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Table 1 Poisson regression model between the air pollutant and hemorrhagic fever with renal syndrome

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

Time lag

Univariate

Multivariate*

 

RR (95% CI)

P

RR (95% CI)

P

No time lag

0.998 (0.995–1.000)

.025

1.013 (1.008–1.017)

< .001

1–month lag

0.972 (0.970–0.975)

< .001

1.001 (0.997–1.004)

.785

2–month lag

0.938 (0.935–0.940)

< .001

0.991 (0.987–0.995)

< .001

3–month lag

0.933 (0.931–0.936)

< .001

0.983 (0.979–0.987)

< .001

4–month lag

0.964 (0.961–0.966)

< .001

0.992 (0.988–0.996)

< .001

5–month lag

0.997 (0.995–1.000)

.022

0.991 (0.988–0.995)

< .001

6–month lag

1.024 (1.022–1.026)

< .001

1.005 (1.002–1.008)

.001

7–month lag

1.036 (1.034–1.037)

< .001

1.006 (1.004–1.009)

< .001

8–month lag

1.031 (1.030–1.033)

< .001

1.002 (1.000–1.005)

.036

9–month lag

1.016 (1.014–1.018)

< .001

0.999 (1.012–1.031)

.360

10–month lag

1.005 (1.002–1.007)

< .001

0.993 (0.989–0.997)

.001

11–month lag

1.000 (0.998–1.002)

.827

0.997 (0.993–1.002)

.208

12–month lag

0.990 (0.988–0.992)

< .001

0.999 (0.995–1.003)

.572

  1. CI confidence interval, RR relative risk.
  2. Dependent variable is the occurrence of hemorrhagic fever with renal syndrome.
  3. *Adjusted for seasonality and climate variables.
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BMC Public Health

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