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Table 2 Multivariate Poisson regression model for hemorrhagic fever with renal syndrome

From: Air pollution and hemorrhagic fever with renal syndrome in South Korea: an ecological correlation study

  Climate model Climate + air pollution model
   RR (95% CI) P   RR (95% CI) P
Seasonality Winter 1 (Reference)   Winter 1 (Reference)  
  Spring 0.998 (0.853–1.168) .981 Spring 0.813 (0.683–0.967) .019
  Summer 1.275 (1.062–1.529) .009 Summer 1.146 (0.952–1.380) .150
  Autumn 1.818 (1.562–2.116) < .001 Autumn 1.656 (1.419–1.933) < .001
Humidity 4–month lag 1.102 (1.094–1.110) < .001 4–month lag 1.102 (1.094–1.110) < .001
Precipitation 3–month lag 1.022 (1.018–1.026) < .001 3–month lag 1.018 (1.014–1.022) < .001
Mean temperature 1–month lag 1.022 (1.013–1.032) < .001 1–month lag 1.038 (1.027–1.049) < .001
PM10     No time lag 1.013 (1.008–1.017) < .001
  1. CI confidence interval, RR relative risk, PM 10 particulate matter smaller than 10 μm.
  2. Dependent variable is the occurrence of hemorrhagic fever with renal syndrome.