Association between Particulate Matter Pollution Concentration and Hospital Admissions for Hypertension in Ganzhou, China

• Published in: International journal of hypertension 2022-02, Vol.2022, p.7413115-11
• Main Authors: Li, Chenwei, Zhou, Xinye, Huang, Kun, Zhang, Xiaokang, Gao, Yanfang
• Format: Article
• Language: English
• Subjects: Admission and discharge; Air pollution; Cardiovascular disease; Computer centers; Generalized linear models; Hospitalization; Hospitals; Humidity; Hypertension; Outdoor air quality; Patient admissions; Pollutants; Risk factors; Software; Time series
• ISSN: 2090-0384; 2090-0392; 2090-0392
• DOI: 10.1155/2022/7413115

Fine particulate matter (PM2.5) and respirable particulate matter (PM10) are two major air pollutants with toxic effects on the cardiovascular system. Hypertension, as a chronic noncommunicable cardiovascular disease, is also a risk factor for several diseases. We applied generalized linear models with a quasi-Poisson link to assess the effect of air pollution exposure on the number of daily admissions for patients with hypertension. In addition, we established a two-pollutant model to evaluate PM2.5 and PM10 hazard effect stability by adjusting the other gaseous pollutants. Results showed that during the study period, 24 h mean concentrations of ambient PM2.5 and PM10 at 38.17 and 59.84 μg/m3, respectively, and a total of 2,611 hypertension hospital admissions were recorded. Air pollution concentrations significantly affected the number of hospitalizations for hypertension approximately 2 months after exposure. For each 10 μg/m3 increase in PM2.5 and PM10 in single-pollutant models, the number of hospitalizations for hypertension increased by 7.92% (95% CI: 5.48% to 10.42%) and 4.46% (95% CI: 2.86% to 5.65%), respectively, at the lag day with the strongest effect. NO2, O3, CO, and SO2 had different significant effects on the number of hospitalizations over the same time period, and PM2.5 and PM10 still showed robust significant effects after adjustment of gas pollutants through a two-pollutant model. These findings may contribute to a better understanding of the health effects of ambient particulate matter.