tetano
Editor, Senior Moderator
BMC Public Health
. 2025 Dec 4.
doi: 10.1186/s12889-025-25316-1. Online ahead of print. Nonlinear and potential driving impacts of meteorological and air pollution factors on influenza-like illness in Jinan, China
Hui Zuo[SUP] 1 [/SUP], Qi Gao[SUP] 1 [/SUP], Michael Tong[SUP] 2 [/SUP], Shujuan Huang[SUP] 3 [/SUP], Chuanlong Cheng[SUP] 1 [/SUP], Shufen Li[SUP] 1 [/SUP], Kemeng Liang[SUP] 1 [/SUP], Caiyun Chang[SUP] #[/SUP][SUP] 3 [/SUP], Xiujun Li[SUP] #[/SUP][SUP] 4 [/SUP]
Affiliations
Background: While many studies have explored the correlation between environmental factors and influenza, research on their potential causal associations remains limited. Further, the impact of temperature changes between neighboring days (TCN) has not been thoroughly investigated.
Methods: Influenza-like illness (ILI) data, meteorological indicators, and air pollutant levels were collected in Jinan, China (2015-2019). Gradient boosting decision trees (GBDT) were used to identify key environmental variables. The distributed lag nonlinear models (DLNM) and empirical dynamic modeling (EDM) framework were then applied to explore their nonlinear associations with and potential causal effects on influenza infection. Subgroup analysis was also performed by different age groups.
Results: GBDT identified absolute humidity (AH), atmospheric pressure (AP), sulfur dioxide (SO[SUB]2[/SUB]), and ozone (O[SUB]3[/SUB]) as key factors, along with TCN as a key variable of interest. DLNM results revealed J-shaped and bimodal exposure-response relationships for TCN and AH, respectively, with increased relative risks (RRs) under low AH. SO[SUB]2[/SUB] was positively associated with influenza risk. The highest RRs were 1.190 (95% confidence interval (CI): 1.070-1.322) observed at 1012 hPa for AP and 3.373 (95% CI: 2.650-4.294) at 125 [Formula: see text]g/m[SUP]3[/SUP] for O[SUB]3[/SUB]. EDM results indicated that long-lag AP and SO[SUB]2[/SUB] may have a potential driving effect on influenza incidence. Positive effects on influenza were observed when TCN > -5 °C, AP > 1100 hPa, and across the full SO[SUB]2[/SUB] range. Children aged 0-4 were more sensitive to AP and SO[SUB]2[/SUB], while the aged 5-59 were more affected by TCN, AH, and O[SUB]3[/SUB].
Conclusion: This study demonstrated that both the DLNM and EDM methods consistently revealed the complex and nonlinear effects of specific environmental factors on influenza infection. Specifically, TCN > 5 ℃, AP > 1100 hPa, and SO[SUB]2[/SUB] > 60 [Formula: see text]g/m[SUP]3[/SUP] were associated with increased risk of influenza infection. Children aged 0-4 years and individuals aged 5-59 years exhibit different susceptibility patterns to environmental factors. These findings can inform public health strategies for influenza prevention, particularly in the context of increasing air pollution and climate variability.
Keywords: Air Pollution; Convergent Cross Mapping; Distributed Lag Nonlinear Models; Influenza; Meteorological Factors; Temperature Variability.
. 2025 Dec 4.
doi: 10.1186/s12889-025-25316-1. Online ahead of print. Nonlinear and potential driving impacts of meteorological and air pollution factors on influenza-like illness in Jinan, China
Hui Zuo[SUP] 1 [/SUP], Qi Gao[SUP] 1 [/SUP], Michael Tong[SUP] 2 [/SUP], Shujuan Huang[SUP] 3 [/SUP], Chuanlong Cheng[SUP] 1 [/SUP], Shufen Li[SUP] 1 [/SUP], Kemeng Liang[SUP] 1 [/SUP], Caiyun Chang[SUP] #[/SUP][SUP] 3 [/SUP], Xiujun Li[SUP] #[/SUP][SUP] 4 [/SUP]
Affiliations
- PMID: 41339835
- DOI: 10.1186/s12889-025-25316-1
Background: While many studies have explored the correlation between environmental factors and influenza, research on their potential causal associations remains limited. Further, the impact of temperature changes between neighboring days (TCN) has not been thoroughly investigated.
Methods: Influenza-like illness (ILI) data, meteorological indicators, and air pollutant levels were collected in Jinan, China (2015-2019). Gradient boosting decision trees (GBDT) were used to identify key environmental variables. The distributed lag nonlinear models (DLNM) and empirical dynamic modeling (EDM) framework were then applied to explore their nonlinear associations with and potential causal effects on influenza infection. Subgroup analysis was also performed by different age groups.
Results: GBDT identified absolute humidity (AH), atmospheric pressure (AP), sulfur dioxide (SO[SUB]2[/SUB]), and ozone (O[SUB]3[/SUB]) as key factors, along with TCN as a key variable of interest. DLNM results revealed J-shaped and bimodal exposure-response relationships for TCN and AH, respectively, with increased relative risks (RRs) under low AH. SO[SUB]2[/SUB] was positively associated with influenza risk. The highest RRs were 1.190 (95% confidence interval (CI): 1.070-1.322) observed at 1012 hPa for AP and 3.373 (95% CI: 2.650-4.294) at 125 [Formula: see text]g/m[SUP]3[/SUP] for O[SUB]3[/SUB]. EDM results indicated that long-lag AP and SO[SUB]2[/SUB] may have a potential driving effect on influenza incidence. Positive effects on influenza were observed when TCN > -5 °C, AP > 1100 hPa, and across the full SO[SUB]2[/SUB] range. Children aged 0-4 were more sensitive to AP and SO[SUB]2[/SUB], while the aged 5-59 were more affected by TCN, AH, and O[SUB]3[/SUB].
Conclusion: This study demonstrated that both the DLNM and EDM methods consistently revealed the complex and nonlinear effects of specific environmental factors on influenza infection. Specifically, TCN > 5 ℃, AP > 1100 hPa, and SO[SUB]2[/SUB] > 60 [Formula: see text]g/m[SUP]3[/SUP] were associated with increased risk of influenza infection. Children aged 0-4 years and individuals aged 5-59 years exhibit different susceptibility patterns to environmental factors. These findings can inform public health strategies for influenza prevention, particularly in the context of increasing air pollution and climate variability.
Keywords: Air Pollution; Convergent Cross Mapping; Distributed Lag Nonlinear Models; Influenza; Meteorological Factors; Temperature Variability.