Analysis of the effects of air pollutants and meteorological factors on upper respiratory tract infection outpatients in Gansu Province.

Abstract

The effects of meteorological factors and air pollutants on upper respiratory tract infection (URTI) varied across different regions depending on climate zones. Previous studies have identified potential interactions between air pollutants and meteorological factors (temperature and relative humidity, i.e., RH) on URTI morbidity. However, research in the inland provinces of Northwest China remains limited. Variations in air pollution levels, pollutant composition, climatic conditions, and population susceptibility across regions contribute to substantial heterogeneity in findings, rendering existing evidence inapplicable to Northwest inland provinces. Therefore, it is necessary to conduct region-specific investigations in representative cities within this area. In this study, we selected cities from different climatic zones in Gansu Province for analysis (temperate continental climate: Jiuquan; temperate semi-arid continental climate: Dingxi; temperate subhumid climate: Tianshui). This study explored several major meteorological factors, including air pollution, temperature and RH, to identify potential modifiable risk factors and their interactive effects on URTI in the three cities in different climate zones. Data from 2017 to 2019 on URTI outpatient visits, air pollutants, and weather in three cities with varying climates were analyzed using generalized additive models and distribution lag nonlinear model (DLNM) to assess the delayed impact of meteorological factors on URTI. Further, bivariate and stratified models explored the interaction between pollutants and meteorological factors on URTI outpatient visits. Our results indicated that PM_2.5, PM₁₀, NO₂, and CO were significantly associated with increased hospital outpatient visits for URTI, with lagged effects observed. The maximum relative risks (RRs) of PM_2.5 were 1.134 (95% CI: 1.057, 1.218) in Jiuquan (lag014), 1.118 (95% CI: 1.069, 1.168) in Dingxi (lag014), and 1.035 (95% CI: 1.013, 1.057) in Tianshui (lag03). For PM₁₀, the maximum RRs were 1.045 (95% CI: 1.026, 1.064) in Jiuquan (lag014) and 1.020 (95% CI: 1.005, 1.035) in Tianshui (lag010), while PM₁₀ has no significant association in Dingxi. For NO₂, the maximum RRs were 1.118 (95% CI: 1.022, 1.224) in Jiuquan (lag011) and 1.158 (95% CI: 1.104, 1.215) in Tianshui (lag011), while NO₂ has no significant association in Dingxi. For CO, the maximum RRs were 5.433 (95% CI: 2.818, 10.475) in Jiuquan (lag014), 2.289 (95% CI: 1.659, 3.156) in Dingxi (lag014), and 1.835 (95% CI: 1.509, 2.231) in Tianshui (lag012). Stratified analyses indicated that the associations were stronger in males and children (0-14 years). Furthermore, the associations were stronger in cold season than in warm season. Our results also revealed that both low and high temperatures could elevate the risk of outpatient visits for URTI. Compared with the median temperature of each city, the maximum RRs of low temperatures were 1.455 (95% CI: 1.365, 1.550) at lag08, 1.073 (95% CI: 1.027, 1.121) at lag014, and 1.127 (95% CI: 1.067, 1.190) at lag014 for Jiuquan, Dingxi, and Tianshui, respectively. For the high temperature exposure, we only observed significant associations in Jiuquan and Tianshui [RR = 1.143 (95% CI: 1.090, 1.200) at lag05 in Jiuquan, RR = 1.023 (95% CI: 1.008, 1.038) at lag14 in Tianshui], while no significant associations with high temperatures were detected in Dingxi. Stratified analyses by gender and age revealed that extremely low temperatures had a more pronounced effect on males and children aged 0-14 years across the three cities, whereas extremely high temperatures exhibited adverse effects only among males and individuals aged 15-64 years in Jiuquan. Similarly, both low and high RH were associated with increased risk of URTI outpatient visits in the three cities, though the impact of extreme RH varied among them. The effect of extremely low RH on URTI outpatient visits was strongest at lag07 for Jiuquan (RR = 1.296, 95% CI: 1.264, 1.329), lag06 for Dingxi (RR = 1.091, 95% CI: 1.031, 1.155), and lag07 for Tianshui (RR = 1.279, 95% CI: 1.176, 1.390). Adverse effects of extremely high RH were observed exclusively in Dingxi and Tianshui, with the strongest associations at lag7 and lag07, respectively. The relative risk (RR) for Dingxi was 1.043 (95% CI: 1.019, 1.069) and for Tianshui it was 1.069 (95% CI: 1.002, 1.140). Stratified analyses by gender and age indicated that extremely low RH had a more pronounced impact on males and children aged 0-14 years across all three cities, while extremely high RH exerted a greater effect on males and children aged 0-14 years in Dingxi and Tianshui. Meteorological factors and air pollutants have an interactive effect on URTI. The response surface analysis indicated that the adverse effects of the four air pollutants on URTI incidence were most pronounced under low temperature and high concentration conditions across the three cities. Stratified analysis demonstrated that, under low temperature, each 10 μg m^-3 increase in pollutant concentration (CO: 1 mg m^-3) was associated with elevated outpatient risk of URTI in Jiuquan, with RRs as follows: PM_2.5 (RR = 1.112, 95% CI: 1.023, 1.203), PM₁₀ (RR = 1.041, 95% CI: 1.021, 1.065), NO₂ (RR = 1.341, 95% CI: 1.230, 1.462), and CO (RR = 2.603, 95% CI: 1.433, 4.728). In Dingxi, the corresponding RRs were: PM_2.5 (RR = 1.148, 95% CI: 1.062, 1.241), PM₁₀ (RR = 1.052, 95% CI: 1.018, 1.087), NO₂ (RR = 1.128, 95% CI: 1.055, 1.206), and CO (RR = 2.294, 95% CI: 1.842, 2.857). In Tianshui, the RRs were: PM_2.5 (RR = 1.150, 95% CI: 1.095, 1.208), PM₁₀ (RR = 1.038, 95% CI: 1.022, 1.054), NO₂ (RR = 1.305, 95% CI: 1.162, 1.466), and CO (RR = 1.682, 95% CI: 1.462, 1.935). Similarly, the response surface plots indicate that the adverse effects of the four air pollutants on URTI incidence in the three cities are most pronounced under low RH and high concentration conditions. Stratified analyses reveal that, under low RH, each 10 μg m^-3 increase in pollutant concentration (CO: 1 mg m^-3) is associated with the following RRs for URTI outpatient visits in Jiuquan: PM_2.5 (RR = 1.101, 95% CI: 1.032, 1.176), PM₁₀ (RR = 1.042, 95% CI: 1.015, 1.069), NO₂ (RR = 1.236, 95% CI: 1.056, 1.446), and CO (RR = 2.569, 95% CI: 1.625, 4.060). In Dingxi, the corresponding RRs are: PM_2.5 (RR = 1.171, 95% CI: 1.129, 1.214), PM₁₀ (RR = 1.063, 95% CI: 1.037, 1.090), NO₂ (RR = 1.141, 95% CI: 1.042, 1.249), and CO (RR = 2.071, 95% CI: 1.645, 2.607). In Tianshui, the RRs are: PM_2.5 (RR = 1.090, 95% CI: 1.058, 1.124), PM₁₀ (RR = 1.043, 95% CI: 1.024, 1.062), NO₂ (RR = 1.180, 95% CI: 1.115, 1.248), and CO (RR = 1.894, 95% CI: 1.631, 2.210). In conclusion, both air pollutants and meteorological factors had an influence on URTI outpatient visits, and the influence on URTI outpatient visits may have an interaction.