Spatial distribution and driving factors of the associations between temperature and influenza-like illness in the United States: a time-stratified case-crossover study

Yongli Yang; Jiao Lian; Xiaocan Jia; Tianrun Wang; Jingwen Fan; Chaojun Yang; Yuping Wang; Junzhe Bao

doi:10.1186/s12889-023-16240-3

Spatial distribution and driving factors of the associations between temperature and influenza-like illness in the United States: a time-stratified case-crossover study

BMC Public Health. 2023 Jul 20;23(1):1403. doi: 10.1186/s12889-023-16240-3.

Authors

Yongli Yang¹, Jiao Lian¹, Xiaocan Jia¹, Tianrun Wang², Jingwen Fan¹, Chaojun Yang¹, Yuping Wang¹, Junzhe Bao³

Affiliations

¹ Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China.
² School of Public Health, Jilin University, Changchun, 130021, Jilin, China.
³ Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan, China. baojz@zzu.edu.cn.

Abstract

Background: Several previous studies investigated the associations between temperature and influenza in a single city or region without a national picture. The attributable risk of influenza due to temperature and the corresponding driving factors were unclear. This study aimed to evaluate the spatial distribution characteristics of attributable risk of Influenza-like illness (ILI) caused by adverse temperatures and explore the related driving factors in the United States.

Methods: ILI, meteorological factors, and PM_2.5 of 48 states in the United States were collected during 2011-2019. The time-stratified case-crossover design with a distributed lag non-linear model was carried out to evaluate the association between temperature and ILI at the state level. The multivariate meta-analysis was performed to obtain the combined effects at the national level. The attributable fraction (AF) was calculated to assess the ILI burden ascribed to adverse temperatures. The ordinary least square model (OLS), spatial lag model (SLM), and spatial error model (SEM) were utilized to identify driving factors.

Results: A total of 7,716,115 ILI cases were included in this study. Overall, the temperature was negatively associated with ILI risk, and lower temperature gave rise to a higher risk of ILI. AF ascribed to adverse temperatures differed across states, from 49.44% (95% eCI: 36.47% ~ 58.68%) in Montana to 6.51% (95% eCI: -6.49% ~ 16.46%) in Wisconsin. At the national level, 29.08% (95% eCI: 27.60% ~ 30.24%) of ILI was attributable to cold. Per 10,000 dollars increase in per-capita income was associated with the increment in AF (OLS: β = -6.110, P = 0.021; SLM: β = -5.496, P = 0.022; SEM: β = -6.150, P = 0.022).

Conclusion: The cold could enhance the risk of ILI and result in a considerable proportion of ILI disease burden. The ILI burden attributed to cold varied across states and was higher in those states with lower economic status. Targeted prevention programs should be considered to lower the burden of influenza.

Keywords: Attributable fraction; Driving factors; Influenza-like illness; Spatial distribution; Time-stratified case-crossover study.

Publication types

Meta-Analysis
Research Support, Non-U.S. Gov't

MeSH terms

Cold Temperature
Cross-Over Studies
Humans
Influenza, Human* / epidemiology
Montana
Temperature
United States / epidemiology