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Table 3 Negative binomial regression model of meteorological factors associated with risk of scrub typhus incidence in Guangzhou, southern China, 2006–2012*

From: Meteorological factors and risk of scrub typhus in Guangzhou, southern China, 2006–2012

  β S.E. P Percent increase = (eβ − 1)*100 95% CI for percent increase (%)
Lower boundary Upper boundary
(A)       
  Intercept −530.11 28.42 0.00
  Aggregate sunshine 0.00 0.00 0.01 0.14 0.03 0.25
  Aggregate rainfall 0.00 0.00 0.00 0.05 0.03 0.08
  Average wind velocity 0.09 0.05 0.08 9.15 −0.92 20.25
  Average relative humidity 0.00 0.00 0.33 −0.45 −1.33 0.45
  Average temperature 0.15 0.01 0.00 15.61 14.00 17.24
  Year 0.26 0.01 0.00 30.20 26.62 33.88
(B)       
  Intercept −307.62 26.69 0.00
  Aggregate sunshine 0.00 0.00 0.00 0.40 0.29 0.50
  Aggregate rainfall 0.00 0.00 0.00 0.08 0.06 0.11
  Average wind velocity −0.19 0.05 0.06 −17.20 −24.16 122.77
  Average relative humidity −0.01 0.03 0.17 −1.32 −2.33 102.00
  Average atmospheric pressure −0.10 0.01 0.00 −9.36 −10.38 −8.34
  Year 0.20 0.01 0.00 22.70 19.47 26.02
(C)       
  Intercept −540.03 18.48 0.00
  Aggregate sunshine 0.00 0.00 0.00 0.17 0.08 0.26
  Aggregate rainfall 0.00 0.00 0.00 0.05 0.02 0.08
  Average temperature 0.14 0.01 0.00 14.98 13.65 16.33
  Year 0.27 0.01 0.00 30.84 28.50 33.22
(D)       
  Intercept −184.32 20.37 0.00
  Aggregate sunshine 0.01 0.00 0.00 0.54 0.47 0.62
  Aggregate rainfall 0.00 0.00 0.00 0.10 0.07 0.12
  Average atmospheric pressure −0.08 0.00 0.00 −8.03 −8.75 −7.31
  Year 0.14 0.01 0.00 14.46 12.42 16.54
  1. Note. *Negative binomial regression model for monthly scrub typhus incidence without atmospheric pressure (A) and without average temperature (B). Final models without atmospheric pressure (C) and without average temperature (D).
  2. CI = Confidence interval, S.E. = Standard error.