher in males than in females, with the opposite trend for SO2 and PM2.5. Effect estimates of air pollutants were higher among return visits for conjunctivitis, the elderly, and white-collar workers. Our study highlights that the vulnerable subpopulations should pay more attention to protect themselves from air pollution.

Original languageEnglish
Pages (from-to)15453–15461
Number of pages9
JournalEnvironmental Science and Pollution Research
Volume28
Issue number12
DOIs
Publication statusPublished - Mar 2021

Keywords

  • Air pollution
  • Conjunctivitis
  • Generalized additive model
  • Time-series analysis
  • Vulnerable populations

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