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.