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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.
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BMC Public Health

ISSN: 1471-2458

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