Table 2

Multivariate Poisson regression model for hemorrhagic fever with renal syndrome
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

CI confidence interval, RR relative risk, PM10 particulate matter smaller than 10 μm.

Dependent variable is the occurrence of hemorrhagic fever with renal syndrome.

Han et al.

Han et al. BMC Public Health 2013 13:347   doi:10.1186/1471-2458-13-347

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