Table 3 Temporal trend term effects, spatiotemporal models of influenza risk with and without covariates, China prefectures, 2005–2018.

From: Spatiotemporal Analysis of Influenza in China, 2005–2018

Year

Adjusted OR (95% CI)*

Adjusted OR (95% CI)**

2005

0.245(0.217~0.272)

0.284(0.227~0.337)

2006

0.328(0.291~0.363)

0.369(0.296~0.437)

2007

0.216(0.191~0.239)

0.250(0.199~0.296)

2008

0.210(0.186~0.233)

0.263(0.209~0.313)

2009

2.221(1.980~2.452)

1.469(0.789~2.013)

2010

0.607(0.540~0.671)

0.993(0.780~1.187)

2011

0.566(0.503~0.626)

0.637(0.509~0.755)

2012

1.316(1.172~1.453)

1.703(1.369~2.009)

2013

1.312(1.168~1.449)

1.410(1.142~1.657)

2014

2.151(1.917~2.375)

2.430(2.054~2.782)

2015

1.795(1.599~1.982)

2.101(1.698~2.473)

2016

2.665(2.375~2.942)

2.457(2.021~2.861)

2017

3.790(3.379~4.183)

2.530(1.602~3.325)

2018

5.763(5.137~6.361)

3.083(1.849~4.123)

  1. *Adjusted by convolutional spatial term, space-time interaction term, e.g., \(\log ({\theta }_{ij})=\alpha +{\nu }_{i}+{\upsilon }_{i}+{\gamma }_{1j}+{\delta }_{ij}\).
  2. **Adjusted by convolutional spatial term, space-time interaction term, and covariates, e.g., \(\log ({\theta }_{ij})={\rm{\alpha }}+{\sum }_{k=1}^{n}{\beta }_{k}{x}_{k}+{\nu }_{i}+{\upsilon }_{i}+{\gamma }_{1j}+{\delta }_{ij}\).
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